article · Applied Sciences
An innovative energy system enables peer-to-peer trading alongside advanced residential energy storage management for smart residential communities. By linking household consumers with nearby energy storage facilities and a community energy pool, users access affordable renewable energy without building new generation plants. The energy pool buys surplus energy from consumers and renewables, reselling it at prices lower than standard retail rates yet higher than feed-in tariffs. Pricing adjusts dynamically to real-time supply and demand, influenced by retail rates, consumer numbers, and renewable generation. The trading and storage processes are formulated as a Markov decision process aimed at cutting costs and increasing renewable energy usage. A reinforcement learning approach using fuzzy Q-learning addresses continuous state space conditions to determine optimal trading strategies, showing quantitative electricity cost reductions when comparing costs before and after implementing the management system.
Rising energy prices and the shift towards renewables require smarter community-level solutions. By allowing neighbours to trade surplus renewable power and share local battery storage, communities can reduce their reliance on main grid power without constructing expensive new facilities. This lowers household electricity bills, encourages clean energy adoption, and supports local power stability.
The system targets smart residential communities, local energy cooperatives, and microgrid operators seeking software solutions to manage demand and peer-to-peer power sales. The research demonstrates algorithmic performance using a quantitative cost comparison, indicating an early-stage to applied analytical model. Further real-world field deployment and testing with physical meters and local network constraints would be required before commercial market release.
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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.
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DOI: 10.3390/app13137838
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