In this paper,a bilevel optimization model of an integrated energy operator(IEO)–load aggregator(LA)is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy sys...In this paper,a bilevel optimization model of an integrated energy operator(IEO)–load aggregator(LA)is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy system(IIES).The upper level represents the integrated energy operator,and the lower level is the electricity-heatgas load aggregator.Owing to the benefit conflict between the upper and lower levels of the IIES,a dynamic pricing mechanism for coordinating the interests of the upper and lower levels is proposed,combined with factors such as the carbon emissions of the IIES,as well as the lower load interruption power.The price of selling energy can be dynamically adjusted to the lower LA in the mechanism,according to the information on carbon emissions and load interruption power.Mutual benefits and win-win situations are achieved between the upper and lower multistakeholders.Finally,CPLEX is used to iteratively solve the bilevel optimization model.The optimal solution is selected according to the joint optimal discrimination mechanism.Thesimulation results indicate that the sourceload coordinate operation can reduce the upper and lower operation costs.Using the proposed pricingmechanism,the carbon emissions and load interruption power of IEO-LA are reduced by 9.78%and 70.19%,respectively,and the capture power of the carbon capture equipment is improved by 36.24%.The validity of the proposed model and method is verified.展开更多
Energy storage power plants are critical in balancing power supply and demand.However,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device ...Energy storage power plants are critical in balancing power supply and demand.However,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device energy utilization.To tackle these challenges,this study proposes an optimal scheduling model for energy storage power plants based on edge computing and the improved whale optimization algorithm(IWOA).The proposed model designs an edge computing framework,transferring a large share of data processing and storage tasks to the network edge.This architecture effectively reduces transmission costs by minimizing data travel time.In addition,the model considers demand response strategies and builds an objective function based on the minimization of the sum of electricity purchase cost and operation cost.The IWOA enhances the optimization process by utilizing adaptive weight adjustments and an optimal neighborhood perturbation strategy,preventing the algorithm from converging to suboptimal solutions.Experimental results demonstrate that the proposed scheduling model maximizes the flexibility of the energy storage plant,facilitating efficient charging and discharging.It successfully achieves peak shaving and valley filling for both electrical and heat loads,promoting the effective utilization of renewable energy sources.The edge-computing framework significantly reduces transmission delays between energy devices.Furthermore,IWOA outperforms traditional algorithms in optimizing the objective function.展开更多
To mitigate the impact of wind power volatility on power system scheduling,this paper adopts the wind-storage combined unit to improve the dispatchability of wind energy.And a three-level optimal scheduling and power ...To mitigate the impact of wind power volatility on power system scheduling,this paper adopts the wind-storage combined unit to improve the dispatchability of wind energy.And a three-level optimal scheduling and power allocation strategy is proposed for the system containing the wind-storage combined unit.The strategy takes smoothing power output as themain objectives.The first level is the wind-storage joint scheduling,and the second and third levels carry out the unit combination optimization of thermal power and the power allocation of wind power cluster(WPC),respectively,according to the scheduling power of WPC and ESS obtained from the first level.This can ensure the stability,economy and environmental friendliness of the whole power system.Based on the roles of peak shaving-valley filling and fluctuation smoothing of the energy storage system(ESS),this paper decides the charging and discharging intervals of ESS,so that the energy storage and wind power output can be further coordinated.Considering the prediction error and the output uncertainty of wind power,the planned scheduling output of wind farms(WFs)is first optimized on a long timescale,and then the rolling correction optimization of the scheduling output of WFs is carried out on a short timescale.Finally,the effectiveness of the proposed optimal scheduling and power allocation strategy is verified through case analysis.展开更多
Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a ...Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.展开更多
From the perspective of a community energy operator,a two-stage optimal scheduling model of a community integrated energy system is proposed by integrating information on controllable loads.The day-ahead scheduling an...From the perspective of a community energy operator,a two-stage optimal scheduling model of a community integrated energy system is proposed by integrating information on controllable loads.The day-ahead scheduling analyzes whether various controllable loads participate in the optimization and investigates the impact of their responses on the operating economy of the community integrated energy system(IES)before and after;the intra-day scheduling proposes a two-stage rolling optimization model based on the day-ahead scheduling scheme,taking into account the fluctuation of wind turbine output and load within a short period of time and according to the different response rates of heat and cooling power,and solves the adjusted output of each controllable device.The simulation results show that the optimal scheduling of controllable loads effectively reduces the comprehensive operating costs of community IES;the two-stage optimal scheduling model can meet the energy demand of customers while effectively and timely suppressing the random fluctuations on both sides of the source and load during the intra-day stage,realizing the economic and smooth operation of IES.展开更多
To adress the problems of insufficient consideration of charging pile resource limitations,discrete-time scheduling methods that do not meet the actual demand and insufficient descriptions of peak-shaving response cap...To adress the problems of insufficient consideration of charging pile resource limitations,discrete-time scheduling methods that do not meet the actual demand and insufficient descriptions of peak-shaving response capability in current electric vehicle(EV)opti-mization scheduling,edge intelligence-oriented electric vehicle optimization scheduling and charging station peak-shaving response capability assessment methods are proposed on the basis of the consideration of electric vehicle and charging pile matching.First,an edge-intelligence-oriented electric vehicle regulation frame for charging stations is proposed.Second,continuous time variables are used to represent the available charging periods,establish the charging station controllable EV load model and the future available charging pile mathematical model,and establish the EV and charging pile matching matrix and constraints.Then,with the goal of maximizing the user charging demand and reducing the charging cost,the charging station EV optimal scheduling model is established,and the EV peak response capacity assessment model is further established by considering the EV load shifting constraints under different peak response capacities.Finally,a typical scenario of a real charging station is taken as an example for the analysis of optimal EV scheduling and peak shaving response capacity,and the proposed method is compared with the traditional method to verify the effectiveness and practicality of the proposed method.展开更多
