Online distributed optimization for spatio-temporally constrained real-time peer-to-peer energy trading

The proliferation of distributed renewable energy triggers the peer-to-peer (P2P) energy market formations. To make profits, prosumers equipped with photovoltaic (PV) panels and even the energy storage system (ESS) can actively participate in the real-time P2P energy market and trade energy. However, in real situations, system states such as energy demands and renewable energy power generation are highly uncertain, making it difficult for prosumers to make optimal real-time decisions. Moreover, severe problems with the physical network can arise from the real-time P2P energy trading, such as bus voltage violations and line overload. To handle these problems, this work first formulates the real-time P2P energy trading problem as a spatio-temporally constrained stochastic optimization problem by considering ESS and the spatial physical network constraints. To deal with the uncertainties online, a modified Lyapunov optimization method is innovatively proposed to approximately reformulate the stochastic optimization problem into an online one by relaxing the time-coupling constraints. Compared with the state-of-the-art online methods, the proposed one renders more flexibility and better per-formance for the real-time P2P energy market operation. Additionally, to protect the prosumers' privacy, an online distributed algorithm based on the consensus alternating direction method of multipliers (ADMM) is developed to solve the reformulated online problem by decoupling the spatial constraints. The theoretical near -optimal performance guarantee of the proposed online distributed algorithm is derived, and its performance can be further improved by minimizing the performance gap. Simulation results demonstrate that the proposed online distributed algorithm can guarantee the fast, stable, and safe long-term operation of the real-time P2P energy market. Allied with a performance gap minimization step, the proposed online distributed algorithm can obtain approximately a 53.10% cost reduction compared to the greedy algorithm for the intraday market operation.

Automated summary

The proliferation of distributed renewable energy has led to the formation of peer-to-peer (P2P) energy markets, where prosumers equipped with photovoltaic (PV) panels and energy storage systems (ESS) can actively participate in real-time energy trading. However, uncertainties in system states and physical network problems pose challenges for prosumers to make optimal real-time decisions. To address these challenges, this work formulates the real-time P2P energy trading problem as a spatio-temporally constrained stochastic optimization problem and proposes a modified Lyapunov optimization method to handle uncertainties online. Additionally, an online distributed algorithm based on consensus alternating direction method of multipliers (ADMM) is developed to protect prosumers' privacy and solve the reformulated problem. The proposed algorithm ensures fast, stable, and safe operation of the real-time P2P energy market, with approximately 53.10% cost reduction compared to the greedy algorithm for intraday market operations.