The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of...The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels.展开更多
Dynamic Economic Emission Dispatch(DEED)aims to optimize control over fuel cost and pollution emission,two conflicting objectives,by scheduling the output power of various units at specific times.Although many methods...Dynamic Economic Emission Dispatch(DEED)aims to optimize control over fuel cost and pollution emission,two conflicting objectives,by scheduling the output power of various units at specific times.Although many methods well-performed on the DEED problem,most of them fail to achieve expected results in practice due to a lack of effective trade-off mechanisms between the convergence and diversity of non-dominated optimal dispatching solutions.To address this issue,a new multi-objective solver called Multi-Objective Golden Jackal Optimization(MOGJO)algorithm is proposed to cope with the DEED problem.The proposed algorithm first stores non-dominated optimal solutions found so far into an archive.Then,it chooses the best dispatching solution from the archive as the leader through a selection mechanism designed based on elite selection strategy and Euclidean distance index method.This mechanism can guide the algorithm to search for better dispatching solutions in the direction of reducing fuel costs and pollutant emissions.Moreover,the basic golden jackal optimization algorithm has the drawback of insufficient search,which hinders its ability to effectively discover more Pareto solutions.To this end,a non-linear control parameter based on the cosine function is introduced to enhance global exploration of the dispatching space,thus improving the efficiency of finding the optimal dispatching solutions.The proposed MOGJO is evaluated on the latest CEC benchmark test functions,and its superiority over the state-of-the-art multi-objective optimizers is highlighted by performance indicators.Also,empirical results on 5-unit,10-unit,IEEE 30-bus,and 30-unit systems show that the MOGJO can provide competitive compromise scheduling solutions compared to published DEED methods.Finally,in the analysis of the Pareto dominance relationship and the Euclidean distance index,the optimal dispatching solutions provided by MOGJO are the closest to the ideal solutions for minimizing fuel costs and pollution emissions simultaneously,compared to the latest published DEED solutions.展开更多
The increasing penetration of renewable energy into power grids is reducing the regulation capacity of automatic generation control(AGC).Thus,there is an urgent demand to coordinate AGC units with active equipment suc...The increasing penetration of renewable energy into power grids is reducing the regulation capacity of automatic generation control(AGC).Thus,there is an urgent demand to coordinate AGC units with active equipment such as energy storage.Current dispatch decision-making methods often ignore the intermittent effects of renewable energy.This paper proposes a two-stage robust optimization model in which energy storage is used to compensate for the intermittency of renewable energy for the dispatch of AGC units.This model exploits the rapid adjustment capability of energy storage to compensate for the slow response speed of AGC units,improve the adjustment potential,and respond to the problems of intermittent power generation from renewable energy.A column and constraint generation algorithm is used to solve the model.In an example analysis,the proposed model was more robust than a model that did not consider energy storage at eliminating the effects of intermittency while offering clear improvements in economy and efficiency.展开更多
With the rapid and large-scale development of renewable energy, the lack of new energy power transportation or consumption, and the shortage of grid peak-shifting ability have become increasingly serious. Aiming to th...With the rapid and large-scale development of renewable energy, the lack of new energy power transportation or consumption, and the shortage of grid peak-shifting ability have become increasingly serious. Aiming to the severe wind power curtailment issue, the characteristics of interactive load are studied upon the traditional day-ahead dispatch model to mitigate the influence of wind power fluctuation. A multi-objective optimal dispatch model with the minimum operating cost and power losses is built. Optimal power flow distribution is available when both generation and demand side participate in the resource allocation. The quantum particle swarm optimization (QPSO) algorithm is applied to convert multi-objective optimization problem into single objective optimization problem. The simulation results of IEEE 30-bus system verify that the proposed method can effectively reduce the operating cost and grid loss simultaneously enhancing the consumption of wind power.展开更多
A reference point based multi-objective optimization using a combination between trust region (TR) algorithm and particle swarm optimization (PSO) to solve the multi-objective environmental/economic dispatch (EED) pro...A reference point based multi-objective optimization using a combination between trust region (TR) algorithm and particle swarm optimization (PSO) to solve the multi-objective environmental/economic dispatch (EED) problem is presented in this paper. The EED problem is handled by Reference Point Interactive Approach. One of the main advantages of the proposed approach is integrating the merits of both TR and PSO, where TR has provided the initial set (close to the Pareto set as possible and the reference point of the decision maker) followed by PSO to improve the quality of the solutions and get all the points on the Pareto frontier. The performance of the proposed algorithm is tested on standard IEEE 30-bus 6-genrator test system and is compared with conventional methods. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto-optimal non-dominated solutions in one single run. The comparison with the classical methods demonstrates the superiority of the proposed approach and confirms its potential to solve the multi-objective EED problem.展开更多
