To enhance the low-carbon economic efficiency and increase the utilization of renewable energy within integrated energy systems(IES),this paper proposes a low-carbon dispatch model integrating power-to-gas(P2G),carbon...To enhance the low-carbon economic efficiency and increase the utilization of renewable energy within integrated energy systems(IES),this paper proposes a low-carbon dispatch model integrating power-to-gas(P2G),carbon capture and storage(CCS),hydrogen fuel cell(HFC),and combined heat and power(CHP).The P2G process is refined into a two-stage structure,and HFC is introduced to enhance hydrogen utilization.Together with CCS and CHP,these devices form a multi-energy conversion system coupling electricity,heat,cooling,and gas.A laddertype carbon trading approach is adopted to flexibly manage carbon output by leveraging marginal cost adjustments.To evaluate the resilience of the proposed low-carbon scheduling strategy involving multiple energy units under the variability of renewable energy,a two-level robust optimization framework is developed.This model captures the most adverse scenarios of wind and solar generation.The dispatch strategy is validated against these extreme conditions to demonstrate its flexibility and effectiveness.The problem is solved using the GUROBI optimization tool.Results from simulations indicate that themodel increases renewable energy integration by 39.1%,and achieves reductions of 15.96%in carbon emissions and 16.29%in operational expenditures.The results demonstrate that the strategy ensures both economic efficiency and environmental performance under uncertain conditions.Compared with existing studies that separately model two-stage P2G or CCS devices,this paper integrates HFC,CHP,and CCS into a unified dispatchable system,enabling refined hydrogen utilization and flexible carbon circulation.Furthermore,the introduction of a laddertype carbon pricing mechanism,combined with multi-energy storage participation in implicit demand response,creates a dynamic and cost-sensitive dispatch framework.These modeling strategies go beyond conventional linear IES formulations and provide more realistic system representations.The proposed approach not only deepens the coupling among electric,thermal,and gas systems,but also offers a feasible pathway for high-penetration renewable integration in low-carbon energy systems.展开更多
Based on a new perspective in coordinating with the traditional"N-1"criteria and system risk,a real-time electricity market model is presented,in which the system risk is employed to model the system’s over...Based on a new perspective in coordinating with the traditional"N-1"criteria and system risk,a real-time electricity market model is presented,in which the system risk is employed to model the system’s overall security level.This new model is called the risk-based security-constrained economic dispatch(RB-SCED).Relative to the securityconstrained economic dispatch(SCED)used in the power industry today,the RB-SCED finds more secure and economic operating conditions.It does this by obtaining solutions that achieve a better balance between post-contingency flows on individual branches and the overall system risk.The method exploits the fact that,in a SCED solution,some postcontingency branch flows which exceed their limits impose little risk while other post-contingency branch flows which are within their limits impose significant risk.The RB-SCED softens constraints for the former and hardens constraints for the latter,thus achieving simultaneous improvement in both security and economy.In this work,the basic concept and the mathematical formulation of the RB-SCED model are systematically described.Experimental results on a 9-bus system and the ISO New England actual system have demonstrated the advantages of RB-SCED over SCED.展开更多
The economic dispatch problem(EDP) of microgrids operating in both grid-connected and isolated modes within an energy internet framework is addressed in this paper. The multi-agent leader-following consensus algorithm...The economic dispatch problem(EDP) of microgrids operating in both grid-connected and isolated modes within an energy internet framework is addressed in this paper. The multi-agent leader-following consensus algorithm is employed to address the EDP of microgrids in grid-connected mode, while the push-pull algorithm with a fixed step size is introduced for the isolated mode. The proposed algorithm of isolated mode is proven to converge to the optimum when the interaction digraph of microgrids is strongly connected. A unified algorithmic framework is proposed to handle the two modes of operation of microgrids simultaneously, enabling our algorithm to achieve optimal power allocation and maintain the balance between power supply and demand in any mode and any mode switching. Due to the push-pull structure of the algorithm and the use of fixed step size,the proposed algorithm can better handle the case of unbalanced graphs, and the convergence speed is improved. It is documented that when the transmission topology is strongly connected and there is bi-directional communication between the energy router and its neighbors, the proposed algorithm in composite mode achieves economic dispatch even with arbitrary mode switching.Finally, we demonstrate the effectiveness and superiority of our algorithm through numerical simulations.展开更多
The exponential growth in the scale of power systems has led to a significant increase in the complexity of dispatch problem resolution,particularly within multi-area interconnected power grids.This complexity necessi...The exponential growth in the scale of power systems has led to a significant increase in the complexity of dispatch problem resolution,particularly within multi-area interconnected power grids.This complexity necessitates the employment of distributed solution methodologies,which are not only essential but also highly desirable.In the realm of computational modelling,the multi-area economic dispatch problem(MAED)can be formulated as a linearly constrained separable convex optimization problem.The proximal point algorithm(PPA)is particularly adept at addressing such mathematical constructs effectively.This study introduces parallel(PPPA)and serial(SPPA)variants of the PPA as distributed algorithms,specifically designed for the computational modelling of the MAED.The PPA introduces a quadratic term into the objective function,which,while potentially complicating the iterative updates of the algorithm,serves to dampen oscillations near the optimal solution,thereby enhancing the convergence characteristics.Furthermore,the convergence efficiency of the PPA is significantly influenced by the parameter c.To address this parameter sensitivity,this research draws on trend theory from stock market analysis to propose trend theory-driven distributed PPPA and SPPA,thereby enhancing the robustness of the computational models.The computational models proposed in this study are anticipated to exhibit superior performance in terms of convergence behaviour,stability,and robustness with respect to parameter selection,potentially outperforming existing methods such as the alternating direction method of multipliers(ADMM)and Auxiliary Problem Principle(APP)in the computational simulation of power system dispatch problems.The simulation results demonstrate that the trend theory-based PPPA,SPPA,ADMM and APP exhibit significant robustness to the initial value of parameter c,and show superior convergence characteristics compared to the residual balancing ADMM.展开更多
