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stochastic economic dispatch of power system with multiple wind farms and pumped-storage hydro stations using approximate dynamic programming

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IET RENEWABLE POWER GENERATION 2020年第13期14卷 2507-2516页

作者： Lin, Shunjiang Fan, Guansheng Jian, Ganyang Liu, Mingbo South China Univ Technol Sch Elect Power Engn Guangzhou 510640 Peoples R China China Southern Power Grid Elect Power Res Inst Guangzhou 510663 Peoples R China

The stochastic economic dispatch problem of power system with multiple wind farms and pumped-storage hydro stations is formulated as a specific stochastic dynamic programming (DP) model, i.e. stochastic storage model, it is impossible to obtain an accurate solution due to the curse of dimensionality. Based on the approximate DP (ADP) method, the stochastic storage model can be transformed into a series of mixed-integer linear programming (MILP) models by describing the approximate value functions (AVFs) as convex piecewise linear functions in post-decision states. The AVFs are first initialised using the results of the deterministic model under a forecast scenario of wind farm output and then trained by scanning stochastic sampling scenarios consecutively with the successive projective approximation routine algorithm. To obtain a near-optimal day-ahead dispatch scheme, the forecast scenario is substituted into the MILP models expressed by the trained AVFs and is solved forward through each time interval. The network constraints are incorporated by the while-loop detection of critical lines. Test results on an actual provincial power system and the modified IEEE 39-bus system, including the comparison among the ADP, DP, scenario-based and chance-constrained programming methods, demonstrate the feasibility and efficiency of the proposed model and algorithm.

关键词： approximation theory stochastic processes dynamic programming power generation economics wind power plants power generation dispatch pumped-storage power stations integer programming stochastic programming power system security optimisation linear programming multiple wind farms pumped-storage hydro stations approximate dynamic programming stochastic economic dispatch problem specific stochastic dynamic programming model stochastic storage model approximate DP method mixed-integer linear programming models approximate value functions AVFs convex piecewise linear functions deterministic model forecast scenario wind farm output stochastic sampling scenarios successive projective approximation routine algorithm near-optimal day-ahead dispatch scheme MILP models actual provincial power system modified IEEE 39-bus system

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Optimisation of port investment strategies considering uncertain cargo demand and environmental pollutions

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 200卷

作者： Lu, Bo Luo, Rifeng Wu, Xin Wang, Huipo Dalian Univ Technol Sch Econ & Management Dalian 116024 Peoples R China Hebei Univ Engn Sch Management Engn & Business Handan 056038 Peoples R China

Port investment is assuming a critical role in contemporary maritime economics, attributable to the escalating demands for enhancing port efficiency and the growing awareness of environmental issues. Currently, numerous ports are operating at full capacity to manage incoming cargoes. Investing in port infrastructure holds the potential to enhance overall port productivity;nevertheless, such investments necessitate substantial financial resources and sufficient time, may encounter limitations due to environmental regulations, and are subject to risks stemming from uncertain demand. Existing investment analysis methods have been utilized to evaluate the impact of uncertainty on port investments, yet they tend to be overly theoretical or lack flexibility in decision- making. In light of these limitations, our study aims to explore a novel approach to optimize port investment strategies considering uncertain cargo demand and environmental concerns, through the development of a multistage stochastic dynamic programming model. This model places emphasis on investment choices related to expanding berth capacity, integrating environmental pollution constraints and financial limitations, all while accounting for demand uncertainties. Ultimately, our proposed methodology demonstrates improved analytical capabilities in addressing uncertainty and environmental pollution, as exemplified in a case study.

关键词： Port investment Demand uncertainty Environmental pollution stochastic programming

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Optimal Trading of a Charging-Station Company in Auction Markets for Electricity

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第5期26卷 6545-6555页

作者： Sohrabi, Farnaz Rohaninejad, Mohammad Hesamzadeh, Mohammad Reza Bems, Julius Czech Tech Univ Fac Elect Engn Dept Econ Management & Humanities Prague 16000 Czech Republic Czech Tech Univ Czech Inst Informat Robot & Cybernet Dept Ind Informat Prague 16000 Czech Republic KTH Royal Inst Technol Sch Elect Engn & Comp Sci S-11428 Stockholm Sweden

This paper addresses a charging-station company (Chargco) for electric and hydrogen vehicles. The optimal trading of the Chargco in day-ahead and intraday auction markets for electricity is modeled as a stochastic Mixed-Integer Quadratic Program (MIQP). We propose a series of linearization and reformulation techniques to reformulate the stochastic MIQP as a mixed-integer linear program (MILP). To model stochasticity, we utilize generative adversarial networks to cluster electricity market price scenarios. Additionally, a combination of random forests and linear regression is employed to model the relationship between Chargco electricity and hydrogen loads and their selling prices. Finally, we propose an Improved L-Shaped Decomposition (ILSD) algorithm to solve our stochastic MILP. Our ILSD algorithm not only addresses infeasibilities through an innovative approach but also incorporates warm starts, valid inequalities and multiple generation cuts, thereby reducing computational complexity. Numerical experiments illustrate the Chargco trading using our proposed stochastic MILP and its solution algorithm.

