Optimal Scheduling of a Residential Energy Prosumer Incorporating Renewable Energy Sources and Energy Storage Systems in a Day-ahead Energy Market
Due to the world rapid population growth, the need for energy is accelerated especially in the residential sector. One of the most efficient ways of responding to energy demand is the utilisation of energy prosumers (EPs). EPs are able to consume and produce energy by using renewable energy sources (RESs) and energy storage systems (ESSs). In this paper, optimal scheduling and operation of a residential EP is proposed considering electricity price forecasting. A hybrid adaptive network-based fuzzy inference system (ANFIS)-genetic algorithm (GA) model is proposed for day-ahead price forecasting. Then, forecasted price values are applied to a real-world EP test system. It is revealed that the proposed hybrid ANFIS-GA model can forecast electricity prices properly. However, due to the high linearity of price patterns, the proposed algorithm was not able to accurately forecast peak-prices. Based on the results, the optimal operation of ESSs is affected by the uncertainty of electricity price.
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