To address the issues of unclear carbon responsibility attribution,insufficient renewable energy absorption,and simplistic carbon trading mechanisms in integrated energy systems,this paper proposes an electricheat-hyd...To address the issues of unclear carbon responsibility attribution,insufficient renewable energy absorption,and simplistic carbon trading mechanisms in integrated energy systems,this paper proposes an electricheat-hydrogen integrated energy system(EHH-IES)optimal scheduling model considering carbon emission stream(CES)and wind-solar accommodation.First,the CES theory is introduced to quantify the carbon emission intensity of each energy conversion device and transmission branch by defining carbon emission rate,branch carbon intensity,and node carbon potential,realizing accurate tracking of carbon flow in the process of multi-energy coupling.Second,a stepped carbon pricing mechanism is established to dynamically adjust carbon trading costs based on the deviation between actual carbon emissions and initial quotas,strengthening the emission reduction incentive.Finally,a lowcarbon economic dispatch model is constructed with the objectives of minimizing operation cost,carbon trading cost,wind-solar curtailment penalty cost,and energy loss.Simulation results show that compared with the traditional economic dispatch scheme 3,the proposed schemel reduces carbon emissions by 53.97%and wind-solar curtailment by 68.89%with a 16.10%increase in total cost.This verifies that the model can effectively improve clean energy utilization and reduce carbon emissions,achieving low-carbon economic operation of EHH-IES,with CES theory ensuring precise carbon flow tracking across multi-energy links.展开更多
Considering the special features of dynamic environment economic dispatch of power systems with high dimensionality,strong coupling,nonlinearity,and non-convexity,a GA-DE multi-objective optimization algorithm based o...Considering the special features of dynamic environment economic dispatch of power systems with high dimensionality,strong coupling,nonlinearity,and non-convexity,a GA-DE multi-objective optimization algorithm based on dual-population pseudo-parallel genetic algorithm-differential evolution is proposed in this paper.The algorithm is based on external elite archive and Pareto dominance,and it adopts the cooperative co-evolution mechanism of differential evolution and genetic algorithm.Average entropy and cubic chaoticmapping initialization strategies are proposed to increase population diversity.In the proposed method,we analyze the distribution of neighboring solutions and apply a new Pareto solution set pruning approach.Unlike traditional models,this work takes the transmission losses as an optimization target and overcomes complex model constraints through a dynamic relaxation constraint approach.To solve the uncertainty caused by integrating wind and photovoltaic energy in power system scheduling,a multi-objective dynamic environment economical dispatch model is set up that takes the system spinning reserve and network highest losses into account.In this paper,the DE algorithm is improved to form the DGAGE algorithm for the objective optimization of the overall power system,The DE algorithm part of DGAGE is combined with the JAYA algorithm to form the system scheduling HDJ algorithm for multiple energy sources connected to the grid.The effectiveness of the proposed method is demonstrated using CEC2022 and CEC2005 test functions,showing robust optimization performance.Validation on a classical 10-unit system confirms the feasibility of the proposed algorithm in addressing power system scheduling issues.This approach provides a novel solution for dynamic power dispatch systems.展开更多
A modified cuckoo search(CS) algorithm is proposed to solve economic dispatch(ED) problems that have nonconvex, non-continuous or non-linear solution spaces considering valve-point effects, prohibited operating zones,...A modified cuckoo search(CS) algorithm is proposed to solve economic dispatch(ED) problems that have nonconvex, non-continuous or non-linear solution spaces considering valve-point effects, prohibited operating zones, transmission losses and ramp rate limits. Comparing with the traditional cuckoo search algorithm, we propose a self-adaptive step size and some neighbor-study strategies to enhance search performance.Moreover, an improved lambda iteration strategy is used to generate new solutions. To show the superiority of the proposed algorithm over several classic algorithms, four systems with different benchmarks are tested. The results show its efficiency to solve economic dispatch problems, especially for large-scale systems.展开更多
By virtue of alternating direction method of multipliers(ADMM), Newton-Raphson method, ratio consensus approach and running sum method, two distributed iterative strategies are presented in this paper to address the e...By virtue of alternating direction method of multipliers(ADMM), Newton-Raphson method, ratio consensus approach and running sum method, two distributed iterative strategies are presented in this paper to address the economic dispatch problem(EDP) in power systems. Different from most of the existing distributed ED approaches which neglect the effects of packet drops or/and time delays, this paper takes into account both packet drops and time delays which frequently occur in communication networks. Moreover, directed and possibly unbalanced graphs are considered in our algorithms, over which many distributed approaches fail to converge. Furthermore, the proposed schemes can address the EDP with local constraints of generators and nonquadratic convex cost functions, not just quadratic ones required in some existing ED approaches. Both theoretical analyses and simulation studies are provided to demonstrate the effectiveness of the proposed schemes.展开更多
Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainti...Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainties of new energies and various types of loads in the IES.Accordingly,a robust optimal dispatching method for the IES based on a robust economic model predictive control(REMPC)strategy considering source-load power interval prediction is proposed.First,an operation model of the IES is established,and an interval prediction model based on the bidirectional long short-term memory network optimized by beetle antenna search and bootstrap is formulated and applied to predict the photovoltaic power and the cooling,heating,and electrical loads.Then,an optimal dispatching scheme based on REMPC is devised for the IES.The source-load interval prediction results are used to improve the robustness of the REPMC and reduce the influence of source-load uncertainties on dispatching.An actual IES case is selected to conduct simulations;the results show that compared with other prediction techniques,the proposed method has higher prediction interval coverage probability and prediction interval normalized averaged width.Moreover,the operational cost of the IES is decreased by the REMPC strategy.With the devised dispatching scheme,the ability of the IES to handle the dispatching risk caused by prediction errors is enhanced.Improved dispatching robustness and operational economy are also achieved.展开更多
