Residential Community Micro Grid Load Scheduling and Management System Using Cooperative Game Theory

Residential Community Micro Grid Load Scheduling and Management System Using Cooperative Game Theory

Descargar PDF Descargar PDF

Publicado en 3C Tecnología. Special Issue – May 2019

eem17052019_portada

Autores

Resumen

Abstract

This paper proposes a residential community based microgrid using cooperative game theory to share excessive energy within a community’s neighbor homes for optimal load scheduling and management. The proposed model is a grid connected residential community where smart homes are connected through central energy management system (EMS) to share the benefits of excessive distributed energy resources (DERs) from solar PV or wind turbine by selling to other community residents at a price lower than the utility gird but higher than the feed–in tariff. The community smart homes are categorized as Externally Importing Homes, Internally Exporting Homes and Externally Exporting Homes which are further classified as passive consumers, active prosumers and proactive prosumers based on the facilities they possess in form of DERs and battery storage (BS). With the cooperative energy transaction mechanism, the smart community homes after fulfilling their own load requirements can place the excessive energy on community poll using decentralized or centralized approach through peer to peer trading or smart community manager (SCM) respectively. The excessive energy can be sold or purchased to and from other community homes as per some defined preferences and priorities. This will benefit the entire community in terms of cost compared to the utility grid’s Time of Use (ToU) pricing. Proposed system will not only share, schedule and manage the community load optimally but will reduce the overall energy cost, system operational stress, improves system operational efficiency and reduces carbon emission.

Artículo

Palabras clave

Keywords

Residential Microgrid, Distributed Energy Resources, Cooperative Game Theory, Load Scheduling, Energy Management System.

Articulos relacionados