When the communication time is relatively shorter than the computation time for every task, the task duplication based scheduling (TDS) algorithm proposed by Darbha and Agrawal generates an optimal schedule. Park and ...When the communication time is relatively shorter than the computation time for every task, the task duplication based scheduling (TDS) algorithm proposed by Darbha and Agrawal generates an optimal schedule. Park and Choe also proposed an extended TDS algorithm whose optimality condition is less restricted than that of TDS algorithm, but the condition is very complex and is difficult to satisfy when the number of tasks is large. An efficient algorithm is proposed whose optimality condition is less restricted and simpler than both of the algorithms, and the schedule length is also shorter than both of the algorithms. The time complexity of the proposed algorithm is O(v2), where v represents the number of tasks.展开更多
This paper presents the optimal scheduling of renewable resources using interior point optimization for grid-connected and islanded microgrids (MG) that operate with no energy storage systems. The German Jordanian Uni...This paper presents the optimal scheduling of renewable resources using interior point optimization for grid-connected and islanded microgrids (MG) that operate with no energy storage systems. The German Jordanian University (GJU) microgrid system is used for illustration. We present analyses for islanded and grid-connected MG with no storage. The results show a feasible islanded MG with a substantial operational cost reduction. We obtain an average of $1 k daily cost savings when operating an islanded compared to a grid-connected MG with capped grid energy prices. This cost saving is 10 times higher when considering varying grid energy prices during the day. Although the PV power is intermittent during the day, the MG continues to operate with a voltage variation that does not 10%. The results imply that MGs of GJU similar topology can optimally and safely operate with no energy storage requirements but considerable renewable generation capacity.展开更多
The electricity-gas transformation problem and related intrinsic mechanisms are considered.First,existing schemes for the optimization of electricity-gas integrated energy systems are analyzed through consideration of...The electricity-gas transformation problem and related intrinsic mechanisms are considered.First,existing schemes for the optimization of electricity-gas integrated energy systems are analyzed through consideration of the relevant literature,and an Electricity Hub(EH)for electricity-gas coupling is proposed.Then,the distribution mechanism in the circuit of the considered electricity-gas integrated system is analyzed.Afterward,a mathematical model for the natural gas pipeline is elaborated according to the power relationship,a node power flow calculation method,and security requirements.Next,the coupling relationship between them is implemented,and dedicated simulations are carried out.Through experimental data,it is found that after 79 data iterations,the optimization results of power generation and gas purchase cost in the new system converge to$54,936 in total,which is consistent with the data obtained by an existing centralized optimization scheme.However,the new proposed optimization scheme is found to be more flexible and convenient.展开更多
With an aim at the job-shop scheduling problem of multiple resource constraints, this paper presents mixed self-adapting Genetic Algorithm （ GA ） , and establishes a job-shop optimal scheduling model of multiple res...With an aim at the job-shop scheduling problem of multiple resource constraints, this paper presents mixed self-adapting Genetic Algorithm （ GA ） , and establishes a job-shop optimal scheduling model of multiple resource constraints based on the effect of priority scheduling rules in the heuristic algorithm upon the scheduling target. New coding regulations or rules are designed. The sinusoidal function is adopted as the self-adapting factor, thus making cross probability and variable probability automatically change with group adaptability in such a way as to overcome the shortcoming in the heuristic algorithm and common GA, so that the operation efficiency is improved. The results from real example simulation and comparison with other algorithms indicate that the mixed self-adapting GA algorithm can well solve the job-shop optimal scheduling problem under the constraints of various kinds of production resources such as machine-tools and cutting tools.展开更多
To address the strong thermoelectric coupling of the combined heat and power(CHP)units,the low utilization rate of energy storage,and the underexploitation of load-side resource flexibility in integrated energy system...To address the strong thermoelectric coupling of the combined heat and power(CHP)units,the low utilization rate of energy storage,and the underexploitation of load-side resource flexibility in integrated energy systems(IESs),this paper proposes an optimal scheduling model of an IES in low-carbon communities considering flexibility of resources and the segmental control of solid oxide fuel cells(SOFCs).Firstly,by replacing the gas turbine(GT)in the CHP unit with an SOFC array to reduce carbon emissions and simultaneously weakening the thermoelectric coupling of the CHP unit,the segmental control method is used to control the SOFC array to improve the overall efficiency of the CHP unit.Secondly,coupled interactions among different types of energy storage equipments are mobilized through the integrated energy storage system to make full use of the remaining space in the heat and natural gas storage tanks.Finally,load-side flexible resources are utilized by considering transferable,substitutable,and heat loads,taking into account the thermal inertia of the building and categorizing rooms based on floors,orientations,and room area.Additionally,different user characteristics are characterized,and the flexible resources of building heating periods in northern cities in China are tapped in depth according to the actual factors.Compared with the traditional model,the optimal scheduling model proposed in this paper can reduce the wind abandonment rate and the carbon emission of community-integrated energy system(CIES)by 4.54%and 70.63%,respectively,and increase the utilization rate of heat and natural gas storage tanks by 12.34%and 30.52%,respectively,and lower the total cost by¥2183.6 under the premise of ensuring user comfort during energy consumption,which promotes the economic and low-carbon operation of the CIES.展开更多