Introducing carbon trading into electricity market can convert carbon dioxide into schedulable resources with economic value.However,the randomness of wind power generation puts forward higher requirements for electri...Introducing carbon trading into electricity market can convert carbon dioxide into schedulable resources with economic value.However,the randomness of wind power generation puts forward higher requirements for electricity market transactions.Therefore,the carbon trading market is introduced into the wind power market,and a new form of low-carbon economic dispatch model is developed.First,the economic dispatch goal of wind power is be considered.It is projected to save money and reduce the cost of power generation for the system.The model includes risk operating costs to account for the impact of wind power output variability on the system,as well as wind farm negative efficiency operating costs to account for the loss caused by wind abandonment.The model also employs carbon trading market metrics to achieve the goal of lowering system carbon emissions,and analyze the impact of different carbon trading prices on the system.A low-carbon economic dispatch model for the wind power market is implemented based on the following two goals.Finally,the solution is optimised using the Ant-lion optimisation method,which combines Levi's flight mechanism and golden sine.The proposed model and algorithm's rationality is proven through the use of cases.展开更多
In this study, we present a Pareto-based chemicalreaction optimization(PCRO) algorithm for solving the multiarea environmental/economic dispatch optimization problems.Two objectives are minimized simultaneously, i.e.,...In this study, we present a Pareto-based chemicalreaction optimization(PCRO) algorithm for solving the multiarea environmental/economic dispatch optimization problems.Two objectives are minimized simultaneously, i.e., total fuel cost and emission. In the proposed algorithm, each solution is represented by a chemical molecule. A novel encoding mechanism for solving the multi-area environmental/economic dispatch optimization problems is designed to dynamically enhance the performance of the proposed algorithm. Then, an ensemble of effective neighborhood approaches is developed, and a selfadaptive neighborhood structure selection mechanism is also embedded in PCRO to increase the search ability while maintaining population diversity. In addition, a grid-based crowding distance strategy is introduced, which can obviously enable the algorithm to easily converge near the Pareto front. Furthermore,a kinetic-energy-based search procedure is developed to enhance the global search ability. Finally, the proposed algorithm is tested on sets of the instances that are generated based on realistic production. Through the analysis of experimental results, the highly effective performance of the proposed PCRO algorithm is favorably compared with several algorithms, with regards to both solution quality and diversity.展开更多
In the recent restructured power system scenario and complex market strategy, operation at absolute minimum cost is no longer the only criterion for dispatching electric power. The economic load dispatch (ELD) problem...In the recent restructured power system scenario and complex market strategy, operation at absolute minimum cost is no longer the only criterion for dispatching electric power. The economic load dispatch (ELD) problem which accounts for minimization of both generation cost and power loss is itself a multiple conflicting objective function problem. In this paper, a modified shuffled frog-leaping algorithm (MSFLA), which is an improved version of memetic algorithm, is proposed for solving the ELD problem. It is a relatively new evolutionary method where local search is applied during the evolutionary cycle. The idea of memetic algorithm comes from memes, which unlike genes can adapt themselves. The performance of MSFLA has been shown more efficient than traditional evolutionary algorithms for such type of ELD problem. The application and validity of the proposed algorithm are demonstrated for IEEE 30 bus test system as well as a practical power network of 203 bus 264 lines 23 machines system.展开更多
This paper presents an efficient and reliable genetic algorithm (GA) based particle swarm optimization (PSO) tech- nique (hybrid GAPSO) for solving the economic dispatch (ED) problem in power systems. The non-linear c...This paper presents an efficient and reliable genetic algorithm (GA) based particle swarm optimization (PSO) tech- nique (hybrid GAPSO) for solving the economic dispatch (ED) problem in power systems. The non-linear characteristics of the generators, such as prohibited operating zones, ramp rate limits and non-smooth cost functions of the practical generator operation are considered. The proposed hybrid algorithm is demonstrated for three different systems and the performance is compared with the GA and PSO in terms of solution quality and computation efficiency. Comparison of results proved that the proposed algo- rithm can obtain higher quality solutions efficiently in ED problems. A comprehensive software package is developed using MATLAB.展开更多
An economic dispatch problem for power system with wind power is discussed.Using discrete scenario to describe uncertain wind powers,a threshold is given to identify bad scenario set.The bad-scenario-set robust econom...An economic dispatch problem for power system with wind power is discussed.Using discrete scenario to describe uncertain wind powers,a threshold is given to identify bad scenario set.The bad-scenario-set robust economic dispatch model is established to minimize the total penalties on bad scenarios.A specialized hybrid particle swarm optimization(PSO)algorithm is developed through hybridizing simulated annealing(SA)operators.The SA operators are performed according to a scenario-oriented adaptive search rule in a neighborhood which is constructed based on the unit commitment constraints.Finally,an experiment is conducted.The computational results show that the developed algorithm outperforms the existing algorithms.展开更多
