Contract design of direct-load control programs and their optimal management by genetic algorithm

被引:19
作者
Lujano-Rojas, Juan M. [1 ,2 ]
Zubi, Ghassan [3 ]
Dufo-Lopez, Rodolfo [4 ]
Bernal-Agustin, Jose L. [4 ]
Garcia-Paricio, Eduardo [4 ]
Cataldo, Joao P. S. [5 ,6 ]
机构
[1] Ton Duc Thang Univ, Dept Management Sci & Technol Dev, Ho Chi Minh City, Vietnam
[2] Ton Duc Thang Univ, Fac Elect & Elect Engn, Ho Chi Minh City, Vietnam
[3] Triangle Res & Dev Ctr, Kfar Qara, Israel
[4] Univ Zaragoza, Dept Elect Engn, Calle Maria de Luna 3, Zaragoza 50018, Spain
[5] Univ Porto, INESC TEC, P-4200465 Porto, Portugal
[6] Univ Porto, Fac Engn, P-4200465 Porto, Portugal
关键词
Demand response; Direct-load control; Microgrid; Genetic algorithm; DEMAND RESPONSE; WIND POWER; OPTIMIZATION; SYSTEMS; BATTERY; HYBRID; MODEL; PERFORMANCE; REANALYSIS; STRATEGY;
D O I
10.1016/j.energy.2019.07.137
中图分类号
O414.1 [热力学];
学科分类号
摘要
A computational model for designing direct-load control (DLC) demand response (DR) contracts is presented in this paper. The critical and controllable loads are identified in each node of the distribution system (DS). Critical loads have to be supplied as demanded by users, while the controllable loads can be connected during a determined time interval. The time interval at which each controllable load can be supplied is determined by means of a contract or compromise established between the utility operator and the corresponding consumers of each node of the DS. This approach allows us to reduce the negative impact of the DLC program on consumers' lifestyles. Using daily forecasting of wind speed and power, solar radiation and temperature, the optimal allocation of DR resources is determined by solving an optimization problem through a genetic algorithm where the energy content of conventional power generation and battery discharging energy are minimized. The proposed approach was illustrated by analyzing a system located in the Virgin Islands. Capabilities and characteristics of the proposed method in daily and annual terms are fully discussed, as well as the influence of forecasting errors. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页数:16
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