Distributed energy storage system scheduling considering tariff structure, energy arbitrage and solar PV penetration

• Published in: Applied energy 2017-11, Vol.205, p.1384-1393
• Main Authors: Babacan, Oytun, Ratnam, Elizabeth L., Disfani, Vahid R., Kleissl, Jan
• Format: Article
• Language: English
• Subjects: Demand charge; Energy storage; Peak-load reduction; Reverse power flow; Solar PV; Time-of-use tariff
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2017.08.025

•A new convex optimization based energy storage charge scheduling algorithm is developed.•A novel supply charge tariff is proposed to incentivize solar PV self-consumption.•The algorithm is benchmarked and compared against two alternate methods proposed in the literature.•The algorithm minimizes the customer energy costs while providing important ancillary grid benefits.•A supply charge successfully restricts reverse power flow without a financial impact on customers. We develop a new convex optimization (CO)-based charge/discharge scheduling algorithm for distributed energy storage systems (ESSs) co-located with solar photovoltaic (PV) systems. The CO-based scheduling algorithm minimizes the monthly electricity expenses of a customer who owns an ESS and incorporates both a time-of-use volumetric tariff and a demand charge tariff. Further, we propose the novel idea of a “supply charge” tariff that incentivizes ESS customers to store excess solar PV generation that may otherwise result in reverse power flow in the distribution grid. By means of a case study we observe the CO-based daily charge/discharge schedules reduce (1) peak net demand (that is, load minus PV generation) of the customer, (2) power fluctuations in the customer net demand profile, and (3) the reliance of the customer on the grid by way of promoting energy self-consumption of local solar PV generation. Two alternate methods for behind-the-meter ESS scheduling are considered as benchmarks for cost minimization, peak net demand reduction, and mitigation of net demand fluctuations. The algorithm is tested using real 30-min interval residential load and solar data of 53 customers over 2-years. Results show that the CO-based scheduling algorithm provides mean peak net demand reductions between 46% and 64%, reduces mean net demand fluctuations by 25–49%, and increases the mean solar PV self-consumption between 24% and 39% when compared to a customer without an ESS. Introduction of a supply charge reduces the maximum solar PV power supply to the grid by 19% on average and does not financially impact ESS owners.