A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrai...A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrained multi-objective optimization problem. The proposed MOPSO approach handles the problem as a multi-objective problem with competing and non-commensurable fuel cost, emission and system loss objectives and has a diversity-preserving mechanism using an external memory (call “repository”) and a geographically-based approach to find widely different Pareto-optimal solutions. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed MOPSO approach were carried out on the standard IEEE 30-bus test system. The results revealed the capabilities of the proposed MOPSO approach to generate well-distributed Pareto-optimal non-dominated solutions of multi-objective economic load dispatch. Com- parison with Multi-objective Evolutionary Algorithm (MOEA) showed the superiority of the proposed MOPSO approach and confirmed its potential for solving multi-objective economic load dispatch.展开更多
Reducing pollutant emissions from electricity production in the power system positively impacts the control of greenhouse gas emissions.Boosting kernel search optimizer(BKSO)is introduced in this research to solve the...Reducing pollutant emissions from electricity production in the power system positively impacts the control of greenhouse gas emissions.Boosting kernel search optimizer(BKSO)is introduced in this research to solve the combined economic emission dispatch(CEED)problem.Inspired by the foraging behavior in the slime mould algorithm(SMA),the kernel matrix of the kernel search optimizer(KSO)is intensified.The proposed BKSO is superior to the standard KSO in terms of exploitation ability,robustness,and convergence rate.The CEC2013 test function is used to assess the improved KSO's performance and compared to 11 well-known optimization algorithms.BKSO performs better in statistical results and convergence curves.At the same time,BKSO achieves better fuel costs and fewer pollution emissions by testing with four real CEED cases,and the Pareto solution obtained is also better than other MAs.Based on the experimental results,BKSO has better performance than other comparable MAs and can provide more economical,robust,and cleaner solutions to CEED problems.展开更多
This paper presents an optimization technique coupling two optimization techniques for solving Economic Emission Load Dispatch Optimization Problem EELD. The proposed approach integrates the merits of both genetic alg...This paper presents an optimization technique coupling two optimization techniques for solving Economic Emission Load Dispatch Optimization Problem EELD. The proposed approach integrates the merits of both genetic algorithm (GA) and local search (LS), where it maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept of ε-dominance. To improve the solution quality, local search technique was applied as neighborhood search engine, where it intends to explore the less-crowded area in the current archive to possibly obtain more non-dominated solutions. TOPSIS technique can incorporate relative weights of criterion importance, which has been implemented to identify best compromise solution, which will satisfy the different goals to some extent. Several optimization runs of the proposed approach are carried out on the standard IEEE 30-bus 6-genrator test system. The comparison demonstrates the superiority of the proposed approach and confirms its potential to solve the multiobjective EELD problem.展开更多
This paper presents a novel approach to economic dispatch in smart grids equipped with diverse energy devices.This method integrates features including photovoltaic(PV)systems,energy storage coupling,varied energy rol...This paper presents a novel approach to economic dispatch in smart grids equipped with diverse energy devices.This method integrates features including photovoltaic(PV)systems,energy storage coupling,varied energy roles,and energy supply and demand dynamics.The systemmodel is developed by considering energy devices as versatile units capable of fulfilling various functionalities and playing multiple roles simultaneously.To strike a balance between optimality and feasibility,renewable energy resources are modeled with considerations for forecasting errors,Gaussian distribution,and penalty factors.Furthermore,this study introduces a distributed event-triggered surplus algorithm designed to address the economic dispatch problem by minimizing production costs.Rooted in surplus theory and finite time projection,the algorithm effectively rectifies network imbalances caused by directed graphs and addresses local inequality constraints.The algorithm greatly reduces the communication burden through event triggering mechanism.Finally,both theoretical proofs and numerical simulations verify the convergence and event-triggered nature of the algorithm.展开更多
This paper presents the solution to the combined heat and power economic dispatch problem using a direct solution algorithm for constrained optimization problems. With the potential of Combined Heat and Power (CHP) pr...This paper presents the solution to the combined heat and power economic dispatch problem using a direct solution algorithm for constrained optimization problems. With the potential of Combined Heat and Power (CHP) production to increase the efficiency of power and heat generation simultaneously having been researched and established, the increasing penetration of CHP systems, and determination of economic dispatch of power and heat assumes higher relevance. The Combined Heat and Power Economic Dispatch (CHPED) problem is a demanding optimization problem as both constraints and objective functions can be non-linear and non-convex. This paper presents an explicit formula developed for computing the system-wide incremental costs corresponding with optimal dispatch. The circumvention of the use of iterative search schemes for this crucial step is the innovation inherent in the proposed dispatch procedure. The feasible operating region of the CHP unit three is taken into account in the proposed CHPED problem model, whereas the optimal dispatch of power/heat outputs of CHP unit is determined using the direct Lagrange multiplier solution algorithm. The proposed algorithm is applied to a test system with four units and results are provided.展开更多
In this paper,an uncertain economic dispatch problem(EDP)is considered for a group of coopertive agents.First,let each agent extract a set of samples(scenarios)from the uncertain set,and then a scenario EDP is obtaine...In this paper,an uncertain economic dispatch problem(EDP)is considered for a group of coopertive agents.First,let each agent extract a set of samples(scenarios)from the uncertain set,and then a scenario EDP is obtained using these scenarios.Based on the scenario theory,a prior certifcation is provided to evaluate the probabilistic feasibility of the scenario solution for uncertain EDP.To facilitate the computational task,a distributed solution strategy is proposed by the alternating direction method of multipliers(ADMM)and a fnite-time consensus strategy.Moreover,the distributed strategy can solve the scenario problem over a weight-balanced directed graph.Finally,the proposed solution strategy is applied to an EDP for a power system involving wind power plants.展开更多
基金supported by the Key Project of Shanghai(Project Number A1-0224-25-002-02-040,Municipal Key Course—Heat Transfer)funded by the National Natural Science Foundation of China(Grant No.52077137).
