City Research Online

Abstract

One of the main goals of Smart grid is to achieve Demand Response by increasing the end users’ participation in decision-making and increasing the awareness that will help consumers to efficiently manage their energy consumption. However the existing demand response (DR) mechanism reduces power consumption based on predetermined policies of load priority (direct load control and pricing techniques) during the peak times without considering consumer comfort and environmental issues. Demand response has been achieved by forcefully shutdown the consumers’ loads during peak hours which violate users’ comfort life style. This is due to lacking of intelligent energy management system and smart automation tools at home level.

The main objective of this thesis paper is to develop a model based intelligent decision supporting Energy Management system which will understand the customer consumption behaviours while simultaneously reduce the energy consumptions. To achieve these, a Fuzzy Multi Criteria Decision Making (MCDM) based load controller has been developed to prioritize the consumers’ preferences and to take decision on behalf of the consumers in order to best manage the use of their appliances. The Fuzzy Multi Criteria Decision Making (MCDM) methodology has been used because it can solve decision and planning problems involving multiple criteria.

Furthermore a comparative analysis for the power consumption and cost saving performance is carried out to show the benefit of using renewable energy sources along with the proposed fuzzy MCDM based load controller. Simulation results show that the proposed load controller successfully limits the power consumption during the peak hours and concurrently maximizes the savings of energy consumption cost without violating consumer comfort level.

Publication Type:	Thesis (Masters)
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments:	School of Science & Technology > Engineering Doctoral Theses School of Science & Technology > School of Science & Technology Doctoral Theses

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