In order to address the synergistic optimization of energy efficiency improvement in the waste incineration power plant(WIPP)and renewable energy accommodation,an electricity-hydrogen-waste multi-energy system integra...In order to address the synergistic optimization of energy efficiency improvement in the waste incineration power plant(WIPP)and renewable energy accommodation,an electricity-hydrogen-waste multi-energy system integrated with phase change material(PCM)thermal storage is proposed.First,a thermal energy management framework is constructed,combining PCM thermal storage with the alkaline electrolyzer(AE)waste heat recovery and the heat pump(HP),while establishing a PCM-driven waste drying system to enhance the efficiency of waste incineration power generation.Next,a flue gas treatment method based on purification-separation-storage coordination is adopted,achieving spatiotemporal decoupling between waste incineration and flue gas treatment.Subsequently,a two-stage optimal dispatching strategy for the multi-energy system is developed:the first stage establishes a dayahead economic dispatch model with the objective of minimizing net system costs,while the second stage introduces model predictive control(MPC)to realize intraday rolling optimization.Finally,The optimal dispatching strategies under different scenarios are obtained using the Gurobi solver,followed by a comparative analysis of the optimized operational outcomes.Simulation results demonstrate that the proposed system optimizes the output and operational states of each unit,simultaneously reducing carbon trading costs while increasing electricity sales revenue.The proposed scheduling strategy demonstrates effective grid peak-shaving functionality,thereby simultaneously improving the system’s economic performance and operational flexibility while providing an innovative technical pathway for municipal solid waste(MSW)resource utilization and low-carbon transformation of energy systems.展开更多
An optimal resource dispatching method is proposed to solve the multiple-response problem under the conditions of potential incidents on freeway networks.Travel time of the response vehicle is selected instead of rout...An optimal resource dispatching method is proposed to solve the multiple-response problem under the conditions of potential incidents on freeway networks.Travel time of the response vehicle is selected instead of route distance as the weight to reflect the impact of traffic conditions on the decisions of rescue resources.According to the characteristics of different types of rescue vehicles the dispatching decision-making time is revised to show the heterogeneity among different rescue vehicle dispatching modes. The genetic algorithm is used to obtain the solutions to the rescue resources dispatching model. A case study shows that the proposed method can accurately reveal the impact of potential incidents on the costs of rescues according to the variations in the types and quantities of rescue resources and the optimal dispatching plan with respect to potential incidents can be obtained.The proposed method is applicable in real world scenarios.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
This paper deals with the use of optimal control techniques in large-scale water distribution networks. According to the network characteristics and actual state of the water supply system in China, the implicit model...This paper deals with the use of optimal control techniques in large-scale water distribution networks. According to the network characteristics and actual state of the water supply system in China, the implicit model, which may be solved by utilizing the hierarchical optimization method, is established. In special, based on the analyses of the water supply system containing variable-speed pumps, a software tool has been developed successfully. The application of this model to the city of Shenyang (China) is compared to experiential strategy. The results of this study show that the developed model is a very promising optimization method to control the large-scale water supply systems.展开更多
Aiming at the problems of large-scale wind and solar grid connection,how to ensure the economy of system operation and how to realize fair scheduling between new energy power stations,a two-stage optimal dispatching m...Aiming at the problems of large-scale wind and solar grid connection,how to ensure the economy of system operation and how to realize fair scheduling between new energy power stations,a two-stage optimal dispatching model of wind power-photovoltaic-solar thermal combined system considering economic optimality and fairness is proposed.Firstly,the first stage dispatching model takes the overall economy optimization of the system as the goal and the principle of maximizing the consumption of wind and solar output,obtains the optimal output value under the economic conditions of each new energy station,and then obtains the maximum consumption space of the new energy station.Secondly,based on the optimization results of the first stage,the second stage dispatching model uses the dispatching method of fuzzy comprehensive ranking priority to prioritize the new energy stations,and then makes a fair allocation to the dispatching of the wind and solar stations.Finally,the analysis of a specific example shows that themodel can take into account the fairness of active power distribution of new energy stations on the basis of ensuring the economy of system operation,make full use of the consumption space,and realize the medium and long-term fairness distribution of dispatching plan.展开更多
