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This paper presents a model for the optimization of a multi-area distribution grid considering the high penetration levels of photovoltaic (PV) resources and electric vehicle charging station (EVCSs) based on distributed flexible AC transmission system (D-FACTS) devices including on-load tap changer (OLTC) transformers and step voltage regulator (SVR) tap control and shunt capacitor (SC) and shunt reactor (ShR) to reduce losses and voltage deviation. The overarching research aim was to avoid the overvoltage caused by power injection by PV sources and the voltage drop caused by charging EVs in distribution grids. The proposed model was designed based on distribution network load flow and was optimized using a modified particle swarm optimization (M-PSO) algorithm. Two different test networks were tested under different scenarios to analyze the proposed model, revealing the efficiency of the proposed model.

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.A mixed-integer quadratic programming model for co-optimization of electric distribution and gas networks in the presence of distributed generation resources, power-to-gas systems, storage facilities, electric vehicles and demand-side management is applied to a 33-bus distribution network. The results of different scenarios show the efficiency of the proposed model.Graphical Abstract