Prosumers matching and energy routing through Yens and SOS algorithms in P2P energy trading systems

Abstract

Growing clean-energy adoption integrates renewables into grids, enabling peer-to-peer energy trading, where the Energy Internet uses routers and efficient routing algorithms to deliver power between trading pairs. Energy routing involves pairing prosumers and choosing a non-congested, efficient transmission path. Given the unavoidable exogenous grid costs associated with peer-to-peer trading, an energy routing algorithm that accommodates these costs is essential. This paper formalizes the energy routing as a nonconvex mixed-integer nonlinear optimization problem that minimizes the consumer energy cost. To solve it, a semi-decentralized energy routing approach incorporating graph theory and metaheuristics is introduced. It efficiently determines for consumers the cost-effective producers, power allocation, and the efficient energy transmission paths with the lowest energy transmission cost while respecting the grid’s physical and market constraints. These constraints encompass exogenous costs, capacity limits, power flow direction constraints, and power losses accurate calculations during transmission—factors often overlooked by existing energy routing algorithms. It involves an energy transmission scheduling mechanism that addresses path and source conflicts, preventing congestion during simultaneous transmissions. The computational load is balanced between energy routers and network system operator. Simulations demonstrate its effectiveness in solving energy routing, preventing physical constraint violations, resolving path and source conflicts, and optimizing energy costs. • Formulating the energy routing problem as a non-convex MINL Optimization Problem. • A novel semi-decentralized approach using graph theory and metaheuristics. • The approach optimizes energy allocation and transmission paths. • Prevents congestion, resolves conflicts, reduces energy costs and transmission losses