The traditional energy hub based model has difficulties in clearly describing the state transition and transition conditions of the energy unit in the integrated energy system(IES).Therefore,this study proposes a stat...The traditional energy hub based model has difficulties in clearly describing the state transition and transition conditions of the energy unit in the integrated energy system(IES).Therefore,this study proposes a state transition modeling method for an IES based on a cyber-physical system(CPS)to optimize the state transition of energy unit in the IES.This method uses the physical,integration,and optimization layers as a three-layer modeling framework.The physical layer is used to describe the physical models of energy units in the IES.In the integration layer,the information flow is integrated into the physical model of energy unit in the IES to establish the state transition model,and the transition conditions between different states of the energy unit are given.The optimization layer aims to minimize the operating cost of the IES and enables the operating state of energy units to be transferred to the target state.Numerical simulations show that,compared with the traditional modeling method,the state transition modeling method based on CPS achieves the observability of the operating state of the energy unit and its state transition in the dispatching cycle,which obtains an optimal state of the energy unit and further reduces the system operating costs.展开更多
The coordinated operation and comprehensive utilization of multi-energy sources require systematic research.A multi-energy microgrid(MEMG)is a coupling system with multiple inputs and outputs.In this paper,a system mo...The coordinated operation and comprehensive utilization of multi-energy sources require systematic research.A multi-energy microgrid(MEMG)is a coupling system with multiple inputs and outputs.In this paper,a system model based on unified energy flows is proposed to describe the static relationship,and an analogue energy storage model is proposed to represent the time-dependency characteristics of energy transfer processes.Then,the optimal dispatching model of an MEMG is established as a mixed-integer linear programming(MILP)problem using piecewise linear approximation and convex relaxation.Finally,the system model and optimal dispatching method are validated in an MEMG,including district electricity,natural gas and heat supply,and renewable generation.The proposed model and method provide an effective way for the energy flow analysis and optimization of MEMGs.展开更多
As a constrained optimization strategy,model predictive control(MPC)is widely used in the optimal dispatch of microgrids.Rolling optimization and feedback correction strategy can effectively deal with the impact due t...As a constrained optimization strategy,model predictive control(MPC)is widely used in the optimal dispatch of microgrids.Rolling optimization and feedback correction strategy can effectively deal with the impact due to the system uncertainty and model mismatch which ensures the stable operation of the microgrid.Due to the uncertainty of distributed power sources,MPC can be applied to solve the problem of renewable energy output in microgrid for fishery.This paper reviews the optimal dispatch of microgrid for fishery based on model predictive control.Firstly,this article introduces the basic principles and classification of model predictive control,and then explains the remaining problems and research direc-tions.The characteristics of the microgrid optimal dispatching problem are described,and the analysis are focused on how MPC solves the uncertain problems in the microgrid optimal dispatching.Finally,perspectives and the future research direction of model pre-dictive control in microgrid energy dispatch is reviewed.展开更多
With the increased promotion of integrated energy power systems(IEPS),renewable energy and energy storage systems(ESS)play a more important role.However,the fluctuation and intermittent nature of wind not only results...With the increased promotion of integrated energy power systems(IEPS),renewable energy and energy storage systems(ESS)play a more important role.However,the fluctuation and intermittent nature of wind not only results in substantial reliability and stability defects,but it also weakens the competitiveness of wind generation in the electric power market.Meanwhile,the way to further enhance the system reliability effectively improving market profits of wind farms is one of the most important aspects of Wind-ESS joint operational design.In this paper,a market-oriented optimized dispatching strategy for a wind farm with a multiple stage hybrid ESS is proposed.The first stage ESS is designed to improve the profits of wind generation through day-ahead market operations,the real-time marketbased second stage ESS is focused on day-ahead forecasting error elimination and wind power fluctuation smoothing,while the backup stage ESS is associated with them to provide the ancillary service.An interval forecasting method is adopted to help to ensure reliable forecast results of day-ahead wind power,electricity prices and loads.With this hybrid ESS design,supply reliability and market profits are simultaneously achieved for wind farms.展开更多