文摘To enhance the low-carbon economic efficiency and increase the utilization of renewable energy within integrated energy systems(IES),this paper proposes a low-carbon dispatch model integrating power-to-gas(P2G),carbon capture and storage(CCS),hydrogen fuel cell(HFC),and combined heat and power(CHP).The P2G process is refined into a two-stage structure,and HFC is introduced to enhance hydrogen utilization.Together with CCS and CHP,these devices form a multi-energy conversion system coupling electricity,heat,cooling,and gas.A laddertype carbon trading approach is adopted to flexibly manage carbon output by leveraging marginal cost adjustments.To evaluate the resilience of the proposed low-carbon scheduling strategy involving multiple energy units under the variability of renewable energy,a two-level robust optimization framework is developed.This model captures the most adverse scenarios of wind and solar generation.The dispatch strategy is validated against these extreme conditions to demonstrate its flexibility and effectiveness.The problem is solved using the GUROBI optimization tool.Results from simulations indicate that themodel increases renewable energy integration by 39.1%,and achieves reductions of 15.96%in carbon emissions and 16.29%in operational expenditures.The results demonstrate that the strategy ensures both economic efficiency and environmental performance under uncertain conditions.Compared with existing studies that separately model two-stage P2G or CCS devices,this paper integrates HFC,CHP,and CCS into a unified dispatchable system,enabling refined hydrogen utilization and flexible carbon circulation.Furthermore,the introduction of a laddertype carbon pricing mechanism,combined with multi-energy storage participation in implicit demand response,creates a dynamic and cost-sensitive dispatch framework.These modeling strategies go beyond conventional linear IES formulations and provide more realistic system representations.The proposed approach not only deepens the coupling among electric,thermal,and gas systems,but also offers a feasible pathway for high-penetration renewable integration in low-carbon energy systems.
基金This work is jointly supported by National High Technology Research and Development Program of China(863 Program)(No.2011AA05A105)a key project from Zhejiang Electric Power Corporation.
文摘Based on a new perspective in coordinating with the traditional"N-1"criteria and system risk,a real-time electricity market model is presented,in which the system risk is employed to model the system’s overall security level.This new model is called the risk-based security-constrained economic dispatch(RB-SCED).Relative to the securityconstrained economic dispatch(SCED)used in the power industry today,the RB-SCED finds more secure and economic operating conditions.It does this by obtaining solutions that achieve a better balance between post-contingency flows on individual branches and the overall system risk.The method exploits the fact that,in a SCED solution,some postcontingency branch flows which exceed their limits impose little risk while other post-contingency branch flows which are within their limits impose significant risk.The RB-SCED softens constraints for the former and hardens constraints for the latter,thus achieving simultaneous improvement in both security and economy.In this work,the basic concept and the mathematical formulation of the RB-SCED model are systematically described.Experimental results on a 9-bus system and the ISO New England actual system have demonstrated the advantages of RB-SCED over SCED.
基金supported by the National Natural Science Foundation of China(62103203)
文摘The economic dispatch problem(EDP) of microgrids operating in both grid-connected and isolated modes within an energy internet framework is addressed in this paper. The multi-agent leader-following consensus algorithm is employed to address the EDP of microgrids in grid-connected mode, while the push-pull algorithm with a fixed step size is introduced for the isolated mode. The proposed algorithm of isolated mode is proven to converge to the optimum when the interaction digraph of microgrids is strongly connected. A unified algorithmic framework is proposed to handle the two modes of operation of microgrids simultaneously, enabling our algorithm to achieve optimal power allocation and maintain the balance between power supply and demand in any mode and any mode switching. Due to the push-pull structure of the algorithm and the use of fixed step size,the proposed algorithm can better handle the case of unbalanced graphs, and the convergence speed is improved. It is documented that when the transmission topology is strongly connected and there is bi-directional communication between the energy router and its neighbors, the proposed algorithm in composite mode achieves economic dispatch even with arbitrary mode switching.Finally, we demonstrate the effectiveness and superiority of our algorithm through numerical simulations.
基金funded by Guangxi Science and Technology Base and Talent Special Project,grant number GuiKeAD20159077Foundation of Guilin University of Technology,grant number GLUTQD2018001.
