Risk-aware multi-objective planning of a renewable hybrid microgrid incorporating energy storage systems and responsive loads

The environmental pollution problem is intensified in recent years due to increasing fossil fuel consumption. In this regard, deployment of renewable resources can be a practical solution to decrease greenhouse gases and global warming. This paper proposes a risk-aware multi-objective programming consisting of operation cost and pollution objective functions to optimize the operation of a renewable hybrid microgrid composed of biomass-based conventional generators, wind turbines, photovoltaics and electrical and heat storage systems. According to the presence of uncertainties in such infrastructures, fluctuations of wind speed, solar radiation, loads and market price are modeled through a scenario generation and reduction procedure and then, the conditional value-at-risk index is used to measure the risk of decisions. Moreover, the epsilon constraint and fuzzy logic approaches are utilized to solve the problem and select the best solution in the Pareto set, respectively. A demand response program is also implemented for electrical and heat loads to analyze the influence of responsive loads. The results validate that the operation cost and pollution increase by about 10.03% and 11.31% in the risk-averse strategy, in return, robustness in the worst-case scenarios improves. As well, responsive loads decrease operation cost by about 9.8% under the uncertainties, however, the pollution increases by about 0.88%.