Value Of solar literature review/Endogenous Assessment of the Capacity Value of Solar PV in Generation Investment Planning Studies

Endogenous Assessment of the Capacity Value of Solar PV in Generation Investment Planning Studies[edit | edit source]

Endogenous Assessment of the Capacity Value of Solar PV in Generation Investment Planning Studies

• Citation

Munoz, Francisco D., and Andrew D. Mills. "Endogenous Assessment of the Capacity Value of Solar PV in Generation Investment Planning Studies." IEEE Transactions on Sustainable Energy 6, no. 4 (October 1, 2015): 1574–85. https://doi.org/10.1109/tste.2015.2456019.

• Abstract

There exist several different reliability-and approximation-based methods to determine the contribution of solar resources toward resource adequacy. However, most of these approaches require knowing in advance the installed capacities of both conventional and solar generators. This is a complication since generator capacities are actually decision variables in capacity planning studies. In this paper, we study the effect of time resolution and solar PV penetration using a planning model that accounts for the full distribution of generator outages and solar resource variability. We also describe a modification of a standard deterministic planning model that enforces a resource adequacy target through a reserve margin constraint. Our numerical experiments show that at least 50 days worth of data are necessary to approximate the results of the full-resolution model with a maximum error of 2.5% on costs and capacity. We also show that the amount of displaced capacity of conventional generation decreases rapidly as the penetration of solar PV increases. We find that using an exogenously defined and constant capacity value based on time-series data can yield relatively accurate results for small penetration levels. For higher penetration levels, the modified deterministic planning model better captures avoided costs and the decreasing value of solar PV.

Climate impacts on the cost of solar energy[edit | edit source]

Climate impacts on the cost of solar energy

• Citation

Flowers, Mallory E., Matthew K. Smith, Ara W. Parsekian, Dmitriy S. Boyuk, Jenna K. McGrath, and Luke Yates. "Climate Impacts on the Cost of Solar Energy." Energy Policy 94 (July 2016): 264–73. https://doi.org/10.1016/j.enpol.2016.04.018.

• Abstract

Photovoltaic (PV) Levelized Cost of Energy (LCOE) estimates are widely utilized by decision makers to predict the long-term cost and benefits of solar PV installations, but fail to consider local climate, which impacts PV panel lifetime and performance. Specific types of solar PV panels are known to respond to climate factors differently. Mono-, poly-, and amorphous-silicon (Si) PV technologies are known to exhibit varying degradation rates and instantaneous power losses as a function of operating temperature, humidity, thermal cycling, and panel soiling. We formulate an extended LCOE calculation, which considers PV module performance and lifespan as a function of local climate. The LCOE is then calculated for crystalline and amorphous Si PV technologies across several climates. Finally, we assess the impact of various policy incentives on reducing the firm's cost of solar deployment when controlling for climate. This assessment is the first to quantify tradeoffs between technologies, geographies, and policies in a unified manner. Results suggest crystalline Si solar panels as the most promising candidate for commercial-scale PV systems due to their low degradation rates compared to amorphous technologies. Across technologies, we note the strong ability of investment subsidies in removing uncertainty and reducing the LCOE, compared to production incentives.

Key Takeaways

Previous calculations of Levelized Cost of Solar in literature do not account for climate effects system lifetime

Local weather conditions impact the performance of panels according to the manufacturing technology.

Development of new cost analysis method where panel lifetime equals lifetime to reach 80% of initial performance

Method account for ambient temperature effect on PV system

Method do not include grid reliability, averted emissions or environmental impacts

MIT Energy Initiative - Utility of the future[edit | edit source]

MIT Energy Initiative - Utility of the future

• Citation

"Utility of the Future." MIT Energy Initiative. MIT, December 2016. http://energy.mit.edu/wp-content/uploads/2016/12/Utility-of-the-Future-Full-Report.pdf.

