[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.
