PV Distributed Load Shifting

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Abstract[edit | edit source]

The duck curve—named after its resemblance to a duck—shows the difference in electricity demand and the amount of available solar energy throughout the day. When the sun is shining, solar floods the market and then drops off as electricity demand peaks in the evening. The Duck Curve is a graph that shows the difference in electricity demand on the grid and the amount of available solar energy throughout the day. It was created by the California Independent System Operator (ISO) to demonstrate the electric load on the ISO grid on an average spring day when it’s sunny but temperatures are cool, meaning that demand is not as high as in height of summer when people are using AC or in the winter when heating homes is needed. Though adapting to changing consumer demand has been an issue that utilities have dealt with for over a century, seasonal usage patterns have changed, and the actual resources used for generating electricity have evolved. The Duck Curve illustrates an important aspect of the challenges that renewable energy such as solar poses to utility managers, and how these have evolved over a much shorter time period. The duck curve is a snapshot of a 24-hour period in California during springtime—when this effect is most extreme because it’s sunny but temperatures remain cool, so demand for electricity is low since people are not using electricity for air conditioning or heating. The duck curve represents a transition point for solar energy. It was, perhaps, the first major acknowledgement by a system operator that solar energy is no longer a niche technology and that utilities need to plan for increasing amounts of solar energy. This is especially true for places that already have high solar adoption, such as California, where one day in March 2020, solar contributed nearly 40% of electricity generation in the state for the first time ever. However, PV penetration into the grid comes with its drawbacks. The inverter-interfaced nature of the PV systems significantly impacts the power system operation from protection, power flow and stability perspectives. It is therefore, important to investigate aggressive methods to ensure proper protection, power flow, and stability of the power grid. There are regions in the world that are already successfully managing an extremely high penetration of renewable VERs. For example, Portugal was run 100 percent on wind, solar, and hydropower for four days straight in May 2016, and Texas hit a record level of 45% instantaneous penetration from wind generation during one evening in February of 2020.

The purpose of this project is to investigate distributed load shifting where the consumer shifts load to help the utility obtain a higher PV penetration without being hammered by the duck curve. First, we dig in deep on appliances that are smart enough to help shift the load by making a table along with normal use kWh move amount. We focused on EPA appliance data, washing machines, dishwashers, and 3D printing during the day at home. In fact, as the coronavirus pandemic forces people across the country to shelter at home, utilities and grid operators are watching typical energy-use patterns change in unpredictable ways and trying to understand how this will affect their grid operations, power purchasing practices and long-term plans. The resulting shifts in typical energy patterns have been illuminating, according to Scott Hinson, Pecan Street’s chief technology officer. We know they’re not indicative of every household in America,” he said in an interview this week. “But it is illustrative of what happens when you tell people to stay home, stay cool and stay productive.” By running the data through a “per cooling-degree day” calculation, Pecan Street estimates that air conditioner usage was up about 40 percent in the last week of March compared to historical averages. Then, we order the list based on most promising. Finally, the investigation shows that the strategy of success of the process implemented for appliances operating like clothes washers and dishwashers depends on consumers' willingness to accept the solution if additional costs are sponsored by energy utility or balanced by savings via energy bill. For appliances operating similar to clothes dryers, utilities must define harmonized signal for shortage of power, and define business model where energy utilities sponsor the implementation of these “Internal energy management agent” modules.

Literature Review (to be revisited)[edit | edit source]

[1] A Case Study on Optimal Sizing of Battery Energy Storage to Solve ‘Duck Curve’ Issues in Malaysia by Wong, L. Ai. & Ramachandaramurthy V. K. in 2020 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE)[edit | edit source]

In this paper, the authors study an Optimal Sizing of Battery Energy Storage to Solve ‘Duck Curve’ Issues in Malaysia. Battery energy storage (BES) has the ability to solve many power system problems especially in systems with renewable energy resources integrated. Due to the intermittent nature of solar photovoltaic (PV) generation, different problems have arise such as voltage fluctuation and reverse power flow. Besides, duck curve issue has become more significant with the increasing amount of PV integrated in the power system as first raised by the Californian Independent System Operator. Studies on these issues has become important in order to solve the steep ramp and over generation problems caused by duck curve issue. The duck curve issues based on general Malaysia load profile and PV pattern are studied in this paper. Besides, the feasibility of BES in solving these issues with suitable BES capacity is investigated. The results show that the BES has the ability to solve the duck curve issues through proper control of BES charging and discharging operation.

[2] A High-Efficiency Network-Constrained Clustered Unit Commitment Model for Power System Planning Studies by E. Du, N. Zhang, C. Kang, & Q. Xia[edit | edit source]

The increasing complexity of power systems, particularly the high renewable energy penetration, raises the necessity of incorporating detailed power system operation models into long-term planning studies. The classic short-term operation model, i.e., network-constrained unit commitment (NCUC), involves many binary variables and introduces computational challenges when applied to long-term planning optimizations. A high-efficiency and simplified NCUC model is required to incorporate operational flexibility in power system planning studies. This paper proposes a linearized NCUC formulation that has a high calculation performance and minor approximation errors compared to the full NCUC model. The proposed model combines the dispatch-only (DO) operation model and clustered unit commitment (CUC) model by introducing linking constraints between them such that the overall model guarantees both the transmission security constraints that are formulated in the DO model and the start-up/shut-down constraints of generating units that are formulated in the CUC model. A case study of a modified IEEE RTS-79 system is provided to demonstrate the validation and efficiency of the proposed simplified NCUC model as well as its effectiveness for power system planning studies.

[3] A Residential Load Scheduling with the Integration of On-Site PV and Energy Storage Systems in Micro-Grid by Rasheed, Muhammad Babar & Alquthami, Shahzadi[edit | edit source]

In this paper, the authors study a Residential Load Scheduling with the Integration of On-Site PV and Energy Storage Systems in Micro-Grid. The smart grid (SG) has emerged as a key enabling technology facilitating the integration of variable energy resources with the objective of load management and reduced carbon-dioxide (CO2) emissions. However, dynamic load consumption trends and inherent intermittent nature of renewable generations may cause uncertainty in active resource management. Eventually, these uncertainties pose serious challenges to the energy management system. To address these challenges, this work establishes an efficient load scheduling scheme by jointly considering an on-site photo-voltaic (PV) system and an energy storage system (ESS). An optimum PV-site matching technique was used to optimally select the highest capacity and lowest cost PV module. Furthermore, the best-fit of PV array in regard with load is anticipated using least square method (LSM). Initially, the mathematical models of PV energy generation, consumption and ESS are presented along with load categorization through Zero and Finite shift methods. Then, the final problem is formulated as a multiobjective optimization problem which is solved by using the proposed Dijkstra algorithm (DA). The proposed algorithm quantifies day-ahead electricity market consumption cost, used energy mixes, curtailed load, and grid imbalances. However, to further analyze and compare the performance of proposed model, the results of the proposed algorithm are compared with the genetic algorithm (GA), binary particle swarm optimization (BPSO), and optimal pattern recognition algorithm (OPRA), respectively. Simulation results show that DA achieved 51.72% cost reduction when grid and renewable sources are used. Similarly, DA outperforms other algorithms in terms of maximum peak to average ratio (PAR) reduction, which is 10.22%.

[4] A Review of Strategies to Increase PV Penetration Level in Smart Grids by Aleem, Sk Abdul, Hussain, S. M. Suhail, & Ustun, Taha Selim.[edit | edit source]

In this paper, the authors study Strategies to Increase PV Penetration Level in Smart Grids. Due to environmental concerns, power system generation is shifting from traditional fossil-fuel resources to renewable energy such as wind, solar and geothermal. Some of these technologies are very location specific while others require high upfront costs. Photovoltaic (PV) generation has become the rising star of this pack, thanks to its versatility. It can be implemented with very little upfront costs, e.g., small solar home systems, or large solar power plants can be developed to generate MWs of power. In contrast with wind or tidal generation, PV can be deployed all around the globe, albeit with varying potentials. These merits have made PV the renewable energy technology with highest installed capacity around the globe. However, PV penetration into the grid comes with its drawbacks. The inverter-interfaced nature of the PV systems significantly impacts the power system operation from protection, power flow and stability perspectives. There must be strategies to mitigate these negative impacts so that PV penetration into the grid can continue. This paper gives a thorough overview of such strategies from different research fields: such as communication, artificial intelligence, power electronics and electric vehicle charging coordination. In addition, possible research directions are given for future work.

[5] An efficient solution method for integrated unit commitment and natural gas network operational scheduling under “Duck Curve” by Fallahi, Farhad, & Maghouli, Pouria[edit | edit source]

In this paper, the authors study an integrated unit commitment and natural gas network operational scheduling with renewable energy (wind and photovoltaic power plants) which changes the load curve to a duck curve. Wind and photovoltaic energy are both uncertain in nature. The availability of photovoltaic energy at certain times of day changes the pattern of the load. This load curve which is called “Duck Curve,” raises the question of whether the power system could provide the necessary ramp rate and capacity for full utilization of photovoltaic energy. The use of gas power plants to deal with the sharp ramp rate would have a negative impact on the flow of gas in the gas network. That is, the uncertain output of renewable resources requires the flexibility of gas power plants which affects the gas network. Storage devices of electricity and gas, line-pack and the technology of electricity to gas transformation (P2G) could be effective solutions for reducing fluctuations and ensuring reliable operation of electricity and gas networks. The underlying problem is known to be a complex nonconvex problem. Compared to existing modeling and solution methods, we propose some efficient reformulation which enables us to rewrite the problem as a convex mixed integer program using Benders decomposition. Given the convex reformulation, we can solve the resulting convex model to optimality. We demonstrate the effectiveness of the proposed method by solving some standard examples such as the modified IEEE 24-Bus test system and the Belgian 20-node gas network.