Key Takeaways

Value of electricity depends on location of supply and demand

Description of locational and non-locational values content in Distributed Energy Resources (DERs)

Value of DERs depends on location of injection nodes:

1. Constraints: line or transformer thermal capacity, node voltage, stability
2. Losses: depends on location of the node

Value of DERs increases where constraints and losses are binding

DERs can sometime contribute to losses by production of reverse power-flow for low voltages (Check Schmalensee 2015 and Goop 2016)

Distributed Solar PV may have more locational value than Centralized plants

LCOE calculated for crystalline and amorphous technologies for different climate regions

Degradation rates collected from field studies data

Method can be applied to broad range of solar PV installations

Incomplete method: could be improve by including environmental impacts and other related costs.

Optimal Policies to Promote Efficient Distributed Generation of Electricity[edit | edit source]

Optimal Policies to Promote Efficient Distributed Generation of Electricity

• Citation

Brown, David P., and David E. M. Sappington. "Optimal Policies to Promote Efficient Distributed Generation of Electricity." Journal of Regulatory Economics 52, no. 2 (October 1, 2017): 159–88. https://doi.org/10.1007/s11149-017-9335-9.

• Abstract

We analyze the design of policies to promote efficient distributed generation (DG) of electricity. The optimal policy varies with the set of instruments available to the regulator and with the prevailing DG production technology. DG capacity charges often play a valuable role in inducing optimal investment in DG capacity, allowing payments for DG production to induce the optimal production of electricity using non-intermittent DG technologies. Net metering can be optimal in certain settings, but often is not optimal, especially for non-intermittent DG technologies.

Key Take aways

Payments methods for DG technologies:

1. Net Metering
2. Value of Production (Value Of Solar in the cas of PV)
3. Avoided Cost

Compensation Policies must be made according to the DG technology considered

Net metering can be optimal for intermittent DG technologies

Net metering is not optimal even for intermittent DG technologies when regulator is unable to set DG capacity charges.

Distributed rate design: A review of early approaches and practical considerations for value of solar tariffs[edit | edit source]

Distributed rate design: A review of early approaches and practical considerations for value of solar tariffs

• Citation

Simshauser, Paul. "Distribution Network Prices and Solar PV: Resolving Rate Instability and Wealth Transfers through Demand Tariffs." Energy Economics 54 (February 1, 2016): 108–22. https://doi.org/10.1016/j.eneco.2015.11.011.

• Abstract

1-in-4 detached households in Southeast Queensland have installed rooftop solar PV—amongst the highest take-up rates in the world. Electricity distribution network capacity is primarily driven by periodic demand, and household load generally peaks in the early evening, whereas solar PV production peaks during the middle of the day and thus a mismatch exists. Compounding matters is the fact that the structure of the regulated two-part network tariff is dominated by a flat-rate variable charge. In this article, interval meter data at the customer switchboard circuit level confirms that solar households use only slightly less peak capacity than non-solar households and, that non-trivial cross-subsidies are rapidly emerging. A tariff model demonstrates that a peak capacity-based 'demand tariff' is a more efficient, cost-reflective and equitable pricing structure that improves the stability of tariffs given a rate-of-return regulatory constraint.

Key Takeaways

The purpose of the article is to identify a more efficient and stable 'structure' for network tariffs that accommodates solar PV and other distributed technology investments in a low growth environment, and for any given asset valuation.

Solar PV households consume dramatically less in lower value shoulder periods and because electricity tariffs are dominated by a flat-rate variable charge, they pay considerably less than non-solar households.

Residential customers in the Southeast Queensland distribution network are modeled using interval meter data at the customer switchboard circuit level for four typical household load types with and without air-conditioners, and with and without solar PV

Cost analysis in the article focuses on network charges

Consumer load data collected in houses at switchboard circuit level

Took into account the seasonal variation of the load

Proposition of a Three-Part Demand Tariff (comprising a fixed charge, a variable kWh charge, and a maximum kW demand charge) will produce a more efficient and more equitable price signal and will correct the incidence of 'implicit subsidies' that are rapidly emerging.