As future ship system,hybrid energy ship system has a wide range of application prospects for solving the serious energy crisis.However,current optimization scheduling works lack the consideration of sea conditions an...As future ship system,hybrid energy ship system has a wide range of application prospects for solving the serious energy crisis.However,current optimization scheduling works lack the consideration of sea conditions and navigational circumstances.There-fore,this paper aims at establishing a two-stage optimization framework for hybrid energy ship power system.The proposed framework considers multiple optimizations of route,speed planning,and energy management under the constraints of sea conditions during navigation.First,a complex hybrid ship power model consisting of diesel generation system,propulsion system,energy storage system,photovoltaic power generation system,and electric boiler system is established,where sea state information and ship resistance model are considered.With objective optimization functions of cost and greenhouse gas(GHG)emissions,a two-stage optimization framework consisting of route planning,speed scheduling,and energy management is constructed.Wherein the improved A-star algorithm and grey wolf optimization algorithm are introduced to obtain the optimal solutions for route,speed,and energy optimization scheduling.Finally,simulation cases are employed to verify that the proposed two-stage optimization scheduling model can reduce load energy consumption,operating costs,and carbon emissions by 17.8%,17.39%,and 13.04%,respectively,compared with the non-optimal control group.展开更多
To promote the utilization of renewable energy,such as photovoltaics,this paper proposes an optimal flexibility dispatch method for demand-side resources(DSR)based on the Stackelberg game theory.First,the concept of t...To promote the utilization of renewable energy,such as photovoltaics,this paper proposes an optimal flexibility dispatch method for demand-side resources(DSR)based on the Stackelberg game theory.First,the concept of the generalized DSR is analyzed and flexibility models for various DSR are constructed.Second,owing to the characteristics of small capacity but large-scale,an outer approximation is proposed to describe the aggregate flexibility of DSR.Then,the optimal flexibility dispatch model of DSR based on the Stackelberg game is established and a decentralized solution algorithm is designed to obtain the Stackelberg equilibrium.Finally,the actual data are utilized for the case study and the results show that,compared to the traditional centralized optimization method,the proposed optimal flexibility dispatch method can not only reduce the net load variability of the DSR aggregator but is beneficial for all DSR owners,which is more suitable for practical applications.展开更多
Owing to the rapid development of microgrids(MGs)and growing applications of renewable energy resources,multiobjective optimal dispatch of MGs need to be studied in detail.In this study,a multiobjective optimal dispat...Owing to the rapid development of microgrids(MGs)and growing applications of renewable energy resources,multiobjective optimal dispatch of MGs need to be studied in detail.In this study,a multiobjective optimal dispatch model is developed for a standalone MG composed of wind turbines,photovoltaics,diesel engine unit,load,and battery energy storage system.The economic cost,environmental concerns,and power supply consistency are expressed via subobjectives with varying priorities.Then,the analytic hierarchy process algorithm is employed to reasonably specify the weight coefficients of the subobjectives.The quantum particle swarm optimization algorithm is thereafter employed as a solution to achieve optimal dispatch of the MG.Finally,the validity of the proposed model and solution methodology are con firmed by case studies.This study provides refere nee for mathematical model of multiojective optimizati on of MG and can be widely used in current research field.展开更多
In response to the underutilization of energy and insufficient flexible operation capability of rural energy supply systems in China,this study proposes an optimal dispatch approach for a rural multi-energy supply sys...In response to the underutilization of energy and insufficient flexible operation capability of rural energy supply systems in China,this study proposes an optimal dispatch approach for a rural multi-energy supply system(RMESS)considering virtual energy storage(VES).First,to enable the flexible utilization of rural biomass resources and the thermal inertia of residential building envelopes,this study constructed VES-I and VES-II models that describe electrical-thermal and electrical-gas coupling from an electrical viewpoint.Subsequently,an RMESS model encompassing these two types of VES was formulated.This model delineates the intricate interplay of multi-energy components within the RMESS framework and facilitates the precise assessment of the adjustable potential for optimizing RMESS operations.Based on the above models,a day-ahead optimal dispatch model for an RMESS considering a VES is proposed to achieve optimal economic performance while ensuring efficient energy allocation.Comparative simulations validated the effectiveness of the VES modeling and the day-ahead optimal dispatch approach for the RMESS.展开更多
