Modelling an integrated blockchain-based energy optimization platform with bilateral trading for microgrid communities

Abstract

The increase in residential solar energy and other Distributed Energy Resources (DER) calls for novel energy management solutions in the low-voltage (LV) distribution grid. Such solutions may come in the form of digital Peer-to-Peer (P2P) energy trading platforms or the emergence of energy communities where households share electricity between them through a microgrid in an optimized manner. In such networks it is common to have a third party as a central coordinator, in which case issues of privacy, security and independence arise. A solution may be found in blockchain and smart contract technology which allows for decentralized and secure coordination of self-interested and independent actors. In this thesis, an integrated blockchain-based energy management platform is modelled that will optimize energy flows in a microgrid whilst implementing a bilateral trading mechanism. physical constraints in the microgrid are respected by formulating an Optimal Power Flow (OPF) problem, which is combined with a bilateral trading mechanism in a single optimization problem. It is one of the first times such an integrated, combined optimization problem has been proposed. The Alternating Direction Method of Multipliers (ADMM) is used to decompose the problem to allow for distributed optimization and a smart contract is used to function as a virtual aggregator. The smart contract fulfills several functions, including distribution of data to all participants and executing part of the ADMM algorithm. The model is run using real data from a prosumer community in Amsterdam and several scenarios are tested to evaluate the impact of combining physical constraints and trading on performance of the algorithm, social welfare of the community and scheduling of energy flows and trading scheme. It is found that the combination of trading and physical constraints in a single optimization problem may mitigate inequality between households within the community. Furthermore, total costs of the whole community are reduced by 22% as compared to a baseline scenario, and total grid energy consumption is reduced by 30%. Total social welfare is found to be highest when optimizing energy flows based on physical constraints without using a trading mechanism, however such a platform is only viable when all costs are equally shared between all households. The combined scenario is found to give only a slightly lower total social welfare than the trade-only scenario, with the added benefit that the inclusion of grid constraints may dampen the market power of prosumers in the community, decreasing inequality between households.