Unlike the Tariff Model contained in Simshauser and Downer (2015) which considered supply chain costs of generation, transmission, distribution and retail, in the present analysis only network tariffs are considered

Data Challenges in Estimating the Capacity Value of Solar Photovoltaics[edit | edit source]

Data Challenges in Estimating the Capacity Valueof Solar Photovoltaics

• Citation

Gami, Dhruv, Ramteen Sioshansi, and Paul Denholm. "Data Challenges in Estimating the Capacity Value of Solar Photovoltaics." IEEE Journal of Photovoltaics 7, no. 4 (July 2017): 1065–73. https://doi.org/10.1109/JPHOTOV.2017.2695328.

• Abstract

We examine the robustness of solar capacity-value estimates to three important data issues. The first is the sensitivity to using hourly averaged as opposed to subhourly solar-insolation data. The second is the sensitivity to errors in recording and interpreting load data. The third is the sensitivity to using modeled as opposed to measured solar-insolation data. We demonstrate that capacity-value estimates of solar are sensitive to all three of these factors, with potentially large errors in the capacity-value estimate in a particular year. If multiple years of data are available, the biases introduced by using hourly averaged solar-insolation can be smoothed out. Multiple years of data will not necessarily address the other data-related issues that we examine. Our analysis calls into question the accuracy of a number of solar capacity-value estimates relying exclusively on modeled solar-insolation data that are reported in the literature (including our own previous works). Our analysis also suggests that multiple years' historical data should be used for remunerating solar generators for their capacity value in organized wholesale electricity markets.

• Key Takeaways

1. Evaluation of PV capacity value estimation methods' robustness
2. Impact of using hourly solar irradiation Data on the PV capacity value
3. Impact of Day Light Savings on Load Data and PV capacity value
4. Sensitivity of PV capacity value to measured data as opposed to simulated sata
5. ELCC, increase of system load when solar is added to system without impact on reliability, is used as capacity value metric
6. ELCC computing methods and steps
7. 10 Locations case study with 1-min available data
8. Use of estimated solar production data

The capacity value of optimal wind and solar portfolios[edit | edit source]

The capacity value of optimal wind and solar portfolios

• Citation

Shahriari, Mehdi, and Seth Blumsack. "The Capacity Value of Optimal Wind and Solar Portfolios." Energy 148 (April 1, 2018): 992–1005. https://doi.org/10.1016/j.energy.2017.12.121.

• Abstract

Using large data sets of simulated wind and solar energy production, we create optimal wind, solar and blended (combined wind and solar) portfolios over various spatial and temporal scales, and use portfolio theory to quantify the capacity benefits in various portions of the electric grid in the Eastern United States. We add to the existing literature on portfolio analysis of renewable energy resources by (i) studying the benefits of optimal aggregation over various spatial and temporal scales, (ii) quantifying the capacity benefits of renewable portfolios over space and time, and (iii) analyzing spatial distributions of renewable installations in optimal renewable portfolios. The results indicate that full time availability of wind and blended portfolios are respectively 14 and 17 times larger than full time availability of an individual wind farm and adding solar to wind portfolios increases the availability factor of renewable portfolios by more than 40% in most regions. Further, optimal hourly portfolios provide higher capacity value relative to daily and weekly portfolios.

Key Takeaways

To fill in

Impacts of wind and solar spatial diversification on its market value: A case study of the Chilean electricity market[edit | edit source]

Impacts of wind and solar spatial diversification on its market value: A case study of the Chilean electricity market

• Citation

Odeh, Rodrigo Pérez, and David Watts. "Impacts of Wind and Solar Spatial Diversification on Its Market Value: A Case Study of the Chilean Electricity Market." Renewable and Sustainable Energy Reviews 111 (September 1, 2019): 442–61. https://doi.org/10.1016/j.rser.2019.01.015.