[6] Analysis of Maximum Intermittent Renewable Energy Source Penetration on South of Sulawesi Power System by H. B. Tambunan, P. A. A. Pramana, & B. S. Munir[edit | edit source]

One of the adverse using renewable energy source is intermittency characteristic. It will have a considerable impact on power system stability and reliability. This study aims to calculate how much intermittent renewable energy source (IRES) penetration into the interconnected power system without causing collapse the system based on applicable standards. Stable conditions by standards must meet the acceptable voltage and system frequency. The level of IRES penetration based on the worst scenario that may occur in the system during intermittent such as penetration in weakest bus by sensitivity analysis, dry season, maintenance of largest generator, peak load, and off-peak load condition. The simulation showed the IRES penetration level into the South of Sulawesi power system are about 0,32% in off-peak load and about 5,24 % in peak load.

[7] Automated demand response strategies using home energy management system in a RES-based smart grid by Zunnurain, L., Maruf, M. N. I.[edit | edit source]

In this paper, the authors study Automated demand response strategies using home energy management system in a RES-based smart grid. Demand Response (DR) applications via Home Energy Management System (HEMS) at the building level in a strategically integrated RES-based electricity grid can help reduce the building peak demand as well as energy consumption and the power inefficiency in the grid respectively. Distributed Energy Generation (DEG) based on Renewable Energy Sources (RES) are seen as a reliable alternative and more efficient grid optimization to the traditional fossil energy sources-based grid. But a proper combination of demand response and the DEG can significantly make a revolutionary change in the electricity grid. This paper focuses on the impact of integrated control of Solar PV and the Wind Turbine on improving the supply-side and coordinated control strategy for automated demand response by ensuring efficient use of home appliances including Electric Vehicle (EV), air cooling, water heating etc. based on HEMS in order to reduce the peak demand and overall power inefficiency in the grid. For that purpose and analysis, a highly accurate aggregated model of Solar PV, Wind turbine and the home appliances has been developed in the Simulink (MATLAB) and an iterative algorithm based on Load Shifting technique has been established for HEMS controller with embedded MATLAB code. The obtained simulation result illustrates the effectiveness of the proposed system.

[8] Bi-Level Load Peak Shifting and Valley Filling Dispatch Model of Distribution Systems With Virtual Power Plants by Luo, Fengzhang, Yang, Xin, Wei, Wei, Zhang, Tianyu, Yao, Liangzhong, Zhu, Lingzhi, & Qian, Minhui[edit | edit source]

In this paper, the authors study Bi-Level Load Peak Shifting and Valley Filling Dispatch Model of Distribution Systems With Virtual Power Plants. Distributed energy resources (DERs) have been widely involved in the optimal dispatch of distribution systems which benefit from the characteristics of reliability, economy, flexibility and environmental protection. And distribution systems are gradually transforming from passive networks to active distribution networks (ADNs). However, it is difficult to manage DERs effectively because of their wide distribution, intermittency and randomness. Virtual power plants (VPPs) can not only coordinate the contradiction between distribution systems and DERs, but also consider the profits of DERs, which can realize the optimal dispatch of distribution systems effectively. In this paper, a bi-level dispatch model based on VPPs is proposed for load peak shaving and valley filling in distribution systems. The VPPs consist of distributed generations (DGs), energy storage devices (ESS) and demand response resources (DR). The objective of the upper-level model is smoothing load curve, and the objective of the lower-level model is maximizing the profits of VPPs. Meanwhile, we consider the quadratic cost function to quantify the deviation between the actual output and the planned output of DGs. The effectiveness of the bi-level dispatch model in load shifting and valley filling is proved by various scenarios. In addition, the flexibility of the model in participating in distribution systems dispatch is also verified.

[9] CES peak demand shaving with energy storage system by S. Park, & W. Park[edit | edit source]

This paper analyzes energy cost reduction from peak demand shaving when a CES provider adopts ESS for the CHP-based CES microgrid site in Seoul, Korea. The simulation results show that about 9% of peak shaving can be realized when a 270kWh ESS is used for three thousand CES households. When two or three ESSs are adopted, peak demand shaving increases at most into 12% or 14%, which concludes that ESS capacity enlargement is not so helpful for the peak reduction. It also concludes that peak shaving is appropriate as a supplementary usage of ESS considering that ESS is required to work only a few days for a year to achieve peak shaving.

[10] Coalitional Game-Based Cost Optimization of Energy Portfolio in Smart Grid Communities by A. Chiş, & V. Koivunen[edit | edit source]

In this paper, we propose two novel coalitional game theory-based optimization methods for minimizing the cost of electricity consumed by households from a smart community. Some households in the community may own renewable energy sources (RESs) conjoined with energy storage systems (ESSs). Some other residences own ESSs only, while the remaining households are simple energy consumers. We first propose a coalitional cost optimization method in which RESs and ESSs owners exchange energy and share their renewable energy and storage spaces. We show that by participating in the proposed game these households may considerably reduce their costs in comparison to performing individual cost optimization. We further propose another coalitional optimization model in which RESs and ESSs owning households not only share their resources, but also sell energy to simple energy consuming households. We show that through this energy trade the RESs and ESSs owners can further reduce their costs, while the simple energy consumers also gain cost savings. The cost savings obtained by the coalition are distributed among its members according to the Shapley value. Simulation examples show that the proposed coalitional optimization methods may reduce the electricity costs for the RESs and ESSs owning households by 18%, while the sole energy consumers may reduce their costs by 3%.

[11] Confronting the Duck Curve: How to Address Over-Generation of Solar Energy[edit | edit source]

In this study, the department of energy (DOE) investigated methods to address over-generation of solar energy. In 2013, the California Independent System Operator published a chart that is now commonplace in conversations about large-scale deployment of solar photovoltaic (PV) power. The duck curve—named after its resemblance to a duck—shows the difference in electricity demand and the amount of available solar energy throughout the day. When the sun is shining, solar floods the market and then drops off as electricity demand peaks in the evening. The duck curve is a snapshot of a 24-hour period in California during springtime—when this effect is most extreme because it’s sunny but temperatures remain cool, so demand for electricity is low since people aren’t using electricity for air conditioning or heating. The duck curve represents a transition point for solar energy. It was, perhaps, the first major acknowledgement by a system operator that solar energy is no longer a niche technology and that utilities need to plan for increasing amounts of solar energy. This is especially true for places that already have high solar adoption, such as California, where one day this past March, solar contributed nearly 40% of electricity generation in the state for the first time ever.

[12] Duck curve problem solving strategies with thermal unit commitment by introducing pumped storage hydroelectricity renewable energy by Howlader, H. O. R., Furukakoi, M., Matayoshi, H., & Senjyu, T.[edit | edit source]

In this paper, the authors study Duck curve problem solving strategies with thermal unit commitment by introducing pumped storage hydroelectricity renewable energy. In recent years, the price of photovoltaic (PV) has been decreasing dramatically, that has been increasing the installation of PVs in smart grid and roof photovoltaic (PVr). There is no doubt, this is a positive development of smart grid for the world. As we know every good thing has a bad side too, PVs definitely generate power in the day time, so huge number of PV's power penetration in the daytime changes the load demand of fossils fuel based thermal generations and load curve becomes duck shape. In duck curve, peak and off-peak gap is very large. It is a challenge to cut the peak and increase the off-peak load which means load leveling is very important. If peak and off-peak gap are more so start-up cost (SUC) of thermal generators will be more. Besides, more thermal units have to be run to fulfill the peak load. Therefore, it is very important to run thermal units optimally. As we already knew, duck curve has very high peak so the only optimization is not enough to bring good results. Therefore, energy storage system plays a vital role to level peak and off-peak load. However, battery energy system (BESS), still installation cost is very high specially NaS battery. That is why in this research considers concentrated solar power (CSP) and pumped storage hydroelectricity (PSH) as the energy storage system (ESS). In this research, optimal unit commitment (UC) of thermal generators and PSH is introduced for saving the fuel cost and SUC of thermal generators. Optimal results of the proposed research model are determined by using MATLAB® INTLINPROG optimization toolbox.

[13] Effect of Taiwan's energy policy on unit commitment in 2025 by Gerard Francesco DG. Apolinario, Chen-Nien Chung, Tai-Ken Lu, Chia-Chi Chu[edit | edit source]

This study presents the effect of Taiwan's energy policy on its unit commitment in the year 2025. Taiwan's energy policy has been changing since 2005, since when it has required the decommissioning of nuclear power plants in response to the crisis in Fukushima, the adoption of the Kyoto Protocol, and the increase of renewable sources of power generation to 20% of its energy mix. The paper models the unit commitment for the Taiwan power system under Taiwan's energy policy. The study compares unit commitments in 2018, summer 2025, and winter 2025 using AMPL Software with the CPLEX 12.9.0 Solver. The results show that the net demand curve will change by 2025, forming a “duck – curve” model due to the high penetration of renewable energy. The daily carbon dioxide emission in 2018 for five percent (5%) spinning reserve is 392,151,000 kg. The said amount is larger than in winter 2025 when it will be 311,314,000 kg indicating the importance of the full implementation of renewable energy projects in helping to reach Taiwan's goal to limit carbon dioxide emission by 2025. Pumped storage scheduling changes from conventional pumping in the early morning to pumping from around noon to late afternoon due to the high penetration of renewable energy. The construction of combined-cycle units will have a critical role in the achieving of the spinning reserve capacity goal set by the Taiwan government, to avoid the importation of energy and to maintain the energy security of the country.

