P2P energy trading among isolated microgrids in the remote area

Abstract

As for the power supply in remote areas, the traditional transmission method by using the central grid has the drawbacks of large transmission loss and long transmission lines, which are easy to be broken. At the same time, there are areas where electricity is not available because of costly transmission losses, for the transmission networks are not yet widespread. This dissertation tends to make use of the local natural resources in remote areas and treat each household in the region as a microgrid to establish a multiple microgrid system. The P2P energy trading method is used in this dissertation to achieve the goal of self-production and self-sale of electricity in the microgrid system. Firstly, this dissertation uses the characteristics of numerous solar energy, wind energy, and biomass energy in remote areas to build the corresponding energy generation model. The heat energy generated by biomass is used to build a heat transfer model, and the CHP system in remote areas is composed of the charging and discharging model of the energy storage devices. Secondly, to eliminate the load imbalance caused by the difference of each MG's power generation, a network framework for each MG in the MMG system to conduct P2P energy trading activity has been established. Combining with the emission standard of carbon allowance, the incentive mechanism of each MG is set to improve the robustness of the model and the rationality of each MG while trading. Considering the delay of contacts in remote areas, the P2P energy transaction model in this dissertation does not use a central aggregator for information integration. Generally speaking, the mode of P2P energy trading is a decentralized process, with each MG becoming the main individual. The optimization goal of P2P energy trading in this dissertation is to maximize the profit of the whole MMG system after ensuring the balance of power supply and optimizing the profits of each MG. Therefore, the problem solved in this dissertation belongs to the category of a distributed optimization problem. Based on these analyses, this dissertation tends to use the Nash Bargaining model to build the relationship among MGs during the P2P trading process. And this dissertation intends to adopt two kinds of methods to solve the P2P energy trading problem: one is to make use of the metaheuristic algorithm, which takes each MG as an individual to solve the problem. The other is to use the distributed optimization algorithm to solve the problem, and thus optimize the profits of individual MG and the whole MMG system. In this dissertation, we have used two kinds of representative algorithms in two kinds of methods: ADMM and CLPSO. In the numerical cases, based on an MMG system composed of three MGs, this dissertation uses the above two algorithms to solve the model of the P2P energy trading problem and compares the variation of each MG load and power generation in different periods as well as the usage of various types of energy. The results of the numerical cases confirm the effectiveness of the proposed models and methods in this dissertation.