Virtual power plants can effectively integrate different types of distributed energy resources,which have become a new operation mode with substantial advantages such as high flexibility,adaptability,and economy.This ...Virtual power plants can effectively integrate different types of distributed energy resources,which have become a new operation mode with substantial advantages such as high flexibility,adaptability,and economy.This paper proposes a distributionally robust optimal dispatch approach for virtual power plants to determine an optimal day-ahead dispatch under uncertainties of renewable energy sources.The proposed distributionally robust approach characterizes probability distributions of renewable power output by moments.In this regard,the faults of stochastic optimization and traditional robust optimization can be overcome.Firstly,a second-order cone-based ambiguity set that incorporates the first and second moments of renewable power output is constructed,and a day-ahead two-stage distributionally robust optimization model is proposed for virtual power plants participating in day-ahead electricity markets.Then,an effective solution method based on the affine policy and second-order cone duality theory is employed to reformulate the proposed model into a deterministic mixed-integer second-order cone programming problem,which improves the computational efficiency of the model.Finally,the numerical results demonstrate that the proposed method achieves a better balance between robustness and economy.They also validate that the dispatch strategy of virtual power plants can be adjusted to reduce costs according to the moment information of renewable power output.展开更多
This paper presents a finite-time economic model predictive control(MPC)algorithm that can be used for frequency regulation and optimal load dispatch in multi-area power systems.Economic MPC can be used in a power sys...This paper presents a finite-time economic model predictive control(MPC)algorithm that can be used for frequency regulation and optimal load dispatch in multi-area power systems.Economic MPC can be used in a power system to ensure frequency stability,real-time economic optimization,control of the system and optimal load dispatch from it.A generalized terminal penalty term was used,and the finite-time convergence of the system was guaranteed.The effectiveness of the proposed model predictive control algorithm was verified by simulating a power system,which had two areas connected by an AC tie line.The simulation results demonstrated the effectiveness of the algorithm.展开更多
The optimal dispatch of energy storage systems(ESSs)in distribution networks poses significant challenges,primarily due to uncertainties of dynamic pricing,fluctuating demand,and the variability inherent in renewable ...The optimal dispatch of energy storage systems(ESSs)in distribution networks poses significant challenges,primarily due to uncertainties of dynamic pricing,fluctuating demand,and the variability inherent in renewable energy sources.By exploiting the generalization capabilities of deep neural networks(DNNs),the deep reinforcement learning(DRL)algorithms can learn good-quality control models that adapt to the stochastic nature of distribution networks.Nevertheless,the practical deployment of DRL algorithms is often hampered by their limited capacity for satisfying operational constraints in real time,which is a crucial requirement for ensuring the reliability and feasibility of control actions during online operations.This paper introduces an innovative framework,named mixed-integer programming based deep reinforcement learning(MIP-DRL),to overcome these limitations.The proposed MIP-DRL framework can rigorously enforce operational constraints for the optimal dispatch of ESSs during the online execution.This framework involves training a Q-function with DNNs,which is subsequently represented in a mixed-integer programming(MIP)formulation.This unique combination allows for the seamless integration of operational constraints into the decision-making process.The effectiveness of the proposed MIP-DRL framework is validated through numerical simulations,demonstrating its superior capability to enforce all operational constraints and achieve high-quality dispatch decisions and showing its advantage over existing DRL algorithms.展开更多
Peer-to-peer(P2P)energy trading in active distribution networks(ADNs)plays a pivotal role in promoting the efficient consumption of renewable energy sources.However,it is challenging to effectively coordinate the powe...Peer-to-peer(P2P)energy trading in active distribution networks(ADNs)plays a pivotal role in promoting the efficient consumption of renewable energy sources.However,it is challenging to effectively coordinate the power dispatch of ADNs and P2P energy trading while preserving the privacy of different physical interests.Hence,this paper proposes a soft actor-critic algorithm incorporating distributed trading control(SAC-DTC)to tackle the optimal power dispatch of ADNs and the P2P energy trading considering privacy preservation among prosumers.First,the soft actor-critic(SAC)algorithm is used to optimize the control strategy of device in ADNs to minimize the operation cost,and the primary environmental information of the ADN at this point is published to prosumers.Then,a distributed generalized fast dual ascent method is used to iterate the trading process of prosumers and maximize their revenues.Subsequently,the results of trading are encrypted based on the differential privacy technique and returned to the ADN.Finally,the social welfare value consisting of ADN operation cost and P2P market revenue is utilized as a reward value to update network parameters and control strategies of the deep reinforcement learning.Simulation results show that the proposed SAC-DTC algorithm reduces the ADN operation cost,boosts the P2P market revenue,maximizes the social welfare,and exhibits high computational accuracy,demonstrating its practical application to the operation of power systems and power markets.展开更多
To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitme...To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.展开更多
