To meet the greenhouse gas reduction targets and address the uncertainty introduced by the surging penetration of stochastic renewable energy sources,energy storage systems are being deployed in microgrids.Relying sol...To meet the greenhouse gas reduction targets and address the uncertainty introduced by the surging penetration of stochastic renewable energy sources,energy storage systems are being deployed in microgrids.Relying solely on short-term uncertainty forecasts can result in substantial costs when making dispatch decisions for a storage system over an entire day.To mitigate this challenge,an adaptive robust optimization approach tailored for a hybrid hydrogen battery energy storage system(HBESS)operating within a microgrid is proposed,with a focus on efficient state-of-charge(SoC)planning to minimize microgrid expenses.The SoC ranges of the battery energy storage(BES)are determined in the day-ahead stage.Concurrently,the power generated by fuel cells and consumed by electrolysis device are optimized.This is followed by the intraday stage,where BES dispatch decisions are made within a predetermined SoC range to accommodate the uncertainties realized.To address this uncertainty and solve the adaptive optimization problem with integer recourse variables in the intraday stage,we proposed an outer-inner column-and-constraint generation algorithm(outer-inner-CCG).Numerical analyses underscored the high effectiveness and efficiency of the proposed adaptive robust operation model in making decisions for HBESS dispatch.展开更多
Rapid development of power-to-gas technology provides a potential solution for virtual power plants(VPP)to achieve near-zero carbon emissions.In this paper,a bi-level hybrid stochastic/robust optimization model is pro...Rapid development of power-to-gas technology provides a potential solution for virtual power plants(VPP)to achieve near-zero carbon emissions.In this paper,a bi-level hybrid stochastic/robust optimization model is proposed for low-carbon VPP day-ahead dispatch considering uncertainties from renewable generation and market prices.First,Karush-Kuhn-Tucker optimality conditions are employed to convert the bi-level model to a single level one.Next,the single level problem is decomposed into a master problem in the base case and several subproblems in extreme cases,which can then be solved by using the column-and-constraint generation algorithm iteratively.Numerical results indicate the proposed approach can effectively satisfy system operation constraints including the carbon emission limit,enhance computational efficiency and algorithm robustness compared with the stochastic method,and improve VPP revenue compared with the robust method.展开更多
This paper proposes a tri-level defense planning model to defend a power system against a coor-dinated cyber-physical attack(CCPA).The defense plan considers not only the standalone physical attack or the cyber attack...This paper proposes a tri-level defense planning model to defend a power system against a coor-dinated cyber-physical attack(CCPA).The defense plan considers not only the standalone physical attack or the cyber attack,but also coordinated attacks.The defense strategy adopts coordinated generation and transmission expansion planning to defend against the attacks.In the process of modeling,the upper-level plan represents the perspective of the planner,aiming to minimize the critical load shedding of the planning system after the attack.The load resources available to planners are extended to flex-ible loads and critical loads.The middle-level plan is from the viewpoint of the attacker,and aims at generating an optimal CCPA scheme in the light of the planning strategy determined by the upper-level plan to maximize the load shedding caused by the attack.The optimal operational behavior of the operator is described by the lower-level plan,which minimizes the load shedding by defending against the CCPA.The tri-level model is analyzed by the column and constraint generation algorithm,which decomposes the defense model into a master problem and subproblem.Case studies on a modified IEEE RTS-79 system are performed to demonstrate the economic effi-ciency of the proposed model.展开更多
The increasing interdependency of electricity and natural gas systems promotes coordination of the two systems for ensuring operational security and economics.This paper proposes a robust day-ahead scheduling model fo...The increasing interdependency of electricity and natural gas systems promotes coordination of the two systems for ensuring operational security and economics.This paper proposes a robust day-ahead scheduling model for the optimal coordinated operation of integrated energy systems while considering key uncertainties of the power system and natural gas system operation cost. Energy hub,with collocated gas-fired units, power-to-gas(Pt G) facilities, and natural gas storages, is considered to store or convert one type of energy(i.e., electricity or natural gas)into the other form, which could analogously function as large-scale electrical energy storages. The column-andconstraint generation(C&CG) is adopted to solve the proposed integrated robust model, in which nonlinear natural gas network constraints are reformulated via a set of linear constraints. Numerical experiments signify the effectiveness of the proposed model for handling volatile electrical loads and renewable generations via the coordinated scheduling of electricity and natural gas systems.展开更多
Quick-start generation units are critical devices and flexible resources to ensure a high penetration level of renewable energy in power systems.By considering the wind uncertainty and both binary and continuous decis...Quick-start generation units are critical devices and flexible resources to ensure a high penetration level of renewable energy in power systems.By considering the wind uncertainty and both binary and continuous decisions of quickstart generation units within the intraday dispatch,we develop a Wasserstein-metric-based distributionally robust optimization model for the day-ahead network-constrained unit commitment(NCUC)problem with mixed-integer recourse.We propose two feasible frameworks for solving the optimization problem.One approximates the continuous support of random wind power with a finite number of events,and the other leverages the extremal distributions instead.Both solution frameworks rely on the classic nested column-and-constraint generation(C&CG)method.It is shown that due to the sparsity of L_(1)-norm Wasserstein metric,the continuous support of wind power generation could be represented by a discrete one with a small number of events,and the rendered extremal distributions are sparse as well.With this reduction,the distributionally robust NCUC model with complicated mixed-integer recourse problems can be efficiently handled by both solution frameworks.Numerical studies are carried out,demonstrating that the model considering quick-start generation units ensures unit commitment(UC)schedules to be more robust and cost-effective,and the distributionally robust optimization method captures the wind uncertainty well in terms of out-of-sample tests.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.72331008,and No.72271211,and PolyU research project 1-YXBL.
