Real-Time Peak Control algorithm using Stochastic Optimization

Battery energy storage systems (BESS) has several uses in electrical system such as peak demand control and energy arbitrage. The benefits of controlling peak can be viewed in both short term and long term. In a long term, it results in a lower electric cost in subsequent years, also it enhances stability and contributes to cost saving in a short term. In this study, we propose a novel Real Time Peak Demand Control, which incorporates a new time series dimensionality reduction technique dubbed TOU base Piecewise Approximation (TPA) for Dynamic Stochastic Optimization. Most peak demand control algorithms employ deterministic approach to controlling peak demand. These methods are not robust and are susceptible to errors. For analysis, we used past load profile obtained from a real site in South Korea. Simulations and the results obtained show that the proposed method achieves a better prediction and as such better peak demand control.