Data Modeling and Simulation for Local Energy Marketplace

Recently, policymakers have been increasingly focused on residential demand response (RDR) programs due to the critical peak load that is generated by residential consumers. However, residential customers often take a reactive approach to price or incentive-based signals, which causes RDR actions to lag behind market changes. This paper presents a comprehensive evaluation of demand response profiles using social and agent-based modeling simulations (ABM). The utility is produced by generation companies, residential customers participate in demand response (DR) events, retailers bridge the gap between the supply and demand sides, and the distributed system operator (DSO) regulates the market for maximum social welfare. Real data from 628 residential households in Qatar was used to verify the proposed methods and model. The results of the study suggest that a distributed energy exchange based on blockchain technology among agents can provide significant benefits for both the demand and supply sides. The proposed methods and models can be used by market operators, retailers, policymakers, and utility companies in Qatar to evaluate proactive RDR results in an interactive multi-entity market.