To deal with uncertainties of renewable energy,demand and price signals in real-time microgrid operation,this paper proposes a model predictive control strategy for microgrid economic dispatch, where hourly schedule i...To deal with uncertainties of renewable energy,demand and price signals in real-time microgrid operation,this paper proposes a model predictive control strategy for microgrid economic dispatch, where hourly schedule is constantly optimized according to the current system state and latest forecast information. Moreover, implicit network topology of the microgrid and corresponding power flow constraints are considered, which leads to a mixed integer nonlinear optimal power flow problem. Given the non-convexity feature of the original problem, the technique of conic programming is applied to efficiently crack the nut. Simulation results from a reconstructed IEEE-33 bus system and comparisons with the routine day-ahead microgrid schedule sufficiently substantiate the effectiveness of the proposed MPC strategy and the conic programming method.展开更多
This paper develops a stochastic framework for the energy management of a microgrid to minimize the energy cost from the grid.It considers the uncertainties in solar photovoltaic(PV)generation,load demand,and electric...This paper develops a stochastic framework for the energy management of a microgrid to minimize the energy cost from the grid.It considers the uncertainties in solar photovoltaic(PV)generation,load demand,and electricity price.Furthermore,the opportunity of flexible load demand,i.e.,the effect of demand response(DR),on the test system is studied.The uncertainties are modeled by using Monte Carlo simulations and the generated scenarios are reduced to improve the computational tractability.In general,microgrid scheduling is implemented by using substation(source node)price as a reference,but that reference price is not the same at all nodes.Therefore,this paper develops the nodal price based energy management in a microgrid to improve the scheduling accuracy.The stochastic energy management framework is formulated as a mixed integer non-linear programming(MINLP).Four case studies are simulated for a modified 15-node radial distribution network integrated with solar PV and battery energy storage system(BESS)to validate the effectiveness of the energy management framework for a microgrid with nodal pricing.展开更多
The increasing penetration of renewable energy sources(RESs)brings great challenges to the frequency security of power systems.The traditional frequency-constrained unit commitment(FCUC)analyzes frequency by simplifyi...The increasing penetration of renewable energy sources(RESs)brings great challenges to the frequency security of power systems.The traditional frequency-constrained unit commitment(FCUC)analyzes frequency by simplifying the average system frequency and ignoring numerous induction machines(IMs)in load,which may underestimate the risk and increase the operational cost.In this paper,we consider a multiarea frequency response(MAFR)model to capture the frequency dynamics in the unit scheduling problem,in which regional frequency security and the inertia of IM load are modeled with high-dimension differential algebraic equations.A multi-area FCUC(MFCUC)is formulated as mixed-integer nonlinear programming(MINLP)on the basis of the MAFR model.Then,we develop a multi-direction decomposition algorithm to solve the MFCUC efficiently.The original MINLP is decomposed into a master problem and subproblems.The subproblems check the nonlinear frequency dynamics and generate linear optimization cuts for the master problem to improve the frequency security in its optimal solution.Case studies on the modified IEEE 39-bus system and IEEE 118-bus system show a great reduction in operational costs.Moreover,simulation results verify the ability of the proposed MAFR model to reflect regional frequency security and the available inertia of IMs in unit scheduling.展开更多
Networked microgrids(NMGs)are critical in theaccommodation of distributed renewable energy.However,theexisting centralized state estimation(SE)cannot meet the demandsof NMGs in distributed energy management.The curren...Networked microgrids(NMGs)are critical in theaccommodation of distributed renewable energy.However,theexisting centralized state estimation(SE)cannot meet the demandsof NMGs in distributed energy management.The currentestimator is also not robust against bad data.This study introducesthe concepts of relative error to construct an improvedrobust SE(IRSE)optimization model with mixed-integer nonlinearprogramming(MINLP)that overcomes the disadvantage ofinaccurate results derived from different measurements whenthe same tolerance range is considered in the robust SE(RSE).To improve the computation efficiency of the IRSE optimizationmodel,the number of binary variables is reduced based on theprojection statistics and normalized residual methods,which effectivelyavoid the problem of slow convergence or divergenceof the algorithm caused by too many integer variables.Finally,an embedded consensus alternating direction of multiplier method(ADMM)distribution algorithm based on outer approximation(OA)is proposed to solve the IRSE optimization model.This algorithm can accurately detect bad data and obtain SE resultsthat communicate only the boundary coupling informationwith neighbors.Numerical tests show that the proposed algorithmeffectively detects bad data,obtains more accurate SE results,and ensures the protection of private information in all microgrids.展开更多
