Techno-Economic Analysis of Decentralized Residential Microgrid With Optimal Prosumer Selection

Government regulations and incentives supporting zero-carbon electricity production have significantly increased prosumers. This article conducts a techno-economic analysis, investigating the impact of significant factors such as fluctuating load needs, solar panel sizes, battery capacities, operating expenditures, and payback duration. A decentralized prosumer consortium comprised of home prosumers with roof-top solar generation is selected for the study. Utilizing the maximum renewable energy generated is a challenge in the prosumer consortium as the energy generation is not consistent throughout. The proposed model outlines a transformative approach to residential energy systems, introducing a prosumer consortium with a peer-to-peer energy trading network. Prosumers, equipped with solar panels and storage solutions, engage in active energy trading with consumers. This detailed economic analysis considers diverse prosumer and consumer profiles and accommodates dynamic load shifts based on prosumer load and generation patterns. The study explores three operating scenarios: a blended consortium of prosumers and consumers, prioritizing renewable energy usage during peak production, and inter-consortium energy pools for efficient resource utilization. Through a comprehensive evaluation of these scenarios, the system aims to identify economically optimal models for each case, ultimately determining the most profitable prosumer consortium model for participating entities. Compared to the traditional decentralized model, in the three scenarios, the utilization of surplus solar energy generated happened. The study shows that adopting decentralized prosumer consortium energy transactions, followed by a 25% to 30% load shifting, a load leveling-based demand response initiative, results in a 35% higher average profit in the system under consideration. When the system contains a suitable balance of consumers and prosumers, with consumers making up 70% to 80% of the overall consortium load, it reduces the average payback time by 40%.