INTEGRATION OF BLOCKCHAIN-BASED PEER-TO-PEER (P2P) ENERGY MARKETS IN BOTH RESIDENTIAL SETTING AND INDUSTRIAL WATER-ENERGY-NETWORK

Abstract

Alongside the proliferation of distributed renewable energy systems (DRESs), the concept of blockchain-based P2P energy markets has been gaining momentum in recent years. This promising concept has proven benefits such as an increase in energy cost savings, improvements in grid resiliency, and decarbonization. In this study, a day-ahead P2P energy market was applied in both residential settings and industrial water-energy-nexus (WEN). This study proposes a hierarchical model to facilitate the P2P energy trading process. The proposed model first uses a long-short-term memory (LSTM) recurrent neural network (RNN) architecture for day-ahead load/generation forecast. A continuous double-sided auction with various pricing mechanisms such as the Vickrey-Clarke-Groves (VCG) mechanism is performed. Trading strategies were implemented using the Zero-Intelligence Plus (ZIP) algorithm. Lastly, a demand-response optimization model is implemented. The proposed model shows how the energy consumption pattern is optimized due to load shifting away from peak hours, contributing to significant cost savings.