Residential microgrid energy management considering flexibility services opportunities and forecast uncertainties

Abstract In the context of smart cities, the growing share of solar power induces uncertainty in power generation due to inherent climatic variations. Accurate forecasting will be a key point for future residential microgrids since inability to do so could dramatically impact power balance and grid stability. This paper proposes an enhanced energy management framework which aims to efficiently address uncertainty issues due to local climatic variations in a peninsula context. The proposed framework uses a ten-state Markov chain to generate stochastic solar irradiation as well as a forecast correction method based on recursive least-squares updated every hours in order to efficiently take part in hour-ahead power bidding process. Numerical results highlight benefits obtained by combining proposed forecast correction method and storage in a practical example of Saint-Nazaire, a city located in peninsula of Guerande, France. Besides, a sensitivity analysis regarding impact of storage size and aggregator penalty on operation cost and commitment indices is investigated. Obtained results demonstrates better accuracy in delivering power to the grid and will lead residential microgrids dealing with strong climatic variations to decrease their operation cost and increase power balance for all grid stakeholders.