Energy Storage Planning for Profitability Maximization by Power Trading and Ancillary Services Participation

One of the main applications of energy storage systems (ESSs) is transmission and distribution systems cost deferral. Further, ESSs are efficient tools for localized reactive power support, peak shaving, and energy arbitrage. This article proposes an ESSs planning algorithm that includes all previous services. The proposed algorithm increases the distribution company profit and minimizes its future system upgrade cost. For a comprehensive planning algorithm, other options, such as including static VAR compensators (SVCs), feeders upgrade, or adding distributed generators, are considered along with ESSs. Different scenarios are utilized to model the load variation, the renewable resources’ intermittency, and the market price fluctuation. The problem constraints include an ESS dynamic model that reflects the capacity and lifetime limitations. Different battery technologies with different costs and dynamic characteristics are compared. The network power flow model is added to account for the voltage level and feeders’ capacity. Power limits constraints are included for the DGs and SVCs as well. The optimization problem is formulated as a mixed-integer quadratic programming problem. To validate the proposed technique, a case study is conducted on a real 41-bus radial feeder in Ontario, Canada using real data for renewable sources, loads, and market prices.