A Simulation-based Decision Support System for Electric Power Demand Management Considering Social Network Interactions

Abstract

A two-level agent-based modeling framework is proposed for the electric power system to solve the problems of renewable energy utilization and demand-side management. While in the detailed level of the framework the customers and utility companies are modeled as agents to represent electricity demand and supply performances, respectively, the high level reflects the aggregated performance of the considered electricity market via state space models. To connect the two levels, a social network is introduced as a dynamic medium for the interactions among customer agents. While the customers' consumption behaviors are modeled at lower level and affected by each other, their individual performances contribute to the system performance in the high level. This dissertation concerns three problems. First, the problem of renewable energy adoption concerns penetration process of distributed solar systems with various incentive policies (i.e., Income Tax Credits and Feed-in Tariff) for renewable energy. The proposed hybrid model incorporates agent-based modeling and system dynamics to simulate the solar system diffusion process among the residential customers. Second, the demand-side management problem focuses on scheduling the Plug-in Hybrid Electric Vehicles (PHEV) charging under different scenarios of demand response programs (i.e., Time-of Use and Real-time Pricing). For the Time-of Use (TOU) program, the decision-support analysis results from simulation-based optimization for both customers and the utility company. For the Real-time Pricing (RTP) program, the discussion is to find proper pricing functions according to different customers. Third, the problem concerns the agent interaction based on different architectures of social network (i.e., small-world and scale-free) and the network evolution based on triadic closure. Such interaction is applied to the first two problems with the effect of changing the customers' social connections, preferences in consumption behaviors and acceptable grid prices. Furthermore, to extend the demand-side management problem, this research also discusses the energy management at individual households integrating PV generation system, battery storage and electric vehicle under demand response programs. The conceptual model is based on the threshold method to suggest residential customers when to use the electricity from which sources (PV generation, storage, or local grid).