• Abstract

Renewable energy is expected to become the main electricity source in the world in the coming decades, with solar and wind power taking a big share of the energy supply. Although there has been a remarkable advance on renewable energy technologies, their integration is still difficult for regulators, market designers and system operators due to the high variability and limited predictability of solar and wind resources. Measures can be adopted to ease their integration, among them geographical diversification. There is plenty of literature about the diversification of solar and wind resources and there is a common conclusion: greater dispersion smooths out power production. However, literature on the effects of spatial diversification on the power system, electricity prices and renewable energy market value is much scarcer. This paper studies the effects of spatial diversification and questions whether integration policies are incentivizing the placement of renewable generators where they provide the highest value to the electricity system in Chile. Using real data and a simplified dispatch model the analysis presented shows evidence of the effects of diversification on wind and solar market value in Chile. Results suggest that spatial diversification has a strong positive effect on the market value of renewable energy, especially in scenarios with active transmission and hydro-storage constraints. Wind market value may vary up to US$10/MW h depending on the level of diversification and the spatial and temporal constraints of the system and, given current storage capacity of hydro reservoirs, the solar market value may increase US$5/MW h due to diversification if transmission capacity is enough. Even though these results must be observed with caution, because they depend on the assumptions made, there is an important effect of renewable spatial diversification that should be observed by regulators.

Key Takeaways

This study shows the impact of transmission and storage constraints on wind and solar market value

The rapid grown of solar and wind power generation the last five years is expected to continue and eventually leave conventional sources behind

If more generators are installed in one location (seeking a good primary resource)the market value of that location decreases (due to the excess of supply)

Economic value of VRE: Revenue that generators earn on the market

Since electricity storage is expensive, production time greatly affects the value of electricity (given that demand and resources availability change over time)

Electricity value depends on location (Losses and transmission)

VRE market value depends strongly on the system and its conditions(such as the energy mix, grid topology, transmission capacity and distance to the consumption centers)

Flexibility is understood as the ability of a system to withstand changes in power demand and generation, and more flexibility helps power systems to adopt more renewable energy, but also to mitigate the reduction of the economic value of VRE with increasing penetrations levels.

VRE market value is impacted by its spatial diversification

As renewable penetration increases, its market value goes down, and it becomes more difficult to integrate them. One of the main effects when renewable energy increase its share is a price drop, known as the"merit-order effect".

Spatial and temporal variation in the value of solar power across United States electricity markets[edit | edit source]

Spatial and temporal variation in the value of solar power across United States electricity markets

• Citation

Brown, Patrick R., and Francis M. O'Sullivan. "Spatial and Temporal Variation in the Value of Solar Power across United States Electricity Markets." Renewable and Sustainable Energy Reviews 121 (April 1, 2020): 109594. https://doi.org/10.1016/j.rser.2019.109594.

• Abstract

The cost of utility-scale photovoltaics (PV) has declined rapidly over the past decade. Yet increased renewable electricity generation, decreased natural gas prices, and deployment of emissions-control technology across the United States have led to concurrent changes in electricity prices and power system emissions rates, each of which influence the value of PV electricity. An ongoing assessment of the economic competitiveness of PV is therefore necessary as PV cost and value continue to evolve. Here, we use historical nodal electricity prices, capacity market prices, marginal power system emissions rates of CO2 and air pollutants, and weather data to model the energy, capacity, health, and climate value of PV electricity at over 10 000 locations across six U.S. Independent System Operators (ISOs) from 2010 to 2017. On the energy and capacity markets, transmission congestion in some locations and years results in PV revenues that are more than double the median across the relevant ISO. While the marginal public health benefits from avoided SO2, NOx, and PM2.5 emissions have declined over time in most ISOs, monetizing the health benefits of PV generation in 2017 would increase median PV energy revenues by 70% in MISO and NYISO and 100% in PJM. Given 2017 PV costs, electricity prices, and grid conditions, PV breaks even at 30% of modeled locations on the basis of energy, capacity, and health benefits, at 75% of modeled locations with the addition of a 50 $/ton CO2 price, and at 100% of modeled locations with a 100 $/ton CO2 price. These results suggest that PV cost decline has outpaced value decline over the past decade, such that in 2017 the net benefits of utility-scale PV outweigh the cost at the majority of modeled locations.