关键词： Hydrogen Electricity Charging stations stochastic processes Electricity supply industry Batteries Uncertainty Indexes Optimization Energy management L-shaped decomposition charging station electricity auction markets generative adversarial networks (GANs) random forest stochastic programming

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Optimal Offering of Energy Storage in UK Day-Ahead Energy and Frequency Response Markets

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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 415-426页

作者： Makedon Karasavvidis Andreas Stratis Dimitrios Papadaskalopoulos Goran Strbac Department of Electrical and Electronic Engineering Imperial College LondonLondonUK Statkraft UK Ltd. LondonUK Department of Electrical and Computer Engineering University of PatrasPatrasGreece IEEE

The offering strategy of energy storage in energy and frequency response(FR) markets needs to account for country-specific market regulations around FR products as well as FR utilization factors, which are highly uncertain. To this end, a novel optimal offering model is proposed for stand-alone price-taking storage participants, which accounts for recent FR market design developments in the UK, namely the trade of FR products in time blocks, and the mutual exclusivity among the multiple FR products. The model consists of a day-ahead stage, devising optimal offers under uncertainty, and a real-time stage, representing the storage operation after uncertainty is materialized. Furthermore, a concrete methodological framework is developed for comparing different approaches around the anticipation of uncertain FR utilization factors(deterministic one based on expected values, deterministic one based on worst-case values, stochastic one, and robust one), by providing four alternative formulations for the real-time stage of the proposed offering model, and carrying out an out-of-sample validation of the four model instances. Finally, case studies employing real data from UK energy and FR markets compare these four instances against achieved profits, FR delivery violations, and computational scalability.

关键词： Energy markets energy storage frequency response optimal offering robust optimization stochastic programming

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A stochastic Sequential Service Restoration Model for Distribution Systems Considering Microgrid Interconnection

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IEEE TRANSACTIONS ON SMART GRID 2024年第3期15卷 2396-2409页

作者： Cai, Sheng Xie, Yunyun Zhang, Menglin Jin, Xiaolong Wu, Qiuwei Guo, Jian Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Tianjin Univ Key Lab Smart Grid Minist Educ Tianjin 30072 Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China

Sequential service restoration (SSR) is essential to restore critical loads after the distribution system outage through gradually forming microgrids (MGs). However, high penetration of renewables induces low-inertia issues, especially in small-scale MGs, which could impede a large-size load restoration for ensuring the MG dynamic stability. To expedite the restoration process, we propose the interconnection of MGs as part of SSR. A developed SSR model is proposed to generate restoration sequence and time of MG interconnection operation. An auxiliary variable is introduced to indicate the system topology before and after MG interconnection, and then the maximum permissive step load in each MG is quantified. By this means, isolated MGs are formed gradually, and adjacent MGs can be reconnected to improve the primary response when picking up a large load. Moreover, to address uncertainties of renewables, the stochastic approach is used. And an improved progressive hedging algorithm (PHA) is utilized to solve the stochastic model. The original model is decomposed by bundles of scenarios, and penalty factors are adaptively updated to accelerate the PHA convergence. Simulation results show the effectiveness of the proposed SSR method in improving the restoration efficiency of distribution systems and reducing computational burdens.

