Multi-Agent Based Optimal Operation of Hybrid Energy Sources Coupled with Demand Response Programs

Tope Roseline Olorunfemi and Nnamdi I. Nwulu
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Tope Roseline Olorunfemi: Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa
Nnamdi I. Nwulu: Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa

Sustainability, 2021, vol. 13, issue 14, 1-20

Abstract: Electricity is an indispensable commodity on which both urban and rural regions heavily rely. Rural areas where the main grid cannot reach make use of distributed energy resources (DER), especially renewable energy sources (RES), in an islanded microgrid. Therefore, it is necessary to make sure there is a sufficient power supply to balance the demand and supply curve and meet people’s demands. The work done in this paper aims to minimize the daily operating cost of the hybrid microgrid while incorporating a demand response strategy built on an incentive-based demand response (IBDR) model. Three case studies were constructed and analyzed to derive the best, most reduced daily operational cost. This was achieved using the CPLEX solver embedded in algebraic modeling language in the Advanced Interactive Multidimensional Modeling Systems (AIMMS) software with multi-agent system (MAS); the MAS was used to make sure that the developed intelligent-based agents work independently to achieve an optimal microgrid system. The sensitivity analysis employed established that case study 2 gave the most reduced daily operation cost (USD 119), which represents an 8% reduction in the daily operational cost from case study 1 and a 9% reduction from case study 3. Then, we achieved 17% and 25% reductions, as compared to specific other approaches.

Keywords: demand response; distributed energy resources; incentive-based DR; multi-agent system; renewable energy sources (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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