The artificial bee colony(ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow(OPF) problem by simultaneously optimizing three conflicting obj...The artificial bee colony(ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow(OPF) problem by simultaneously optimizing three conflicting objectives of OPF, instead of transforming multi-objective functions into a single objective function. The main idea of HMOABC is to extend original ABC algorithm to multi-objective and cooperative mode by combining the Pareto dominance and divide-and-conquer approach. HMOABC is then used in the 30-bus IEEE test system for solving the OPF problem considering the cost, loss, and emission impacts. The simulation results show that the HMOABC is superior to other algorithms in terms of optimization accuracy and computation robustness.展开更多
The output uncertainty of high-proportion distributed power generation severely affects the system voltage and frequency.Simultaneously,controllable loads have also annually increased,which markedly improve the capabi...The output uncertainty of high-proportion distributed power generation severely affects the system voltage and frequency.Simultaneously,controllable loads have also annually increased,which markedly improve the capability for nodal-power control.To maintain the system frequency and voltage magnitude around rated values,a new multi-objective optimization model for both voltage and frequency control is proposed.Moreover,a great similarity between the multiobjective optimization and game problems appears.To reduce the strong subjectivity of the traditional methods,the idea and method of the game theory are introduced into the solution.According to the present situational data and analysis of the voltage and frequency sensitivities to nodal-power variations,the design variables involved in the voltage and frequency control are classified into two strategy spaces for players using hierarchical clustering.Finally,the effectiveness and rationality of the proposed control are verified in MATLAB.展开更多
This paper studies the economic environmental energy-saving day-ahead scheduling problem of power systems considering wind generation(WG)and demand response(DR)by means of multi-objective dynamic optimal power flow(MD...This paper studies the economic environmental energy-saving day-ahead scheduling problem of power systems considering wind generation(WG)and demand response(DR)by means of multi-objective dynamic optimal power flow(MDOPF).Within the model,fuel cost,carbon emission and active power losses are taken as objectives,and an integrated dispatch modeof conventional coal-fired generation,WG and DRis utilized.The corresponding solution process to the MDOPF is based on ahybrid of a non-dominated sorting genetic algorithm-II(NSGA-II)and fuwzy satisfaction-maximizing method,where NSGA-II obtains the Pareto frontier and the fuzzy satisfaction-maximizing method is the chosen strategy.Illustrative cases of different scenarios are performed based on an IEEE 6-units\,30-nodes system,to verify the proposed model and the solution process,as well as the benefits obtained by the DR into power system.展开更多
In view of the reactive power coordination difficulties caused by reactive power strong coupling,the provincial power grids in the interconnected system are formed by the multi-AC/DC transmission.Wind power channels a...In view of the reactive power coordination difficulties caused by reactive power strong coupling,the provincial power grids in the interconnected system are formed by the multi-AC/DC transmission.Wind power channels are under the conditions of large-scale long-distance transmission of wind power and other forms of renewable power generation.The AC-DC hybrid power flow equation of the interconnected system,including the AC-DC tie lines,is presented in this paper,along with the robust dynamic evolutionary optimization of the reactive power system in interconnected systems under fluctuating and uncertain wind power conditions.Therefore,the rapid collaborative optimization of reactive power flow and the exchange of reactive power between tie lines between provincial power grids are realized.The analysis was made by taking four interconnected large-scale provincial power grids of Eastern Mongolia,Jilin,Liaoning and Shandong as an example.The simulation results demonstrate the effectiveness and superiority of the proposed reactive power dynamic multi-objective optimization method for interconnected power grids.展开更多
In this paper, a simple strategy based differential evolution was proposed for solving the problem of multi-objective environmental optimal power flow considering a hybrid model （Wind-Shunt-FACTS）. The DE algorithm ...In this paper, a simple strategy based differential evolution was proposed for solving the problem of multi-objective environmental optimal power flow considering a hybrid model （Wind-Shunt-FACTS）. The DE algorithm optimized simultaneously a combined vector control based active power of wind sources and reactive power of multi STATCOM exchanged with the electrical power system to minimize fuel cost and emissions. The proposed strategy was examined and applied to the standard IEEE 30-bus with smooth cost function to solve the problem of security environmental economic dispatch considering multi distributed hybrid model based wind and STATCOM controllers. In addition, the proposed approach was validated on a large practical electrical power system 40 generating units considering valve point effect. Simulation results demonstrate that choosing the installation of multi type of FACTS devices in coordination with many distributed wind sources is a vital research area.展开更多