[14] Energy storage in high renewable penetration power systems: Technologies, applications, supporting policies and suggestions by H. Zhu, H. Li, G. Liu, Y. Ge, J. Shi, H. Li, & N. Zhang[edit | edit source]

Integrating renewable energy is one of the most effective ways to achieve a low-carbon energy system. The high penetration of variable renewable energy, such as wind power and photovoltaic, increases the challenge of balancing the power system. Energy storage technology is regarded as one of the key technologies for balancing the intermittency of variable renewable energy to achieve high penetration. This study reviews the energy storage technology that can accommodate the high penetration of renewable energy. The basic energy storage technologies that can accommodate time-scale variation are reviewed first. The role of energy storage in the generation, transmission, distribution, and consumption for the high renewable energy penetration system is then analyzed. The supporting energy storage policies in the United States, the United Kingdom and China are summarized. Specific suggestions are proposed from the perspectives of technology, business and policy. This paper provides guidelines for planning energy storage to enable a high renewable penetration power system.

[15] Energy Storage in High Penetration of Renewable Energy Power System: Technologies, Application and Supporting Policies by L. Ma, H. Li, Y. Ge, J. Shi, G. Liu, & B. Li[edit | edit source]

Integrating renewable energy is one of the most effective way to achieve low-carbon energy system. High penetration of variable renewable energy such as wind power and photovoltaic rises the challenge of balancing the power system. Energy storage technology is regarded one of the keys technology for balancing the intermittency of variable renewable energy to achieve high penetration. This study reviews the energy storage technology that is able to accommodate high penetration of renewable energy. The basic technologies of energy storage that is effective in accommodating the variation in different time scale is firstly reviewed. The role of energy storage in the generation, transmission, distribution, and consumption for high renewable energy penetration is then analyzed. The energy storage supporting policies in the United States and Chinese are summarized. This paper provides guidelines for planning energy storage towards high penetration of renewable energy power system.

[16] Flattening the "duck curve" to get more renewable energy on the grid by Robert, David in Vox.[edit | edit source]

According to the author, the first big strategy to flatten the duck is interconnection. The more grids can be connected with one another to form larger grids over larger areas, the more spread out the potential for wind and solar will be and the more spread out load will be, both of which will serve to smooth out the peaks and valleys in the curve. The second big strategy is energy storage. If you can store some of that wind and solar energy rather than automatically sending it to the grid, you make it "dispatchable," meaning you can time it. It becomes a movable piece of the puzzle. He offers 10 practical ideas to start flattening the duck:

  1. Target energy efficiency to the hours when load ramps up sharply
  2. Acquire and deploy peak-oriented renewable resources
  3. Manage water and wastewater pumping loads
  4. Control electric water heaters to reduce peak demand and increase load at strategic hours
  5. Convert commercial air conditioning to ice storage or chilled-water storage
  6. Rate design: Focus utility prices on the "ramping hours" to enable price-induced changes in load
  7. Deploy electrical energy storage in targeted locations
  8. Implement aggressive demand response programs
  9. Use inter-regional power exchanges to take advantage of diversity in loads and resources
  10. Retire inflexible generating plants with high off-peak must-run requirements

[17] Future Challenges of Grid-Connected Photovoltaic in Java Bali Power System by H. B. Tambunan, P. A. A. Pramana, B. B. S. D. A. Harsono, A. A. Kusuma, J. Hartono, & B. S. Munir[edit | edit source]

Utilization of renewable energy sources (RES) especially photovoltaic (PV) into electrical power system including Indonesia grid massively increasing. Furthermore, high PV penetration level can create duck curve phenomenon in net load profile. This study aim to describe the challenges introduced by grid-connected PV system into Java Bali power system. High PV penetration level changes the load shape in the middle of the day. The calculation results show that system operator must prepare high ramp rate from conventional power plant before reaching the peak load time. Therefore, system operator should make proper planning to deal with duck curve phenomenon.

[18] Flattening the Duck Curve Using Grid-friendly Solar Panel Orientation by Doroshenko M., Keshav, M., & Keshav, C.[edit | edit source]

In this paper, the authors proposed the use of a Grid-friendly Solar Panel Orientation to flattening the Duck Curve. By adopting grid-scale solar power, a utility can reduce both its carbon footprint and its fuel bills for legacy thermal generation plants. However, as solar penetration increases, generation can exceed load during the middle of the day, and diurnal variations in solar generation cause rapid ramps every morning and evening. This so-called 'duck-curve' causes increased wear and tear of thermal plants and wasteful curtailment. We study how flexibility in solar panel orientation at the time of installation can be used to flatten the duck curve mitigating these ramping problems. We find that grid-friendly panel orientation can indeed reduce ramping by 25-30%, also reducing overgeneration during mid-day periods, without significantly increasing net load. Thus, it is an attractive approach for future solar deployments.

[19] INCORPORATING RENEWABLES INTO THE ELECTRIC GRID: EXPANDING OPPORTUNITIES FOR SMART MARKETS AND ENERGY STORAGE[edit | edit source]

The cost of renewable energy has been quickly dropping and renewable energy generation has been rapidly growing in the United States, spurred by state and federal policies and technological advances. Moreover, projections going forward suggest ever increasing penetration of renewables into the electricity grid. The two most rapidly growing renewable energy sources, wind and solar, provide variable energy output that depends on the time of day, location, season, weather, and other factors. Integrating high levels of these renewables onto the grid will require a reimagining of the management of the grid. It will increase the demand for grid management services, opening up a new set of important opportunities for promising technologies and approaches. This report examines economic and technical considerations related to increasing integration of variable renewable energy resources onto the existing electric grid, which highlight the importance of emerging technologies and approaches in smart markets and energy storage that can help smooth this transition. Smart markets use new communications technologies to develop integrated approaches allowing for electricity demand to respond during times of high value. Energy storage technologies allow the temporary storage of electricity so it can be released during times of high value. The key report findings are outlined below.

Wind and solar are known as “variable energy resources” (VERs) because their output is variable. Generation of wind and solar depends on when the sun is shining and the wind is blowing, which is imperfectly predictable. There are regions in the world that are already successfully managing an extremely high penetration of renewable VERs. For example, Portugal was run 100 percent on wind, solar, and hydropower for four days straight in May 2016, and Texas hit a record level of 45 percent instantaneous penetration from wind generation during one evening in February of this year.

[20] Impact of High Renewable Penetration on the Power System Operation Mode: A Data-Driven Approach by Q. Hou, E. Du, N. Zhang, & C. Kang[edit | edit source]

The high penetration of renewable energy will substantially change the power system operation. Traditionally, the annual operation of a power system can be represented by some typical operation modes and acts as the basis for the power-system-related analysis. The introduction of highly penetrated renewable energy will make the power system operation mode highly diversified and variable. These modes may not follow traditional empirical patterns. In this paper, we propose a data-driven method based on high-dimensional power system operation data (including power flow, unit generation, and load demand) to identify the pattern of the operation modes and analyze the impact of high renewable penetration. Specifically, the proposed data-driven method is composed of simulation, preprocessing, clustering, dimension reduction, and visualization with the aim to provide an intuitive understanding of the operation mode variety under high renewable penetration. In addition, several indices are introduced to quantify the space dispersion, time variation, and seasonal consistency of operation modes. A case study on actual Qinghai provincial power system in China validates the effectiveness of the proposed data-driven method and indicates that the dispersion and time variation of operation mode will significantly increase in the beginning and then saturate with the increase in renewable penetration level. The operation mode is also less correlated with seasons in renewable energy dominated power system.

[21] Improving Duck Curve Profile, Enabling Peak-shaving and Increasing Self-sufficiency by Properly Designing Community Solar Projects by M. A. Hayat, F. Shahnia, & G. M. Shafiullah[edit | edit source]

Community solar consisting of rooftop photovoltaic systems (RPVs) and battery energy storage systems, when designed properly, can address and solve many of the design and adoptability challenges brought by the individual RPVs and battery energy storage. Such systems can benefit many of the remote and rural communities, that are usually supplied by diesel generators, or long traditional distribution lines, which in addition to being expensive often don't provide the reliability at desired level. These systems can also benefit most of the urban areas since the unmanaged penetration of RPVs has resulted in the undesired duck curve profile in the network. To this end, this paper proposes and verifies the appropriate design criteria for community solar projects with an aim to improve the network duck curve profile, enable peak-shaving and increase the self-sufficiency of the community.

[22] Integrating solar into Florida's power system: Potential roles for flexibility by Hale, Elaine T., Stoll, Brady L., & Novacheck, Joshua E.[edit | edit source]

Although Florida has very little photovoltaic (PV) generation to date, it is reasonable to expect significant deployment in the 2020s under a variety of future policy and cost scenarios. To understand these potential futures, the authors model Florida Reliability Coordinating Council operations in 2026 over a wide range of PV penetrations with various combinations of battery storage capacity, demand response, and increased operational flexibility. By calculating the value of PV under a wide range of conditions, we find that at least 5%, and more likely 10–24%, PV penetration is cost competitive in Florida within the next decade with baseline flexibility and all but the most pessimistic of assumptions. For high PV penetrations, we demonstrate Florida’s electrical net-load variability (duck curve) challenges, the associated reduction of PV’s value to the system, and the ability of flexibility options–in particular energy-shifting resources–to preserve value and increase the economic carrying capacity of PV. A high level of demand response boosts the economic carrying capacity of PV by up to 0.5–2 percentage points, which is comparable to the impact of deploying 1 GW of battery storage. Adding 4 GW of battery storage expands the economic carrying capacity of PV by up to 6 percentage points.

