The stochastic dual dynamic programming (SDDP) algorithm is becoming increasingly used. In this paper we present analysis of different methods of lattice construction for SDDP exemplifying a realistic variant of the n...The stochastic dual dynamic programming (SDDP) algorithm is becoming increasingly used. In this paper we present analysis of different methods of lattice construction for SDDP exemplifying a realistic variant of the newsvendor problem, incorporating storage of production. We model several days of work and compare the profits realized using different methods of the lattice construction and the corresponding computer time spent in lattice construction. Our case differs from the known one because we consider not only a multidimensional but also a multistage case with stage dependence. We construct scenario lattice for different Markov processes which play a crucial role in stochastic modeling. The novelty of our work is comparing different methods of scenario lattice construction. We considered a realistic variant of the newsvendor problem. The results presented in this article show that the Voronoi method slightly outperforms others, but the k-means method is much faster overall.展开更多
The large-scale integration of renewable energy sources(RES)is the global trend to deal with the energy crisis and greenhouse emissions.Due to the intermittent nature of RES together with the uncertainty of load deman...The large-scale integration of renewable energy sources(RES)is the global trend to deal with the energy crisis and greenhouse emissions.Due to the intermittent nature of RES together with the uncertainty of load demand,the problem of transmission expansion planning(TEP)is facing more and more challenges from uncertainties.In this paper,the TEP problem is modeled as a two-stage formulation,so as to minimize the total of investment costs and generation costs.To ensure the utilization level of the RES generation,the expansion plan is required to provide sufficient transmission capacity for the integration of RES.Also,N-k security criterion is considered into the model,so the expansion plan can meet the required security criteria.The stochastic dual dynamic programming(SDDP)approach is applied to consider the uncertainties,and the whole model is solved by Benders’decomposition technique.Two case studies are carried out to compare the performance of the SDDP approach and the deterministic approach.Results show that the expansion plan obtained by the SDDP approach has a better performance than that of the deterministic approach.展开更多
The rapid expansion of renewable energy,particularly wind and photovoltaic(PV)power generation,increases the vulnerability of power systems to persistent low output scenarios(PLOS),which pose significant security risk...The rapid expansion of renewable energy,particularly wind and photovoltaic(PV)power generation,increases the vulnerability of power systems to persistent low output scenarios(PLOS),which pose significant security risks.To address uncertainties in the timing,duration,and frequency of PLOS while considering operational non-anticipativity,this paper proposes a multi-stage energy storage planning model incorporating a Markov process.A nested conditional value at risk(CVaR)framework is employed to manage uncertainty.To efficiently solve the large-scale multi-stage mixed-integer stochastic problem,a modified stochastic dual dynamic integer programming(SDDiP)algorithm is proposed.In order to accelerate the convergence speed of the algorithm,techniques such as regularization,dynamic sampling,dynamic cut selection,and parallel computation are designed.Case studies on the IEEE 118-bus system validate the effectiveness of the proposed approach.展开更多
With the participation of large quantities of renewable energy in power system operations,their volatility and intermittence increases the difficulties and challenges of power system economic scheduling.Considering th...With the participation of large quantities of renewable energy in power system operations,their volatility and intermittence increases the difficulties and challenges of power system economic scheduling.Considering the uncertainty of renewable energy generation,based on the distributionally robust optimization method,a two-stage economic dispatch model is proposed to minimize the total operation costs.In this paper,it is assumed that the fluctuating of renewable power generation follows the unknown probability distribution that is restricted in an ambiguity set,which is established by utilizing the first-order moment information of available historical data.Furthermore,the theory of conditional value-at-risk is introduced to transform the model into a tractable model,which we call robust counterpart formulation.Based on the stochastic dual dynamic programming method,an improved iterative algorithm is proposed to solve the robust counterpart problem.Specifically,the convergence optimum can be obtained by the improved iterative algorithm,which performs a forward pass and backward pass repeatedly in each iterative process.Finally,by comparing with other methods,the results on the modified IEEE 6-bus,118-bus,and 300-bus system show the effectiveness and advantages of the proposed model and method.展开更多
文摘The stochastic dual dynamic programming (SDDP) algorithm is becoming increasingly used. In this paper we present analysis of different methods of lattice construction for SDDP exemplifying a realistic variant of the newsvendor problem, incorporating storage of production. We model several days of work and compare the profits realized using different methods of the lattice construction and the corresponding computer time spent in lattice construction. Our case differs from the known one because we consider not only a multidimensional but also a multistage case with stage dependence. We construct scenario lattice for different Markov processes which play a crucial role in stochastic modeling. The novelty of our work is comparing different methods of scenario lattice construction. We considered a realistic variant of the newsvendor problem. The results presented in this article show that the Voronoi method slightly outperforms others, but the k-means method is much faster overall.
基金special project(CEPRI:XT71-12-028)funded by the State Grid of China。
文摘The large-scale integration of renewable energy sources(RES)is the global trend to deal with the energy crisis and greenhouse emissions.Due to the intermittent nature of RES together with the uncertainty of load demand,the problem of transmission expansion planning(TEP)is facing more and more challenges from uncertainties.In this paper,the TEP problem is modeled as a two-stage formulation,so as to minimize the total of investment costs and generation costs.To ensure the utilization level of the RES generation,the expansion plan is required to provide sufficient transmission capacity for the integration of RES.Also,N-k security criterion is considered into the model,so the expansion plan can meet the required security criteria.The stochastic dual dynamic programming(SDDP)approach is applied to consider the uncertainties,and the whole model is solved by Benders’decomposition technique.Two case studies are carried out to compare the performance of the SDDP approach and the deterministic approach.Results show that the expansion plan obtained by the SDDP approach has a better performance than that of the deterministic approach.
基金supported by the National Key Research and Development Program of China under Grant 2022YFB2403000.
文摘The rapid expansion of renewable energy,particularly wind and photovoltaic(PV)power generation,increases the vulnerability of power systems to persistent low output scenarios(PLOS),which pose significant security risks.To address uncertainties in the timing,duration,and frequency of PLOS while considering operational non-anticipativity,this paper proposes a multi-stage energy storage planning model incorporating a Markov process.A nested conditional value at risk(CVaR)framework is employed to manage uncertainty.To efficiently solve the large-scale multi-stage mixed-integer stochastic problem,a modified stochastic dual dynamic integer programming(SDDiP)algorithm is proposed.In order to accelerate the convergence speed of the algorithm,techniques such as regularization,dynamic sampling,dynamic cut selection,and parallel computation are designed.Case studies on the IEEE 118-bus system validate the effectiveness of the proposed approach.
基金supported by the National Natural Science Foundation of China(No.51777126)。
文摘With the participation of large quantities of renewable energy in power system operations,their volatility and intermittence increases the difficulties and challenges of power system economic scheduling.Considering the uncertainty of renewable energy generation,based on the distributionally robust optimization method,a two-stage economic dispatch model is proposed to minimize the total operation costs.In this paper,it is assumed that the fluctuating of renewable power generation follows the unknown probability distribution that is restricted in an ambiguity set,which is established by utilizing the first-order moment information of available historical data.Furthermore,the theory of conditional value-at-risk is introduced to transform the model into a tractable model,which we call robust counterpart formulation.Based on the stochastic dual dynamic programming method,an improved iterative algorithm is proposed to solve the robust counterpart problem.Specifically,the convergence optimum can be obtained by the improved iterative algorithm,which performs a forward pass and backward pass repeatedly in each iterative process.Finally,by comparing with other methods,the results on the modified IEEE 6-bus,118-bus,and 300-bus system show the effectiveness and advantages of the proposed model and method.