In this paper, the optimal dispatching model for multilake-reservoir system of KaiduKongque River and Tarim river is studied with mathematical programming.The optimal dispatching models belong to large system theory,...In this paper, the optimal dispatching model for multilake-reservoir system of KaiduKongque River and Tarim river is studied with mathematical programming.The optimal dispatching models belong to large system theory,which considers both wator supply and the effect of science and technology. The computation results shows that the model is feasible.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
文摘In order to address the synergistic optimization of energy efficiency improvement in the waste incineration power plant(WIPP)and renewable energy accommodation,an electricity-hydrogen-waste multi-energy system integrated with phase change material(PCM)thermal storage is proposed.First,a thermal energy management framework is constructed,combining PCM thermal storage with the alkaline electrolyzer(AE)waste heat recovery and the heat pump(HP),while establishing a PCM-driven waste drying system to enhance the efficiency of waste incineration power generation.Next,a flue gas treatment method based on purification-separation-storage coordination is adopted,achieving spatiotemporal decoupling between waste incineration and flue gas treatment.Subsequently,a two-stage optimal dispatching strategy for the multi-energy system is developed:the first stage establishes a dayahead economic dispatch model with the objective of minimizing net system costs,while the second stage introduces model predictive control(MPC)to realize intraday rolling optimization.Finally,The optimal dispatching strategies under different scenarios are obtained using the Gurobi solver,followed by a comparative analysis of the optimized operational outcomes.Simulation results demonstrate that the proposed system optimizes the output and operational states of each unit,simultaneously reducing carbon trading costs while increasing electricity sales revenue.The proposed scheduling strategy demonstrates effective grid peak-shaving functionality,thereby simultaneously improving the system’s economic performance and operational flexibility while providing an innovative technical pathway for municipal solid waste(MSW)resource utilization and low-carbon transformation of energy systems.
基金The National Natural Science Foundation of China(No.71101025)the Science and Technology Key Plan Project of Changzhou(No.CE20125001)
文摘An optimal resource dispatching method is proposed to solve the multiple-response problem under the conditions of potential incidents on freeway networks.Travel time of the response vehicle is selected instead of route distance as the weight to reflect the impact of traffic conditions on the decisions of rescue resources.According to the characteristics of different types of rescue vehicles the dispatching decision-making time is revised to show the heterogeneity among different rescue vehicle dispatching modes. The genetic algorithm is used to obtain the solutions to the rescue resources dispatching model. A case study shows that the proposed method can accurately reveal the impact of potential incidents on the costs of rescues according to the variations in the types and quantities of rescue resources and the optimal dispatching plan with respect to potential incidents can be obtained.The proposed method is applicable in real world scenarios.
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This work has been partly funded by the National Natural Science Foundation of China(No.50078048).
文摘This paper deals with the use of optimal control techniques in large-scale water distribution networks. According to the network characteristics and actual state of the water supply system in China, the implicit model, which may be solved by utilizing the hierarchical optimization method, is established. In special, based on the analyses of the water supply system containing variable-speed pumps, a software tool has been developed successfully. The application of this model to the city of Shenyang (China) is compared to experiential strategy. The results of this study show that the developed model is a very promising optimization method to control the large-scale water supply systems.
基金a phased achievement of Gansu Province’s Major Science and Technology Project(19ZD2GA003)“Key Technologies and Demonstrative Applications of Market Consumption and Dispatching Control of Photothermal-Photovoltaic-Wind PowerNew Energy Base(Multi Energy System Optimization)”.
文摘Aiming at the problems of large-scale wind and solar grid connection,how to ensure the economy of system operation and how to realize fair scheduling between new energy power stations,a two-stage optimal dispatching model of wind power-photovoltaic-solar thermal combined system considering economic optimality and fairness is proposed.Firstly,the first stage dispatching model takes the overall economy optimization of the system as the goal and the principle of maximizing the consumption of wind and solar output,obtains the optimal output value under the economic conditions of each new energy station,and then obtains the maximum consumption space of the new energy station.Secondly,based on the optimization results of the first stage,the second stage dispatching model uses the dispatching method of fuzzy comprehensive ranking priority to prioritize the new energy stations,and then makes a fair allocation to the dispatching of the wind and solar stations.Finally,the analysis of a specific example shows that themodel can take into account the fairness of active power distribution of new energy stations on the basis of ensuring the economy of system operation,make full use of the consumption space,and realize the medium and long-term fairness distribution of dispatching plan.