Exploring optimal operational schemes for synergistic development is crucial for sustainable management in river basins.This study introduces a multi-objective synergistic optimization framework aimed at analyzing the...Exploring optimal operational schemes for synergistic development is crucial for sustainable management in river basins.This study introduces a multi-objective synergistic optimization framework aimed at analyzing the interplay among flood control,ecological integrity,and desilting objectives under varying watersediment conditions.The framework encompasses multi-objective reservoir optimal operation,scheme decision,and trade-off analysis among competing objectives.To address the optimization model,an elite mutation-based multiobjective particle swarm optimization(MOPSO)algorithm that integrates genetic algorithms(GA)is developed.The coupling coordination degree is employed for optimal scheme decision-making,allowing for the adjustment of weight ratios to investigate the trade-offs between objectives.This research focuses on the Sanmenxia and Xiaolangdi cascade reservoirs in the Yellow River,utilizing three representative hydrological years:1967,1969,and 2002.The findings reveal that:(1)the proposed model effectively generates Pareto fronts for multi-objective operations,facilitating the recommendation of optimal schemes based on coupling coordination degrees;(2)as water-sediment conditions shift from flooding to drought,competition intensifies between the flood control and desilting objectives.While flood control and ecological objectives compete during flood and dry years,they demonstrate synergies in normal years(r=0.22);conversely,ecological and desilting objectives are consistently competitive across all three typical years,with the strongest competition observed in the normal year(r=-0.95);(3)the advantages conferred to ecological objectives increase as water-sediment conditions shift from flooding to drought.However,the promotion of the desilting objective requires more complex trade-offs.This study provides a model and methodological approach for the multi-objective optimization of flood control,sediment management,and ecological considerations in reservoir clusters.Moreover,the methodologies presented herein can be extended to other water resource systems for multi-objective optimization and decision-making.展开更多
Cross-line trains, as a link between high-speed and conventional rail networks, will increase the complexity of transport organization and lead to significant challenges in dispatch coordination between the two system...Cross-line trains, as a link between high-speed and conventional rail networks, will increase the complexity of transport organization and lead to significant challenges in dispatch coordination between the two systems. Based on the characteristics of high-speed transport organization, this paper deals with the necessity of dispatch coordination between high-speed and conventional lines from the following two perspectives： the operation of cross-line trains and work coordination in connection stations. An adjustment model for the operation of high-speed trains, taking cross-line trains into account, is established. Finally, the dispatch system is described in terms of construction and process. Methods for organizing dispatch are proposed, and the processes of coordination adjustment under normal and unexpected situations are analyzed. The discussion in this paper may serve as a theoretical basis for the development of high-speed rail dispatch systems.展开更多
Confronted with the requirement of higher efficiency and higher quality of distribution network fault rush-repair, the subject addressed in this paper is the optimal resource dispatching issue of the distribution netw...Confronted with the requirement of higher efficiency and higher quality of distribution network fault rush-repair, the subject addressed in this paper is the optimal resource dispatching issue of the distribution network rush-repair when single resource center cannot meet the emergent resource demands. A multi-resource and multi-center dispatching model is established with the objective of “the shortest repair start-time” and “the least number of the repair centers”. The optimal and worst solutions of each objective are both obtained, and a “proximity degree method” is used to calculate the optimal resource dispatching plan. The feasibility of the proposed algorithm is illustrated by an example of a distribution network fault. The proposed method provides a practical technique for efficiency improvement of fault rush-repair work of distribution network, and thus mostly abbreviates power recovery time and improves the management level of the distribution network.展开更多
基金funded by the National Key Research and Development Program of China(2024YFE0106800)Natural Science Foundation of Shandong Province(ZR2021ME199).
