Solving the quadratically constrained quadratic programming(QCQP)problem is in general NP-hard.Only a few subclasses of the QCQP problem are known to be polynomial-time solvable.Recently,the QCQP problem with a noncon...Solving the quadratically constrained quadratic programming(QCQP)problem is in general NP-hard.Only a few subclasses of the QCQP problem are known to be polynomial-time solvable.Recently,the QCQP problem with a nonconvex quadratic objective function over one ball and two parallel linear constraints is proven to have an exact computable representation,which reformulates the original problem as a linear semidefinite program with additional linear and second-order cone constraints.In this paper,we provide exact computable representations for some more subclasses of the QCQP problem,in particular,the subclass with one secondorder cone constraint and two special linear constraints.展开更多
This paper studies the nonhomogeneous quadratic programming problem over a second-order cone with linear equality constraints.When the feasible region is bounded,we show that an optimal solution of the problem can be ...This paper studies the nonhomogeneous quadratic programming problem over a second-order cone with linear equality constraints.When the feasible region is bounded,we show that an optimal solution of the problem can be found in polynomial time.When the feasible region is unbounded,a semidefinite programming(SDP)reformulation is constructed to find the optimal objective value of the original problem in polynomial time.In addition,we provide two sufficient conditions,under which,if the optimal objective value is finite,we show the optimal solution of SDP reformulation can be decomposed into the original space to generate an optimal solution of the original problem in polynomial time.Otherwise,a recession direction can be identified in polynomial time.Numerical examples are included to illustrate the effectiveness of the proposed approach.展开更多
To reduce the loss of the integrated electricity-gas energy system(IEGS)during typhoons,this paper proposes a two-stage resilience planning method for the IEGS considering hydrogen refueling stations(HRSs).Monte Carlo...To reduce the loss of the integrated electricity-gas energy system(IEGS)during typhoons,this paper proposes a two-stage resilience planning method for the IEGS considering hydrogen refueling stations(HRSs).Monte Carlo and simultaneous backward scenario reduction methods are used to generate typical power distribution network(PDN)line fault scenarios,which are then input into the two-stage resilience planning model.The first stage solves the resilience enhancement investment decision problem.It decides the locations and capacities of HRSs and gas-fired distributed generations(DGs).The second stage solves the system operation problem to minimize the cost in all fault scenarios.It de-termines the power of fuel cells and gas-fired DGs,the load shedding,as well as the line connection status.The resilience planning scheme with the minimum sum of the investment and system operation cost can be obtained by coordinating two stages.Finally,the simulation is verified via the IEEE 33-node PDN coupled with the 7-node gas network using historical data from the 2023 super typhoon“Saola”.The results indicate that the proposed method can not only reduce the investment and system operation cost;but also reduce load shedding and improve the ability to restore power supply in fault scenarios,therefore enhancing IEGS's resilience.展开更多
The proliferation of electric vehicles(EVs)introduces transformative opportunities and challenges for the stability of distribution networks.Unregulated EV charging will further exacerbate the inherent three-phase imb...The proliferation of electric vehicles(EVs)introduces transformative opportunities and challenges for the stability of distribution networks.Unregulated EV charging will further exacerbate the inherent three-phase imbalance of the power grid,while regulated EV charging will alleviate such imbalance.To systematically address this challenge,this study proposes a two-stage bidding strategy with dispatch potential of electric vehicle aggregators(EVAs).By constructing a coordinated framework that integrates the day-ahead and real-time markets,the proposed two-stage bidding strategy reconfigures distributed EVA clusters into a controllable dynamic energy storage system,with a particular focus on dynamic compensation for deviations between scheduled and real-time operations.A bilevel Stackelberg game resolves three-phase imbalance by achieving Nash equilibrium for inter-phase balance,with Karush-Kuhn-Tucker(KKT)conditions and mixed-integer secondorder cone programming(MISOCP)ensuring feasible solutions.The proposed coordinated framework is validated with different bidding modes includes independent bidding,full price acceptance,and cooperative bidding modes.The proposed twostage bidding strategy provides an EVA-based coordinated scheduling solution that balances the economic efficiency and phase stability in electricity market.展开更多
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
