To maximize energy profit with the participation of electricity,natural gas,and district heating networks in the day-ahead market,stochastic scheduling of energy hubs taking into account the uncertainty of photovoltai...To maximize energy profit with the participation of electricity,natural gas,and district heating networks in the day-ahead market,stochastic scheduling of energy hubs taking into account the uncertainty of photovoltaic and wind resources,has been carried out.This has been done using a new meta-heuristic algorithm,improved artificial rabbits optimization(IARO).In this study,the uncertainty of solar and wind energy sources is modeled using Hang’s two-point estimating method(TPEM).The IARO algorithm is applied to calculate the best capacity of hub energy equipment,such as solar and wind renewable energy sources,combined heat and power(CHP)systems,steamboilers,energy storage,and electric cars in the day-aheadmarket.The standard ARO algorithmis developed to mimic the foraging behavior of rabbits,and in this work,the algorithm’s effectiveness in avoiding premature convergence is improved by using the dystudynamic inertia weight technique.The proposed IARO-based scheduling framework’s performance is evaluated against that of traditional ARO,particle swarm optimization(PSO),and salp swarm algorithm(SSA).The findings show that,in comparison to previous approaches,the suggested meta-heuristic scheduling framework based on the IARO has increased energy profit in day-ahead electricity,gas,and heating markets by satisfying the operational and energy hub limitations.Additionally,the results show that TPEM approach dependability consideration decreased hub energy’s profit by 8.995%as compared to deterministic planning.展开更多
This study suggests an optimal renewable energy source(RES)allocation and distribution-static synchronous compensator(D-STATCOM)and passive power filters(PPFs)for an electrical distribution network(EDN)to improve its ...This study suggests an optimal renewable energy source(RES)allocation and distribution-static synchronous compensator(D-STATCOM)and passive power filters(PPFs)for an electrical distribution network(EDN)to improve its performance and power quality(PQ).First,the latest metaheuristic artificial rabbits optimization(ARO)is used to locate and size solar photovoltaic(PV),wind turbine(WT)and D-STATCOM units.In the second stage,ratings of single-tuned PPFs and D-STATCOMs at the RESs are determined,considering non-linear loads in the network.The multi-objective function reduces power loss,improves the voltage stability index(VSI)and limits total harmonic distortion.Simulations using the IEEE 33-bus EDN compared the ARO results with those of previous studies.In the first scenario,ideally integrated D-STATCOMs,PVs and WTs reduced losses by 34.79%,64.74%and 94.15%,respectively.VSI increases from 0.6965 to 0.7749,0.8804 and 0.967.The optimal WT integration of the first scenario outperformed the PVs and D-STATCOMs.The second step optimizes the WTs and PQ devices for non-linear loads.WTs and D-STATCOMs reduce the maximum total harmonic distortion of the voltage waveform by 5.21%with non-linear loads to 3.23%,while WTs and PPFs reduce it to 4.39%.These scenarios demonstrate how WTs and D-STATCOMs can improve network performance and PQ.The computational efficiency of ARO is compared to that of the pathfinder algorithm,future search algorithm,butterfly optimization algorithm and coyote optimization algorithm.ARO speeds up convergence and improves solution quality and comprehension.展开更多
基金This research is supported by the Deputyship forResearch&Innovation,Ministry of Education in Saudi Arabia under Project Number(IFP-2022-35).
文摘To maximize energy profit with the participation of electricity,natural gas,and district heating networks in the day-ahead market,stochastic scheduling of energy hubs taking into account the uncertainty of photovoltaic and wind resources,has been carried out.This has been done using a new meta-heuristic algorithm,improved artificial rabbits optimization(IARO).In this study,the uncertainty of solar and wind energy sources is modeled using Hang’s two-point estimating method(TPEM).The IARO algorithm is applied to calculate the best capacity of hub energy equipment,such as solar and wind renewable energy sources,combined heat and power(CHP)systems,steamboilers,energy storage,and electric cars in the day-aheadmarket.The standard ARO algorithmis developed to mimic the foraging behavior of rabbits,and in this work,the algorithm’s effectiveness in avoiding premature convergence is improved by using the dystudynamic inertia weight technique.The proposed IARO-based scheduling framework’s performance is evaluated against that of traditional ARO,particle swarm optimization(PSO),and salp swarm algorithm(SSA).The findings show that,in comparison to previous approaches,the suggested meta-heuristic scheduling framework based on the IARO has increased energy profit in day-ahead electricity,gas,and heating markets by satisfying the operational and energy hub limitations.Additionally,the results show that TPEM approach dependability consideration decreased hub energy’s profit by 8.995%as compared to deterministic planning.
文摘This study suggests an optimal renewable energy source(RES)allocation and distribution-static synchronous compensator(D-STATCOM)and passive power filters(PPFs)for an electrical distribution network(EDN)to improve its performance and power quality(PQ).First,the latest metaheuristic artificial rabbits optimization(ARO)is used to locate and size solar photovoltaic(PV),wind turbine(WT)and D-STATCOM units.In the second stage,ratings of single-tuned PPFs and D-STATCOMs at the RESs are determined,considering non-linear loads in the network.The multi-objective function reduces power loss,improves the voltage stability index(VSI)and limits total harmonic distortion.Simulations using the IEEE 33-bus EDN compared the ARO results with those of previous studies.In the first scenario,ideally integrated D-STATCOMs,PVs and WTs reduced losses by 34.79%,64.74%and 94.15%,respectively.VSI increases from 0.6965 to 0.7749,0.8804 and 0.967.The optimal WT integration of the first scenario outperformed the PVs and D-STATCOMs.The second step optimizes the WTs and PQ devices for non-linear loads.WTs and D-STATCOMs reduce the maximum total harmonic distortion of the voltage waveform by 5.21%with non-linear loads to 3.23%,while WTs and PPFs reduce it to 4.39%.These scenarios demonstrate how WTs and D-STATCOMs can improve network performance and PQ.The computational efficiency of ARO is compared to that of the pathfinder algorithm,future search algorithm,butterfly optimization algorithm and coyote optimization algorithm.ARO speeds up convergence and improves solution quality and comprehension.