文摘The exponential growth in the scale of power systems has led to a significant increase in the complexity of dispatch problem resolution,particularly within multi-area interconnected power grids.This complexity necessitates the employment of distributed solution methodologies,which are not only essential but also highly desirable.In the realm of computational modelling,the multi-area economic dispatch problem(MAED)can be formulated as a linearly constrained separable convex optimization problem.The proximal point algorithm(PPA)is particularly adept at addressing such mathematical constructs effectively.This study introduces parallel(PPPA)and serial(SPPA)variants of the PPA as distributed algorithms,specifically designed for the computational modelling of the MAED.The PPA introduces a quadratic term into the objective function,which,while potentially complicating the iterative updates of the algorithm,serves to dampen oscillations near the optimal solution,thereby enhancing the convergence characteristics.Furthermore,the convergence efficiency of the PPA is significantly influenced by the parameter c.To address this parameter sensitivity,this research draws on trend theory from stock market analysis to propose trend theory-driven distributed PPPA and SPPA,thereby enhancing the robustness of the computational models.The computational models proposed in this study are anticipated to exhibit superior performance in terms of convergence behaviour,stability,and robustness with respect to parameter selection,potentially outperforming existing methods such as the alternating direction method of multipliers(ADMM)and Auxiliary Problem Principle(APP)in the computational simulation of power system dispatch problems.The simulation results demonstrate that the trend theory-based PPPA,SPPA,ADMM and APP exhibit significant robustness to the initial value of parameter c,and show superior convergence characteristics compared to the residual balancing ADMM.
文摘To address the issues of unclear carbon responsibility attribution,insufficient renewable energy absorption,and simplistic carbon trading mechanisms in integrated energy systems,this paper proposes an electricheat-hydrogen integrated energy system(EHH-IES)optimal scheduling model considering carbon emission stream(CES)and wind-solar accommodation.First,the CES theory is introduced to quantify the carbon emission intensity of each energy conversion device and transmission branch by defining carbon emission rate,branch carbon intensity,and node carbon potential,realizing accurate tracking of carbon flow in the process of multi-energy coupling.Second,a stepped carbon pricing mechanism is established to dynamically adjust carbon trading costs based on the deviation between actual carbon emissions and initial quotas,strengthening the emission reduction incentive.Finally,a lowcarbon economic dispatch model is constructed with the objectives of minimizing operation cost,carbon trading cost,wind-solar curtailment penalty cost,and energy loss.Simulation results show that compared with the traditional economic dispatch scheme 3,the proposed schemel reduces carbon emissions by 53.97%and wind-solar curtailment by 68.89%with a 16.10%increase in total cost.This verifies that the model can effectively improve clean energy utilization and reduce carbon emissions,achieving low-carbon economic operation of EHH-IES,with CES theory ensuring precise carbon flow tracking across multi-energy links.
基金funded by the Major Humanities and Social Sciences Research Projects in Zhejiang Higher Education Institutions,grant number 2023QN131National Innovation Training Program Project in China,grant number 202410451009.
文摘Considering the special features of dynamic environment economic dispatch of power systems with high dimensionality,strong coupling,nonlinearity,and non-convexity,a GA-DE multi-objective optimization algorithm based on dual-population pseudo-parallel genetic algorithm-differential evolution is proposed in this paper.The algorithm is based on external elite archive and Pareto dominance,and it adopts the cooperative co-evolution mechanism of differential evolution and genetic algorithm.Average entropy and cubic chaoticmapping initialization strategies are proposed to increase population diversity.In the proposed method,we analyze the distribution of neighboring solutions and apply a new Pareto solution set pruning approach.Unlike traditional models,this work takes the transmission losses as an optimization target and overcomes complex model constraints through a dynamic relaxation constraint approach.To solve the uncertainty caused by integrating wind and photovoltaic energy in power system scheduling,a multi-objective dynamic environment economical dispatch model is set up that takes the system spinning reserve and network highest losses into account.In this paper,the DE algorithm is improved to form the DGAGE algorithm for the objective optimization of the overall power system,The DE algorithm part of DGAGE is combined with the JAYA algorithm to form the system scheduling HDJ algorithm for multiple energy sources connected to the grid.The effectiveness of the proposed method is demonstrated using CEC2022 and CEC2005 test functions,showing robust optimization performance.Validation on a classical 10-unit system confirms the feasibility of the proposed algorithm in addressing power system scheduling issues.This approach provides a novel solution for dynamic power dispatch systems.
基金supported in part by the National Key Research and Development Program of China(2017YFB0306400)in part by the National Natural Science Foundation of China(61573089,71472080,71301066)Liaoning Province Dr.Research Foundation of China(20175032)
文摘A modified cuckoo search(CS) algorithm is proposed to solve economic dispatch(ED) problems that have nonconvex, non-continuous or non-linear solution spaces considering valve-point effects, prohibited operating zones, transmission losses and ramp rate limits. Comparing with the traditional cuckoo search algorithm, we propose a self-adaptive step size and some neighbor-study strategies to enhance search performance.Moreover, an improved lambda iteration strategy is used to generate new solutions. To show the superiority of the proposed algorithm over several classic algorithms, four systems with different benchmarks are tested. The results show its efficiency to solve economic dispatch problems, especially for large-scale systems.
基金supported by the National Natural Science Foundation of China(61673077)。
文摘By virtue of alternating direction method of multipliers(ADMM), Newton-Raphson method, ratio consensus approach and running sum method, two distributed iterative strategies are presented in this paper to address the economic dispatch problem(EDP) in power systems. Different from most of the existing distributed ED approaches which neglect the effects of packet drops or/and time delays, this paper takes into account both packet drops and time delays which frequently occur in communication networks. Moreover, directed and possibly unbalanced graphs are considered in our algorithms, over which many distributed approaches fail to converge. Furthermore, the proposed schemes can address the EDP with local constraints of generators and nonquadratic convex cost functions, not just quadratic ones required in some existing ED approaches. Both theoretical analyses and simulation studies are provided to demonstrate the effectiveness of the proposed schemes.