基金supported by the National Natural Science Foundation of China(No.52107108)。
文摘The traditional energy hub based model has difficulties in clearly describing the state transition and transition conditions of the energy unit in the integrated energy system(IES).Therefore,this study proposes a state transition modeling method for an IES based on a cyber-physical system(CPS)to optimize the state transition of energy unit in the IES.This method uses the physical,integration,and optimization layers as a three-layer modeling framework.The physical layer is used to describe the physical models of energy units in the IES.In the integration layer,the information flow is integrated into the physical model of energy unit in the IES to establish the state transition model,and the transition conditions between different states of the energy unit are given.The optimization layer aims to minimize the operating cost of the IES and enables the operating state of energy units to be transferred to the target state.Numerical simulations show that,compared with the traditional modeling method,the state transition modeling method based on CPS achieves the observability of the operating state of the energy unit and its state transition in the dispatching cycle,which obtains an optimal state of the energy unit and further reduces the system operating costs.
基金supported by the Technology Program of State Grid Corporation of China(No.SGSDJY00GPJS1900058)
文摘The coordinated operation and comprehensive utilization of multi-energy sources require systematic research.A multi-energy microgrid(MEMG)is a coupling system with multiple inputs and outputs.In this paper,a system model based on unified energy flows is proposed to describe the static relationship,and an analogue energy storage model is proposed to represent the time-dependency characteristics of energy transfer processes.Then,the optimal dispatching model of an MEMG is established as a mixed-integer linear programming(MILP)problem using piecewise linear approximation and convex relaxation.Finally,the system model and optimal dispatching method are validated in an MEMG,including district electricity,natural gas and heat supply,and renewable generation.The proposed model and method provide an effective way for the energy flow analysis and optimization of MEMGs.
基金This work is support by Key Research and Development Pro-gram of Hebei Province,Grant No.20327217D.
文摘As a constrained optimization strategy,model predictive control(MPC)is widely used in the optimal dispatch of microgrids.Rolling optimization and feedback correction strategy can effectively deal with the impact due to the system uncertainty and model mismatch which ensures the stable operation of the microgrid.Due to the uncertainty of distributed power sources,MPC can be applied to solve the problem of renewable energy output in microgrid for fishery.This paper reviews the optimal dispatch of microgrid for fishery based on model predictive control.Firstly,this article introduces the basic principles and classification of model predictive control,and then explains the remaining problems and research direc-tions.The characteristics of the microgrid optimal dispatching problem are described,and the analysis are focused on how MPC solves the uncertain problems in the microgrid optimal dispatching.Finally,perspectives and the future research direction of model pre-dictive control in microgrid energy dispatch is reviewed.
基金This work was supported in part by the National Natural Science Foundation of China(No.51607025).
文摘With the increased promotion of integrated energy power systems(IEPS),renewable energy and energy storage systems(ESS)play a more important role.However,the fluctuation and intermittent nature of wind not only results in substantial reliability and stability defects,but it also weakens the competitiveness of wind generation in the electric power market.Meanwhile,the way to further enhance the system reliability effectively improving market profits of wind farms is one of the most important aspects of Wind-ESS joint operational design.In this paper,a market-oriented optimized dispatching strategy for a wind farm with a multiple stage hybrid ESS is proposed.The first stage ESS is designed to improve the profits of wind generation through day-ahead market operations,the real-time marketbased second stage ESS is focused on day-ahead forecasting error elimination and wind power fluctuation smoothing,while the backup stage ESS is associated with them to provide the ancillary service.An interval forecasting method is adopted to help to ensure reliable forecast results of day-ahead wind power,electricity prices and loads.With this hybrid ESS design,supply reliability and market profits are simultaneously achieved for wind farms.
基金supported by the National Natural Science Foundation of China under Grant 62473328by the Open Research Fund of Jiangsu Collaborative Innovation Center for Smart Distribution Network,Nanjing Institute of Technology under No.XTCX202203.
