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

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.

Multi-objective energy management of island microgrids with D-FACTS devices considering clean energy, storage systems and electric vehicles

The development of microgrids is progressing due to intelligent load demands,clean energy,batteries and electric vehicles.The presence of such systems in microgrids causes power balance inconsistency,leading to increased power losses and deviation in voltage.In this pa-per,a mixed-integer non-linear programming model is proposed for modelling island microgrid energy management considering smart loads,clean energy resources,electric vehicles and batteries.Similarly,a flexible distributed AC transmission system device is proposed to prevent voltage deviation and reduce power losses.A scenario-based multi-objective function has been proposed to decrease energy losses and voltage deviations and energy outages of clean energy resources,reduce emissions from fossil-fired distributed generation and finally decrease load outages to reduce the vulnerability of the islanded microgrid.Regarding the proposed mixed-integer non-linear model and the high number of variables and constraints,a modified evolutionary algorithm based on particle swarm optimization has been proposed to solve the proposed model,which can be more efficient than other algorithms to achieve global optimal solutions.The model presented is implemented on a 33-node island microgrid and the results illustrate that the proposed algorithm and model are effective in reducing energy losses and voltage deviation,as well as reducing the vulnerability of the microgrid.The simulation results demonstrate that the proposed approach can lead to significant improvements in the performance of the microgrid.Specifically,the ap-proach can result in a 27%reduction in losses,a 6%reduction in pollution and a 31%improvement in voltage.Additionally,the approach allows maximum utilization of renewable energy sources,making it a promising solution for sustainable energy management.