Hybrid energy storage is considered as an effective means to improve the economic and environmental performance of integrated energy systems(IESs).Although the optimal scheduling of IES has been widely studied,few stu...Hybrid energy storage is considered as an effective means to improve the economic and environmental performance of integrated energy systems(IESs).Although the optimal scheduling of IES has been widely studied,few studies have taken into account the property that the uncertainty of the forecasting error decreases with the shortening of the forecasting time scale.Combined with hybrid energy storage,the comprehensive use of various uncertainty optimization methods under different time scales will be promising.This paper proposes a multi-time-scale optimal scheduling method for an IES with hybrid energy storage under wind and solar uncertainties.Firstly,the proposed system framework of an IES including electric-thermal-hydrogen hybrid energy storage is established.Then,an hour-level robust optimization based on budget uncertainty set is performed for the day-ahead stage.On this basis,a scenario-based stochastic optimization is carried out for intra-day and real-time stages with time intervals of 15 min and 5 min,respectively.The results show that①the proposed method improves the economic benefits,and the intra-day and real-time scheduling costs are reduced,respectively;②by adjusting the uncertainty budget in the model,a flexible balance between economic efficiency and robustness in day-ahead scheduling can be achieved;③reasonable design of the capacity of electric-thermal-hydrogen hybrid energy storage can significantly reduce the electricity curtailment rate and carbon emissions,thus reducing the cost of system scheduling.展开更多
The existing researches on the flexibility evaluation and optimal scheduling of flexible loads in residential buildings do not fully consider the association characteristics of different loads,resulting in a large dev...The existing researches on the flexibility evaluation and optimal scheduling of flexible loads in residential buildings do not fully consider the association characteristics of different loads,resulting in a large deviation between the calculated results and experimental results of optimization scheduling.A flexibility evaluation methodology and an optimization model considering load associations characteristics are proposed for flexible loads in residential buildings.Temporal flexibility ratio,which is the ratio of temporal flexibility considering association characteristics to that without considering association characteristics,is defined in this study.The optimization model is solved using the CPLEX solver under three different scenarios,namely,a scenario only considering the temporal overlapping load associations,a scenario only considering the temporal non-overlapping load associations,and a scenario considering both types of load associations.It was shown that in the residential building case in this study,the cooking loads with association characteristics exhibit less temporal flexibility but higher temporal flexibility ratio of up to 71.21%,while laundry loads exhibit higher temporal flexibility,but their temporal flexibility ratio is only around 36.84%.Additionally,when the users adopted the time of use(TOU)price,their electricity costs under the three considered scenarios increased by 0.00%,7.57%,and 7.57%relative to the scenario without considering load associations,respectively.When installing a 3-kW household photovoltaic system,the electricity costs under the three scenarios increased by 0.00%,1.28%,and 1.28%,respectively.As highlighted in the results,temporal non-overlapping association characteristics greatly affect the optimal scheduling of flexible energy loads,especially under TOU,while temporal overlapping association characteristics have little effect on that.展开更多
Though an accurate discretization approach for gas flow dynamics, the method of characteristics (MOC) is liable to instability for inappropriate step sizes. This letter addresses the numerical stability limitation of ...Though an accurate discretization approach for gas flow dynamics, the method of characteristics (MOC) is liable to instability for inappropriate step sizes. This letter addresses the numerical stability limitation of MOC, in the context of lEGS's optimal scheduling. Specifically, the proposed method enables flexible temporal step sizes without sacrificing accuracy, significantly reducing non-convergence due to numerical oscillations. The effectiveness of the proposed method is validated through case studies in different simulation settings.展开更多
Building integrated energy systems(BIESs)are pivotal for enhancing energy efficiency by accounting for a significant proportion of global energy consumption.Two key barriers that reduce the BIES operational efficiency...Building integrated energy systems(BIESs)are pivotal for enhancing energy efficiency by accounting for a significant proportion of global energy consumption.Two key barriers that reduce the BIES operational efficiency mainly lie in the renewable generation uncertainty and operational non-convexity of combined heat and power(CHP)units.To this end,this paper proposes a soft actor-critic(SAC)algorithm to solve the scheduling problem of BIES,which overcomes the model non-convexity and shows advantages in robustness and generalization.This paper also adopts a temporal fusion transformer(TFT)to enhance the optimal solution for the SAC algorithm by forecasting the renewable generation and energy demand.The TFT can effectively capture the complex temporal patterns and dependencies that span multiple steps.Furthermore,its forecasting results are interpretable due to the employment of a self-attention layer so as to assist in more trustworthy decision-making in the SAC algorithm.The proposed hybrid data-driven approach integrating TFT and SAC algorithm,i.e.,TFT-SAC approach,is trained and tested on a real-world dataset to validate its superior performance in reducing the energy cost and computational time compared with the benchmark approaches.The generalization performance for the scheduling policy,as well as the sensitivity analysis,are examined in the case studies.展开更多
The hydrogen energy storage system(HESS)integrated with renewable energy power generation exhibits low reliability and flexibility under source-load uncertainty.To address the above issues,a two-stage optimal scheduli...The hydrogen energy storage system(HESS)integrated with renewable energy power generation exhibits low reliability and flexibility under source-load uncertainty.To address the above issues,a two-stage optimal scheduling model considering the operation sequences of HESSs is proposed for commercial community integrated energy systems(CIESs)with power to hydrogen and heat(P2HH)capability.It aims to optimize the energy flow of HESS and improve the flexibility of hydrogen production and the reliability of energy supply for loads.First,the refined operation model of HESS is established,and its operation model is linearized according to the operation domain of HESS,which simplifies the difficulty in solving the optimization problem under the premise of maintaining high approximate accuracy.Next,considering the flexible start-stop of alkaline electrolyzer(AEL)and the avoidance of multiple energy conversions,the operation sequences of HESS are formulated.Finally,a two-stage optimal scheduling model combining day-ahead economic optimization and intra-day rolling optimization is established,and the model is simulated and verified using the source-load prediction data of typical days in each season.The simulation results show that the two-stage optimal scheduling reduces the total load offset by about 14%while maintaining similar operating cost to the day-ahead economic optimal scheduling.Furthermore,by formulating the operation sequences of HESS,the operating cost of CIES is reduced by up to about 4.4%.展开更多
