Agent-Based Modeling of Peer-to-Peer Energy Trading in a Smart Grid Environment

Abstract

The energy system is undergoing a drastic transition towards a system where previously passive consumers will play important roles. These consumers who actively participate in the energy system with a variety of distributed energy resources, such as electric vehicles, solar panels, and battery energy storage systems, become so-called prosumers as they can also generate electricity. This electricity can then be self-consumed, sold to the existing grid, or be sold to other consumers connected to the same electric network through Peer-to-Peer (P2P) trading schemes. This P2P energy trading may offer significant advantages to consumers involved as well as the wider electric system. The use of Agent-Based Modelling (ABM) can help address these problems. ABM models allow to understand complex and dynamic systems by incorporating the behavior of individual agents into the model as the individual behavior of the agents has a direct influence on the outcomes of the systems. In this paper, an ABM model is developed to examine the effects of increased consumer participation within a local energy system. This model utilizes a diverse set of consumers based on real-world data to model and provide insight into the interactions within a P2P energy trading system. The effects of P2P trading on financial outcomes as well as the share of renewable energy utilized within the local energy system is investigated. Results show that ABM models can accurately model P2P energy trading systems and can capture the effects of individual behavior of many active consumers within electrical systems. Also, it is shown that there may be a tradeoff between maximizing P2P energy trades within a community and maximizing the revenues of the prosumers.