Demand response for variable renewable energy integration: A proposed approach and its impacts
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McPherson, Madeleine↗
Engineering topics
Stoll, Brady
Publications and source records attributed to Stoll, Brady.
Estimating the Value of Improved Distributed Photovoltaic Adoption Forecasts for Utility Resource Planning
Incorrectly forecasting distributed PV can impact both the capital and operations costs of future grid systems.
14 SOLAR ENERGY↗
Estimating the Value of Improved Distributed Photovoltaic Adoption Forecasts for Utility Resource Planning
Misforecasting the adoption of customer-owned distributed photovoltaics (DPV) can have operational and financial implications for utilities - forecasting capabilities can be improved, but generally at a cost.This paper informs this decision-space by quantifying the costs of misforecasting across a wide range of DPV growth rates and misforecast severities. Using a simplified probabilistic method presented within, an analyst can make a first-order estimate of the financial benefit of improving a utility's forecasting capabilities, and thus be better informed about whether to make such an investment. For example, we show that a utility with 10 TWh per year of retail electric sales who initially estimates that the increase in DPV's contribution to total generation could range from 2 percent to 7.5 percent over the next 15 years could expect total present-value savings of approximately 4 million if they could keep the severity of successive five-year misforecasts within +/- 25 percent. We also have more general discussions about how misforecasting DPV impacts the buildout and operation of the bulk power system - for example, we observed that misforecasting DPV most strongly influenced the amount of utility-scale PV that gets built, due to the similarity in the energy and capacity services offered by the two solar technologies.
14 SOLAR ENERGY↗
Estimating the Value of Improved Distributed Photovoltaic Adoption Forecasts for Utility Resource Planning
Misforecasting the adoption of customer-owned distributed photovoltaics (DPV) can have operational and financial implications for utilities; forecasting capabilities can be improved, but generally at a cost. This paper informs this decision-space by using a suite of models to explore the capacity expansion and operation of the Western Interconnection over a 15-year period across a wide range of DPV growth rates and misforecast severities. The system costs under a misforecast are compared against the costs under a perfect forecast, to quantify the costs of misforecasting. Using a simplified probabilistic method applied to these modeling results, an analyst can make a first-order estimate of the financial benefit of improving a utility’s forecasting capabilities, and thus be better informed about whether to make such an investment. For example, under our base assumptions, a utility with 10 TWh per year of retail electric sales who initially estimates that DPV growth could range from 2% to 7.5% of total generation over the next 15 years could expect total present-value savings of approximately $4 million if they could reduce the severity of misforecasting to within ±25%. Utility resource planners can compare those savings against the costs needed to achieve that level of precision, to guide their decision on whether to make an investment in tools or resources.
14 SOLAR ENERGY↗
Estimating the Value of Improved Distributed Photovoltaic Adoption Forecasts for Utility Resource Planning
Misforecasting the adoption of customer-owned distributed photovoltaics (DPV) can have operational and financial implications for utilities - forecasting capabilities can be improved, but generally at a cost. This paper informs this decision-space by quantifying the costs of misforecasting across a wide range of DPV growth rates and misforecast severities. Using a simplified probabilistic method presented within, an analyst can make a first-order estimate of the financial benefit of improving a utility's forecasting capabilities, and thus be better informed about whether to make such an investment. For example, we show that a utility with 10 TWh per year of retail electric sales who initially estimates that the increase in DPV's contribution to total generation could range from 2 to 7.5 percent over the next 15 years could expect total present-value savings of approximately 4 million dollars if they could keep the severity of successive five-year misforecasts within plus or minus 25 percent. We also have more general discussions about how misforecasting DPV impacts the buildout and operation of the bulk power system - for example, we observed that misforecasting DPV most strongly influenced the amount of utility-scale PV that gets built, due to the similarity in the energy and capacity services offered by the two solar technologies.
14 SOLAR ENERGY↗
The value of demand response in Florida
Many electrical loads may be operated flexibly to provide grid services, including peaking capacity, reserves, and load shifting. The authors model 14 demand end uses in Florida and analyze their operational impacts and overall value for a wide range of solar penetrations and grid flexibility options. They find demand response is able to reduce production costs, reduce the number of low-load hours for traditional generators, reduce starting of gas generators, and reduce curtailment.
14 SOLAR ENERGY↗
Renewable Energy Deployment in Colorado and the West: Extended Policy Sensitivities
The Resource Planning Model is a capacity expansion model designed for a regional power system, such as a utility service territory, state, or balancing authority. We apply a geospatial analysis to Resource Planning Model renewable energy capacity expansion results to understand the likelihood of renewable development on various lands within Colorado.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Hydropower Modeling Challenges
Hydropower facilities are important assets for the electric power sector and represent a key source of flexibility for electric grids with large amounts of variable generation. As variable renewable generation sources expand, understanding the capabilities and limitations of the flexibility from hydropower resources is important for grid planning. Appropriately modeling these resources, however, is difficult because of the wide variety of constraints these plants face that other generators do not. These constraints can be broadly categorized as environmental, operational, and regulatory. This report highlights several key issues involving incorporating these constraints when modeling hydropower operations in terms of production cost and capacity expansion. Many of these challenges involve a lack of data to adequately represent the constraints or issues of model complexity and run time. We present several potential methods for improving the accuracy of hydropower representation in these models to allow for a better understanding of hydropower's capabilities.
13 HYDRO ENERGY↗