文摘The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels.
基金supported by the National Natural Science Foundation of China under Grant No.61802328,61972333,and 61771415.
文摘Dynamic Economic Emission Dispatch(DEED)aims to optimize control over fuel cost and pollution emission,two conflicting objectives,by scheduling the output power of various units at specific times.Although many methods well-performed on the DEED problem,most of them fail to achieve expected results in practice due to a lack of effective trade-off mechanisms between the convergence and diversity of non-dominated optimal dispatching solutions.To address this issue,a new multi-objective solver called Multi-Objective Golden Jackal Optimization(MOGJO)algorithm is proposed to cope with the DEED problem.The proposed algorithm first stores non-dominated optimal solutions found so far into an archive.Then,it chooses the best dispatching solution from the archive as the leader through a selection mechanism designed based on elite selection strategy and Euclidean distance index method.This mechanism can guide the algorithm to search for better dispatching solutions in the direction of reducing fuel costs and pollutant emissions.Moreover,the basic golden jackal optimization algorithm has the drawback of insufficient search,which hinders its ability to effectively discover more Pareto solutions.To this end,a non-linear control parameter based on the cosine function is introduced to enhance global exploration of the dispatching space,thus improving the efficiency of finding the optimal dispatching solutions.The proposed MOGJO is evaluated on the latest CEC benchmark test functions,and its superiority over the state-of-the-art multi-objective optimizers is highlighted by performance indicators.Also,empirical results on 5-unit,10-unit,IEEE 30-bus,and 30-unit systems show that the MOGJO can provide competitive compromise scheduling solutions compared to published DEED methods.Finally,in the analysis of the Pareto dominance relationship and the Euclidean distance index,the optimal dispatching solutions provided by MOGJO are the closest to the ideal solutions for minimizing fuel costs and pollution emissions simultaneously,compared to the latest published DEED solutions.
基金supported by Theoretical study of power system synergistic dispatch National Science Foundation of China(51477091).
文摘The increasing penetration of renewable energy into power grids is reducing the regulation capacity of automatic generation control(AGC).Thus,there is an urgent demand to coordinate AGC units with active equipment such as energy storage.Current dispatch decision-making methods often ignore the intermittent effects of renewable energy.This paper proposes a two-stage robust optimization model in which energy storage is used to compensate for the intermittency of renewable energy for the dispatch of AGC units.This model exploits the rapid adjustment capability of energy storage to compensate for the slow response speed of AGC units,improve the adjustment potential,and respond to the problems of intermittent power generation from renewable energy.A column and constraint generation algorithm is used to solve the model.In an example analysis,the proposed model was more robust than a model that did not consider energy storage at eliminating the effects of intermittency while offering clear improvements in economy and efficiency.
文摘With the rapid and large-scale development of renewable energy, the lack of new energy power transportation or consumption, and the shortage of grid peak-shifting ability have become increasingly serious. Aiming to the severe wind power curtailment issue, the characteristics of interactive load are studied upon the traditional day-ahead dispatch model to mitigate the influence of wind power fluctuation. A multi-objective optimal dispatch model with the minimum operating cost and power losses is built. Optimal power flow distribution is available when both generation and demand side participate in the resource allocation. The quantum particle swarm optimization (QPSO) algorithm is applied to convert multi-objective optimization problem into single objective optimization problem. The simulation results of IEEE 30-bus system verify that the proposed method can effectively reduce the operating cost and grid loss simultaneously enhancing the consumption of wind power.
文摘A reference point based multi-objective optimization using a combination between trust region (TR) algorithm and particle swarm optimization (PSO) to solve the multi-objective environmental/economic dispatch (EED) problem is presented in this paper. The EED problem is handled by Reference Point Interactive Approach. One of the main advantages of the proposed approach is integrating the merits of both TR and PSO, where TR has provided the initial set (close to the Pareto set as possible and the reference point of the decision maker) followed by PSO to improve the quality of the solutions and get all the points on the Pareto frontier. The performance of the proposed algorithm is tested on standard IEEE 30-bus 6-genrator test system and is compared with conventional methods. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto-optimal non-dominated solutions in one single run. The comparison with the classical methods demonstrates the superiority of the proposed approach and confirms its potential to solve the multi-objective EED problem.