文摘As future ship system,hybrid energy ship system has a wide range of application prospects for solving the serious energy crisis.However,current optimization scheduling works lack the consideration of sea conditions and navigational circumstances.There-fore,this paper aims at establishing a two-stage optimization framework for hybrid energy ship power system.The proposed framework considers multiple optimizations of route,speed planning,and energy management under the constraints of sea conditions during navigation.First,a complex hybrid ship power model consisting of diesel generation system,propulsion system,energy storage system,photovoltaic power generation system,and electric boiler system is established,where sea state information and ship resistance model are considered.With objective optimization functions of cost and greenhouse gas(GHG)emissions,a two-stage optimization framework consisting of route planning,speed scheduling,and energy management is constructed.Wherein the improved A-star algorithm and grey wolf optimization algorithm are introduced to obtain the optimal solutions for route,speed,and energy optimization scheduling.Finally,simulation cases are employed to verify that the proposed two-stage optimization scheduling model can reduce load energy consumption,operating costs,and carbon emissions by 17.8%,17.39%,and 13.04%,respectively,compared with the non-optimal control group.
基金supported by Science and Technology Project of State Grid Hebei Electric Power Company(SGHE0000DKJS2000228)
文摘To promote the utilization of renewable energy,such as photovoltaics,this paper proposes an optimal flexibility dispatch method for demand-side resources(DSR)based on the Stackelberg game theory.First,the concept of the generalized DSR is analyzed and flexibility models for various DSR are constructed.Second,owing to the characteristics of small capacity but large-scale,an outer approximation is proposed to describe the aggregate flexibility of DSR.Then,the optimal flexibility dispatch model of DSR based on the Stackelberg game is established and a decentralized solution algorithm is designed to obtain the Stackelberg equilibrium.Finally,the actual data are utilized for the case study and the results show that,compared to the traditional centralized optimization method,the proposed optimal flexibility dispatch method can not only reduce the net load variability of the DSR aggregator but is beneficial for all DSR owners,which is more suitable for practical applications.
基金State Grid Corporation Science and Technology Project(520605190010).
文摘Owing to the rapid development of microgrids(MGs)and growing applications of renewable energy resources,multiobjective optimal dispatch of MGs need to be studied in detail.In this study,a multiobjective optimal dispatch model is developed for a standalone MG composed of wind turbines,photovoltaics,diesel engine unit,load,and battery energy storage system.The economic cost,environmental concerns,and power supply consistency are expressed via subobjectives with varying priorities.Then,the analytic hierarchy process algorithm is employed to reasonably specify the weight coefficients of the subobjectives.The quantum particle swarm optimization algorithm is thereafter employed as a solution to achieve optimal dispatch of the MG.Finally,the validity of the proposed model and solution methodology are con firmed by case studies.This study provides refere nee for mathematical model of multiojective optimizati on of MG and can be widely used in current research field.
基金supported by Science and Technology Project of SGCC(5108-202218280A-2-375-XG)。
文摘In response to the underutilization of energy and insufficient flexible operation capability of rural energy supply systems in China,this study proposes an optimal dispatch approach for a rural multi-energy supply system(RMESS)considering virtual energy storage(VES).First,to enable the flexible utilization of rural biomass resources and the thermal inertia of residential building envelopes,this study constructed VES-I and VES-II models that describe electrical-thermal and electrical-gas coupling from an electrical viewpoint.Subsequently,an RMESS model encompassing these two types of VES was formulated.This model delineates the intricate interplay of multi-energy components within the RMESS framework and facilitates the precise assessment of the adjustable potential for optimizing RMESS operations.Based on the above models,a day-ahead optimal dispatch model for an RMESS considering a VES is proposed to achieve optimal economic performance while ensuring efficient energy allocation.Comparative simulations validated the effectiveness of the VES modeling and the day-ahead optimal dispatch approach for the RMESS.
基金supported by the Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.,China,under Grant J2020090.
文摘Virtual power plants can effectively integrate different types of distributed energy resources,which have become a new operation mode with substantial advantages such as high flexibility,adaptability,and economy.This paper proposes a distributionally robust optimal dispatch approach for virtual power plants to determine an optimal day-ahead dispatch under uncertainties of renewable energy sources.The proposed distributionally robust approach characterizes probability distributions of renewable power output by moments.In this regard,the faults of stochastic optimization and traditional robust optimization can be overcome.Firstly,a second-order cone-based ambiguity set that incorporates the first and second moments of renewable power output is constructed,and a day-ahead two-stage distributionally robust optimization model is proposed for virtual power plants participating in day-ahead electricity markets.Then,an effective solution method based on the affine policy and second-order cone duality theory is employed to reformulate the proposed model into a deterministic mixed-integer second-order cone programming problem,which improves the computational efficiency of the model.Finally,the numerical results demonstrate that the proposed method achieves a better balance between robustness and economy.They also validate that the dispatch strategy of virtual power plants can be adjusted to reduce costs according to the moment information of renewable power output.
