Peer-to-Peer Energy Trading Case Study Using an AI-Powered Community Energy Management System

Abstract

The Internet of Energy (IoE) is a topic that industry and academics find intriguing and promising, since it can aid in developing technology for smart cities. This study suggests an innovative energy system with peer-to-peer trading and more sophisticated residential energy storage system management. It proposes a smart residential community strategy that includes household customers and nearby energy storage installations. Without constructing new energy-producing facilities, users can consume affordable renewable energy by exchanging energy with the community energy pool. The community energy pool can purchase any excess energy from consumers and renewable energy sources and sell it for a price higher than the feed-in tariff but lower than the going rate. The energy pricing of the power pool is based on a real-time link between supply and demand to stimulate local energy trade. Under this pricing structure, the cost of electricity may vary depending on the retail price, the number of consumers, and the amount of renewable energy. This maximizes the advantages for customers and the utilization of renewable energy. A Markov decision process (MDP) depicts the recommended power to maximize consumer advantages, increase renewable energy utilization, and provide the optimum option for the energy trading process. The reinforcement learning technique determined the best option in the renewable energy MDP and the energy exchange process. The fuzzy inference system, which takes into account infinite opportunities for the energy exchange process, enables Q-learning to be used in continuous state space problems (fuzzy Q-learning). The analysis of the suggested demand-side management system is successful. The efficacy of the advanced demand-side management system is assessed quantitatively by comparing the cost of power before and after the deployment of the proposed energy management system.

Techno-Economic Assessment of Peer to Peer Energy Trading: An Egyptian Case Study

“…In a case study in South Africa, a P2P energy trading scheme reduced the operating costs of prosumers by regulating internal energy trading between the prosumers, increasing the usage of energy from renewable energy sources, and decreasing the use of electrical energy supplied [22]. In order to improve community energy sharing in Tunis, an intelligent P2P energy trading strategy including smart homes, non-smart homes, and a local energy pool is proposed [23].…”

Environmental Sustainability in the Age of Deep Learning: Balancing Technological Advancement with Ecological Responsibility

“…Mahmoud and Slama [14] created a powered AI communal system for energy administration that is centred on peer-to-energy trading. The method maximizes customer benefits and renewable energy use through reinforcement learning techniques.…”

Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health

“…Mahmoud and Slama [62] introduced an AI-powered community energy management system that emphasizes peer-to-peer energy trading. Their approach focuses on maximizing consumer advantages and renewable energy utilization through reinforcement learning techniques.…”