Relieving network congestions is a critical goal for the safe and flexible operation of modern power systems, especially in the presence of intermittent renewables or distributed generation. This paper deals with the ...Relieving network congestions is a critical goal for the safe and flexible operation of modern power systems, especially in the presence of intermittent renewables or distributed generation. This paper deals with the real-time coordinated operation of distributed static series compensators(DSSCs) to remove network congestions by suitable modifications of the branch reactance. Several objective functions are considered and discussed to minimize the number of the devices involved in the control actions, the total losses or the total reactive power exchanged, leading to a non-convex mixed-integer non-linear programming problem. Then, a heuristic methodology combining the solution of a regular NLP with k-means clustering algorithm is proposed to get rid of the binary variables, in an attempt to reduce the computational cost. The proposed coordinated operation strategy of the DSSCs is tested on several benchmark systems, providing feasible and sufficiently optimal solutions in a reasonable time frame for practical systems.展开更多
One battery energy storage system(BESS)can be used to provide different services,such as energy arbitrage(EA)and frequency regulation(FR)support,etc.,which have different revenues and lead to different battery degrada...One battery energy storage system(BESS)can be used to provide different services,such as energy arbitrage(EA)and frequency regulation(FR)support,etc.,which have different revenues and lead to different battery degradation profiles.This paper proposes a whole-lifetime coordinated service strategy to maximize the total operation profit of BESS.A multi-stage battery aging model is developed to characterize the battery aging rates during the whole lifetime.Considering the uncertainty of electricity price in EA service and frequency deviation in FR service,the whole problem is formulated as a twostage stochastic programming problem.At the first stage,the optimal service switching scheme between the EA and FR services are formulated to maximize the expected value of the whole-lifetime operation profit.At the second stage,the output power of BESS in EA service is optimized according to the electricity price in the hourly timescale,whereas the output power of BESS in FR service is directly determined according to the frequency deviation in the second timescale.The above optimization problem is then converted as a deterministic mixed-integer nonlinear programming(MINLP)model with bilinear items.Mc Cormick envelopes and a bound tightening algorithm are used to solve it.Numerical simulation is carried out to validate the effectiveness and advantages of the proposed strategy.展开更多
In this paper,we develop a profit-sharing-based optimal routing mechanism to incentivize horizontal collaboration among urban goods distributors.The core of this mechanism is based on exchanging goods at meet points,w...In this paper,we develop a profit-sharing-based optimal routing mechanism to incentivize horizontal collaboration among urban goods distributors.The core of this mechanism is based on exchanging goods at meet points,which is optimally planned en route.We propose a Collaborative Electric Vehicle Routing Problem with Meet Points(CoEVRPMP)considering constraints such as time windows,opportunity charging,and meet-point synchronization.The proposed CoEVRPMP is formulated as a mixed-integer nonlinear programming model.We present an exact method via branching and a matheuristic that combines adaptive large neighborhood search with linear programming.The viability and scalability of the collaborative method are demonstrated through numerical case studies,including a real-world case and a large-scale experiment with up to 500 customers.The findings underscore the significance of horizontal collaboration among delivery companies in attaining both higher individual profits and lower total costs.Moreover,collaboration helps to reduce the environmental footprint by decreasing travel distance.展开更多
基金supported by the National Natural Science Foundation of China(No.51277170)the National Key Basic Research Program of China(No.2012CB215204)
文摘To deal with uncertainties of renewable energy,demand and price signals in real-time microgrid operation,this paper proposes a model predictive control strategy for microgrid economic dispatch, where hourly schedule is constantly optimized according to the current system state and latest forecast information. Moreover, implicit network topology of the microgrid and corresponding power flow constraints are considered, which leads to a mixed integer nonlinear optimal power flow problem. Given the non-convexity feature of the original problem, the technique of conic programming is applied to efficiently crack the nut. Simulation results from a reconstructed IEEE-33 bus system and comparisons with the routine day-ahead microgrid schedule sufficiently substantiate the effectiveness of the proposed MPC strategy and the conic programming method.