The increasing integration of photovoltaic generators(PVGs) and the uneven economic development in different regions may cause the unbalanced spatial-temporal distribution of load demands in an urban distribution netw...The increasing integration of photovoltaic generators(PVGs) and the uneven economic development in different regions may cause the unbalanced spatial-temporal distribution of load demands in an urban distribution network(UDN). This may lead to undesired consequences, including PVG curtailment, load shedding, and equipment inefficiency, etc. Global dynamic reconfiguration provides a promising method to solve those challenges. However, the power flow transfer capabilities for different kinds of switches are diverse, and the willingness of distribution system operators(DSOs) to select them is also different. In this paper, we formulate a multi-objective dynamic reconfiguration optimization model suitable for multi-level switching modes to minimize the operation cost, load imbalance, and the PVG curtailment. The multi-level switching includes feeder-level switching, transformer-level switching, and substation-level switching. A novel load balancing index is devised to quantify the global load balancing degree at different levels. Then, a stochastic programming model based on selected scenarios is established to address the uncertainties of PVGs and loads. Afterward, the fuzzy c-means(FCMs) clustering is applied to divide the time periods of reconfiguration. Furthermore, the modified binary particle swarm optimization(BPSO)and Cplex solver are combined to solve the proposed mixed-integer second-order cone programming(MISOCP) model. Numerical results based on the 148-node and 297-node systems are obtained to validate the effectiveness of the proposed method.展开更多
Active splitting control utilizes real-time decision and system-level splitting to prevent cascading blackouts and to maintain power supply under severe disturbances. Splitting strategy searching(SSS) is one of the mo...Active splitting control utilizes real-time decision and system-level splitting to prevent cascading blackouts and to maintain power supply under severe disturbances. Splitting strategy searching(SSS) is one of the most crucial issues in active splitting control for deciding‘‘where to split’’. SSS determines the splitting surface in real time to properly divide the asynchronous generators into isolated islands with an optimal control effect. In this paper, an SSS approach that focuses on island stability is presented. The proposed SSS approach is designed to ensure a rational stability margin and regulation ability on each island during and after the transient process of system splitting. This method includes the active/reactive power flow feasibility constraints and voltage/angle stability constraints in the steady state as well as the frequencyresponse capability constraints in the transient process. By considering the island stability constraints in the SSS, the proposed approach can avoid the splitting strategies with poor stability performance. Therefore, the major advantage of the proposed approach is that it can ensure better island static and transient stability during and after the splitting control. In addition, the entire model is formulated as a mixed-integer second-order cone programming(MISOCP)model. Thus, it can be rapidly solved by using commercial optimization solvers. Numerical simulations of a realistic provincial power system in central China demonstrate thevalidity of the proposed approach and the necessity of considering the island stability issues.展开更多
In the existing multi-period robust optimization methods for the optimal power flow in radial distribution systems,the capability of distributed generators(DGs)to regulate the reactive power,the operation costs of the...In the existing multi-period robust optimization methods for the optimal power flow in radial distribution systems,the capability of distributed generators(DGs)to regulate the reactive power,the operation costs of the regulation equipment,and the current of the shunt capacitor of the cables are not considered.In this paper,a multi-period two-stage robust scheduling strategy that aims to minimize the total cost of the power supply is developed.This strategy considers the time-ofuse price,the capability of the DGs to regulate the active and reactive power,the action costs of the regulation equipment,and the current of the shunt capacitors of the cables in a radial distribution system.Furthermore,the numbers of variables and constraints in the first-stage model remain constant during the iteration to enhance the computation efficiency.To solve the second-stage model,only the model of each period needs to be solved.Then,their objective values are accumulated,revealing that the computation rate using the proposed method is much higher than that of existing methods.The effectiveness of the proposed method is validated by actual 4-bus,IEEE 33-bus,and PG 69-bus distribution systems.展开更多
基金supported by US Army Research Office Grant(No.W911NF-04-D-0003)by the North Carolina State University Edward P.Fitts Fellowship and by National Natural Science Foundation of China(No.11171177)。
文摘Solving the quadratically constrained quadratic programming(QCQP)problem is in general NP-hard.Only a few subclasses of the QCQP problem are known to be polynomial-time solvable.Recently,the QCQP problem with a nonconvex quadratic objective function over one ball and two parallel linear constraints is proven to have an exact computable representation,which reformulates the original problem as a linear semidefinite program with additional linear and second-order cone constraints.In this paper,we provide exact computable representations for some more subclasses of the QCQP problem,in particular,the subclass with one secondorder cone constraint and two special linear constraints.