[23] Investigation of Daytime Peak Loads to Improve the Power Generation Costs of Solar-Integrated Power Systems by Afonaa-Mensah, Stephen, Wang, Qian, & Uzoejinwa, Benjamin B.[edit | edit source]

In this paper, the authors investigated Daytime Peak Loads to Improve the Power Generation Costs of Solar-Integrated Power Systems. Improving daytime loads can mitigate some of the challenges posed by solar variations in solar-integrated power systems. Thus, this simulation study investigated the different levels of daytime peak loads under varying solar penetration conditions in solar-integrated power systems to improve power generation cost performance based on different load profiles and to mitigate the challenges encountered due to solar variation. The daytime peak loads during solar photovoltaic generation hours were determined by measuring the solar load correlation coefficients between each load profile and the solar irradiation, and the generation costs were determined using a dynamic economic dispatch method with particle swarm optimization in a MATLAB environment. The results revealed that the lowest generation costs were generally associated with load profiles that had low solar load correlation coefficients. Conversely, the load profile with the highest positive solar load correlation coefficient exhibited the highest generation costs, which were mainly associated with violations of the supply-demand balance requirement. However, this profile also exhibited the lowest generation costs at high levels of solar penetration. This result indicates that improving daytime load management could improve generation costs under high solar penetration conditions. However, if the generation system lacks sufficient ramping capability, this technique could pose operational challenges that adversely impact power generation costs.

[24] Joint power generation schedules of wind farms and pumped storage power stations based on load tracking by Wenxia You, Yangjian Xiong, & Junxiao Chang[edit | edit source]

In this paper, the authors used load tracking to create Joint power generation schedules of wind farms and pumped storage power stations. Joint power generation of wind farms and a pumped storage power station is an important way to improve reliability for wind power paralleling in power grids. Based on load tracking, joint power generation schedules of wind farms and pumped storage power stations are researched. Two kinds of joint power generation schedules are put forward including fluctuation tracking mode and constant tracking mode. In order to analyze them, generation reliability index is set up, considering maximal output and generation schedule power of wind farms and pumped storage plants joint generation. The index can quantify generation reliability, by which proposed schedules are compared. Two kinds of proposed generation schedules are analyzed by an example. The results show that constant tracking generation schedule is better than fluctuation tracking generation schedule.

[25] Maximum Allowable Intermittent Renewable Energy Source Penetration in Java-Bali Power System by H. B. Tambunan, A. A. Kusuma, & B. S. Munir[edit | edit source]

The significant characteristic of renewable energy source especially solar and wind is intermittency. It could cause significant impact on power system stability and reliability. This study was conducted to estimate maximum allowable penetration of intermittent renewable energy source (IRES) where frequency and voltage must meet the stability and reliability requirement of the electric power system. The worst possible scenario that may occur in the system was used, such as penetration in weakest bus by sensitivity analysis, dry season, one time sudden loss of largest generator and IRES generation, and peak load and off-peak load condition. The result showed IRES penetration level into the Java-Bali power system using 2017 scenario in peak load is about 0.83% while in off-peak load is about 0.44% from total available generation in current condition without any action taken to compensate the penetration.

[26] Microgrid Energy Management System (EMS) using Optimization by LeSage, Jonathan[edit | edit source]

Energy management systems (EMS) help to optimize the usages of distributed energy resources (DERs) in microgrids, particularly when variable pricing and generation are involved. This example walks through the process of developing an optimization routine that uses forecast pricing and loading conditions to optimally store/sell energy from a grid-scale battery system. Two approaches are demonstrated: a heuristic state machine strategy and the linear program-based optimization approach. The main example uses a full microgrid simulation for validation of the EMS optimization algorithm. However, there is a purely MATLAB/Optimization Toolbox example that shows the formulation of the optimization without the validation study.

[19] Microgrid Modeling and Grid Interconnection Studies by Saleem, Hira Amna[edit | edit source]

The demand for renewable energies and their integration to the grid has become more pressing than ever before due to the various reasons including increasing population energy demand, depleting fossil fuels, increasing atmospheric population, etc. Thus the vision of a sustainable future requires easy and reliable integration of renewable distributed generators to the grid. This master’s thesis studies the dynamics of distributed generators when they are connected with the main grid. Simulink MATLAB is used for the design and simulations of this system. Three distributed generators are used in this system: Photo-voltaic converter, Fuel cell and diesel generator. The control and design of the power electronics converters is done to function properly in both grid-connected and islanding mode. The turbine governors in diesel generators control the proper functioning of diesel generator in both modes. The converters in both battery and PV make sure that they work properly in both grid-connected and islanding mode. The control of battery converter is designed in a way to function for load-shaving during unplanned load changes in the microgrid. This fully functioning microgrid is then connected with the main grid using Kundur’s two-area system and simulated for various faults and load changes. A collection of data at the point of common coupling which is the point of connection of microgrid and main grid is gathered for various cases in the grid-connected mode. The cases for faults in the external grid are simulated and then WEKA software is used to develop decision trees. The development of the decision trees can help in predicting the decision of islanding of microgrid. By increasing this database for more scenarios; the response of the generators in grid and distributed generators in microgrid can be studied with decision trees giving more accurate results

[27] Mitigation Strategy for Duck Curve in High Photovoltaic Penetration Power System Using Concentrating Solar Power Station by Wang, Qi, Chang, Ping, Bai, Runqing, Liu, Wenfei, Dai, Jianfeng, & Tang, Yi[edit | edit source]

Concentrating solar power (CSP) station is counted as a promising flexible power supply when the net load power curve is duck-shaped in high photovoltaic (PV) penetration power system, which may lead to the serious phenomenon of PV curtailment and a large-capacity power shortage. This paper presents a mitigation strategy that replaces thermal power station with CSP station to participate in the optimal operation of power system for solving the duck-shaped net load power curve problem. The proposed strategy utilizes the dispatchability of thermal storage system (TSS) and the fast output regulation of unit in the CSP station. Simultaneously, considering the operation constraints of CSP station and network security constraints of the system, an optimization model is developed to minimize the overall cost including operation and penalty. The results obtained by nonlinear optimization function demonstrate that the replacement of concentrating solar power (CSP) station contributes to reducing the PV curtailment and lost load, while increasing the available equivalent slope for power balance. Thus, the proposed mitigation strategy can promote the penetration of PV generation and improve the flexibility of power system.

[28] Model Predictive Control Approach for Optimal Power Dispatch and Duck Curve Handling Under High Photovoltaic Power Penetration by Ahmad, S. S., Al-Ismail, F. S., Almehizia, A. A., & Khalid, M.[edit | edit source]

In this paper, an energy management system (EMS) has been developed based on model predictive control (MPC) to optimally dispatch the power units and particularly handle the duck curve fast ramping events. The methodology is specifically developed considering higher penetration of solar photovoltaic power subjected to realistic physical constraints. Battery energy storage, load shedding and solar curtailment have been utilized to effectively control the duck curve fast ramping events. The proposed system has been assessed with the help of a case study using a 24-bus RTS system. Consequently, detailed flexibility analyses were carried out and it has been proven that the given energy management and control system is capable of handling fast ramping events of duck curve. Furthermore, it has been observed that the overall operation cost of the system is also minimized. The performance of the developed model is compared with traditional non-MPC based mixed-integer linear programming approaches and it has been concluded that MPC-based optimization is more economical and effective in handling the duck curve challenges.

[29] Modeling and Control of Wind/PV/Battery Micro-grid Based on MATLAB/Simulink by Li, Qiang, Yang, Libin, Ma, Liangyu, Liu, Weiliang, & Wang, Yinsong[edit | edit source]

Isolated micro-grid makes a great sense of supplying power in the area without large power grid covers. In this paper, an isolated operating micro-grid model is built based on the Matlab/Simulink environment, which contains miniature wind power system, PV system and energy storage system. In order to improve the quality of electrical energy, a composite control method with PI controller and neural network is designed. Simulation result shows the presented control method is effective.

[30] Modeling and simulation of a microgrid with multiple energy resources by Almada, J. B., Leão, R. P. S., Montenegro, F. F. D., Miranda, S. S. V., & Sampaio, R. F.[edit | edit source]

This work presents the modeling, control and operation of a microgrid with three power sources and a storage system. The sources are solar photovoltaic, fuel cell and wind turbine. The objective of this work is to describe useful models of typical sources, the calculation of its parameters and control design of the power electronic converters of the energy sources. The modeling of the full system including all the stages of the converters is performed using PSCAD/EMTDC software package. The microgrid was designed to operate connected to the main network. The microgrid operated appropriately for different steady state operating conditions.

[31] Modeling, Control, and Simulation of Battery Storage Photovoltaic-Wave Energy Hybrid Renewable Power Generation Systems for Island Electrification in Malaysia by Samrat, Nahidul Hoque, Ahmad, Norhafizan Bin, Choudhury, Imtiaz Ahmed, & Taha, Zahari Bin[edit | edit source]

Today, the whole world faces a great challenge to overcome the environmental problems related to global energy production. Most of the islands throughout the world depend on fossil fuel importation with respect to energy production. Recent development and research on green energy sources can assure sustainable power supply for the islands. But unpredictable nature and high dependency on weather conditions are the main limitations of renewable energy sources. To overcome this drawback, different renewable sources and converters need to be integrated with each other. This paper proposes a standalone hybrid photovoltaic- (PV-) wave energy conversion system with energy storage. In the proposed hybrid system, control of the bidirectional buck-boost DC-DC converter (BBDC) is used to maintain the constant dc-link voltage. It also accumulates the excess hybrid power in the battery bank and supplies this power to the system load during the shortage of hybrid power. A three-phase complex vector control scheme voltage source inverter (VSI) is used to control the load side voltage in terms of the frequency and voltage amplitude. Based on the simulation results obtained from Matlab/Simulink, it has been found that the overall hybrid framework is capable of working under the variable weather and load conditions.

