Peak Load Curtailment in a Smart Grid Via Fuzzy System Approach
Blerim Qela, Hussein T. Mouftah
Abstract
Blerim Qela, Hussein T. Mouftah
Abstract
Among many significant smart grid initiatives and challenges considered by many utilities and within the research community, are those associated with the energy management and conservation, in particular the management of energy demand during peak load periods. In this paper, a novel method for peak load curtailment by using a fuzzy system approach is presented. The proposed method is based on the application of fuzzy logic principles for peak load curtailment in a smart grid environment. The inputs to the system are the utility peak load data consisting of many energy demand scenarios, and the outputs are the necessary demand response power reductions required for the load curtailment during the peak load periods. The proposed method considers different peak load profiles and power consumption sources for multiple city regions. Furthermore, it is adaptable for use in many scenarios, such as those encompassing many input sources of power consumption with diverse input parameters of control (i.e., temperature offsets, duty cycle control, etc.) within numerous city regions. Thus, it can be applied to multiple output variables of control.
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Among many significant smart grid initiatives and challenges considered by many utilities and within the research community, are those associated with the energy management and conservation, in particular the management of energy demand during peak load periods. In this paper, a novel method for peak load curtailment by using a fuzzy system approach is presented. The proposed method is based on the application of fuzzy logic principles for peak load curtailment in a smart grid environment. The inputs to the system are the utility peak load data consisting of many energy demand scenarios, and the outputs are the necessary demand response power reductions required for the load curtailment during the peak load periods. The proposed method considers different peak load profiles and power consumption sources for multiple city regions. Furthermore, it is adaptable for use in many scenarios, such as those encompassing many input sources of power consumption with diverse input parameters of control (i.e., temperature offsets, duty cycle control, etc.) within numerous city regions. Thus, it can be applied to multiple output variables of control.
Key concepts: Smart grid, Load management, Demand response, Load balancing (electrical power), Fuzzy logic, Computer science, Electric power system, Grid