Flexible load can optimize the load curve,which is an important means to promote renewable energy consumption.The peculiarities of electricity,heat,cooling and gas loads are analyzed in this paper,considering the fuzz...Flexible load can optimize the load curve,which is an important means to promote renewable energy consumption.The peculiarities of electricity,heat,cooling and gas loads are analyzed in this paper,considering the fuzzy degree of human perception for water temperature,and the characteristic model of hot water load is established.Considering the fuzzy degree of human perception of ambient temperature,the characteristic model of cooling load is established by using PMV and PPD index.Meanwhile,considering four combinations of cut load,translatable load,transferable load and alternative load,and considering the coupling relationship of composite parts,different response models of load are established respectively.With the minimum cost of the system,including operation and compensation costs as the objective function,the optimization scheduling model of the regional integrated energy system is established,and the Gurobi solver is used for simulation analysis to solve the optimal output and load response curve of each piece of equipment.The results show that the load curve can be optimized,the flexible regulation ability of the regional integrated energy system can be enhanced,the energy loss of the system can be reduced,and the wind power consumption ability of the system can be increased by considering the integrated demand response.展开更多
Hybrid energy storage can enhance the economic performance and reliability of energy systems in industrial parks,while lowering the industrial parks’carbon emissions and accommodating diverse load demands from users....Hybrid energy storage can enhance the economic performance and reliability of energy systems in industrial parks,while lowering the industrial parks’carbon emissions and accommodating diverse load demands from users.However,most optimization research on hybrid energy storage has adopted rulebased passive-control principles,failing to fully leverage the advantages of active energy storage.To address this gap in the literature,this study develops a detailed model for an industrial park energy system with hybrid energy storage(IPES-HES),taking into account the operational characteristics of energy devices such as lithium batteries and thermal storage tanks.An active operation strategy for hybrid energy storage is proposed that uses decision variables based on hourly power outputs from the energy storage of the subsequent day.An optimization configuration model for an IPES-HES is formulated with the goals of reducing costs and lowering carbon emissions and is solved using the non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ).A method using the improved NSGA-Ⅱ is developed for day-ahead nonlinear scheduling,based on configuration optimization.The research findings indicate that the system energy bill and the peak power of the IPES-HES under the optimization-based operational strategy are reduced by 181.4 USD(5.5%)and 1600.3 kW(43.7%),respectively,compared with an operation strategy based on proportional electricity storage on a typical summer day.Overall,the day-ahead nonlinear optimal scheduling method developed in this study offers guidance to fully harness the advantages of active energy storage.展开更多
A centralized-distributed scheduling strategy for distribution networks based on multi-temporal and hierarchical cooperative game is proposed to address the issues of difficult operation control and energy optimizatio...A centralized-distributed scheduling strategy for distribution networks based on multi-temporal and hierarchical cooperative game is proposed to address the issues of difficult operation control and energy optimization interaction in distribution network transformer areas,as well as the problem of significant photovoltaic curtailment due to the inability to consume photovoltaic power locally.A scheduling architecture combiningmulti-temporal scales with a three-level decision-making hierarchy is established:the overall approach adopts a centralized-distributed method,analyzing the operational characteristics and interaction relationships of the distribution network center layer,cluster layer,and transformer area layer,providing a“spatial foundation”for subsequent optimization.The optimization process is divided into two stages on the temporal scale:in the first stage,based on forecasted electricity load and demand response characteristics,time-of-use electricity prices are utilized to formulate day-ahead optimization strategies;in the second stage,based on the charging and discharging characteristics of energy storage vehicles and multi-agent cooperative game relationships,rolling electricity prices and optimal interactive energy solutions are determined among clusters and transformer areas using the Nash bargaining theory.Finally,a distributed optimization algorithm using the bisection method is employed to solve the constructed model.Simulation results demonstrate that the proposed optimization strategy can facilitate photovoltaic consumption in the distribution network and enhance grid economy.展开更多
This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts,which are integral to an optimal energy storage control system.By expanding on...This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts,which are integral to an optimal energy storage control system.By expanding on an existing algorithm,this study resolves issues discovered during implementation and addresses previously overlooked concerns,resulting in significant enhancements in both performance and reliability.The refined real-time control scheme is integrated with a day-ahead optimization engine and forecast model,which is utilized for illustrative simulations to highlight its potential efficacy on a real site.Furthermore,a comprehensive comparison with the original formulation was conducted to cover all possible scenarios.This analysis validated the operational effectiveness of the scheme and provided a detailed evaluation of the improvements and expected behavior of the control system.Incorrect or improper adjustments to mitigate forecast uncertainties can result in suboptimal energy management,significant financial losses and penalties,and potential contract violations.The revised algorithm optimizes the operation of the battery system in real time and safeguards its state of health by limiting the charging/discharging cycles and enforcing adherence to contractual agreements.These advancements yield a reliable and efficient real-time correction algorithm for optimal site management,designed as an independent white box that can be integrated with any day-ahead optimization control system.展开更多
