Open AccessOptimal Scheduling of a Residential Energy Prosumer Incorporating Renewable Energy Sources and Energy Storage Systems in a Day-ahead Energy Market
Author(s) -
Hui Huang,
Wenyuan Liao,
Hesam Parvaneh
Publication year - 2021
Publication title -
distributed generation and alternative energy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.19
H-Index - 12
eISSN - 2156-3306
pISSN - 2156-6550
DOI - 10.13052/dgaej2156-3306.3542
Subject(s) - renewable energy , prosumer , energy storage , electricity , population , genetic algorithm , computer science , mathematical optimization , adaptive neuro fuzzy inference system , demand response , fuzzy logic , environmental economics , engineering , economics , fuzzy control system , mathematics , electrical engineering , power (physics) , artificial intelligence , physics , demography , quantum mechanics , sociology
Due to the world rapid population growth, the need for energy is acceler-ated especially in the residential sector. One of the most efficient ways ofresponding to energy demand is the utilisation of energy prosumers (EPs).EPs are able to consume and produce energy by using renewable energysources (RESs) and energy storage systems (ESSs). In this paper, optimalscheduling and operation of a residential EP is proposed considering elec-tricity price forecasting. A hybrid adaptive network-based fuzzy inferencesystem (ANFIS)-genetic algorithm (GA) model is proposed for day-aheadprice forecasting. Then, forecasted price values are applied to a real-worldEP test system. It is revealed that the proposed hybrid ANFIS-GA modelcan forecast electricity prices properly. However, due to the high linearityof price patterns, the proposed algorithm was not able to accurately forecast peak-prices. Based on the results, the optimal operation of ESSs is affectedby the uncertainty of electricity price.