基金Fang was supported by the US National Science Foundation(No.DMI-0553310)Guo,Wang and Xing were supported by the National Natural Science Foundation of China(Nos.11171177 and 11371216)Deng was supported by the Edward P.Fitts Fellowship at North Carolina State University.
文摘This paper studies the nonhomogeneous quadratic programming problem over a second-order cone with linear equality constraints.When the feasible region is bounded,we show that an optimal solution of the problem can be found in polynomial time.When the feasible region is unbounded,a semidefinite programming(SDP)reformulation is constructed to find the optimal objective value of the original problem in polynomial time.In addition,we provide two sufficient conditions,under which,if the optimal objective value is finite,we show the optimal solution of SDP reformulation can be decomposed into the original space to generate an optimal solution of the original problem in polynomial time.Otherwise,a recession direction can be identified in polynomial time.Numerical examples are included to illustrate the effectiveness of the proposed approach.
基金supported by the National Natural Science Foundation of China(No.52177110).
文摘To reduce the loss of the integrated electricity-gas energy system(IEGS)during typhoons,this paper proposes a two-stage resilience planning method for the IEGS considering hydrogen refueling stations(HRSs).Monte Carlo and simultaneous backward scenario reduction methods are used to generate typical power distribution network(PDN)line fault scenarios,which are then input into the two-stage resilience planning model.The first stage solves the resilience enhancement investment decision problem.It decides the locations and capacities of HRSs and gas-fired distributed generations(DGs).The second stage solves the system operation problem to minimize the cost in all fault scenarios.It de-termines the power of fuel cells and gas-fired DGs,the load shedding,as well as the line connection status.The resilience planning scheme with the minimum sum of the investment and system operation cost can be obtained by coordinating two stages.Finally,the simulation is verified via the IEEE 33-node PDN coupled with the 7-node gas network using historical data from the 2023 super typhoon“Saola”.The results indicate that the proposed method can not only reduce the investment and system operation cost;but also reduce load shedding and improve the ability to restore power supply in fault scenarios,therefore enhancing IEGS's resilience.
基金supported by Science and Technology Project of State Grid Corporation of China(No.5400-202318246A-1-1-ZN)。
文摘The proliferation of electric vehicles(EVs)introduces transformative opportunities and challenges for the stability of distribution networks.Unregulated EV charging will further exacerbate the inherent three-phase imbalance of the power grid,while regulated EV charging will alleviate such imbalance.To systematically address this challenge,this study proposes a two-stage bidding strategy with dispatch potential of electric vehicle aggregators(EVAs).By constructing a coordinated framework that integrates the day-ahead and real-time markets,the proposed two-stage bidding strategy reconfigures distributed EVA clusters into a controllable dynamic energy storage system,with a particular focus on dynamic compensation for deviations between scheduled and real-time operations.A bilevel Stackelberg game resolves three-phase imbalance by achieving Nash equilibrium for inter-phase balance,with Karush-Kuhn-Tucker(KKT)conditions and mixed-integer secondorder cone programming(MISOCP)ensuring feasible solutions.The proposed coordinated framework is validated with different bidding modes includes independent bidding,full price acceptance,and cooperative bidding modes.The proposed twostage bidding strategy provides an EVA-based coordinated scheduling solution that balances the economic efficiency and phase stability in electricity market.