基金supported by the Central Government Guides Local Science and Technology Development Fund Project(2023ZY0020)Key R&D and Achievement Transformation Project in InnerMongolia Autonomous Region(2022YFHH0019)+3 种基金the Fundamental Research Funds for Inner Mongolia University of Science&Technology(2022053)Natural Science Foundation of Inner Mongolia(2022LHQN05002)National Natural Science Foundation of China(52067018)Metallurgical Engineering First-Class Discipline Construction Project in Inner Mongolia University of Science and Technology,Control Science and Engineering Quality Improvement and Cultivation Discipline Project in Inner Mongolia University of Science and Technology。
文摘In this paper,a bilevel optimization model of an integrated energy operator(IEO)–load aggregator(LA)is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy system(IIES).The upper level represents the integrated energy operator,and the lower level is the electricity-heatgas load aggregator.Owing to the benefit conflict between the upper and lower levels of the IIES,a dynamic pricing mechanism for coordinating the interests of the upper and lower levels is proposed,combined with factors such as the carbon emissions of the IIES,as well as the lower load interruption power.The price of selling energy can be dynamically adjusted to the lower LA in the mechanism,according to the information on carbon emissions and load interruption power.Mutual benefits and win-win situations are achieved between the upper and lower multistakeholders.Finally,CPLEX is used to iteratively solve the bilevel optimization model.The optimal solution is selected according to the joint optimal discrimination mechanism.Thesimulation results indicate that the sourceload coordinate operation can reduce the upper and lower operation costs.Using the proposed pricingmechanism,the carbon emissions and load interruption power of IEO-LA are reduced by 9.78%and 70.19%,respectively,and the capture power of the carbon capture equipment is improved by 36.24%.The validity of the proposed model and method is verified.
基金supported by the Changzhou Science and Technology Support Project(CE20235045)Open Subject of Jiangsu Province Key Laboratory of Power Transmission and Distribution(2021JSSPD12)+1 种基金Talent Projects of Jiangsu University of Technology(KYY20018)Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX23_1633).
文摘Energy storage power plants are critical in balancing power supply and demand.However,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device energy utilization.To tackle these challenges,this study proposes an optimal scheduling model for energy storage power plants based on edge computing and the improved whale optimization algorithm(IWOA).The proposed model designs an edge computing framework,transferring a large share of data processing and storage tasks to the network edge.This architecture effectively reduces transmission costs by minimizing data travel time.In addition,the model considers demand response strategies and builds an objective function based on the minimization of the sum of electricity purchase cost and operation cost.The IWOA enhances the optimization process by utilizing adaptive weight adjustments and an optimal neighborhood perturbation strategy,preventing the algorithm from converging to suboptimal solutions.Experimental results demonstrate that the proposed scheduling model maximizes the flexibility of the energy storage plant,facilitating efficient charging and discharging.It successfully achieves peak shaving and valley filling for both electrical and heat loads,promoting the effective utilization of renewable energy sources.The edge-computing framework significantly reduces transmission delays between energy devices.Furthermore,IWOA outperforms traditional algorithms in optimizing the objective function.
基金supported by the State Grid Jiangsu Electric Power Co.,Ltd.Technology Project(J2023035).
文摘To mitigate the impact of wind power volatility on power system scheduling,this paper adopts the wind-storage combined unit to improve the dispatchability of wind energy.And a three-level optimal scheduling and power allocation strategy is proposed for the system containing the wind-storage combined unit.The strategy takes smoothing power output as themain objectives.The first level is the wind-storage joint scheduling,and the second and third levels carry out the unit combination optimization of thermal power and the power allocation of wind power cluster(WPC),respectively,according to the scheduling power of WPC and ESS obtained from the first level.This can ensure the stability,economy and environmental friendliness of the whole power system.Based on the roles of peak shaving-valley filling and fluctuation smoothing of the energy storage system(ESS),this paper decides the charging and discharging intervals of ESS,so that the energy storage and wind power output can be further coordinated.Considering the prediction error and the output uncertainty of wind power,the planned scheduling output of wind farms(WFs)is first optimized on a long timescale,and then the rolling correction optimization of the scheduling output of WFs is carried out on a short timescale.Finally,the effectiveness of the proposed optimal scheduling and power allocation strategy is verified through case analysis.
文摘Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.
基金supported in part by the National Natural Science Foundation of China(51977127)Shanghai Municipal Science and Technology Commission(19020500800)“Shuguang Program”(20SG52)Shanghai Education Development Foundation and Shanghai Municipal Education Commission.
文摘From the perspective of a community energy operator,a two-stage optimal scheduling model of a community integrated energy system is proposed by integrating information on controllable loads.The day-ahead scheduling analyzes whether various controllable loads participate in the optimization and investigates the impact of their responses on the operating economy of the community integrated energy system(IES)before and after;the intra-day scheduling proposes a two-stage rolling optimization model based on the day-ahead scheduling scheme,taking into account the fluctuation of wind turbine output and load within a short period of time and according to the different response rates of heat and cooling power,and solves the adjusted output of each controllable device.The simulation results show that the optimal scheduling of controllable loads effectively reduces the comprehensive operating costs of community IES;the two-stage optimal scheduling model can meet the energy demand of customers while effectively and timely suppressing the random fluctuations on both sides of the source and load during the intra-day stage,realizing the economic and smooth operation of IES.
基金supported by the Science and Technology Project of State Grid Jiangsu Electric Power Company(J2023114).