To solve the optimal power flow(OPF)problem,reinforcement learning(RL)emerges as a promising new approach.However,the RL-OPF literature is strongly divided regarding the exact formulation of the OPF problem as an RL e...To solve the optimal power flow(OPF)problem,reinforcement learning(RL)emerges as a promising new approach.However,the RL-OPF literature is strongly divided regarding the exact formulation of the OPF problem as an RL environment.In this work,we collect and implement diverse environment design decisions from the literature regarding training data,observation space,episode definition,and reward function choice.In an experimental analysis,we show the significant impact of these environment design options on RL-OPF training performance.Further,we derive some first recommendations regarding the choice of these design decisions.The created environment framework is fully open-source and can serve as a benchmark for future research in the RL-OPF field.展开更多
Power flow optimization control,which governs the energy flow among engine,battery,and motor,plays a very important role in plug-in hybrid electric vehicles(PHEVs).Its performance directly affects the fuel economy of ...Power flow optimization control,which governs the energy flow among engine,battery,and motor,plays a very important role in plug-in hybrid electric vehicles(PHEVs).Its performance directly affects the fuel economy of PHEVs.For the purpose of improving fuel economy,the electric system including battery and motor will be frequently scheduled,which would affect battery life.Therefore,a multi-objective optimization mechanism taking fuel economy and battery life into account is necessary,which is also a research focus in field of hybrid vehicles.Motivated by this issue,this paper proposes a multi-objective power flow optimization control strategy for a power split PHEV using game theory.Firstly,since the demand power of driver which is necessary for the power flow optimization control,cannot be known in advance,the demand power of driver can be modelled using a Markov chain to obtain predicted demand power.Secondly,based on the predicted demand power,the multi-objective optimization control problem is transformed into a game problem.A novel non-cooperative game model between engine and battery is established,and the benefit function with fuel economy and battery life as the optimization objective is proposed.Thirdly,under the premise of satisfying various constraints,the participants of the above game maximize their own benefit function to obtain the Nash equilibrium,which comprises of optimal power split scheme.Finally,the proposed strategy is verified compared with two baseline strategies,and results show that the proposed strategy can reduce equivalent fuel consumption by about 15%compared with baseline strategy 1,and achieve similar fuel economy while greatly extend battery life simultaneously compared with baseline strategy 2.展开更多
Optimal power flow(OPF)is one of the key tools for optimal operation and planning of modern power systems.Due to the high complexity with continuous and discrete control variables,modern heuristic optimization algorit...Optimal power flow(OPF)is one of the key tools for optimal operation and planning of modern power systems.Due to the high complexity with continuous and discrete control variables,modern heuristic optimization algorithms(HOAs)have been widely employed for the solution of OPF.This paper provides an overview of the latest applications of advanced HOAs in OPF problems.The most frequently applied HOAs for solving the OPF problem in recent years are covered and briefly introduced,including genetic algorithm(GA),differential evolution(DE),particle swarm optimization(PSO),and evolutionary programming(EP),etc.展开更多
Various kinds of new engineering technologies have been studied to realize the low-carbon and sustainable power supply systems all over the world.In actual implementation of these technologies,mostly,there are multipl...Various kinds of new engineering technologies have been studied to realize the low-carbon and sustainable power supply systems all over the world.In actual implementation of these technologies,mostly,there are multiple objectives with trade off relationships among each other,and also various constraints in the achievement of these objectives.Therefore,it should be essential to solve multiobjective optimization problems effectively in the applications of these new technologies in power systems.This paper proposes an improved method to realize multiobjective optimization for critical challenges in advanced power systems.To realize that,in an optimal dispersed generation installation problem,that is,one of effective measures for low-carbon power systems,various optimization methods and their combination methods are evaluated and a hybrid method for evolutionary algorithms was developed.The method can provide improved results compared with other state-of-the-art multi-objective optimization methods.展开更多