[32] Modelling, Control and Simulation of a Microgrid based on PV System, Battery System and VSC by Ma Lu, Silvia[edit | edit source]

Nowadays, where the renewable energies are the new trend and sustainable technologies are starting to play a big role in the society in order to eliminate the dependence on fossil fuels, finding the way to collect these clean energies and to convert them into electricity at their highest performance is without any doubt essential. Nevertheless, the way to link the power generated from these renewable sources to the main grid is as well significant. The present project studies step by step the design, modelling, control and simulation of a microgrid based on several elements with a special focus to the Photovoltaic (PV) System and to the Voltage Source Converters (VSC). Modelling of the equivalent electric circuit model to simulate the working principle of a PV cell is studied in detail and a Maximum Power Point Tracking (MPPT) control algorithm to force the PV system works at its highest operation point is applied. A complete review of the two level VSC simplified model is elaborated and implemented to connect two DC sources (PV system and Battery system) to the main AC three-phase grid. Additionally, examples using the two level VSC real model based on six Insulated Gate Bipolar Transistors (IGBT) are tested, where the voltages modulation are obtained by applying Sinusoidal Pulse Width Modulation (SPWM). The control methodology for the several elements of the microgrid, most of them based on Proportional – Integral (PI) controllers to reduce the steady-state error to zero, ensures that a change in an input parameter of the system alters other components in order to reach a new balanced state in a small amount of time. The operation and behavior of the entire microgrid is checked using software MATLAB Simulink and the results show a proper performance.

[33] On operational flexibility in power systems by A. Ulbig, & G. Andersson[edit | edit source]

Operational flexibility is an important property of electric power systems. The term flexibility is widely used in the context of power systems although at times without a proper definition. The role of operational flexibility for the transition of existing power systems, many of them based on fossil fuels, towards power systems effectively accommodating high shares of variable Renewable Energy Sources (RES) has been widely recognized. Availability of sufficient operational flexibility is a necessary precondition for the grid integration of large shares of power in-feed from variable RES, for example wind power and photovoltaics (PV). The paper analyzes the role of operational flexibility in power systems. Necessary flexibility metrics for categorizing different types of operational flexibility are discussed. A new methodology for assessing the technically available operational flexibility is presented. Qualitative insights are derived, notably regarding the limits of RES integration for a given power system with its specific flexibility properties. An extensive simulation study is performed, assessing the role that operational flexibility has for the mitigation of challenges, namely curtailment, arising from high shares of variable RES in-feed.

[34] Optimal Operation of Concentrating Solar Power Station in Power System with High Penetration of Photovoltaic Generation by Chang, P., Tang, Y., Huang, Y., Zhao, J., Li, C., & Yuan, C.[edit | edit source]

For coal-dominated power system, there're difficulties in the adjustment ability with increase in photovoltaic (PV) permeability. Concentrating solar power (CSP) plant is a potential solar power generation worth studying to alleviate the influences of duck curve caused by high PV permeability. Because in CSP plant, the thermal storage device (TSD) can be dispatched flexibly and the unit controlled by TSD can regulate output quickly. If the advantages of CSP plant mentioned above are applied, the adjustment ability of high PV permeability power system can be enhanced. The effectiveness of the proposed method is verified by the simulation of IEEE-RTS24 node system. The simulation results indicate that the total cost, PV curtailment power and load loss power have been decreased after using the strategy proposed in this paper at the same PV permeability. Meanwhile, the duck curve can be smoothed to increase the maximum PV permeability.

[35] Optimal Thermal Unit Commitment for Solving Duck Curve Problem by Introducing CSP, PSH and Demand Response by Howlader, H. O. R., Sediqi, M. M., Ibrahimi, A. M., & Senjyu, T.[edit | edit source]

Nowadays, the installations of photovoltaics (PVs) in the smart grid have been growing dramatically because the price of PVs is falling drastically. Undoubtedly, this is a great achievement for the recent smart grid technology. However, the colossal penetration of PVs' power at the day-time changes the load demand of thermal generations (TGs) of a smart grid which creates duck shape load curve called duck curve. In a duck curve, peak and off-peak gaps are very large which increase the start-up cost (SUC) of TGs because the units of TGs must be turned ON and turned OFF frequently. Therefore, it is very significant to run TGs units optimally. Only an optimization technique is not enough to bring a good solution. This research considers concentrated solar power and pumped storage hydroelectricity (PSH) as the energy storages. Also, fuel cells are considered as the controllable loads in the demand side's smart houses. In addition, this paper considers the real-time price-based demand response. The optimal unit commitment (UC) of TGs, PSHs, and other generators is introduced for saving the fuel cost and SUC of TGs. The optimal results of the proposed model are determined by using MATLAB® INTLINPROG optimization toolbox. To evaluate the effectiveness of the proposed method, simulation results have been compared with some other methods.

[36] Optimization of Union College’s Hybrid Microgrid to Meet All Load by McMahon, Caitlin, & Dosiek.[edit | edit source]

In this study, the authors study a scenario with PVs and wind turbines should be analyzed, as well as a combined system with PVs, turbines, and storage. This creates the two scenarios which will be explored in this paper. Each scenario needs to be able to meet all of Union’s load throughout the entire year, or else it cannot be considered optimal. Currently the microgrid can supply up to 70 percent of the campus’ load, which peaks at approximately 2.4MW. The cogeneration plant supplies most of the load, peaking at 1.8MW, with solar panels providing about 6.3kW and the wind turbine providing less than 1.2kW. Hence, to reach 100 percent of the campus’ load, there needs to be at least 592.5kW of added power generation, or approximately 0.6MW. Based on [13], the microgrid may require more than this to reliably meet load, due to the variability in power supply from PVs and turbines. The simulation will reveal how much extra power needs to be added above 0.6MW.

[37] Probabilistic duck curve in high PV penetration power system: Concept, modeling, and empirical analysis in China by Qingchun Hou, Ning Zhang, Ershun Du, Miao Miao, Fei Peng, & Chongqing Kang (2019)[edit | edit source]

The high penetration of photovoltaic (PV) is reshaping the electricity net-load curve and has a significant impact on power system operation and planning. The concept of duck curve is widely used to describe the timing imbalance between peak demand and PV generation. The traditional duck curve is deterministic and only shows a single extreme or typical scenario during a day. Thus, it cannot capture both the probability of that scenario and the uncertainty of PV generation and loads. These weaknesses limit the application of the duck curve on power system planning under high PV penetration. To address this issue, the novel concepts of probabilistic duck curve (PDC) and probabilistic ramp curve (PRC) are proposed to accurately model the uncertainty and variability of electricity net load and ramp under high PV penetration. An efficient method is presented for modeling PDC and PRC using kernel density estimation, copula function, and dependent discrete convolution. Several indices are designed to quantify the characteristics of the PDC and PRC. For the application, we demonstrate how the PDC and PRC will benefit flexible resource planning. Finally, an empirical study on the Qinghai provincial power system of China validates the effectiveness of the presented method. The results of PDC and PRC intuitively illustrate that the ramp demand and the valley of net load face considerable uncertainty under high PV penetration. The results of flexible resource planning indicate that retrofitting coal-fired units has remarkable performance on enhancing the power system flexibility in Qinghai. In average, reducing the minimal output of coal-fired units by 1 MW will increase PV accommodation by over 4 MWh each day.

[38] Simplified Model of a Small Scale Micro-Grid - MATLAB & Simulink by Hiroumi Mita[edit | edit source]

This example shows the behavior of a simplified model of a small-scale micro grid during 24 hours on a typical day. The model uses Phasor solution provided by Specialized Power Systems in order to accelerate simulation speed. The micro-grid is a single-phase AC network. Energy sources are an electricity network, a solar power generation system and a storage battery. The storage battery is controlled by a battery controller. It absorbs surplus power when there is excess energy in the micro-network, and provides additional power if there is a power shortage in the micro-network. Three ordinary houses consume energy (maximum of 2.5 kW) as electric charges. The micro-array is connected to the power network via a transformer mounted on a post which lowers the voltage of 6.6 kV to 200 V.

The solar power generation and storage battery are DC power sources that are converted to single-phase AC. The control strategy assumes that the microarray does not depend entirely on the power supplied by the power grid, and the power supplied by the solar power generation and storage are sufficient at all times.

From 20h to 4h, the solar power generation is 0 W. It reaches the peak amount (5 kW) from 14h to 15h.

As a typical load change in ordinary houses, the amount of electric power load reaches peak consumption at 9h (6,500 W), 19h, and 22h (7,500 W).

From 0h to 12h and from 18h to 24h, battery control is performed by battery controller. The battery control performs tracking control of the current so that active power which flows into system power from the secondary side of the pole transformer is set to 0. Then, the active power of secondary side of the pole mounted transformer is always around zero.

The storage battery supplies the insufficient current when the power of the micro-grid is insufficient and absorbs surplus current from the micro-grid when its power is surpasses the electric load.

From 12h to 18h, battery control is not performed. SOC (State Of Charge) of the storage battery is fixed to a constant and does not change since charge or discharge of the storage

battery are not performed by the battery controller. When there is a power shortage in the micro- grid, the system power supplies insufficient power. When there is a surplus power in the micro-grid, surplus power is returned to the system power.

At 8h, electricity load No. 3 of an ordinary house is set to OFF for 10 sec by the breaker. A spike is observed in the active power on the secondary side of the pole transformer and the electric power of the storage battery.

[39] Solar Plus: A Holistic Approach to Distributed Solar PV by Eric O'Shaughnessy, Kristen Ardani, Dylan Cutler, & Robert Margolis[edit | edit source]

The authors use the National Renewable Energy Laboratory’s Renewable Energy Optimization (REopt) model to explore the customer-side economics of solar plus under various utility rate structures and net metering rates. We also explore optimal solar plus applications in five case studies with different net metering rates and rate structures. REopt deploys different configurations of PV, batteries, smart domestic water heaters, and smart AC units in response to different rate structures and customer load profiles.

