Fuzzy risk-based framework for scheduling of energy storage systems in photovoltaic-rich networks

Recently, the need to generate electricity with less carbon dioxide emissions has resulted in a significant integration of renewable energy sources (RESs) into the distribution networks. However, the electric power generated by many RESs, such as wind power and solar power, are intermittent. To compensate for this issue, energy storage systems (ESSs) are utilized as a potential alternative. For appropriate integration of ESSs and RESs in active distribution networks, optimization tools are indispensable. In this paper a risk-based framework is explored to optimize day-to-day charging/discharge schedule of ESS embedded photovoltaic (PV) and diesel generators in interconnected microgrids. Two primary objective functions, including the capital expenditure, and the Voltage Stability Factor (VSF) are developed and then simultaneously optimized through Multi-Objective Teaching Learning Based Optimization algorithm, which gives a set of solutions instead of a single global solution. Three final solutions are then chosen from the point of view of risk-averse, risk-neutral and risk-seeker operators among a set of solutions via a fuzzy concept. Moreover, due to integration of RERs in the system, an effective and time-efficient classification is utilized for modeling the uncertainty of such resources. Finally, Profitability and feasibility of the proposed approach are identified by implementing it on a practical 51-bus system that is exploited as the interconnected microgrids under various case studies.