As high amounts of new energy and electric vehicle(EV)charging stations are connected to the distribution network,the voltage deviations are likely to occur,which will further affect the power quality.It is challengin...As high amounts of new energy and electric vehicle(EV)charging stations are connected to the distribution network,the voltage deviations are likely to occur,which will further affect the power quality.It is challenging to manage high quality voltage control of a distribution network only relying on the traditional reactive power control mode.If the reactive power regulation potentials of new energy and EVs can be tapped,it will greatly reduce the reactive power optimization pressure on the network.Keeping this in mind,our reasearch first adds EVs to the traditional distribution network model with new forms of energy,and then a multi-objective optimization model,with achieving the lowest line loss,voltage deviation,and the highest static voltage stability margin as its objectives,is constructed.Meanwihile,the corresponding model parameters are set under different climate and equipment conditions.Ultimately,the optimization model under specific scenarios is obtained.Furthermore,considering the supply and demand relation-ship of the network,an improved technique for order preference by similarity to an ideal solution decision method is proposed,which aims to judge the adaptability of different algorithms to the optimized model,so as to select a most suitable algorithm for the problem.Finally,a comparison is made between the constructed model and a model without new energy.The results reveal that the constructed model can provide a high quality reactive power regula-tion strategy.展开更多
A distributed active and reactive power control(DARPC)strategy based on the alternating direction method of multipliers(ADMM)is proposed for regional AC transmission system(TS)with wind farms(WFs).The proposed DARPC s...A distributed active and reactive power control(DARPC)strategy based on the alternating direction method of multipliers(ADMM)is proposed for regional AC transmission system(TS)with wind farms(WFs).The proposed DARPC strategy optimizes the power distribution among the WFs to minimize the power losses of the AC TS while tracking the active power reference from the transmission system operator(TSO),and minimizes the voltage deviation of the buses inside the WF from the rated voltage as well as the power losses of the WF collection system.The optimal power flow(OPF)of the TS is relaxed by using the semidefinite programming(SDP)relaxation while the branch flow model is used to model the WF collection system.In the DARPC strategy,the large-scale strongly-coupled optimization problem is decomposed by using the ADMM,which is solved in the regional TS controller and WF controllers in parallel without loss of the global optimality.The boundary information is exchanged between the regional TS controller and WF controllers.Compared with the conventional OPF method of the TS with WFs,the optimality and accuracy of the system operation can be improved.Moreover,the proposed strategy efficiently reduces the computation burden of the TS controller and eliminates the need of a central controller.The protection of the information privacy can be enhanced.A modified IEEE 9-bus system with two WFs consisting of 64 wind turbines(WTs)is used to validate the proposed DARPC strategy.展开更多
As an important process during the cement production,grate cooler plays significance roles on clinker cooling and waste heat recovery.In this paper,we measured experimentally the heat balance of the grate cooler,which...As an important process during the cement production,grate cooler plays significance roles on clinker cooling and waste heat recovery.In this paper,we measured experimentally the heat balance of the grate cooler,which provided initial operating parameters for optimization.Then,the grate cooler was simplified into a series-connected heat exchanger network by power flow method.Constructing the equivalent thermal resistance network provided the global constraints by Kirchhoff’s law.On this basis,with the objectives of the minimum entropy generation numbers caused by heat transfer and viscous dissipation,solving a multi-objective optimization model achieved the Pareto Front by genetic algorithm.Then selecting the scheme of the lowest fan power consumption obtained the optimal operating parameters of the grate cooler.The results showed that the total mass flow of the optimized scheme did not change significantly compared with the original scheme,but the fan power consumption was 25.44%lower,and the heat recovery efficiency was 88.43%,which was improved by 11.35%.Furthermore,the analysis showed that the optimal operating parameters were affected by the local heat load.After optimizing the diameter of clinker particles within the allowable industrial range,the clinker with particle diameter of 0.02 m had the optimal performance.展开更多
基金Projects(61105067,61174164)supported by the National Natural Science Foundation of China
文摘The artificial bee colony(ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow(OPF) problem by simultaneously optimizing three conflicting objectives of OPF, instead of transforming multi-objective functions into a single objective function. The main idea of HMOABC is to extend original ABC algorithm to multi-objective and cooperative mode by combining the Pareto dominance and divide-and-conquer approach. HMOABC is then used in the 30-bus IEEE test system for solving the OPF problem considering the cost, loss, and emission impacts. The simulation results show that the HMOABC is superior to other algorithms in terms of optimization accuracy and computation robustness.
