Optimization of battery/supercapacitor-based photovoltaic household-prosumers providing self-consumption and frequency containment reserve as influenced by temporal data granularity

Abstract

Service complementarity between a frequency containment reserve and PV self-consumption can increase incomes for household-prosumers. Moreover, battery/supercapacitor-based hybrid energy storage systems (HESSs) play a major role. Fitting power and energy management improve HESS performance, and therefore increase the profitability of the asset. Furthermore, component sizing is critical. To achieve both targets, we developed a hybrid meta-heuristic optimization algorithm that deals with the management strategies and sizing. Accordingly, a four-dimensional, non-linear, non-convex, and mixed-integer optimization problem was formulated, and a cost function was minimized by combining the Haar wavelet (WT) transform and the teaching-learning-based optimization (TLBO) method. The algorithm has a flexible design, which is adapted in terms of a number of discrete states to suit input profiles defined according to different time discretizations. The effectiveness of the algorithm was proved by using different data granularity for a PV prosumer in Spain in various service scenarios. The simulations performed in this study reflected both technical and economic impacts. The results suggest that for optimization purposes, high-resolution data should be used to consider the full range of input fluctuations. However, these results largely depend on the service scenario setup. Indeed, in some scenarios, accurate results were obtained by using coarse-grained data, which entailed a lower computational burden. In contrast, in other scenarios, it was preferable to use data with a higher resolution. The optimal combination of services significantly increased the profitability of the asset.