[40] Solar plus: Optimization of distributed solar PV through battery storage and dispatchable load in residential buildings by Eric O'Shaughnessy, Kristen Ardani, Dylan Cutler, & Robert Margolis[edit | edit source]

As utility electricity rates evolve, pairing solar photovoltaic (PV) systems with battery storage has potential to ensure the value proposition of residential solar by mitigating economic uncertainty. In addition to batteries, load control technologies can reshape customer load profiles to optimize PV system use. The combination of PV, energy storage, and load control provides an integrated approach to PV deployment, which we call “solar plus”. The U.S. National Renewable Energy Laboratory’s Renewable Energy Optimization (REopt) model is utilized to evaluate cost-optimal technology selection, sizing, and dispatch in residential buildings under a variety of rate structures and locations. The REopt model is extended to include a controllable or “smart” domestic hot water heater model and smart air conditioner model. We find that the solar plus approach improves end user economics across a variety of rate structures – especially those that are challenging for PV – including lower grid export rates, non-coincident time-of-use structures, and demand charges.

[41] Solving the duck curve in a smart grid environment using a non-cooperative game theory and dynamic pricing profiles by Moataz Sheha, Kasra Mohammadi, & Kody Powell[edit | edit source]

With the intermittency that comes with electricity generation from renewables, utilizing dynamic pricing will encourage the demand-side to respond in a smart way that would minimize the electricity costs and flatten the net electricity demand curve. Determining the optimal dynamic pricing profile that would leverage distributed storage to flatten the curve is a novel idea that needs to be studied. Moreover, the economic feasibility of utilizing distributed electrical energy storage is still not given in the literature. Therefore, in this paper, a novel way of solving a citywide dynamic model using a bilevel programming algorithm is introduced. The problem is developed as a novel non-cooperative Stackelberg game that utilizes air-conditioning systems and electrical storage through the end-users to determine the optimal dynamic pricing profile. The results show that the combined effect of utilizing demand-side air-conditioning systems and distributed storage together can flatten the curve while employing the optimal dynamic pricing profile. An economic study is performed to determine the economic feasibility of 20 different cases with different battery designs and the level of solar penetration. Three metrics were used to evaluate the economic performance of each case: the levelized cost of storage, the levelized cost of energy, and the simple payback period. Most cases had levelized cost of storage values lower than 0.457 $/kWh, which is the lower bound available in the literature. Seven out of 16 cases have a simple payback period shorter than the lifetime of the system (25 years). The case with a 100 MW PV power plant and a battery storage of size 597 MWh, was found to be the most promising case with a simple payback period of 12.71 years for the photovoltaic plant and 19.86 years for the demand-side investments.

[42] Teaching the “Duck” to Fly by Jim Lazar[edit | edit source]

Jim Lazar confirms that electric grid managers and utilities can integrate high quantities of variable renewable energy, like solar and wind power, and dramatically reduce carbon emissions by using several existing, and dependable market-proven strategies and technologies in this update to the 2014 “Teaching the Duck to Fly.” The original analysis included ten strategies designed to reduce strain on the grid during daily periods of high renewable energy generation. The strategies, most of which still apply, include such measures as timing the use of energy-intensive equipment to coincide with high renewable energy production. This updated report identifies several new approaches that have proven effective and valuable to utilities already integrating high levels of renewable energy. These include the use of ice storage for air conditioning, controlling water and wastewater pumping, and focusing renewable energy purchases on projects that produce energy when demand is greatest, such as wind farms that peak in late afternoon.

The duck curve describes the new shape of consumer energy demand in markets with high levels of renewable energy. Demand in such markets, which used to peak in the early afternoon, now peaks later in the day, and grids may experience lower demand during the former mid-day peak. The updated strategies continue to enable substantially greater renewable energy integration, better system reliability, and lower costs by modifying the load profiles and better utilizing existing assets.

[43] Modelling, Control and Simulation of a Microgrid based on PV System, Battery System and VSC by Silvia Ma Lu[edit | edit source]

Nowadays, where the renewable energies are the new trend and sustainable technologies are starting to play a big role in the society in order to eliminate the dependence on fossil fuels, finding the way to collect these clean energies and to convert them into electricity at their highest performance is without any doubt essential. Nevertheless, the way to link the power generated from these renewable sources to the main grid is as well significant. The present project studies step by step the design, modelling, control and simulation of a microgrid based on several elements with a special focus to the Photovoltaic (PV) System and to the Voltage Source Converters (VSC). Modelling of the equivalent electric circuit model to simulate the working principle of a PV cell is studied in detail and a Maximum Power Point Tracking (MPPT) control algorithm to force the PV system works at its highest operation point is applied. A complete review of the two level VSC simplified model is elaborated and implemented to connect two DC sources (PV system and Battery system) to the main AC three-phase grid. Additionally, examples using the two level VSC real model based on six Insulated Gate Bipolar Transistors (IGBT) are tested, where the voltages modulation are obtained by applying Sinusoidal Pulse Width Modulation (SPWM). The control methodology for the several elements of the microgrid, most of them based on Proportional – Integral (PI) controllers to reduce the steady-state error to zero, ensures that a change in an input parameter of the system alters other components in order to reach a new balanced state in a small amount of time. The operation and behavior of the entire microgrid is checked using software MATLAB Simulink and the results show a proper performance.

[44] The Challenges and Opportunities of Renewable Energy Source (RES) Penetration in Indonesia: Case Study of Java-Bali Power System by Handrea Bernando Tambunan, Dzikri Firmansyah Hakam, Iswan Prahastono, Anita Pharmatrisanti, Andreas Putro Purnomoadi, Siti Aisyah, Yonny Wicaksono, & I. Gede Ryan Sandy[edit | edit source]

Nowadays, the integration of renewable energy sources, especially grid-connected photovoltaic, into electrical power systems, is increasing dramatically. There are several stimulants especially in the Java-Bali power system, including huge solar potential, a national renewable energy (RE) target, regulation support for prosumers, photovoltaic technology development, and multi-year power system planning. However, significant annual photovoltaic penetration can lead to critical issues, including a drop of netload during the day, ramping capability, and minimal load operation for thermal power plants. This study analyses the duck curve phenomenon in the Java-Bali power system that considers high shares of the baseload power plant and specific scenarios in photovoltaic (PV) penetration and electricity demand growth. This study also analyses future netload, need for fast ramping rate capability, and oversupply issues in the Java-Bali power system. The results showed that the duck curve phenomenon appears with a significant netload drop in the middle of the day because of high power generation from grid-connected PV. Furthermore, the need for fast ramp rate capability is critical for a higher peak load combined with the lowest netload valley. Moreover, the significant load growth with high grid-connected PV penetration level caused unit commitment issues for thermal power plants as baseload operators.

[45] The Duck Curve by Lazar, j. (2014)[edit | edit source]

The Duck Curve is a graph that shows the difference in electricity demand on the grid and the amount of available solar energy throughout the day. It was created by the California Independent System Operator (ISO) to demonstrate the electric load on the ISO grid on an average spring day when it’s sunny but temperatures are cool, meaning that demand is not as high as in height of summer when people are using AC or in the winter when heating homes is needed. Though adapting to changing consumer demand has been an issue that utilities have dealt with for over a century, seasonal usage patterns have changed, and the actual resources used for generating electricity have evolved. The Duck Curve illustrates an important aspect of the challenges that renewable energy such as solar poses to utility managers, and how these have evolved over a much shorter time period.

[46] The Duck Curve Characteristic and Storage Requirements for Greening the Island of Porto Santo by R. Torabi, A. Gomes, & F. Morgado-Dias[edit | edit source]

One of the barriers to a higher dissemination of renewable generation is the mismatch that may exist between availability and energy consumption. Also, when the penetration of solar generation is very high, and due to the natural variation of that energy source some challenging situations may occur if demand varies in the opposite direction, leading to what is called "duck curve". In this situation, conventional generation may be no longer able to accommodate the ramp rate and range needed to fully utilize solar energy in power systems with considerable amount of solar generation. It was revealed that this could result in "overgeneration" leading to generation curtailment, with consequent decrease in benefits and wasted investment Storage and demand-side management are tools that may be used for dealing with that discrepancy between solar and wind generation and the demand, including facilitating the accommodation of higher solar electricity, and at the same time to allow a more efficient operation of the power system. This paper explores the duck chart in Porto Santo island, examining how much photovoltaic generation might need to be cut off if additional grid flexibility operations are not taken into account. It is shown that the solar penetration as low as 40% of annual energy could lead to marginal curtailment rates that exceed 30% of the solar generation. However, employing energy storage systems allow much greater penetration of solar resources, achieving a 50% renewable portfolio standard.

[47] The Duck Curve on California’s Grid Will Encourage Innovation and Creative Thinking by Jim Lazar[edit | edit source]

If you work in solar power, the duck curve is proof of your growing industry’s ability to meet early afternoon peak power demand. If you work in efficiency, storage, or demand response, the curve is an opportunity to prove your value during fast-ramping periods in the late afternoon. If you work for a utility or regulatory agency, the duck is a triple challenge -- straining your grid, threatening profits, and challenging old system control and management models.

Thankfully, the duck curve is perfectly solvable. A set of proven strategies can teach the duck to fly. These strategies (explained below) tighten the duck’s belly and flatten its neck -- moving it from a “sitting duck” position to a “flight” pose, thereby helping solve one of the more pressing problems facing the power sector.