基金supported by the National Key Research and Development Project of China(2018YFE0122200).
文摘Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainties of new energies and various types of loads in the IES.Accordingly,a robust optimal dispatching method for the IES based on a robust economic model predictive control(REMPC)strategy considering source-load power interval prediction is proposed.First,an operation model of the IES is established,and an interval prediction model based on the bidirectional long short-term memory network optimized by beetle antenna search and bootstrap is formulated and applied to predict the photovoltaic power and the cooling,heating,and electrical loads.Then,an optimal dispatching scheme based on REMPC is devised for the IES.The source-load interval prediction results are used to improve the robustness of the REPMC and reduce the influence of source-load uncertainties on dispatching.An actual IES case is selected to conduct simulations;the results show that compared with other prediction techniques,the proposed method has higher prediction interval coverage probability and prediction interval normalized averaged width.Moreover,the operational cost of the IES is decreased by the REMPC strategy.With the devised dispatching scheme,the ability of the IES to handle the dispatching risk caused by prediction errors is enhanced.Improved dispatching robustness and operational economy are also achieved.
基金National Natural Science Foundation of China,Grant/Award Number:51677059。
文摘Introducing carbon trading into electricity market can convert carbon dioxide into schedulable resources with economic value.However,the randomness of wind power generation puts forward higher requirements for electricity market transactions.Therefore,the carbon trading market is introduced into the wind power market,and a new form of low-carbon economic dispatch model is developed.First,the economic dispatch goal of wind power is be considered.It is projected to save money and reduce the cost of power generation for the system.The model includes risk operating costs to account for the impact of wind power output variability on the system,as well as wind farm negative efficiency operating costs to account for the loss caused by wind abandonment.The model also employs carbon trading market metrics to achieve the goal of lowering system carbon emissions,and analyze the impact of different carbon trading prices on the system.A low-carbon economic dispatch model for the wind power market is implemented based on the following two goals.Finally,the solution is optimised using the Ant-lion optimisation method,which combines Levi's flight mechanism and golden sine.The proposed model and algorithm's rationality is proven through the use of cases.
基金partially supported by the National Natural Science Foundation of China(61773192,61773246,61603169,61803192)Shandong Province Higher Educational Science and Technology Program(J17KZ005)+1 种基金Special Fund Plan for Local Science and Technology Development Lead by Central AuthorityMajor Basic Research Projects in Shandong(ZR2018ZB0419)
文摘In this study, we present a Pareto-based chemicalreaction optimization(PCRO) algorithm for solving the multiarea environmental/economic dispatch optimization problems.Two objectives are minimized simultaneously, i.e., total fuel cost and emission. In the proposed algorithm, each solution is represented by a chemical molecule. A novel encoding mechanism for solving the multi-area environmental/economic dispatch optimization problems is designed to dynamically enhance the performance of the proposed algorithm. Then, an ensemble of effective neighborhood approaches is developed, and a selfadaptive neighborhood structure selection mechanism is also embedded in PCRO to increase the search ability while maintaining population diversity. In addition, a grid-based crowding distance strategy is introduced, which can obviously enable the algorithm to easily converge near the Pareto front. Furthermore,a kinetic-energy-based search procedure is developed to enhance the global search ability. Finally, the proposed algorithm is tested on sets of the instances that are generated based on realistic production. Through the analysis of experimental results, the highly effective performance of the proposed PCRO algorithm is favorably compared with several algorithms, with regards to both solution quality and diversity.
文摘In the recent restructured power system scenario and complex market strategy, operation at absolute minimum cost is no longer the only criterion for dispatching electric power. The economic load dispatch (ELD) problem which accounts for minimization of both generation cost and power loss is itself a multiple conflicting objective function problem. In this paper, a modified shuffled frog-leaping algorithm (MSFLA), which is an improved version of memetic algorithm, is proposed for solving the ELD problem. It is a relatively new evolutionary method where local search is applied during the evolutionary cycle. The idea of memetic algorithm comes from memes, which unlike genes can adapt themselves. The performance of MSFLA has been shown more efficient than traditional evolutionary algorithms for such type of ELD problem. The application and validity of the proposed algorithm are demonstrated for IEEE 30 bus test system as well as a practical power network of 203 bus 264 lines 23 machines system.
文摘This paper presents an efficient and reliable genetic algorithm (GA) based particle swarm optimization (PSO) tech- nique (hybrid GAPSO) for solving the economic dispatch (ED) problem in power systems. The non-linear characteristics of the generators, such as prohibited operating zones, ramp rate limits and non-smooth cost functions of the practical generator operation are considered. The proposed hybrid algorithm is demonstrated for three different systems and the performance is compared with the GA and PSO in terms of solution quality and computation efficiency. Comparison of results proved that the proposed algo- rithm can obtain higher quality solutions efficiently in ED problems. A comprehensive software package is developed using MATLAB.
基金supported by the National Natural Science Foundation of China(62173219,62073210).
文摘An economic dispatch problem for power system with wind power is discussed.Using discrete scenario to describe uncertain wind powers,a threshold is given to identify bad scenario set.The bad-scenario-set robust economic dispatch model is established to minimize the total penalties on bad scenarios.A specialized hybrid particle swarm optimization(PSO)algorithm is developed through hybridizing simulated annealing(SA)operators.The SA operators are performed according to a scenario-oriented adaptive search rule in a neighborhood which is constructed based on the unit commitment constraints.Finally,an experiment is conducted.The computational results show that the developed algorithm outperforms the existing algorithms.
