Stochastic optimisation model to optimise the contractual generation capacity of a battery-integrated distributed energy resource in a balancing services contract

Popular heuristic approaches applied by demand response aggregators often use conservative tactics and may fall short of the contractual generation capacity of a distributed energy resource (DER) allocation in a balancing services (BS) contract. Hence, the possibility of optimising revenue remains generally unexplored. This research presents a novel framework for aggregators by employing a two-stage stochastic mixed integer nonlinear programming model to tackle site electricity demand unpredictability and the uncertainty of short-term operating reserve (STOR) calls to find the optimal generation capacity of a diesel generator (DG) to contract in STOR service. In the first stage, K-means clustering for innovative segmentation and rigorously categorising half-hourly site electricity demand data into optimal demand bins is employed. In the second stage, the model integrates a behind-the-meter battery energy storage system (BESS) to enhance performance and evaluate scenarios with and without BESS. Additionally, the study evaluates the effects of varying BESS capacities to enhance the contractual capacity of the DG, resulting in significantly improved revenue. A rigorous sensitivity analysis of penalty cost, utilization payment, and storage capacity ensures the robustness of the model across varied conditions. Results show the site revenue increases between 7.91 % and 20.27 % compared to the deterministic MIQCP approach previously employed.