文摘To adress the problems of insufficient consideration of charging pile resource limitations,discrete-time scheduling methods that do not meet the actual demand and insufficient descriptions of peak-shaving response capability in current electric vehicle(EV)opti-mization scheduling,edge intelligence-oriented electric vehicle optimization scheduling and charging station peak-shaving response capability assessment methods are proposed on the basis of the consideration of electric vehicle and charging pile matching.First,an edge-intelligence-oriented electric vehicle regulation frame for charging stations is proposed.Second,continuous time variables are used to represent the available charging periods,establish the charging station controllable EV load model and the future available charging pile mathematical model,and establish the EV and charging pile matching matrix and constraints.Then,with the goal of maximizing the user charging demand and reducing the charging cost,the charging station EV optimal scheduling model is established,and the EV peak response capacity assessment model is further established by considering the EV load shifting constraints under different peak response capacities.Finally,a typical scenario of a real charging station is taken as an example for the analysis of optimal EV scheduling and peak shaving response capacity,and the proposed method is compared with the traditional method to verify the effectiveness and practicality of the proposed method.
文摘When the communication time is relatively shorter than the computation time for every task, the task duplication based scheduling (TDS) algorithm proposed by Darbha and Agrawal generates an optimal schedule. Park and Choe also proposed an extended TDS algorithm whose optimality condition is less restricted than that of TDS algorithm, but the condition is very complex and is difficult to satisfy when the number of tasks is large. An efficient algorithm is proposed whose optimality condition is less restricted and simpler than both of the algorithms, and the schedule length is also shorter than both of the algorithms. The time complexity of the proposed algorithm is O(v2), where v represents the number of tasks.
文摘This paper presents the optimal scheduling of renewable resources using interior point optimization for grid-connected and islanded microgrids (MG) that operate with no energy storage systems. The German Jordanian University (GJU) microgrid system is used for illustration. We present analyses for islanded and grid-connected MG with no storage. The results show a feasible islanded MG with a substantial operational cost reduction. We obtain an average of $1 k daily cost savings when operating an islanded compared to a grid-connected MG with capped grid energy prices. This cost saving is 10 times higher when considering varying grid energy prices during the day. Although the PV power is intermittent during the day, the MG continues to operate with a voltage variation that does not 10%. The results imply that MGs of GJU similar topology can optimally and safely operate with no energy storage requirements but considerable renewable generation capacity.
文摘The electricity-gas transformation problem and related intrinsic mechanisms are considered.First,existing schemes for the optimization of electricity-gas integrated energy systems are analyzed through consideration of the relevant literature,and an Electricity Hub(EH)for electricity-gas coupling is proposed.Then,the distribution mechanism in the circuit of the considered electricity-gas integrated system is analyzed.Afterward,a mathematical model for the natural gas pipeline is elaborated according to the power relationship,a node power flow calculation method,and security requirements.Next,the coupling relationship between them is implemented,and dedicated simulations are carried out.Through experimental data,it is found that after 79 data iterations,the optimization results of power generation and gas purchase cost in the new system converge to$54,936 in total,which is consistent with the data obtained by an existing centralized optimization scheme.However,the new proposed optimization scheme is found to be more flexible and convenient.
基金This paper is supported by Shaanxi Natural Science Foundation of China under Grant No2004E202
文摘With an aim at the job-shop scheduling problem of multiple resource constraints, this paper presents mixed self-adapting Genetic Algorithm （ GA ） , and establishes a job-shop optimal scheduling model of multiple resource constraints based on the effect of priority scheduling rules in the heuristic algorithm upon the scheduling target. New coding regulations or rules are designed. The sinusoidal function is adopted as the self-adapting factor, thus making cross probability and variable probability automatically change with group adaptability in such a way as to overcome the shortcoming in the heuristic algorithm and common GA, so that the operation efficiency is improved. The results from real example simulation and comparison with other algorithms indicate that the mixed self-adapting GA algorithm can well solve the job-shop optimal scheduling problem under the constraints of various kinds of production resources such as machine-tools and cutting tools.
基金supported by the Industrial Technology R&D Program of Jilin Province(No.2023C033-5).
文摘To address the strong thermoelectric coupling of the combined heat and power(CHP)units,the low utilization rate of energy storage,and the underexploitation of load-side resource flexibility in integrated energy systems(IESs),this paper proposes an optimal scheduling model of an IES in low-carbon communities considering flexibility of resources and the segmental control of solid oxide fuel cells(SOFCs).Firstly,by replacing the gas turbine(GT)in the CHP unit with an SOFC array to reduce carbon emissions and simultaneously weakening the thermoelectric coupling of the CHP unit,the segmental control method is used to control the SOFC array to improve the overall efficiency of the CHP unit.Secondly,coupled interactions among different types of energy storage equipments are mobilized through the integrated energy storage system to make full use of the remaining space in the heat and natural gas storage tanks.Finally,load-side flexible resources are utilized by considering transferable,substitutable,and heat loads,taking into account the thermal inertia of the building and categorizing rooms based on floors,orientations,and room area.Additionally,different user characteristics are characterized,and the flexible resources of building heating periods in northern cities in China are tapped in depth according to the actual factors.Compared with the traditional model,the optimal scheduling model proposed in this paper can reduce the wind abandonment rate and the carbon emission of community-integrated energy system(CIES)by 4.54%and 70.63%,respectively,and increase the utilization rate of heat and natural gas storage tanks by 12.34%and 30.52%,respectively,and lower the total cost by¥2183.6 under the premise of ensuring user comfort during energy consumption,which promotes the economic and low-carbon operation of the CIES.