  1. Peak-oriented renewable energy is the most valuable
  2. Water is a battery, and we should use it that way
  3. Some resources are less valuable than we previously assessed

[48] The Duck Curve: What is it and what does it mean? - Energy Alabama by Daniel Tait[edit | edit source]

One probable solution for the duck curve can be found in a method called interconnection. This strategy involves connecting multiple energy grids together to make a large energy grid. In theory, this would broaden and disperse the load and availability of solar and wind across a larger area, which in turn would flatten the duck curve.

This strategy could provide a long term solution to the problem. However, although the technology already exists, the politics of a large, interconnected grid is unlikely due to “not in my backyard” concerns and securing the rights of way.

The second method of smoothing out the duck curve is committing to the storage of energy generated by solar and wind, instead of immediately sending that energy directly to the grid. The energy can then be “dispatched” when it’s needed, and would almost definitely flatten the curve. This method could prove very expensive to execute in near term however battery storage continues to fall in price and more utilities are actively seeking it as a viable solution.

[49] The Solar ‘Duck Curve’ Might Look Different Under Coronavirus by Jeff ST. John[edit | edit source]

As the coronavirus pandemic forces people across the country to shelter at home, utilities and grid operators are watching typical energy-use patterns change in unpredictable ways and trying to understand how this will affect their grid operations, power purchasing practices and long-term plans. The resulting shifts in typical energy patterns have been illuminating, according to Scott Hinson, Pecan Street’s chief technology officer. We know they’re not indicative of every household in America,” he said in an interview this week. “But it is illustrative of what happens when you tell people to stay home, stay cool and stay productive.” By running the data through a “per cooling-degree day” calculation, Pecan Street estimates that air conditioner usage was up about 40 percent in the last week of March compared to historical averages. That could have implications for utilities trying to predict increases in AC energy demand under lockdown conditions as the country moves into the warmer summer months.

[50] The SWIS DUCK – Value pricing analysis of commercial scale photovoltaic generation in the South West Interconnected System by Maticka, Martin J.[edit | edit source]

The Duck Curve is an illustrative term used to describe the compounding effect of small-scale renewable generation on the electricity demand curve in electricity markets. The “Duck Curve”11The effect is also been referenced as the “Duck Chart” in early literature on the topic. problem has become a topical subject in the electricity industry in recent years and is associated with issues such as minimum system demand, changes to ancillary service requirements, unsustainable ramp rates, risk of over generation, occurrences of negative wholesale prices and exit of large-scale thermal generation. Unfortunately in the South West Interconnected System (SWIS)22The SWIS is the name of the power system located in the south west corner in the state of Western Australia, Australia. the Duck Curve has been referenced at a cursory level with a limited amount of formal analysis with poorly formed views of the effect of what it is trying to describe. This paper summarizes a literature review undertaken of the Duck Curve focusing on econometric aspects rather than power system engineering in the SWIS. The paper develops a hypothesis and substantiates that behind the meter generation should be displacing the revenue from all generation, but the effect will be more pronounced in commercial scale PV farms as the generation profile of both rooftop and commercial PV is determined by irradiation levels that are in similar geographic locations. Due to high variance in electricity spot prices a value analysis approach was used to create a model suitable for Ordinary Least Squares analysis. This analysis showed evidence for the proposed hypothesis and more importantly that econometric rigger using easily accessible tools and techniques can be applied to the Duck Curve concept with significant results.

[51] The value of operational flexibility in power systems with significant wind power generation by F. Bouffard, & M. Ortega-Vazquez[edit | edit source]

There is a growing body of evidence demonstrating how large penetrations of wind power generation in power systems contribute to increase the cost and the complexity of grid operations. Those costs and increased complexity are directly linked to the random nature of the wind over time, which requires system operators to carry more reserve capacity to cope with that randomness if current security and reliability standards are to be maintained. Moreover, as the frequency spectrum of the wind generation random process is relatively wide (from 10-6 to about slightly above 1 Hz), the reserves available must be capable to be deployed fast enough to counter this variability. Therefore, in systems with significant wind power penetrations the security-constrained unit commitment programs should be capable of capturing the reserve capacity deployment requirements entailed by the random wind dynamics. More fundamentally, however, what is required is that the dispatchable portion of the generation system providing reserves is flexible enough. In other words, there must be enough flexible capacity available to ramp up and down so to shadow the wind's caprices. In this paper, we formulate a modification of the classic unit commitment formulation of assess the value of such operational flexibility in power systems with large proportions of wind capacity. We discuss some economic and technical indicators of flexibility.

Conclusion[edit | edit source]

The implementation of load shifting on smart appliances is not limited only to clothes washers, tumble dryers, and dishwashers. The methods used in this research can be extended to electrical heating circulation pump, electric heating of storage units, electric water heaters, room air conditioners, freezers, refrigerators, and electric oven and stove. We saw that the strategy of success of the process implemented for appliances operating like clothes washers and dishwashers depends on consumers' willingness to accept the solution if additional costs are sponsored by energy utility or balanced by savings via energy bill. For appliances operating similar to clothes dryers, utilities must define harmonized signal for shortage of power, and define business model where energy utilities sponsor the implementation of these “Internal energy management agent” modules.

References[edit | edit source]

[1] Wong, L. A., & Ramachandaramurthy, V. K. (2020). A Case Study on Optimal Sizing of Battery Energy Storage to Solve ‘Duck Curve’ Issues in Malaysia. 2020 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE), 1–4. https://doi.org/10.1109/ICSGCE49177.2020.9275649

[2] E. Du, N. Zhang, C. Kang, & Q. Xia. (2019). A High-Efficiency Network-Constrained Clustered Unit Commitment Model for Power System Planning Studies. IEEE Transactions on Power Systems, 34(4), 2498–2508. https://doi.org/10.1109/TPWRS.2018.2881512

[3] Ullah, I., Rasheed, M. B., Alquthami, T., & Tayyaba, S. (2020). A Residential Load Scheduling with the Integration of On-Site PV and Energy Storage Systems in Micro-Grid. Sustainability, 12(1), 184. https://doi.org/10.3390/su12010184

[4] Aleem, S. A., Hussain, S. M. S., & Ustun, T. S. (2020). A Review of Strategies to Increase PV Penetration Level in Smart Grids. Energies, 13(3), 636. https://doi.org/10.3390/en13030636

[5] Fallahi, F., & Maghouli, P. (2020). An efficient solution method for integrated unit commitment and natural gas network operational scheduling under “Duck Curve.” International Transactions on Electrical Energy Systems, 30(12), e12662. https://doi.org/10.1002/2050-7038.12662

[6] H. B. Tambunan, P. A. A. Pramana, & B. S. Munir. (2018). Analysis of Maximum Intermittent Renewable Energy Source Penetration on South of Sulawesi Power System. 2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS), 32–35. https://doi.org/10.1109/EECCIS.2018.8692960

[7] Zunnurain, I., & Maruf, M. N. I. (2017). Automated demand response strategies using home energy management system in a RES-based smart grid. 2017 4th International Conference on Advances in Electrical Engineering (ICAEE), 664–668. https://doi.org/10.1109/ICAEE.2017.8255439

[8] Luo, F., Yang, X., Wei, W., Zhang, T., Yao, L., Zhu, L., & Qian, M. (2020). Bi-Level Load Peak Shifting and Valley Filling Dispatch Model of Distribution Systems With Virtual Power Plants. Frontiers in Energy Research, 8. https://doi.org/10.3389/fenrg.2020.596817

[9] S. Park & W. Park. (2017). CES peak demand shaving with energy storage system. 2017 International Conference on Information and Communication Technology Convergence (ICTC), 1124–1126. https://doi.org/10.1109/ICTC.2017.8190874

[10] A. Chiş & V. Koivunen. (2019). Coalitional Game-Based Cost Optimization of Energy Portfolio in Smart Grid Communities. IEEE Transactions on Smart Grid, 10(2), 1960–1970. https://doi.org/10.1109/TSG.2017.2784902

[11] Confronting the Duck Curve: How to Address Over-Generation of Solar Energy. (n.d.). Energy.Gov. Retrieved January 20, 2021, from https://www.energy.gov/eere/articles/confronting-duck-curve-how-address-over-generation-solar-energy

[12] Howlader, H. O. R., Furukakoi, M., Matayoshi, H., & Senjyu, T. (2017). Duck curve problem solving strategies with thermal unit commitment by introducing pumped storage hydroelectricity renewable energy. 2017 IEEE 12th International Conference on Power Electronics and Drive Systems (PEDS), 502–506. https://doi.org/10.1109/PEDS.2017.8289132

[13] Gerard Francesco DG. Apolinario, Chen-Nien Chung, Tai-Ken Lu, & Chia-Chi Chu. (2020). Effect of Taiwan’s energy policy on unit commitment in 2025. Applied Energy, 277, 115585. https://doi.org/10.1016/j.apenergy.2020.115585

[14] L. Ma, H. Li, Y. Ge, J. Shi, G. Liu, & B. Li. (2019). Energy Storage in High Penetration of Renewable Energy Power System: Technologies, Application and Supporting Policies. 2019 IEEE Sustainable Power and Energy Conference (ISPEC), 1428–1433. https://doi.org/10.1109/iSPEC48194.2019.8975195

[15] H. Zhu, H. Li, G. Liu, Y. Ge, J. Shi, H. Li, & N. Zhang. (2020). Energy storage in high renewable penetration power systems: Technologies, applications, supporting policies and suggestions. CSEE Journal of Power and Energy Systems, 1–9. https://doi.org/10.17775/CSEEJPES.2020.00090

[16] Roberts, D. (2016, February 12). Flattening the “duck curve” to get more renewable energy on the grid. Vox. https://www.vox.com/2016/2/12/10970858/flattening-duck-curve-renewable-energy

