Ice Storage Air-Conditioning System Simulation with Dynamic Electricity Pricing: A Demand Response Study
AbstractThis paper presents an optimal dispatch model of an ice storage air-conditioning system for participants to quickly and accurately perform energy saving and demand response, and to avoid the over contact with electricity price peak. The schedule planning for an ice storage air-conditioning system of demand response is mainly to transfer energy consumption from the peak load to the partial-peak or off-peak load. Least Squares Regression (LSR) is used to obtain the polynomial function for the cooling capacity and the cost of power consumption with a real ice storage air-conditioning system. Based on the dynamic electricity pricing, the requirements of cooling loads, and all technical constraints, the dispatch model of the ice-storage air-conditioning system is formulated to minimize the operation cost. The Improved Ripple Bee Swarm Optimization (IRBSO) algorithm is proposed to solve the dispatch model of the ice storage air-conditioning system in a daily schedule on summer. Simulation results indicate that reasonable solutions provide a practical and flexible framework allowing the demand response of ice storage air-conditioning systems to demonstrate the optimization of its energy savings and operational efficiency and offering greater energy efficiency. View Full-Text
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Lo, C.-C.; Tsai, S.-H.; Lin, B.-S. Ice Storage Air-Conditioning System Simulation with Dynamic Electricity Pricing: A Demand Response Study. Energies 2016, 9, 113.
Lo C-C, Tsai S-H, Lin B-S. Ice Storage Air-Conditioning System Simulation with Dynamic Electricity Pricing: A Demand Response Study. Energies. 2016; 9(2):113.Chicago/Turabian Style
Lo, Chi-Chun; Tsai, Shang-Ho; Lin, Bor-Shyh. 2016. "Ice Storage Air-Conditioning System Simulation with Dynamic Electricity Pricing: A Demand Response Study." Energies 9, no. 2: 113.
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