文摘To meet the greenhouse gas reduction targets and address the uncertainty introduced by the surging penetration of stochastic renewable energy sources,energy storage systems are being deployed in microgrids.Relying solely on short-term uncertainty forecasts can result in substantial costs when making dispatch decisions for a storage system over an entire day.To mitigate this challenge,an adaptive robust optimization approach tailored for a hybrid hydrogen battery energy storage system(HBESS)operating within a microgrid is proposed,with a focus on efficient state-of-charge(SoC)planning to minimize microgrid expenses.The SoC ranges of the battery energy storage(BES)are determined in the day-ahead stage.Concurrently,the power generated by fuel cells and consumed by electrolysis device are optimized.This is followed by the intraday stage,where BES dispatch decisions are made within a predetermined SoC range to accommodate the uncertainties realized.To address this uncertainty and solve the adaptive optimization problem with integer recourse variables in the intraday stage,we proposed an outer-inner column-and-constraint generation algorithm(outer-inner-CCG).Numerical analyses underscored the high effectiveness and efficiency of the proposed adaptive robust operation model in making decisions for HBESS dispatch.
基金supported by the Shenzhen Science and Technology Program(JCYJ20210324130811031)Tsinghua Shenzhen International Graduate School Interdisciplinary Research and Innovation Fund(JC2021004).
文摘Rapid development of power-to-gas technology provides a potential solution for virtual power plants(VPP)to achieve near-zero carbon emissions.In this paper,a bi-level hybrid stochastic/robust optimization model is proposed for low-carbon VPP day-ahead dispatch considering uncertainties from renewable generation and market prices.First,Karush-Kuhn-Tucker optimality conditions are employed to convert the bi-level model to a single level one.Next,the single level problem is decomposed into a master problem in the base case and several subproblems in extreme cases,which can then be solved by using the column-and-constraint generation algorithm iteratively.Numerical results indicate the proposed approach can effectively satisfy system operation constraints including the carbon emission limit,enhance computational efficiency and algorithm robustness compared with the stochastic method,and improve VPP revenue compared with the robust method.
基金supported by the National Natural Science Foundation of China(No.52022016).
文摘This paper proposes a tri-level defense planning model to defend a power system against a coor-dinated cyber-physical attack(CCPA).The defense plan considers not only the standalone physical attack or the cyber attack,but also coordinated attacks.The defense strategy adopts coordinated generation and transmission expansion planning to defend against the attacks.In the process of modeling,the upper-level plan represents the perspective of the planner,aiming to minimize the critical load shedding of the planning system after the attack.The load resources available to planners are extended to flex-ible loads and critical loads.The middle-level plan is from the viewpoint of the attacker,and aims at generating an optimal CCPA scheme in the light of the planning strategy determined by the upper-level plan to maximize the load shedding caused by the attack.The optimal operational behavior of the operator is described by the lower-level plan,which minimizes the load shedding by defending against the CCPA.The tri-level model is analyzed by the column and constraint generation algorithm,which decomposes the defense model into a master problem and subproblem.Case studies on a modified IEEE RTS-79 system are performed to demonstrate the economic effi-ciency of the proposed model.
基金supported in part by the U.S.National Science Foundation Grant(No.CMMI-1635339)
文摘The increasing interdependency of electricity and natural gas systems promotes coordination of the two systems for ensuring operational security and economics.This paper proposes a robust day-ahead scheduling model for the optimal coordinated operation of integrated energy systems while considering key uncertainties of the power system and natural gas system operation cost. Energy hub,with collocated gas-fired units, power-to-gas(Pt G) facilities, and natural gas storages, is considered to store or convert one type of energy(i.e., electricity or natural gas)into the other form, which could analogously function as large-scale electrical energy storages. The column-andconstraint generation(C&CG) is adopted to solve the proposed integrated robust model, in which nonlinear natural gas network constraints are reformulated via a set of linear constraints. Numerical experiments signify the effectiveness of the proposed model for handling volatile electrical loads and renewable generations via the coordinated scheduling of electricity and natural gas systems.
基金supported by the Guangdong R&D Program in Key Areas (No.2021B0101230004)supported in part by the U.S.National Science Foundation (No.CMMI-1635472)supported by the Key Program of National Natural Science Foundation of China (No.51937005)。
文摘Quick-start generation units are critical devices and flexible resources to ensure a high penetration level of renewable energy in power systems.By considering the wind uncertainty and both binary and continuous decisions of quickstart generation units within the intraday dispatch,we develop a Wasserstein-metric-based distributionally robust optimization model for the day-ahead network-constrained unit commitment(NCUC)problem with mixed-integer recourse.We propose two feasible frameworks for solving the optimization problem.One approximates the continuous support of random wind power with a finite number of events,and the other leverages the extremal distributions instead.Both solution frameworks rely on the classic nested column-and-constraint generation(C&CG)method.It is shown that due to the sparsity of L_(1)-norm Wasserstein metric,the continuous support of wind power generation could be represented by a discrete one with a small number of events,and the rendered extremal distributions are sparse as well.With this reduction,the distributionally robust NCUC model with complicated mixed-integer recourse problems can be efficiently handled by both solution frameworks.Numerical studies are carried out,demonstrating that the model considering quick-start generation units ensures unit commitment(UC)schedules to be more robust and cost-effective,and the distributionally robust optimization method captures the wind uncertainty well in terms of out-of-sample tests.