基金the National Key Research and Development Program of China(Basic Research Class)(No.2017YFB0903000)the National Natural Science Foundation of China(No.U1909201).
文摘The output uncertainty of high-proportion distributed power generation severely affects the system voltage and frequency.Simultaneously,controllable loads have also annually increased,which markedly improve the capability for nodal-power control.To maintain the system frequency and voltage magnitude around rated values,a new multi-objective optimization model for both voltage and frequency control is proposed.Moreover,a great similarity between the multiobjective optimization and game problems appears.To reduce the strong subjectivity of the traditional methods,the idea and method of the game theory are introduced into the solution.According to the present situational data and analysis of the voltage and frequency sensitivities to nodal-power variations,the design variables involved in the voltage and frequency control are classified into two strategy spaces for players using hierarchical clustering.Finally,the effectiveness and rationality of the proposed control are verified in MATLAB.
基金This work was supported in part by the National Natural Science Foundation of China under Grant 51277015,51677007 and 51977012.
文摘This paper studies the economic environmental energy-saving day-ahead scheduling problem of power systems considering wind generation(WG)and demand response(DR)by means of multi-objective dynamic optimal power flow(MDOPF).Within the model,fuel cost,carbon emission and active power losses are taken as objectives,and an integrated dispatch modeof conventional coal-fired generation,WG and DRis utilized.The corresponding solution process to the MDOPF is based on ahybrid of a non-dominated sorting genetic algorithm-II(NSGA-II)and fuwzy satisfaction-maximizing method,where NSGA-II obtains the Pareto frontier and the fuzzy satisfaction-maximizing method is the chosen strategy.Illustrative cases of different scenarios are performed based on an IEEE 6-units\,30-nodes system,to verify the proposed model and the solution process,as well as the benefits obtained by the DR into power system.
基金This work was supported by the National Key Research and Development Program of China under Grant No.2017YFB0902100.
文摘In view of the reactive power coordination difficulties caused by reactive power strong coupling,the provincial power grids in the interconnected system are formed by the multi-AC/DC transmission.Wind power channels are under the conditions of large-scale long-distance transmission of wind power and other forms of renewable power generation.The AC-DC hybrid power flow equation of the interconnected system,including the AC-DC tie lines,is presented in this paper,along with the robust dynamic evolutionary optimization of the reactive power system in interconnected systems under fluctuating and uncertain wind power conditions.Therefore,the rapid collaborative optimization of reactive power flow and the exchange of reactive power between tie lines between provincial power grids are realized.The analysis was made by taking four interconnected large-scale provincial power grids of Eastern Mongolia,Jilin,Liaoning and Shandong as an example.The simulation results demonstrate the effectiveness and superiority of the proposed reactive power dynamic multi-objective optimization method for interconnected power grids.