基金supported by the Science and Technology Project of State Grid Zhejiang Electric Power Co.,Ltd.“Research on coordinated optimal configuration and operation control technology for long-term and short-term hybrid energy storage considering multi-time scale matching requirements”(No.5211DS230001).
文摘Hybrid energy storage is considered as an effective means to improve the economic and environmental performance of integrated energy systems(IESs).Although the optimal scheduling of IES has been widely studied,few studies have taken into account the property that the uncertainty of the forecasting error decreases with the shortening of the forecasting time scale.Combined with hybrid energy storage,the comprehensive use of various uncertainty optimization methods under different time scales will be promising.This paper proposes a multi-time-scale optimal scheduling method for an IES with hybrid energy storage under wind and solar uncertainties.Firstly,the proposed system framework of an IES including electric-thermal-hydrogen hybrid energy storage is established.Then,an hour-level robust optimization based on budget uncertainty set is performed for the day-ahead stage.On this basis,a scenario-based stochastic optimization is carried out for intra-day and real-time stages with time intervals of 15 min and 5 min,respectively.The results show that①the proposed method improves the economic benefits,and the intra-day and real-time scheduling costs are reduced,respectively;②by adjusting the uncertainty budget in the model,a flexible balance between economic efficiency and robustness in day-ahead scheduling can be achieved;③reasonable design of the capacity of electric-thermal-hydrogen hybrid energy storage can significantly reduce the electricity curtailment rate and carbon emissions,thus reducing the cost of system scheduling.
基金supported by the National Natural Science Foundation of China(52378109)Innovation Capability Support Program of Shaanxi(2023KJXX-043)+1 种基金Young Talent Fund of Association for Science and Technology in Shaanxi(20220425)the Key Research and Development Program of Shaanxi,General Project(2023-YBSF-177).
文摘The existing researches on the flexibility evaluation and optimal scheduling of flexible loads in residential buildings do not fully consider the association characteristics of different loads,resulting in a large deviation between the calculated results and experimental results of optimization scheduling.A flexibility evaluation methodology and an optimization model considering load associations characteristics are proposed for flexible loads in residential buildings.Temporal flexibility ratio,which is the ratio of temporal flexibility considering association characteristics to that without considering association characteristics,is defined in this study.The optimization model is solved using the CPLEX solver under three different scenarios,namely,a scenario only considering the temporal overlapping load associations,a scenario only considering the temporal non-overlapping load associations,and a scenario considering both types of load associations.It was shown that in the residential building case in this study,the cooking loads with association characteristics exhibit less temporal flexibility but higher temporal flexibility ratio of up to 71.21%,while laundry loads exhibit higher temporal flexibility,but their temporal flexibility ratio is only around 36.84%.Additionally,when the users adopted the time of use(TOU)price,their electricity costs under the three considered scenarios increased by 0.00%,7.57%,and 7.57%relative to the scenario without considering load associations,respectively.When installing a 3-kW household photovoltaic system,the electricity costs under the three scenarios increased by 0.00%,1.28%,and 1.28%,respectively.As highlighted in the results,temporal non-overlapping association characteristics greatly affect the optimal scheduling of flexible energy loads,especially under TOU,while temporal overlapping association characteristics have little effect on that.
文摘Though an accurate discretization approach for gas flow dynamics, the method of characteristics (MOC) is liable to instability for inappropriate step sizes. This letter addresses the numerical stability limitation of MOC, in the context of lEGS's optimal scheduling. Specifically, the proposed method enables flexible temporal step sizes without sacrificing accuracy, significantly reducing non-convergence due to numerical oscillations. The effectiveness of the proposed method is validated through case studies in different simulation settings.
文摘Building integrated energy systems(BIESs)are pivotal for enhancing energy efficiency by accounting for a significant proportion of global energy consumption.Two key barriers that reduce the BIES operational efficiency mainly lie in the renewable generation uncertainty and operational non-convexity of combined heat and power(CHP)units.To this end,this paper proposes a soft actor-critic(SAC)algorithm to solve the scheduling problem of BIES,which overcomes the model non-convexity and shows advantages in robustness and generalization.This paper also adopts a temporal fusion transformer(TFT)to enhance the optimal solution for the SAC algorithm by forecasting the renewable generation and energy demand.The TFT can effectively capture the complex temporal patterns and dependencies that span multiple steps.Furthermore,its forecasting results are interpretable due to the employment of a self-attention layer so as to assist in more trustworthy decision-making in the SAC algorithm.The proposed hybrid data-driven approach integrating TFT and SAC algorithm,i.e.,TFT-SAC approach,is trained and tested on a real-world dataset to validate its superior performance in reducing the energy cost and computational time compared with the benchmark approaches.The generalization performance for the scheduling policy,as well as the sensitivity analysis,are examined in the case studies.
基金supported by the Major Science and Technology Innovation Project of Jiangsu Province of China(No.BE2022038)。
文摘The hydrogen energy storage system(HESS)integrated with renewable energy power generation exhibits low reliability and flexibility under source-load uncertainty.To address the above issues,a two-stage optimal scheduling model considering the operation sequences of HESSs is proposed for commercial community integrated energy systems(CIESs)with power to hydrogen and heat(P2HH)capability.It aims to optimize the energy flow of HESS and improve the flexibility of hydrogen production and the reliability of energy supply for loads.First,the refined operation model of HESS is established,and its operation model is linearized according to the operation domain of HESS,which simplifies the difficulty in solving the optimization problem under the premise of maintaining high approximate accuracy.Next,considering the flexible start-stop of alkaline electrolyzer(AEL)and the avoidance of multiple energy conversions,the operation sequences of HESS are formulated.Finally,a two-stage optimal scheduling model combining day-ahead economic optimization and intra-day rolling optimization is established,and the model is simulated and verified using the source-load prediction data of typical days in each season.The simulation results show that the two-stage optimal scheduling reduces the total load offset by about 14%while maintaining similar operating cost to the day-ahead economic optimal scheduling.Furthermore,by formulating the operation sequences of HESS,the operating cost of CIES is reduced by up to about 4.4%.
