Integration of demand side and supply side energy management resources for optimal scheduling of demand response loads – South Africa in focus

Monyei, Chukwuka; Adewumi, A. O.

Abstract

© 2018 Elsevier B.V. The energy crisis of 2008 in South Africa, due to electricity demand surpassing supply and a depleted electricity reserve margin has exposed the need for more synergy between home energy management systems (HEMS) and supply side energy management systems (SSEMS). Demand side management (DSM) techniques have been investigated and proven to be viable means of regulating electricity demand from the consumer side. However, the viability of DSM is dependent on the participation of willing consumers. In this paper, a combined energy management system (CEMS) is proposed to provide a platform for incorporating the demands and constraints of consumers (time of dispatch, reduction of electricity costs, etc.) and suppliers (reduced operations cost, reduced emissions, etc.). The proposed CEMS utilizes dynamic pricing (DP) and a standard deviation biased genetic algorithm (SDBGA) in minimizing the DSM window to be allocated to the DSM loads of consumers based on the multi-objective constraints. The Medupi power plant which has been modelled to utilize carbon capture and sequestration (CCS) technology is used in carrying out the dispatch of the participating DSM loads (cloth washers, cloth dryers and dish washers) for 100,000 random residential customers. Results show that in dispatch option 1 (in which the user is in control of the start time), a lower cost of electricity of ZAR 373,218.40 is obtained compared to ZAR 416,280.20 by dispatch option 2 (in which the utility selects dispatch time for participating DSM loads) for the consumers. However, dispatch option 2 achieves a better minimized DSM window (14.94 MW), lower operating cost (about 1.6% lower than dispatch option 1), higher plant capacity utilization (87.92% efficiency) and a more evenly distributed profile.

Citation

Monyei, C., & Adewumi, A. O. (2018). Integration of demand side and supply side energy management resources for optimal scheduling of demand response loads – South Africa in focus. Electric Power Systems Research, 158, 92-104. https://doi.org/10.1016/j.epsr.2017.12.033

• Journal Article Type: Article
• Acceptance Date: Dec 30, 2017
• Publication Date: May 1, 2018
• Deposit Date: Dec 20, 2018
• Publicly Available Date: Jan 30, 2019
• Journal: Electric Power Systems Research
• Print ISSN: 0378-7796
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 158
• Pages: 92-104
• DOI: https://doi.org/10.1016/j.epsr.2017.12.033
• Keywords: demand side management, combined energy management system, home energy management system, supply side energy management system, standard deviation biased genetic algorithm
• Public URL: https://uwe-repository.worktribe.com/output/868748
• Publisher URL: https://doi.org/10.1016/j.epsr.2017.12.033