基金supported by the National Natural Science Foundation of China(Grant 62103101)the Natural Science Foundation of Jiangsu Province of China(Grant BK20210217)+5 种基金the China Postdoctoral Science Foundation(Grant 2022M710680)the National Natural Science Foundation of China(Grant 62273094)the"Zhishan"Scholars Programs of Southeast Universitythe Fundamental Science(Natural Science)General Program of Jiangsu Higher Education Institutions(No.21KJB470020)the Open Research Fund of Jiangsu Collaborative Innovation Center for Smart Distribution Network,Nanjing Institute of Technology(No.XTCX202102)the Introduced Talents Scientific Research Start-up Fund Project,Nanjing Institute of Technology(No.YKJ202133).
文摘This paper presents a finite-time economic model predictive control(MPC)algorithm that can be used for frequency regulation and optimal load dispatch in multi-area power systems.Economic MPC can be used in a power system to ensure frequency stability,real-time economic optimization,control of the system and optimal load dispatch from it.A generalized terminal penalty term was used,and the finite-time convergence of the system was guaranteed.The effectiveness of the proposed model predictive control algorithm was verified by simulating a power system,which had two areas connected by an AC tie line.The simulation results demonstrated the effectiveness of the algorithm.
基金supported by the DATALESs project(No.482.20.602)jointly financed by the Netherlands Organization for Scientific Research(NWO)and the National Natural Science Foundation of China.
文摘The optimal dispatch of energy storage systems(ESSs)in distribution networks poses significant challenges,primarily due to uncertainties of dynamic pricing,fluctuating demand,and the variability inherent in renewable energy sources.By exploiting the generalization capabilities of deep neural networks(DNNs),the deep reinforcement learning(DRL)algorithms can learn good-quality control models that adapt to the stochastic nature of distribution networks.Nevertheless,the practical deployment of DRL algorithms is often hampered by their limited capacity for satisfying operational constraints in real time,which is a crucial requirement for ensuring the reliability and feasibility of control actions during online operations.This paper introduces an innovative framework,named mixed-integer programming based deep reinforcement learning(MIP-DRL),to overcome these limitations.The proposed MIP-DRL framework can rigorously enforce operational constraints for the optimal dispatch of ESSs during the online execution.This framework involves training a Q-function with DNNs,which is subsequently represented in a mixed-integer programming(MIP)formulation.This unique combination allows for the seamless integration of operational constraints into the decision-making process.The effectiveness of the proposed MIP-DRL framework is validated through numerical simulations,demonstrating its superior capability to enforce all operational constraints and achieve high-quality dispatch decisions and showing its advantage over existing DRL algorithms.
基金supported by the National Natural Science Foundation of China(No.52177085).
文摘Peer-to-peer(P2P)energy trading in active distribution networks(ADNs)plays a pivotal role in promoting the efficient consumption of renewable energy sources.However,it is challenging to effectively coordinate the power dispatch of ADNs and P2P energy trading while preserving the privacy of different physical interests.Hence,this paper proposes a soft actor-critic algorithm incorporating distributed trading control(SAC-DTC)to tackle the optimal power dispatch of ADNs and the P2P energy trading considering privacy preservation among prosumers.First,the soft actor-critic(SAC)algorithm is used to optimize the control strategy of device in ADNs to minimize the operation cost,and the primary environmental information of the ADN at this point is published to prosumers.Then,a distributed generalized fast dual ascent method is used to iterate the trading process of prosumers and maximize their revenues.Subsequently,the results of trading are encrypted based on the differential privacy technique and returned to the ADN.Finally,the social welfare value consisting of ADN operation cost and P2P market revenue is utilized as a reward value to update network parameters and control strategies of the deep reinforcement learning.Simulation results show that the proposed SAC-DTC algorithm reduces the ADN operation cost,boosts the P2P market revenue,maximizes the social welfare,and exhibits high computational accuracy,demonstrating its practical application to the operation of power systems and power markets.
基金supported by the Special Research Project on Power Planning of the Guangdong Power Grid Co.,Ltd.
文摘To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.
文摘In this paper, the optimal dispatching model for multilake-reservoir system of KaiduKongque River and Tarim river is studied with mathematical programming.The optimal dispatching models belong to large system theory,which considers both wator supply and the effect of science and technology. The computation results shows that the model is feasible.
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.