Key Takeaways

Focus on benefits provided by solar in terms of displaced energy, capacity, public health, and climate change from 2010 to 2017

"Merit-order effect" is most pronounced during times of day when solar energy generation is highest, causing the average market value of solar electricity to decline even more rapidly than the average electricity price

Simulation Used

1. Historical data for solar irradiation from NSRDB
2. PV Model in PVLIB Toolbox : PV orientation / inverter ration / system losses / module temperature losses
3. Horizontal 1-axis tracking crystalline silicon
4. Non-curtailable PV systems
5. A companion analysis uses this dataset to explore the impact of temporal PV output shaping

Four components of PV gen value are modeled:

1. Energy
2. Resource adequacy
3. Public Health benefits
4. Climate Change mitigation

Data Sources:

1. Meteorological data from NSRDB
2. Electricity prices from ISO
3. Marginal emissions and damages from Azevedo et al. and Siler-Evans et al. [17,48].
4. PV Capacity: open PV project

Limitations

1. Impact of explicit subsidies not included
2. The value is only assess for a short fixed period of time, not for the medium or long-run
3. Impact of solar forecasting not included
4. GHG emissions have not been quantified using a life cycle analysis
5. Monetized health and climate benefits are difficult to assess
6. The presence of an emissions cap and trade program can complicate an assessment of the marginal emissions offset of solar

Sunniest places are not always the most suitable for solar

Data and computer codes available here

Future wind and solar power market values in Germany — Evidence of spatial and technological dependencies?[edit | edit source]

Future wind and solar power market values in Germany — Evidence of spatial and technological dependencies?

• Citation

Eising, Manuel, Hannes Hobbie, and Dominik Möst. "Future Wind and Solar Power Market Values in Germany — Evidence of Spatial and Technological Dependencies?" Energy Economics 86 (February 1, 2020): 104638. https://doi.org/10.1016/j.eneco.2019.104638.

• Abstract

Achieving ambitious climate targets entails an extensive utilization of renewable energy sources. However, due to weather-dependent fluctuations, generation from variable renewable energy (VRE) sources is characterized by significantly lower market values in comparison to conventional technologies, reinforced by a decline in electricity prices. This development poses interesting questions as to its drivers and to what extent market values of wind and solar power can be influenced by the design of electricity markets. Against this background, a scenario-based analysis is conducted to trace the future development of market values using endogenously derived electricity prices considering different regional and technological VRE diversification strategies and investments into VRE technologies on a myopic basis. The results show a continued decline in market values with increasing regional discrepancies indicating a growing importance of inter-regional inter-dependencies for assessing the profitability of VRE. Furthermore, from a system point of view, a more distributed allocation of onshore wind capacities to contend with declining market values does not always prove to stabilize market values by facilitating a more constant feed-in pattern, contrary to expectation. Finally, replacing onshore with offshore wind energy appears to be beneficial as it can lead to an overall increase in the market values of offshore, onshore as well as PV generation technologies compared to other mitigation strategies. This result raise interesting questions about the systemic economic value of offshore wind despite its higher LCOE in the context of market integration of VRE.

Key Takeaways

The paper aims at contributing to the extant literature by assessing the development of the market value and its drivers with a spatially dis-aggregated representation

The weather-dependent variability of VRE generation as well as varying demand and high storage costs affect daily and seasonal price patterns

From a welfare- theoretical perspective,the market value can be seen as the marginal social benefit of VRE generation.

