Co-optimization of integrated energy systems in the presence of renewable energy, electric vehicles, power-to-gas systems and energy storage systems with demand-side management

Abstract

With the widespread penetration of renewable energy sources and energy storage systems, the problem of energy management has received increasing attention. One of the systems that network owners consider today is the power-to-gas (P2G) system. This system causes surplus electricity generated from renewable energy resources or batteries in the network to be converted into gas and sold to the gas network. Two reasons for the existence of gas distributed generation resources and P2G systems cause the two power and gas networks to interact. Energy management and profit making considering these two networks, as a co-optimization of integrated energy systems, is a topic that has been discussed in this study to achieve the best optimal answer. Since the production of renewable energy resources and the purchase price of energy are uncertain, a scenario-based method has been chosen for modelling. Demand-side management is also one of the important problems in optimal operation of the electricity network, which can have a significant impact on reducing peak load and increasing profits. In this paper, a mixed-integer quadratic programming model for co-optimization of electric distribution and gas networks in the presence of distributed generation resources, P2G systems, storage facilities, electric vehicles and demand-side management is presented. The 33-bus distribution network is intended to analyse the proposed model. The results of different scenarios show the efficiency of the proposed model. Several key points are deduced from the obtained results: (i) demand-side management is able to reduce the peak load of the network, (ii) the presence of renewable resources and batteries can cause the network to convert excess electricity into gas and sell it to the gas network in the market and (iii) distributed generation can reduce the purchase of energy from the upstream network and cause a 36% reduction in the cost function.