[17] Doroshenko, M., Keshav, S., & Rosenberg, C. (2018). Flattening the Duck Curve Using Grid-friendly Solar Panel Orientation. Proceedings of the Ninth International Conference on Future Energy Systems, 375–377. https://doi.org/10.1145/3208903.3212029

[18] Tambunan, H. B., Pramana, P. A. A., Harsono, B. B. S. D. A., Kusuma, A. A., Hartono, J., & Munir, B. S. (2019). Future Challenges of Grid-Connected Photovoltaic in Java Bali Power System. 2019 5th International Conference on Science and Technology (ICST), 1, 1–4. https://doi.org/10.1109/ICST47872.2019.9166258

[19] Q. Hou, E. Du, N. Zhang, & C. Kang. (2020). Impact of High Renewable Penetration on the Power System Operation Mode: A Data-Driven Approach. IEEE Transactions on Power Systems, 35(1), 731–741. https://doi.org/10.1109/TPWRS.2019.2929276

[20] M. A. Hayat, F. Shahnia, & G. M. Shafiullah. (2019). Improving Duck Curve Profile, Enabling Peak-shaving and Increasing Self-sufficiency by Properly Designing Community Solar Projects. 2019 9th International Conference on Power and Energy Systems (ICPES), 1–5. https://doi.org/10.1109/ICPES47639.2019.9105403

[21] Hale, E. T., Stoll, B. L., & Novacheck, J. E. (2018). Integrating solar into Florida’s power system: Potential roles for flexibility. Solar Energy, 170, 741–751. https://doi.org/10.1016/j.solener.2018.05.045

[22] Afonaa-Mensah, S., Wang, Q., & Uzoejinwa, B. B. (2019, November 11). Investigation of Daytime Peak Loads to Improve the Power Generation Costs of Solar-Integrated Power Systems [Research Article]. International Journal of Photoenergy; Hindawi. https://doi.org/10.1155/2019/5986874

[23] Wenxia You, Yangjian Xiong, & Junxiao Chang. (2013). Joint power generation schedules of wind farms and pumped storage power stations based on load tracking. 2013 5th International Conference on Modelling, Identification and Control (ICMIC), 153–157.

[24] H. B. Tambunan, A. A. Kusuma, & B. S. Munir. (2018). Maximum Allowable Intermittent Renewable Energy Source Penetration in Java-Bali Power System. 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE), 325–328. https://doi.org/10.1109/ICITEED.2018.8534845

[25] LeSage, J. (n.d.). Microgrid Energy Management System (EMS) using Optimization. Retrieved February 22, 2021, from https://www.mathworks.com/matlabcentral/fileexchange/73139-microgrid-energy-management-system-ems-using-optimization

[26] Saleem, H. A. (2014). Microgrid Modeling and Grid Interconnection Studies. Masters Theses. https://trace.tennessee.edu/utk_gradthes/2756

[27] Saleem, H. A. (n.d.). Microgrid Modeling and Grid Interconnection Studies. 71.

[28] Wang, Q., Chang, P., Bai, R., Liu, W., Dai, J., & Tang, Y. (2019). Mitigation Strategy for Duck Curve in High Photovoltaic Penetration Power System Using Concentrating Solar Power Station. Energies, 12(18), 3521. https://doi.org/10.3390/en12183521

[29] Ahmad, S. S., Al-Ismail, F. S., Almehizia, A. A., & Khalid, M. (2020). Model Predictive Control Approach for Optimal Power Dispatch and Duck Curve Handling Under High Photovoltaic Power Penetration. IEEE Access, 8, 186840–186850. https://doi.org/10.1109/ACCESS.2020.3030100

[30] Ahmad, S. S., Al-Ismail, F. S., Almehizia, A. A., & Khalid, M. (2020). Model Predictive Control Approach for Optimal Power Dispatch and Duck Curve Handling Under High Photovoltaic Power Penetration. IEEE Access, 8, 186840–186850. https://doi.org/10.1109/ACCESS.2020.3030100

[31] Li, Q., Yang, L., Ma, L., Liu, W., & Wang, Y. (2015, January 1). Modeling and Control of Wind/PV/Battery Micro-grid Based on Matlab/Simulink. https://doi.org/10.2991/icitmi-15.2015.150

[32] Almada, J. B., Leão, R. P. S., Montenegro, F. F. D., Miranda, S. S. V., & Sampaio, R. F. (2013). Modeling and simulation of a microgrid with multiple energy resources. Eurocon 2013, 1150–1157. https://doi.org/10.1109/EUROCON.2013.6625126

[33] Samrat, N. H., Ahmad, N. B., Choudhury, I. A., & Taha, Z. B. (2014, April 30). Modeling, Control, and Simulation of Battery Storage Photovoltaic-Wave Energy Hybrid Renewable Power Generation Systems for Island Electrification in Malaysia [Research Article]. The Scientific World Journal; Hindawi. https://doi.org/10.1155/2014/436376

[34] Ma Lu, S. (2018). Modelling, Control and Simulation of a Microgrid based on PV System, Battery System and VSC. https://upcommons.upc.edu/handle/2117/114079

[35] A. Ulbig & G. Andersson. (2012). On operational flexibility in power systems. 2012 IEEE Power and Energy Society General Meeting, 1–8. https://doi.org/10.1109/PESGM.2012.6344676

[36] Chang, P., Tang, Y., Huang, Y., Zhao, J., Li, C., & Yuan, C. (2019). Optimal Operation of Concentrating Solar Power Station in Power System with High Penetration of Photovoltaic Generation. 2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), 1–6. https://doi.org/10.1109/APPEEC45492.2019.8994347

[37] Howlader, H. O. R., Sediqi, M. M., Ibrahimi, A. M., & Senjyu, T. (2018). Optimal Thermal Unit Commitment for Solving Duck Curve Problem by Introducing CSP, PSH and Demand Response. IEEE Access, 6, 4834–4844. https://doi.org/10.1109/ACCESS.2018.2790967

[38] McMahon, C., & Dosiek, P. (n.d.). Optimization of Union College’s Hybrid Microgrid to Meet All Load. 34.

[39] Hou, Q., Zhang, N., Du, E., Miao, M., Peng, F., & Kang, C. (2019). Probabilistic duck curve in high PV penetration power system: Concept, modeling, and empirical analysis in China. Applied Energy, 242, 205–215. https://doi.org/10.1016/j.apenergy.2019.03.067

[40] Simplified Model of a Small Scale Micro-Grid—MATLAB & Simulink. (n.d.). Retrieved February 22, 2021, from https://www.mathworks.com/help/physmod/sps/ug/simplified-model-of-a-small-scale-micro-grid.html;jsessionid=0fa3d76f77d51efb9d50e4201293

[41] O’Shaughnessy, E., Ardani, K., Cutler, D., & Margolis, R. (2017). Solar Plus: A Holistic Approachto Distributed Solar PV.

[42] O’Shaughnessy, E., Cutler, D., Ardani, K., & Margolis, R. (2018). Solar plus: Optimization of distributed solar PV through battery storage and dispatchable load in residential buildings. Applied Energy, 213, 11–21. https://doi.org/10.1016/j.apenergy.2017.12.118

[43] Sheha, M., Mohammadi, K., & Powell, K. (2020). Solving the duck curve in a smart grid environment using a non-cooperative game theory and dynamic pricing profiles. Energy Conversion and Management, 220, 113102. https://doi.org/10.1016/j.enconman.2020.113102

[44] Lazar, J. (n.d.). Teaching the “Duck” to Fly. 28.

[45] Tambunan, H. B., Hakam, D. F., Prahastono, I., Pharmatrisanti, A., Purnomoadi, A. P., Aisyah, S., Wicaksono, Y., & Sandy, I. G. R. (2020). The Challenges and Opportunities of Renewable Energy Source (RES) Penetration in Indonesia: Case Study of Java-Bali Power System. Energies, 13(22), 5903. https://doi.org/10.3390/en13225903

[46] The Duck Curve | NuScale Power. (n.d.). Retrieved January 20, 2021, from https://www.nuscalepower.com/environment/renewables/the-duck-curve

[47] Torabi, R., Gomes, A., & Morgado-Dias, F. (2018). The Duck Curve Characteristic and Storage Requirements for Greening the Island of Porto Santo. 2018 Energy and Sustainability for Small Developing Economies (ES2DE), 1–7. https://doi.org/10.1109/ES2DE.2018.8494235

[48] The Duck Curve on California’s Grid Will Encourage Innovation and Creative Thinking. (n.d.). Retrieved February 22, 2021, from https://www.greentechmedia.com/articles/read/californias-duck-curve-will-encourage-innovation

[49] Tait, D. (n.d.). The Duck Curve: What is it and what does it mean? - Energy Alabama. Retrieved January 20, 2021, from https://alcse.org/the-duck-curve-what-is-it-and-what-does-it-mean/

[50] The Solar ‘Duck Curve’ Might Look Different Under Coronavirus. (n.d.). Retrieved February 22, 2021, from https://www.greentechmedia.com/articles/read/tracking-the-duck-curve-under-coronavirus-lockdown-and-other-findings-from-pecan-street

[51] Maticka, M. J. (2019). The SWIS DUCK – Value pricing analysis of commercial scale photovoltaic generation in the South West Interconnected System. The Electricity Journal, 32(6), 57–65. https://doi.org/10.1016/j.tej.2019.05.020

[52] F. Bouffard & M. Ortega-Vazquez. (2011). The value of operational flexibility in power systems with significant wind power generation. 2011 IEEE Power and Energy Society General Meeting, 1–5. https://doi.org/10.1109/PES.2011.6039031

[53] Peak Shaving | What it is & how it works. (2019, February 13). https://www.next-kraftwerke.com/knowledge/what-is-peak-shaving