From an investor's perspective, the market value cor-responds to the specific market revenues (without subsidies) earned

The paper provides formulas to evaluate:

1. The short-term market value of VRE
2. The long-term market value of VRE
3. The Value Factor of VRE

Explanation of "merit-order" effect: exogenous and endogenous drivers influence the value factor

Exogenous drivers:

1. VRE market shares and feed-in pattern
2. Storage and demand flexibility
3. Fuel and CO2 prices
4. Cross-border network expansion
5. Power plant portfolios
6. Market structure and market design

Endogenous drivers

1. Technological diversity (TDiv)
2. Technological design (TDes)
3. Geographical diversity (GDiv)

Findings

1. Technological composition and geographical distribution of VRE impact their cost
2. PV value factor decreases significantly as VRE shares increase
3. Geographical diversification does not necessarily counteract falling VRE value factor
4. A geographically diverse generation portfolio was employed to level out the variability of the generation from VRE dueto a decreasing pair-correlation across distances
5. When pursuing investments in VRE plants, it is important not only to consider current local market revenues in conjunction with site-specific LCOE but also the future development of regional market values

A geographically resolved method to estimate levelized power plant costs with environmental externalities[edit | edit source]

A geographically resolved method to estimate levelized power plant costs with environmental externalities

• Citation

Rhodes, Joshua D., Carey King, Gürcan Gulen, Sheila M. Olmstead, James S. Dyer, Robert E. Hebner, Fred C. Beach, Thomas F. Edgar, and Michael E. Webber. "A Geographically Resolved Method to Estimate Levelized Power Plant Costs with Environmental Externalities." Energy Policy 102 (March 1, 2017): 491–99. https://doi.org/10.1016/j.enpol.2016.12.025.

• Abstract

In this analysis we developed and applied a geographically-resolved method to calculate the Levelized Cost of Electricity (LCOE) of new power plants on a county-by-county basis while including estimates of some environmental externalities. We calculated the LCOE for each county of the contiguous United States for 12 power plant technologies. The minimum LCOE option for each county varies based on local conditions, capital and fuel costs, environmental externalities, and resource availability. We considered ten scenarios that vary input assumptions. We present the results in a map format to facilitate comparisons by fuel, technology, and location. For our reference analysis, which includes a cost of $62/tCO2 for CO2 emissions natural gas combined cycle, wind, and nuclear are most often the lowest-LCOE option. While the average cost increases when internalizing the environmental externalities (carbon and air pollutants) is small for some technologies, the local cost differences are as high as $0.62/kWh for coal (under our reference analysis). These results display format, and online tools could serve as an educational tool for stakeholders when considering which technologies might or might not be a good fit for a given locality subject to system integration considerations.

Key Takeaways

LCOE typically only considers costs that are internal to the plant itself such as:

1. Capital costs (CAPEX)
2. Debt service costs
3. Fixed Operations and Maintenance costs
4. Variable O & M costs
5. The heat rate
6. The fuel cost
7. The capacity factor

The study incorporates region-specific data on CAPEX, O & M, and fuel costs, where available, and uses geographical interpolation techniques to calculate them on a county-by-county basis in the United States.

Not considered in the study:

1. Temporal fidelity
2. Levelized avoided cost of electricity (LACE)
3. The impact of subsidies
4. And the ability to incorporate performance factors

Methods: The approach used in the paper is the conventional LCOE formulation to which environmental externalities are integrated, after which the calculations are executed with geographical differentiation.

Formula for calculations of LCOE are provided

Summaries of Recent State Actions on Net Energy Metering Policies in Five Vertically Integrated and Five Restructured States[edit | edit source]

• Citation

Barber, Jamie, and Tom Stanton. "Summaries of Recent State Actions on Net Energy Metering Policies in Five Vertically Integrated and Five Restructured States," n.d.

Key Takeaways

Georgia

1. Discussion on VOS in play in the state of Georgia since 2014
2. Georgia Power defined 21 separate elements of system costs that could change, up or down, when a renewable resource is added to the grid.

Hawaii

1. One of the first states to end its net metering program and approve successor tariffs
2. New DG tariffs: grid-supply tariff and customer self-supply (CSS) tariff

Minnesota

1. VOS components: energy and its delivery; generation capacity; transmission capacity; transmission and distribution line losses; and environmental value.