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 National Key R&D Program of China (No.2019YFE0123600)National Natural Science Foundation of China (No.52077146)Young Elite Scientists Sponsorship Program by CSEE (No.CESS-YESS-2019027)。
文摘The increasing integration of photovoltaic generators(PVGs) and the uneven economic development in different regions may cause the unbalanced spatial-temporal distribution of load demands in an urban distribution network(UDN). This may lead to undesired consequences, including PVG curtailment, load shedding, and equipment inefficiency, etc. Global dynamic reconfiguration provides a promising method to solve those challenges. However, the power flow transfer capabilities for different kinds of switches are diverse, and the willingness of distribution system operators(DSOs) to select them is also different. In this paper, we formulate a multi-objective dynamic reconfiguration optimization model suitable for multi-level switching modes to minimize the operation cost, load imbalance, and the PVG curtailment. The multi-level switching includes feeder-level switching, transformer-level switching, and substation-level switching. A novel load balancing index is devised to quantify the global load balancing degree at different levels. Then, a stochastic programming model based on selected scenarios is established to address the uncertainties of PVGs and loads. Afterward, the fuzzy c-means(FCMs) clustering is applied to divide the time periods of reconfiguration. Furthermore, the modified binary particle swarm optimization(BPSO)and Cplex solver are combined to solve the proposed mixed-integer second-order cone programming(MISOCP) model. Numerical results based on the 148-node and 297-node systems are obtained to validate the effectiveness of the proposed method.
文摘Active splitting control utilizes real-time decision and system-level splitting to prevent cascading blackouts and to maintain power supply under severe disturbances. Splitting strategy searching(SSS) is one of the most crucial issues in active splitting control for deciding‘‘where to split’’. SSS determines the splitting surface in real time to properly divide the asynchronous generators into isolated islands with an optimal control effect. In this paper, an SSS approach that focuses on island stability is presented. The proposed SSS approach is designed to ensure a rational stability margin and regulation ability on each island during and after the transient process of system splitting. This method includes the active/reactive power flow feasibility constraints and voltage/angle stability constraints in the steady state as well as the frequencyresponse capability constraints in the transient process. By considering the island stability constraints in the SSS, the proposed approach can avoid the splitting strategies with poor stability performance. Therefore, the major advantage of the proposed approach is that it can ensure better island static and transient stability during and after the splitting control. In addition, the entire model is formulated as a mixed-integer second-order cone programming(MISOCP)model. Thus, it can be rapidly solved by using commercial optimization solvers. Numerical simulations of a realistic provincial power system in central China demonstrate thevalidity of the proposed approach and the necessity of considering the island stability issues.
基金supported in part by the Fundamental Research Funds for the Central Universities of China(No.PA2021GDSK0083)in part by the State Key Program of National Natural Science of China(No.51637004)in part by the National Key Research and Development Plan“Important Scientific Instruments and Equipment Development”(No.2016YFF0102200)。
文摘In the existing multi-period robust optimization methods for the optimal power flow in radial distribution systems,the capability of distributed generators(DGs)to regulate the reactive power,the operation costs of the regulation equipment,and the current of the shunt capacitor of the cables are not considered.In this paper,a multi-period two-stage robust scheduling strategy that aims to minimize the total cost of the power supply is developed.This strategy considers the time-ofuse price,the capability of the DGs to regulate the active and reactive power,the action costs of the regulation equipment,and the current of the shunt capacitors of the cables in a radial distribution system.Furthermore,the numbers of variables and constraints in the first-stage model remain constant during the iteration to enhance the computation efficiency.To solve the second-stage model,only the model of each period needs to be solved.Then,their objective values are accumulated,revealing that the computation rate using the proposed method is much higher than that of existing methods.The effectiveness of the proposed method is validated by actual 4-bus,IEEE 33-bus,and PG 69-bus distribution systems.
