The state of California is leading the nation with respect to solar energy and storage. The California Energy Commission has mandated that starting in 2020 all new homes must be solar powered. In 2010 the California state legislature adopted an energy storage mandate AB 2514. This required California's three largest utilities to contract for an additiona11.3 GW of energy storage by 2020, coming online by 2024. Therefore, there is keen interest in the potential advantages of deploying solar combined with energy storage. This paper formulates the optimization problem to identify the maximum potential revenue from pairing storage with solar and participating in the California Independent System Operator (CAISO) day ahead market for energy. Using the optimization formulation, five years of historical market data (2014-2018) for 2, 172 price nodes were analyzed to identify trends and opportunities for the deployment of solar plus storage.
This paper analyzes how two Kalman Filter (KF) based frequency estimation algorithms react to phase steps. It is demonstrated that phase steps are interpreted as sharp changes in frequency. The paper studies whether the location of the phase step, within the sinusoidal waveform, has any effect on the frequency estimate. Because phase steps are not the product of a permanent change in the underlying frequency, the paper proposes an algorithm to correct frequency estimates deemed erroneous. The algorithm makes use of the residual of the KF to determine when an estimate is incorrect and to trigger a corrective action in which the frequency estimate is replaced by an average of the previous values that were considered accurate. Using synthesized and simulated data with distortions the paper shows the effectiveness of the correction algorithm in fixing frequency estimates.
This paper explores the revenue potential for electric storage resources (ESRs), also referred to as electrical energy storage, in the Southwest Power Pool Integrated Marketplace. In particular, opportunities in the day-ahead market with the energy and frequency regulation products are considered. The revenue maximization problem is formulated as a linear program model, where an ESR seeks to maximize its revenue through the available revenue streams. The ESR has perfect foresight of historical prices and determines the optimal policy accordingly. A case study using FY2018 data shows that frequency regulation services are the most lucrative for revenue potential. This paper also explores different methods of using area control error data to infer the regulation control signal and the consequent effect on the optimization. Finally, the paper conducts a sensitivity analysis of ESR payback period to energy capacity and power rating.
This paper focuses on a transmission system with a high penetration of converter-interfaced generators participating in its primary frequency regulation. In particular, the effects on system stability of widespread misconfiguration of frequency regulation schemes are considered. Failures in three separate primary frequency control schemes are analyzed by means of time domain simulations where control action was inverted by, for example, negating controller gain. The results indicate that in all cases the frequency response of the system is greatly deteriorated and, in multiple scenarios, the system loses synchronism. It is also shown that including limits to the control action can mitigate the deleterious effects of inverted control configurations.
Energy storage is a unique grid asset in that it is capable of providing a number of grid services. In market areas, these grid services are only as valuable as the market prices for the services provided. This paper formulates the optimization problem for maximizing energy storage revenue from arbitrage and frequency regulation in the CAISO market. The optimization algorithm was then applied to three years of historical market data (2014-2016) at 2200 nodes to quantify the locational and time-varying nature of potential revenue. The optimization assumed perfect foresight, so it provides an upper bound on the maximum expected revenue. Since California is starting to experience negative locational marginal prices (LMPs) because of increased renewable generation, the optimization includes a duty cycle constraint to handle negative LMPs. The results show that participating in frequency regulation provides approximately 3.4 times the revenue of arbitrage. In addition, arbitrage potential revenue is highly location-specific. Since there are only a handful of zones for frequency regulation, the distribution of potential revenue from frequency regulation is much tighter.