This study builds a decision support tool to evaluate when it is a good economic decision (least cost with minimum discomfort) for the residential customer to invest in distributed energy resources (DERs) based on different electricity rate structures, DER ownership frameworks, and DER rebates offered by electric utilities. The tool is demonstrated using empirical electricity consumption data from Pecan Street Inc. (a non-profit entity based on Austin, Texas), residential rates from Austin Energy (the municipal electric utility in Austin, Texas), DER ownership costs from various nationwide pilot programs, and incentives offered by electric utilities in the United States. Results show that for constant electricity rates, the overall expenditure is least when the customer owns solar panels without storage, while for time-varying pricing structures, the least expensive scenario is one where the customer does not own any DERs. As the capital costs for DERs decline, utilities incentivize customer ownership of DERs, and more residential customers face the decision of whether to invest in DERs, this study aims to be a key tool in aiding that decision-making process.
Microgrids are small-scale power networks where distributed generation and inverter interfaced power sources are common. These networks are faced with more significant control challenges; a smaller system can less effectively dampen and distribute power disturbances or fluctuations, and the system frequency is less robust without synchronous generators to provide rotational inertia. In this paper we will develop optimal control algorithms to control the voltage and frequency in an islanded inverter-based microgrid. The voltages and frequency of this system are controlled using decentralized ℋ∞ control. The decentralized controllers operate using only local data, making the control methodolgy scalable. In addition, the studied controllers can be tuned to achieve the desired transient behavior. For voltage and frequency control of microgrids, transient performance is still an area of weakness. The proposed control scheme extends optimal control to the field of microgrid control and can improve the state of microgrid technology.
This study builds a generalized tool to forecast the change of 4 coincident peak (4CP) loads and payments based on varying amounts of solar, storage capacity, and population estimates over a 10-year period for utilities within the Electric Reliability Council of Texas (ERCOT). It also incorporates an optimization model for the energy storage systems (ESSs) that maximizes the sum of the revenue earned from their operation as well as the net 4CP payments received by the utility by attempting to arbitrage the ERCOT energy market. The tool is illustrated by using empirical data from the municipally-owned utility in Austin, TX (Austin Energy). 4CP payments can be on the order of tens of millions of dollars. Results indicate that solar and storage capacity can substantially lower these payments. For example, a 20 MW increase in solar capacity in 2018 would reduce Austin Energy’s payment by an estimated $200,000 for each subsequent year. By using the novel approach of incorporating coincident peak demand charge reductions at the DSP level, this study highlights the economic value of local generation and storage.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.