文摘In this paper, a simple strategy based differential evolution was proposed for solving the problem of multi-objective environmental optimal power flow considering a hybrid model （Wind-Shunt-FACTS）. The DE algorithm optimized simultaneously a combined vector control based active power of wind sources and reactive power of multi STATCOM exchanged with the electrical power system to minimize fuel cost and emissions. The proposed strategy was examined and applied to the standard IEEE 30-bus with smooth cost function to solve the problem of security environmental economic dispatch considering multi distributed hybrid model based wind and STATCOM controllers. In addition, the proposed approach was validated on a large practical electrical power system 40 generating units considering valve point effect. Simulation results demonstrate that choosing the installation of multi type of FACTS devices in coordination with many distributed wind sources is a vital research area.
文摘To solve the optimal power flow(OPF)problem,reinforcement learning(RL)emerges as a promising new approach.However,the RL-OPF literature is strongly divided regarding the exact formulation of the OPF problem as an RL environment.In this work,we collect and implement diverse environment design decisions from the literature regarding training data,observation space,episode definition,and reward function choice.In an experimental analysis,we show the significant impact of these environment design options on RL-OPF training performance.Further,we derive some first recommendations regarding the choice of these design decisions.The created environment framework is fully open-source and can serve as a benchmark for future research in the RL-OPF field.
基金the National Natural Science Foundation of China(Grant Nos.51975048,U1764257 and 51705480)the Beijing Institute of Technology Research Fund Program for Young Scholars。
文摘Power flow optimization control,which governs the energy flow among engine,battery,and motor,plays a very important role in plug-in hybrid electric vehicles(PHEVs).Its performance directly affects the fuel economy of PHEVs.For the purpose of improving fuel economy,the electric system including battery and motor will be frequently scheduled,which would affect battery life.Therefore,a multi-objective optimization mechanism taking fuel economy and battery life into account is necessary,which is also a research focus in field of hybrid vehicles.Motivated by this issue,this paper proposes a multi-objective power flow optimization control strategy for a power split PHEV using game theory.Firstly,since the demand power of driver which is necessary for the power flow optimization control,cannot be known in advance,the demand power of driver can be modelled using a Markov chain to obtain predicted demand power.Secondly,based on the predicted demand power,the multi-objective optimization control problem is transformed into a game problem.A novel non-cooperative game model between engine and battery is established,and the benefit function with fuel economy and battery life as the optimization objective is proposed.Thirdly,under the premise of satisfying various constraints,the participants of the above game maximize their own benefit function to obtain the Nash equilibrium,which comprises of optimal power split scheme.Finally,the proposed strategy is verified compared with two baseline strategies,and results show that the proposed strategy can reduce equivalent fuel consumption by about 15%compared with baseline strategy 1,and achieve similar fuel economy while greatly extend battery life simultaneously compared with baseline strategy 2.
基金This work was partially supported by Hong Kong RGC Theme Based Research Scheme Grants No.T23-407/13 N and T23-701/14 N.
文摘Optimal power flow(OPF)is one of the key tools for optimal operation and planning of modern power systems.Due to the high complexity with continuous and discrete control variables,modern heuristic optimization algorithms(HOAs)have been widely employed for the solution of OPF.This paper provides an overview of the latest applications of advanced HOAs in OPF problems.The most frequently applied HOAs for solving the OPF problem in recent years are covered and briefly introduced,including genetic algorithm(GA),differential evolution(DE),particle swarm optimization(PSO),and evolutionary programming(EP),etc.
文摘Various kinds of new engineering technologies have been studied to realize the low-carbon and sustainable power supply systems all over the world.In actual implementation of these technologies,mostly,there are multiple objectives with trade off relationships among each other,and also various constraints in the achievement of these objectives.Therefore,it should be essential to solve multiobjective optimization problems effectively in the applications of these new technologies in power systems.This paper proposes an improved method to realize multiobjective optimization for critical challenges in advanced power systems.To realize that,in an optimal dispersed generation installation problem,that is,one of effective measures for low-carbon power systems,various optimization methods and their combination methods are evaluated and a hybrid method for evolutionary algorithms was developed.The method can provide improved results compared with other state-of-the-art multi-objective optimization methods.