DC Value Of Solar: On May 19, 2017, the DC Office of the People's Counsel (OPC) filed a value of solar study

New York: Value of Distributed energy Resources to replace Net Metering (Work in progress)

Review of State Net Energy Metering and Successor Rate Designs[edit | edit source]

• Citation

Stanton T. Review of State Net Energy Metering and Successor Rate Designs. National Regulatory Research Institute; 2019.

• Executive Summary

The objective of this paper is to summarize actions now being taken in many states to change rate designs for distributed energy resources (DER) on the customer side of the meter. Net energy metering (NEM) has been the most common rate design used for customers with small-scale generators that provide what is sometimes known as self-service power. Recently, there has been considerable interest in finding alternatives to net metering by legislatures and public utility commissions (PUCs), with some related deliberations underway or recently concluded in at least 48 states and the District of Columbia. These actions sometimes arise from preexisting legislative or regulatory requirements that trigger reviews when the total installed NEM system capacity or energy production, either for individual utilities or statewide, reaches a predetermined threshold. In other cases, regulatory reviews have been requested by utility companies through proposals to replace net-metering with other alternatives. Alternative proposals to supplant net metering include rate designs with various combinations of: (a) compensating for energy delivered to the grid at a price other than the retail service rate; (b) increasing fixed charges and sometimes also minimum bills; (c) time-varying rates; and (d) adding demand-charges to bills for customers who did not previously have them. Several states have considered creating a separate rate class for customers with distributed generation (DG), whereas others have made provisions for utility ownership of DG under specific circumstances. Another important factor included in this review is the treatment of customers who entered into NEM arrangements under previous rate designs. There is often some provision for grandfathering, allowing customers to continue operating under a previous rate design for some period after the new rate becomes effective. In some jurisdictions, the proposed or adopted changes affect all residential and small commercial customers, whereas in others the changes apply only to NEM customers. In some jurisdictions, NEM or successor rate designs also apply to customers participating in variations of aggregated, neighborhood, or virtual net metering, which often also includes participants in community-solar projects. The rates for community solar participants are often somewhat different from customers with on-site DG, but they are sometimes considered part of the same overall tariff. This NRRI briefing paper includes reviews of changes to NEM or DG program rate designs. The changes resulted from either new legislation that directs state commissions to make changes, or changes that state public utility regulatory commission have already adopted, or both. The review encompasses legislative changes that have occurred since 2014 and regulatory changes that took effect by mid-2018 or earlier. It draws from and expands upon information provided in the North Carolina Clean Energy Technology Center's 50 States of Solar series; particularly, the 4th Quarter Report and Annual Summary for 2017 (2018a) and Q1 and Q2 2018 Update Reports (2018b and 2018c).

Key Takeaways

• Many utilities and some other interested parties subsequently described NEM as a program that was inherently causing cross-subsidies to be paid by non-participating customers to participating customers.
• NEM programs, originally intended to support nascent markets for marginally cost-effective solar PV, have served their purpose and the time has come to replace them with cost-based or value-based tariffs.
• Exports from NEM customers are a service that the customer provides to the utility system, and the regulatory treatment should appropriately compensate NEM customers for the services they are producing and delivering
• Including subsidy for distributed solar in DG cost calculation is a complex question, which cannot be answered without detailed analysis, utility by utility
• Solar market moved from Uneconomic, to Pre-economic and is now competitive to grid electricity
• There are already roughly two dozen completed studies in over one dozen states (RMI 2013; Taylor, McLaren et al. 2015) to determine whether VOS should be based on the value of solar, cost of solar, or a mix of the two. Interestingly, there is little consistency in the findings from those studies to date.
• The studies have not all included the same list of potential benefits or costs, nor have they all used the same measurement methods. Thus, the resulting values range widely, from as little as 4¢/kWh to as much as 30¢/kWh, with a mean value of 16¢/kWh (RMI 2013).
• At present, there is no "one size fits all" system for completing these studies: fundamental differences of opinions remain among different interested parties about both the identification of benefit and cost categories to be included, and the appropriate methods and time horizons to use for estimating what those benefits and costs might be.
• There is a recognition that distributed resources are generally more valuable than bulk power in the wholesale market, due mainly to cost savings because of:

1. Reduced transmission and distribution system losses
2. Estimated value for environmental benefits

• On the other hand, several studies have also concluded that distributed resources are less valuable than the full retail rate
• VOS prices do not apply to all type of customers
• The state of Georgia identified nine distributed generation cost components providing a net benefit, six components providing a cost, and two components providing either a cost or a benefit.
• A 2014 Mississippi VOS study concluded that VOS was positive under all but one of the scenarios and sensitivities studied.
• Remaining question: Are studies of VOS, VDER, and utility costs of service measuring the right benefits and costs? Are they measuring all of them? And are the measuring methods valid and reliable?

The 50 States of Solar: 2019 Policy Review Q4 2019 Quarterly Report[edit | edit source]

• Citation

Proudlove, Autumn, Brian Lips, and David Sarkisian. "The 50 States of Solar: 2019 Policy Review Q4 2019 Quarterly Report." NC CLEAN ENERGY TECHNOLOGY CENTER, January 2020.

• Key Takeaways:

Topics of interest to us in the report

• Significant changes to state or utility net metering laws and rules, including program caps, system size limits, meter aggregation rules, and compensation rates for net excess generation
• Legislative or regulatory-led efforts to study the value of solar, net metering, or distributed solar generation policy, e.g., through a regulatory docket or a cost-benefit analysis

Increasing Solar market in the US as for end of 2019

Policy changes in discussion in most US States

In 2019, state and utility solar policies were undergoing review in nearly every state in the country, with 46 states and DC considering changes in 2019

Changes need to be done to fairly compensate new participant to the interconnected electricity grid web

States are conducting Extensive Value of Solar Studies to Inform Net Metering Successors

Table 2 in the report describes DG Compensation and Credit Rate Structures

DISTRIBUTED SOLAR VALUATION STUDIES REVIEW

• In 2019, 22 states plus DC were in the process of examining some element of the value of distributed generation.
• A study quantifying the value of distributed solar was completed in Mississippi.
• New DG valuation studies were launched in Connecticut and South Carolina, while utilities in Minnesota and Oregon filed updated value of solar calculations

Value Of solar study components

• Integration Cost
• Administrative Cost
• Avoided Energy
• Avoided Generation Capacity
• Avoided Transmission
• Avoided Distribution
• System/Line Losses
• Ancillary Services
• Risk/Price Hedging
• Market Price Suppression
• Environmental Benefits

Table 5 gives another list of component included in the valuation of solar in 3 US states

The Value of Solar Writ Large: A Modest Proposal for Applying 'Value of Solar' Analysis and Principles to the Entire Electricity Market[edit | edit source]

The Value of Solar Writ Large: A Modest Proposal for Applying 'Value of Solar' Analysis and Principles to the Entire Electricity Market

• Citation

Brown, Ashley C. "The Value of Solar Writ Large: A Modest Proposal for Applying 'Value of Solar' Analysis and Principles to the Entire Electricity Market." The Electricity Journal 29, no. 9 (November 2016): 27–30. https://doi.org/10.1016/j.tej.2016.10.009.

• Abstract

The essay sets out what electric markets might look like if the pricing proposed in value of solar studies were adopted for every resource.

• Key Takeaways

Criteria include in calculation of VOS

• Grid management benefits (e.g. dispatch, congestion, line losses, and ancillary services); :*Grid capacity benefits :*Generation benefits (including generating capacity)
• Carbon and other environmental benefits
• Jobs benefits
• Fuel and price hedge benefits

This paper argues that other sources of energy have the same benefits