A Two-Stage Stochastic Optimization Scheduling Approach for Integrating Renewable Energy Sources and Deferrable Demand in the Spinning Reserve Market
Mohammad Mohsen Hayati, Ali Aminlou, Kazem Zare, Mehdi Abapour
Abstract
An ideal way to reap the benefits of demand flexibility is to use real-time pricing at the retail level. However, due to political situations and the current state of the industry, it is unlikely that this procedure will happen quickly in the future. An alternative method to achieve this goal is the use of deferrable loads and an aggregator to introduce these loads in retail markets. An alternative method for applying load flexibility in existing markets and operation protocols is the direct connection of renewable energy sources, energy storage systems, and deferrable load aggregators. Based on the type of coupling proposed in this paper, deferrable load aggregators can be coupled with renewable generators and large-scale energy storages, which, as a result, allocate their production to these loads. Also, the considered load aggregator is the type of plug-in electric vehicle connected to the grid. In this paper, a two-stage stochastic model is presented to evaluate the effects of integrating renewable energy sources and energy storage systems with large-scale deferrable load aggregators. Also, to examine the benefits of demand flexibility, a demand response method is analyzed. In addition, to coordinate the deferrable load aggregator with energy-producing sources, a dynamic planning algorithm is proposed. This research study is covered by probabilistic modeling. For the validation of the models and the proposed algorithm, the Western Electricity Coordinating Council (WECC) of the U.S. has been considered.