基金supported by the Science and Engineering Research Board(SERB)a statutory body of Department of Science and Technology(DST)Government of India(Go I)(No.EMR/2016/002037).
文摘This paper develops a stochastic framework for the energy management of a microgrid to minimize the energy cost from the grid.It considers the uncertainties in solar photovoltaic(PV)generation,load demand,and electricity price.Furthermore,the opportunity of flexible load demand,i.e.,the effect of demand response(DR),on the test system is studied.The uncertainties are modeled by using Monte Carlo simulations and the generated scenarios are reduced to improve the computational tractability.In general,microgrid scheduling is implemented by using substation(source node)price as a reference,but that reference price is not the same at all nodes.Therefore,this paper develops the nodal price based energy management in a microgrid to improve the scheduling accuracy.The stochastic energy management framework is formulated as a mixed integer non-linear programming(MINLP).Four case studies are simulated for a modified 15-node radial distribution network integrated with solar PV and battery energy storage system(BESS)to validate the effectiveness of the energy management framework for a microgrid with nodal pricing.
基金supported by the Science and Technology Project of State Grid Hebei Electric Power Company Limited(No.kj2021-073)。
文摘The increasing penetration of renewable energy sources(RESs)brings great challenges to the frequency security of power systems.The traditional frequency-constrained unit commitment(FCUC)analyzes frequency by simplifying the average system frequency and ignoring numerous induction machines(IMs)in load,which may underestimate the risk and increase the operational cost.In this paper,we consider a multiarea frequency response(MAFR)model to capture the frequency dynamics in the unit scheduling problem,in which regional frequency security and the inertia of IM load are modeled with high-dimension differential algebraic equations.A multi-area FCUC(MFCUC)is formulated as mixed-integer nonlinear programming(MINLP)on the basis of the MAFR model.Then,we develop a multi-direction decomposition algorithm to solve the MFCUC efficiently.The original MINLP is decomposed into a master problem and subproblems.The subproblems check the nonlinear frequency dynamics and generate linear optimization cuts for the master problem to improve the frequency security in its optimal solution.Case studies on the modified IEEE 39-bus system and IEEE 118-bus system show a great reduction in operational costs.Moreover,simulation results verify the ability of the proposed MAFR model to reflect regional frequency security and the available inertia of IMs in unit scheduling.
基金supported by the National Natural Science Foundation of China(No.5217070269).
文摘Networked microgrids(NMGs)are critical in theaccommodation of distributed renewable energy.However,theexisting centralized state estimation(SE)cannot meet the demandsof NMGs in distributed energy management.The currentestimator is also not robust against bad data.This study introducesthe concepts of relative error to construct an improvedrobust SE(IRSE)optimization model with mixed-integer nonlinearprogramming(MINLP)that overcomes the disadvantage ofinaccurate results derived from different measurements whenthe same tolerance range is considered in the robust SE(RSE).To improve the computation efficiency of the IRSE optimizationmodel,the number of binary variables is reduced based on theprojection statistics and normalized residual methods,which effectivelyavoid the problem of slow convergence or divergenceof the algorithm caused by too many integer variables.Finally,an embedded consensus alternating direction of multiplier method(ADMM)distribution algorithm based on outer approximation(OA)is proposed to solve the IRSE optimization model.This algorithm can accurately detect bad data and obtain SE resultsthat communicate only the boundary coupling informationwith neighbors.Numerical tests show that the proposed algorithmeffectively detects bad data,obtains more accurate SE results,and ensures the protection of private information in all microgrids.
