Open AccessCoordinated multi‐objective scheduling of a multi‐energy virtual power plant considering storages and demand response
Author(s) -
Ghasemi Olanlari Farzin,
Amraee Turaj,
MoradiSepahvand Mojtaba,
Ahmadian Ali
Publication year - 2022
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/gtd2.12543
Subject(s) - demand response , virtual power plant , renewable energy , distributed generation , thermal energy storage , electric power , natural gas , automotive engineering , electricity , energy storage , photovoltaic system , computer science , engineering , electrical engineering , power (physics) , waste management , ecology , physics , quantum mechanics , biology
A virtual power plant (VPP) is a solution that brings distributed generation (DG) resources together and allows them to be optimally utilized to meet load demands in the presence of technical and pollution constraints. Electricity, heat, and natural gas are interdependent at the levels of generation, transmission, and consumption, and the interactions of these energy sources need to be considered. This paper presents an optimal model for daily operation of a multi‐energy virtual power plant (MEVPP), including electric, thermal, and natural gas sectors. MEVPP includes small‐scale gas‐fired and non‐gas‐fired DGs, combined heat and power (CHP), power to gas (P2G), boilers, electrical storage, electric vehicles (EV), and thermal storage. Renewable energy resources (RES), including wind turbines (WT), photovoltaic (PV), and PV‐thermal (PVT), also supply P2G technology. Smart grid technologies such as price‐based demand response (PBDR) and incentive‐based demand response (IBDR) are employed for electric loads. The proposed MEVPP model is eligible to participate in day‐ahead electricity, natural gas, heat markets, and electrical spinning reserve market. The scheduling model is multi‐objective to maximize MEVPP profit and minimize carbon dioxide emissions. The Epsilon constraint method is utilized to solve the problem, and the best Pareto point is chosen using the fuzzy satisfying approach.