基金Project (Nos. 60074040 and 6022506) supported by the NationalNatural Science Foundation of China
文摘A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrained multi-objective optimization problem. The proposed MOPSO approach handles the problem as a multi-objective problem with competing and non-commensurable fuel cost, emission and system loss objectives and has a diversity-preserving mechanism using an external memory (call “repository”) and a geographically-based approach to find widely different Pareto-optimal solutions. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed MOPSO approach were carried out on the standard IEEE 30-bus test system. The results revealed the capabilities of the proposed MOPSO approach to generate well-distributed Pareto-optimal non-dominated solutions of multi-objective economic load dispatch. Com- parison with Multi-objective Evolutionary Algorithm (MOEA) showed the superiority of the proposed MOPSO approach and confirmed its potential for solving multi-objective economic load dispatch.
基金This research was supported by the Science&Technology Development Project of Jilin Province,China(YDZJ202201ZYTS555)the Science&Technology Research Project of the Education Department of Jilin Province,China(JJKH20220244KJ)。
文摘Reducing pollutant emissions from electricity production in the power system positively impacts the control of greenhouse gas emissions.Boosting kernel search optimizer(BKSO)is introduced in this research to solve the combined economic emission dispatch(CEED)problem.Inspired by the foraging behavior in the slime mould algorithm(SMA),the kernel matrix of the kernel search optimizer(KSO)is intensified.The proposed BKSO is superior to the standard KSO in terms of exploitation ability,robustness,and convergence rate.The CEC2013 test function is used to assess the improved KSO's performance and compared to 11 well-known optimization algorithms.BKSO performs better in statistical results and convergence curves.At the same time,BKSO achieves better fuel costs and fewer pollution emissions by testing with four real CEED cases,and the Pareto solution obtained is also better than other MAs.Based on the experimental results,BKSO has better performance than other comparable MAs and can provide more economical,robust,and cleaner solutions to CEED problems.
文摘This paper presents an optimization technique coupling two optimization techniques for solving Economic Emission Load Dispatch Optimization Problem EELD. The proposed approach integrates the merits of both genetic algorithm (GA) and local search (LS), where it maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept of ε-dominance. To improve the solution quality, local search technique was applied as neighborhood search engine, where it intends to explore the less-crowded area in the current archive to possibly obtain more non-dominated solutions. TOPSIS technique can incorporate relative weights of criterion importance, which has been implemented to identify best compromise solution, which will satisfy the different goals to some extent. Several optimization runs of the proposed approach are carried out on the standard IEEE 30-bus 6-genrator test system. The comparison demonstrates the superiority of the proposed approach and confirms its potential to solve the multiobjective EELD problem.
基金The Science and Technology Project of the State Grid Corporation of China(Research and Demonstration of Loss Reduction Technology Based on Reactive Power Potential Exploration and Excitation of Distributed Photovoltaic-Energy Storage Converters:5400-202333241A-1-1-ZN).
文摘This paper presents a novel approach to economic dispatch in smart grids equipped with diverse energy devices.This method integrates features including photovoltaic(PV)systems,energy storage coupling,varied energy roles,and energy supply and demand dynamics.The systemmodel is developed by considering energy devices as versatile units capable of fulfilling various functionalities and playing multiple roles simultaneously.To strike a balance between optimality and feasibility,renewable energy resources are modeled with considerations for forecasting errors,Gaussian distribution,and penalty factors.Furthermore,this study introduces a distributed event-triggered surplus algorithm designed to address the economic dispatch problem by minimizing production costs.Rooted in surplus theory and finite time projection,the algorithm effectively rectifies network imbalances caused by directed graphs and addresses local inequality constraints.The algorithm greatly reduces the communication burden through event triggering mechanism.Finally,both theoretical proofs and numerical simulations verify the convergence and event-triggered nature of the algorithm.
文摘This paper presents the solution to the combined heat and power economic dispatch problem using a direct solution algorithm for constrained optimization problems. With the potential of Combined Heat and Power (CHP) production to increase the efficiency of power and heat generation simultaneously having been researched and established, the increasing penetration of CHP systems, and determination of economic dispatch of power and heat assumes higher relevance. The Combined Heat and Power Economic Dispatch (CHPED) problem is a demanding optimization problem as both constraints and objective functions can be non-linear and non-convex. This paper presents an explicit formula developed for computing the system-wide incremental costs corresponding with optimal dispatch. The circumvention of the use of iterative search schemes for this crucial step is the innovation inherent in the proposed dispatch procedure. The feasible operating region of the CHP unit three is taken into account in the proposed CHPED problem model, whereas the optimal dispatch of power/heat outputs of CHP unit is determined using the direct Lagrange multiplier solution algorithm. The proposed algorithm is applied to a test system with four units and results are provided.
文摘In this paper,an uncertain economic dispatch problem(EDP)is considered for a group of coopertive agents.First,let each agent extract a set of samples(scenarios)from the uncertain set,and then a scenario EDP is obtained using these scenarios.Based on the scenario theory,a prior certifcation is provided to evaluate the probabilistic feasibility of the scenario solution for uncertain EDP.To facilitate the computational task,a distributed solution strategy is proposed by the alternating direction method of multipliers(ADMM)and a fnite-time consensus strategy.Moreover,the distributed strategy can solve the scenario problem over a weight-balanced directed graph.Finally,the proposed solution strategy is applied to an EDP for a power system involving wind power plants.
