The paper proposes a stochastic unit commitment(UC)model to realize the low-carbon operation requirement and cope with wind power prediction errors for power systems with intensive wind power and carbon capture power ...The paper proposes a stochastic unit commitment(UC)model to realize the low-carbon operation requirement and cope with wind power prediction errors for power systems with intensive wind power and carbon capture power plant(CCPP).A linear re-dispatch strategy is introduced to compensate the wind power deviation from the spot forecast.The robust optimization technique is employed to obtain a reliable commitment plan against all realizations of wind power within the uncertainty set given by probabilistic forecast.The proposed model is validated with IEEE 39-bus system.The advantages of flexible CCPPs are compared to the normal coal-fueled plants and the impacts of robustness controlling are discussed.展开更多
This paper proposes a novel method for transmission network expansion planning(TNEP)that take into account uncertainties in loads and renewable energy resources.The goal of TNEP is to minimize the expansion cost of ca...This paper proposes a novel method for transmission network expansion planning(TNEP)that take into account uncertainties in loads and renewable energy resources.The goal of TNEP is to minimize the expansion cost of candidate lines without any load curtailment.A robust linear optimization algorithm is adopted to minimize the load curtailment with uncertainties considered under feasible expansion costs.Hence,the optimal planning scheme obtained through an iterative process would be to serve loads and provide a sufficient margin for renewable energy integration.In this paper,two uncertainty budget parameters are introduced in the optimization process to limit the considered variation ranges for both the load and the renewable generation.Simulation results obtained from two test systems indicate that the uncertainty budget parameters used to describe uncertainties are essential to arrive at a compromise for the robustness and optimality,and hence,offer a range of preferences to power system planners and decision makers.展开更多
This paper presents an optimization for transmission network expansion planning(TNEP)under uncertainty circumstances.This TNEP model introduces the approach of parameter sets to describe the range that all possible re...This paper presents an optimization for transmission network expansion planning(TNEP)under uncertainty circumstances.This TNEP model introduces the approach of parameter sets to describe the range that all possible realizations of uncertainties in load and renewable generation can reach.While optimizing the TNEP solution,the output of each generator is modeled as an uncertain variable to linearly respond to changes caused by uncertainties,which is constrained by the extent to which uncertain parameters may change the operational range of generators,and network topology.This paper demonstrates that the robust optimization approach is effective to make the problem with uncertainties tractable by converting it into a deterministic optimization,and with the genetic algorithm,the optimal TNEP solution is derived iteratively.Compared with other robust TNEP results tested on IEEE 24-bus systems,the proposed method produces a least-cost expansion plan without losing robustness.In addition,the contribution that each generator can make to accommodate with every uncertainty is optimally quantified.Effects imposed by different uncertainty levels are analyzed to provide a compromise of the conservativeness of TNEP solutions.展开更多
基金This work was supported by the National Basic Research Program of China(No.2012CB215106)State Grid Corporation of China(No.52150014006W).
文摘The paper proposes a stochastic unit commitment(UC)model to realize the low-carbon operation requirement and cope with wind power prediction errors for power systems with intensive wind power and carbon capture power plant(CCPP).A linear re-dispatch strategy is introduced to compensate the wind power deviation from the spot forecast.The robust optimization technique is employed to obtain a reliable commitment plan against all realizations of wind power within the uncertainty set given by probabilistic forecast.The proposed model is validated with IEEE 39-bus system.The advantages of flexible CCPPs are compared to the normal coal-fueled plants and the impacts of robustness controlling are discussed.
基金supported by the National Basic Research Program of China(2012CB215106).
文摘This paper proposes a novel method for transmission network expansion planning(TNEP)that take into account uncertainties in loads and renewable energy resources.The goal of TNEP is to minimize the expansion cost of candidate lines without any load curtailment.A robust linear optimization algorithm is adopted to minimize the load curtailment with uncertainties considered under feasible expansion costs.Hence,the optimal planning scheme obtained through an iterative process would be to serve loads and provide a sufficient margin for renewable energy integration.In this paper,two uncertainty budget parameters are introduced in the optimization process to limit the considered variation ranges for both the load and the renewable generation.Simulation results obtained from two test systems indicate that the uncertainty budget parameters used to describe uncertainties are essential to arrive at a compromise for the robustness and optimality,and hence,offer a range of preferences to power system planners and decision makers.
基金This work was supported in part by the National Key Research and Development Program of China(2016YFB0900400,2016YFB0900403).
文摘This paper presents an optimization for transmission network expansion planning(TNEP)under uncertainty circumstances.This TNEP model introduces the approach of parameter sets to describe the range that all possible realizations of uncertainties in load and renewable generation can reach.While optimizing the TNEP solution,the output of each generator is modeled as an uncertain variable to linearly respond to changes caused by uncertainties,which is constrained by the extent to which uncertain parameters may change the operational range of generators,and network topology.This paper demonstrates that the robust optimization approach is effective to make the problem with uncertainties tractable by converting it into a deterministic optimization,and with the genetic algorithm,the optimal TNEP solution is derived iteratively.Compared with other robust TNEP results tested on IEEE 24-bus systems,the proposed method produces a least-cost expansion plan without losing robustness.In addition,the contribution that each generator can make to accommodate with every uncertainty is optimally quantified.Effects imposed by different uncertainty levels are analyzed to provide a compromise of the conservativeness of TNEP solutions.
