TY - GEN
T1 - Optimal operation of combined cooling heat and power microgrid with PEVs
AU - Wu, Ting
AU - Mai, Weijie
AU - Qin, Mingwen
AU - Zhang, Chunxue
AU - Li, Jiayong
AU - Nie, Yongquan
AU - Liu, Junwei
AU - Chung, C. Y.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/8/31
Y1 - 2015/8/31
N2 - Combined cooling heat and power microgrid with renewable energy sources and plug-in electric vehicles (PEVs) offers an effective solution to energy-related problems, such as growing energy demand, increasing energy costs, environmental concerns and grid security. Taking the advantage of bidirectional power flow characteristic of PEVs and time-of-use pricing structure, this paper proposes an energy dispatch model to optimally coordinate the electrical, cooling and heat output of various energy sources and minimize both operational costs and environmental pollution, considering various operational constraints of micro-generation, PEV charge/discharge constraints and distribution of PEV state-of-charge. On the basis of the prediction of the wind speed, power, cooling and heat demand for the next 24 hours, dynamic inertia weight particle swarm optimization algorithm is applied to optimize the operation schedule. Promising simulation results are presented to demonstrate the well-coordinated performance between the flexible PEV loads, micro-generations and external power grid by peak clipping and valley filling with minimal operational costs and pollution.
AB - Combined cooling heat and power microgrid with renewable energy sources and plug-in electric vehicles (PEVs) offers an effective solution to energy-related problems, such as growing energy demand, increasing energy costs, environmental concerns and grid security. Taking the advantage of bidirectional power flow characteristic of PEVs and time-of-use pricing structure, this paper proposes an energy dispatch model to optimally coordinate the electrical, cooling and heat output of various energy sources and minimize both operational costs and environmental pollution, considering various operational constraints of micro-generation, PEV charge/discharge constraints and distribution of PEV state-of-charge. On the basis of the prediction of the wind speed, power, cooling and heat demand for the next 24 hours, dynamic inertia weight particle swarm optimization algorithm is applied to optimize the operation schedule. Promising simulation results are presented to demonstrate the well-coordinated performance between the flexible PEV loads, micro-generations and external power grid by peak clipping and valley filling with minimal operational costs and pollution.
KW - combined cooling heat and power system
KW - microgrid
KW - particle swarm optimization
KW - plug-in electric vehicles
UR - http://www.scopus.com/inward/record.url?scp=84951335470&partnerID=8YFLogxK
U2 - 10.1109/PTC.2015.7232270
DO - 10.1109/PTC.2015.7232270
M3 - Conference article published in proceeding or book
AN - SCOPUS:84951335470
T3 - 2015 IEEE Eindhoven PowerTech, PowerTech 2015
BT - 2015 IEEE Eindhoven PowerTech, PowerTech 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Eindhoven PowerTech, PowerTech 2015
Y2 - 29 June 2015 through 2 July 2015
ER -