基金National Natural Science Foundation of China,Grant/Award Number:U2243228The Belt and Road Special Foundation of the National Key Laboratory of Water Disaster Prevention,Grant/Award Number:2022nkms04+1 种基金MOE(Ministry of Education in China)Liberal Arts and Social Sciences Foundation,Grant/Award Number:23YJCZH332Natural Science Foundation of Anhui Province,Grant/Award Numbers:2208085US03,2308085US13。
文摘Exploring optimal operational schemes for synergistic development is crucial for sustainable management in river basins.This study introduces a multi-objective synergistic optimization framework aimed at analyzing the interplay among flood control,ecological integrity,and desilting objectives under varying watersediment conditions.The framework encompasses multi-objective reservoir optimal operation,scheme decision,and trade-off analysis among competing objectives.To address the optimization model,an elite mutation-based multiobjective particle swarm optimization(MOPSO)algorithm that integrates genetic algorithms(GA)is developed.The coupling coordination degree is employed for optimal scheme decision-making,allowing for the adjustment of weight ratios to investigate the trade-offs between objectives.This research focuses on the Sanmenxia and Xiaolangdi cascade reservoirs in the Yellow River,utilizing three representative hydrological years:1967,1969,and 2002.The findings reveal that:(1)the proposed model effectively generates Pareto fronts for multi-objective operations,facilitating the recommendation of optimal schemes based on coupling coordination degrees;(2)as water-sediment conditions shift from flooding to drought,competition intensifies between the flood control and desilting objectives.While flood control and ecological objectives compete during flood and dry years,they demonstrate synergies in normal years(r=0.22);conversely,ecological and desilting objectives are consistently competitive across all three typical years,with the strongest competition observed in the normal year(r=-0.95);(3)the advantages conferred to ecological objectives increase as water-sediment conditions shift from flooding to drought.However,the promotion of the desilting objective requires more complex trade-offs.This study provides a model and methodological approach for the multi-objective optimization of flood control,sediment management,and ecological considerations in reservoir clusters.Moreover,the methodologies presented herein can be extended to other water resource systems for multi-objective optimization and decision-making.
基金one of the key parts of an NNFF (Na-tional Natural Science Foundation) project under grant 60776827:‘Train network operation program with optimization theory and method research’meanwhile is the key research in ‘Study of optimization method and adjustment theory of high-speed train operation’ supported by the Doctoral Program Foundation of Ministry of Education under grant 20090184110011
文摘Cross-line trains, as a link between high-speed and conventional rail networks, will increase the complexity of transport organization and lead to significant challenges in dispatch coordination between the two systems. Based on the characteristics of high-speed transport organization, this paper deals with the necessity of dispatch coordination between high-speed and conventional lines from the following two perspectives： the operation of cross-line trains and work coordination in connection stations. An adjustment model for the operation of high-speed trains, taking cross-line trains into account, is established. Finally, the dispatch system is described in terms of construction and process. Methods for organizing dispatch are proposed, and the processes of coordination adjustment under normal and unexpected situations are analyzed. The discussion in this paper may serve as a theoretical basis for the development of high-speed rail dispatch systems.
文摘Confronted with the requirement of higher efficiency and higher quality of distribution network fault rush-repair, the subject addressed in this paper is the optimal resource dispatching issue of the distribution network rush-repair when single resource center cannot meet the emergent resource demands. A multi-resource and multi-center dispatching model is established with the objective of “the shortest repair start-time” and “the least number of the repair centers”. The optimal and worst solutions of each objective are both obtained, and a “proximity degree method” is used to calculate the optimal resource dispatching plan. The feasibility of the proposed algorithm is illustrated by an example of a distribution network fault. The proposed method provides a practical technique for efficiency improvement of fault rush-repair work of distribution network, and thus mostly abbreviates power recovery time and improves the management level of the distribution network.