基金supported by National Key R&D Program of China (2021ZD0111502)National Natural Science Foundation of China (51907112,U2066212)+1 种基金Natural Science Foundation of Guangdong Province of China (2019A1515011671,2021A1515011709)Scientific Research Staring Foundation of Shantou University (NTF19028,NTF20009).
文摘As high amounts of new energy and electric vehicle(EV)charging stations are connected to the distribution network,the voltage deviations are likely to occur,which will further affect the power quality.It is challenging to manage high quality voltage control of a distribution network only relying on the traditional reactive power control mode.If the reactive power regulation potentials of new energy and EVs can be tapped,it will greatly reduce the reactive power optimization pressure on the network.Keeping this in mind,our reasearch first adds EVs to the traditional distribution network model with new forms of energy,and then a multi-objective optimization model,with achieving the lowest line loss,voltage deviation,and the highest static voltage stability margin as its objectives,is constructed.Meanwihile,the corresponding model parameters are set under different climate and equipment conditions.Ultimately,the optimization model under specific scenarios is obtained.Furthermore,considering the supply and demand relation-ship of the network,an improved technique for order preference by similarity to an ideal solution decision method is proposed,which aims to judge the adaptability of different algorithms to the optimized model,so as to select a most suitable algorithm for the problem.Finally,a comparison is made between the constructed model and a model without new energy.The results reveal that the constructed model can provide a high quality reactive power regula-tion strategy.
基金supported in part by Technical University of Denmark(DTU)in part by China Scholarship Council(No.201806130202)。
文摘A distributed active and reactive power control(DARPC)strategy based on the alternating direction method of multipliers(ADMM)is proposed for regional AC transmission system(TS)with wind farms(WFs).The proposed DARPC strategy optimizes the power distribution among the WFs to minimize the power losses of the AC TS while tracking the active power reference from the transmission system operator(TSO),and minimizes the voltage deviation of the buses inside the WF from the rated voltage as well as the power losses of the WF collection system.The optimal power flow(OPF)of the TS is relaxed by using the semidefinite programming(SDP)relaxation while the branch flow model is used to model the WF collection system.In the DARPC strategy,the large-scale strongly-coupled optimization problem is decomposed by using the ADMM,which is solved in the regional TS controller and WF controllers in parallel without loss of the global optimality.The boundary information is exchanged between the regional TS controller and WF controllers.Compared with the conventional OPF method of the TS with WFs,the optimality and accuracy of the system operation can be improved.Moreover,the proposed strategy efficiently reduces the computation burden of the TS controller and eliminates the need of a central controller.The protection of the information privacy can be enhanced.A modified IEEE 9-bus system with two WFs consisting of 64 wind turbines(WTs)is used to validate the proposed DARPC strategy.
基金supported by the Shandong Provincial Natural Science Foundation(Grant No.ZR2019QEE016)。
文摘As an important process during the cement production,grate cooler plays significance roles on clinker cooling and waste heat recovery.In this paper,we measured experimentally the heat balance of the grate cooler,which provided initial operating parameters for optimization.Then,the grate cooler was simplified into a series-connected heat exchanger network by power flow method.Constructing the equivalent thermal resistance network provided the global constraints by Kirchhoff’s law.On this basis,with the objectives of the minimum entropy generation numbers caused by heat transfer and viscous dissipation,solving a multi-objective optimization model achieved the Pareto Front by genetic algorithm.Then selecting the scheme of the lowest fan power consumption obtained the optimal operating parameters of the grate cooler.The results showed that the total mass flow of the optimized scheme did not change significantly compared with the original scheme,but the fan power consumption was 25.44%lower,and the heat recovery efficiency was 88.43%,which was improved by 11.35%.Furthermore,the analysis showed that the optimal operating parameters were affected by the local heat load.After optimizing the diameter of clinker particles within the allowable industrial range,the clinker with particle diameter of 0.02 m had the optimal performance.