基金supported by the Spanish Ministry of Economy and Competitiveness (No. ENE2017-84813-R)the CERVERA research program of the Spanish Industrial and Technological Development Centre (CDTI) under the research project HySGrid+(CER-20191019)Junta de Andalucía (No. P18-TP-3655)。
文摘Relieving network congestions is a critical goal for the safe and flexible operation of modern power systems, especially in the presence of intermittent renewables or distributed generation. This paper deals with the real-time coordinated operation of distributed static series compensators(DSSCs) to remove network congestions by suitable modifications of the branch reactance. Several objective functions are considered and discussed to minimize the number of the devices involved in the control actions, the total losses or the total reactive power exchanged, leading to a non-convex mixed-integer non-linear programming problem. Then, a heuristic methodology combining the solution of a regular NLP with k-means clustering algorithm is proposed to get rid of the binary variables, in an attempt to reduce the computational cost. The proposed coordinated operation strategy of the DSSCs is tested on several benchmark systems, providing feasible and sufficiently optimal solutions in a reasonable time frame for practical systems.
基金partially supported by T-RECs Energy Pte.Ltd.under project(No.04IDS000719N014)。
文摘One battery energy storage system(BESS)can be used to provide different services,such as energy arbitrage(EA)and frequency regulation(FR)support,etc.,which have different revenues and lead to different battery degradation profiles.This paper proposes a whole-lifetime coordinated service strategy to maximize the total operation profit of BESS.A multi-stage battery aging model is developed to characterize the battery aging rates during the whole lifetime.Considering the uncertainty of electricity price in EA service and frequency deviation in FR service,the whole problem is formulated as a twostage stochastic programming problem.At the first stage,the optimal service switching scheme between the EA and FR services are formulated to maximize the expected value of the whole-lifetime operation profit.At the second stage,the output power of BESS in EA service is optimized according to the electricity price in the hourly timescale,whereas the output power of BESS in FR service is directly determined according to the frequency deviation in the second timescale.The above optimization problem is then converted as a deterministic mixed-integer nonlinear programming(MINLP)model with bilinear items.Mc Cormick envelopes and a bound tightening algorithm are used to solve it.Numerical simulation is carried out to validate the effectiveness and advantages of the proposed strategy.
基金Transport Area of Advanced within Chalmers University of Technology through the project COLLECT:Horizontal Cooperation in Urban Distribution Logistics-a Trusted-Cooperative Electric Vehicle Routing Method.This work was in part supported by the European Commission via the ERA-Net Urban Accessibility and Connectivity Sino-European program and the project E-LaaS:Energy optimal urban Logistics As A Service and by the Swedish Energy Agency(P2023-00021).
文摘In this paper,we develop a profit-sharing-based optimal routing mechanism to incentivize horizontal collaboration among urban goods distributors.The core of this mechanism is based on exchanging goods at meet points,which is optimally planned en route.We propose a Collaborative Electric Vehicle Routing Problem with Meet Points(CoEVRPMP)considering constraints such as time windows,opportunity charging,and meet-point synchronization.The proposed CoEVRPMP is formulated as a mixed-integer nonlinear programming model.We present an exact method via branching and a matheuristic that combines adaptive large neighborhood search with linear programming.The viability and scalability of the collaborative method are demonstrated through numerical case studies,including a real-world case and a large-scale experiment with up to 500 customers.The findings underscore the significance of horizontal collaboration among delivery companies in attaining both higher individual profits and lower total costs.Moreover,collaboration helps to reduce the environmental footprint by decreasing travel distance.