基金supported by the National Natural Science Foundation of China(51577086)Jiangsu Key University Science Research Project(19KJA510012)+1 种基金Six talent peaks project in Jiangsu Province(TD-XNY004)Jiangsu Qinglan Project.
文摘Flexible load can optimize the load curve,which is an important means to promote renewable energy consumption.The peculiarities of electricity,heat,cooling and gas loads are analyzed in this paper,considering the fuzzy degree of human perception for water temperature,and the characteristic model of hot water load is established.Considering the fuzzy degree of human perception of ambient temperature,the characteristic model of cooling load is established by using PMV and PPD index.Meanwhile,considering four combinations of cut load,translatable load,transferable load and alternative load,and considering the coupling relationship of composite parts,different response models of load are established respectively.With the minimum cost of the system,including operation and compensation costs as the objective function,the optimization scheduling model of the regional integrated energy system is established,and the Gurobi solver is used for simulation analysis to solve the optimal output and load response curve of each piece of equipment.The results show that the load curve can be optimized,the flexible regulation ability of the regional integrated energy system can be enhanced,the energy loss of the system can be reduced,and the wind power consumption ability of the system can be increased by considering the integrated demand response.
基金supported by National Key Research and Development Program of China(2022YFB4201003)the National Natural Science Foundation of China(52278104 and 52108076)the Science and Technology Innovation Program of Hunan Province(2023RC1042).
文摘Hybrid energy storage can enhance the economic performance and reliability of energy systems in industrial parks,while lowering the industrial parks’carbon emissions and accommodating diverse load demands from users.However,most optimization research on hybrid energy storage has adopted rulebased passive-control principles,failing to fully leverage the advantages of active energy storage.To address this gap in the literature,this study develops a detailed model for an industrial park energy system with hybrid energy storage(IPES-HES),taking into account the operational characteristics of energy devices such as lithium batteries and thermal storage tanks.An active operation strategy for hybrid energy storage is proposed that uses decision variables based on hourly power outputs from the energy storage of the subsequent day.An optimization configuration model for an IPES-HES is formulated with the goals of reducing costs and lowering carbon emissions and is solved using the non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ).A method using the improved NSGA-Ⅱ is developed for day-ahead nonlinear scheduling,based on configuration optimization.The research findings indicate that the system energy bill and the peak power of the IPES-HES under the optimization-based operational strategy are reduced by 181.4 USD(5.5%)and 1600.3 kW(43.7%),respectively,compared with an operation strategy based on proportional electricity storage on a typical summer day.Overall,the day-ahead nonlinear optimal scheduling method developed in this study offers guidance to fully harness the advantages of active energy storage.
基金funded by the Jilin Province Science and Technology Development Plan Project(20230101344JC).
文摘A centralized-distributed scheduling strategy for distribution networks based on multi-temporal and hierarchical cooperative game is proposed to address the issues of difficult operation control and energy optimization interaction in distribution network transformer areas,as well as the problem of significant photovoltaic curtailment due to the inability to consume photovoltaic power locally.A scheduling architecture combiningmulti-temporal scales with a three-level decision-making hierarchy is established:the overall approach adopts a centralized-distributed method,analyzing the operational characteristics and interaction relationships of the distribution network center layer,cluster layer,and transformer area layer,providing a“spatial foundation”for subsequent optimization.The optimization process is divided into two stages on the temporal scale:in the first stage,based on forecasted electricity load and demand response characteristics,time-of-use electricity prices are utilized to formulate day-ahead optimization strategies;in the second stage,based on the charging and discharging characteristics of energy storage vehicles and multi-agent cooperative game relationships,rolling electricity prices and optimal interactive energy solutions are determined among clusters and transformer areas using the Nash bargaining theory.Finally,a distributed optimization algorithm using the bisection method is employed to solve the constructed model.Simulation results demonstrate that the proposed optimization strategy can facilitate photovoltaic consumption in the distribution network and enhance grid economy.
基金supported by the Israeli Ministry of Infrastructure,Energy and Water Resources.
文摘This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts,which are integral to an optimal energy storage control system.By expanding on an existing algorithm,this study resolves issues discovered during implementation and addresses previously overlooked concerns,resulting in significant enhancements in both performance and reliability.The refined real-time control scheme is integrated with a day-ahead optimization engine and forecast model,which is utilized for illustrative simulations to highlight its potential efficacy on a real site.Furthermore,a comprehensive comparison with the original formulation was conducted to cover all possible scenarios.This analysis validated the operational effectiveness of the scheme and provided a detailed evaluation of the improvements and expected behavior of the control system.Incorrect or improper adjustments to mitigate forecast uncertainties can result in suboptimal energy management,significant financial losses and penalties,and potential contract violations.The revised algorithm optimizes the operation of the battery system in real time and safeguards its state of health by limiting the charging/discharging cycles and enforcing adherence to contractual agreements.These advancements yield a reliable and efficient real-time correction algorithm for optimal site management,designed as an independent white box that can be integrated with any day-ahead optimization control system.