关键词： Load modeling Renewable energy sources Topology stochastic processes Uncertainty Switches Stability analysis Microgrid interconnection sequential service restoration renewables stochastic programming progressive hedging algorithm

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Two-Stage stochastic Optimization for Customer-Centric Aircraft Routing Considering stochastic Travel Times

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TRANSPORTATION RESEARCH RECORD 2025年第4期2679卷 509-523页

作者： Kwon, Yeongjun Lu, Bo Moon, Ilkyeong Seoul Natl Univ Dept Ind Engn Seoul South Korea Seoul Natl Univ Inst Ind Syst Innovat Seoul South Korea Dalian Univ Technol Sch Econ & Management Dalian Peoples R China

This study addresses the problem of creating single aircraft route within strict time constraints, taking into account the uncertainty of travel times. The approach involves a two-phase decision-making process: the initial phase focuses on selecting airports from a pool of candidates by optimizing for maximum customer satisfaction;the subsequent phase involves arranging the sequence of itinerary to these airports, factoring in variable travel times to enhance the overall experience. To tackle this complex issue, we have developed a two-stage stochastic optimization model that incorporates probability-based constraints. This model is then analyzed using the sample average approximation method. To validate the efficacy of our proposed model, we compared its performance with two established benchmark models. Additionally, case studies focused on the U.S. airline network suggest that operators can optimize airline route recommendations by enhancing both customer utility and their own profitability. This approach demonstrates that strategic adjustments in route planning can lead to mutual benefits, effectively balancing customer satisfaction with financial performance.

关键词： single aircraft routing stochastic programming sample average approximation case study

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Economic evaluation of a CHP biomass plant using stochastic dual dynamic programming

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ELECTRICAL ENGINEERING 2020年第4期102卷 2605-2615页

作者： Leocadio, Caio Monteiro Gomide Richa, Carlos Salvador Fortes, Marcio Zamboti Ferreira, Vitor Hugo Dias, Bruno Henriques Univ Fed Fluminense UFF Postgrad Program Elect Engn & Telecommun Passo da Patria St 156 BR-24210240 Niteroi RJ Brazil Univ Fed Juiz de Fora UFJF Elect Energy Dept Jose Lourenco Kelmer StUniv Campus BR-36036330 Juiz De Fora MG Brazil

Cogeneration systems have gained prominence in recent years driven by the simultaneous systematic production of heat and electricity, which are involved in industrial and commercial processes. Sugar and alcohol industry has become a reference in the use of cogeneration through the burning of sugarcane bagasse, with an important role in the future policies for generation expansion in Brazil. However, in the economic evaluation of this type of project, some previously ignored variables had a gain of relevance, such as the possibility of selling surplus energy produced by the plant. This paper presents a multiobjective stochastic optimization model using stochastic dynamic dual programming techniques, which seeks to maximize net revenue of a cogeneration project, indicating also the installed power adequate to the fuel availability and the needs of the plant, considering different energy scenarios in the spot market. The results indicate the best configuration for a cogeneration plant, considering the possibility of energy revenue, allowing an easy cost comparison of different alternatives for decision making from the investor's point of view.

关键词： Cogeneration stochastic programming SDDP Biomass Sugarcane

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Multi-Time-Scale Resource Allocation Based on Long-Term Contracts and Real-Time Rental Business Models for Shared Energy Storage Systems

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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 454-465页

作者： Yuxuan Zhuang Zhiyi Li Qipeng Tan Yongqi Li Minhui Wan College of Electrical Engineering Zhejiang UniversityHangzhouChina China Southern Power Grid Power Generation Company Energy Storage Research Institute GuangzhouChina IEEE

The push for renewable energy emphasizes the need for energy storage systems(ESSs)to mitigate the unpre-dictability and variability of these sources,yet challenges such as high investment costs,sporadic utilization,and demand mismatch hinder their broader *** response,shared energy storage systems(SESSs)offer a more cohesive and efficient use of ESS,providing more accessible and cost-effective energy storage solutions to overcome these *** enhance the profitability of SESSs,this paper designs a multi-time-scale resource allocation strategy based on long-term contracts and real-time rental business *** initially construct a life cycle cost model for SESS and introduce a method to estimate the degradation costs of multiple battery groups by cycling numbers and depth of discharge within the ***,we design various long-term contracts from both capacity and energy perspectives,establishing associated models and real-time rental ***,multi-time-scale resource allocation based on the decomposition of user demand is *** analysis validates that the business model based on long-term contracts excels over models operating solely in the real-time market in economic viability and user satisfaction,effectively reducing battery degradation,and leveraging the aggregation effect for SESS can generate an additional increase of 10.7%in net revenue.

关键词： Capacity allocation long-term contracts shared energy storage system stochastic programming

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Integrated energy scheduling under uncertainty for sustainable ports

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 197卷

作者： Gao, Yinping Yang, Linying Wang, Miaomiao Zhen, Lu Shanghai Maritime Univ Coll Transport & Commun Shanghai 201306 Peoples R China Shanghai Univ Sch Management Shanghai 200444 Peoples R China

Renewable energy generation has attracted increasing attention in port energy systems due to the urgent need for sustainable development. This study focuses on an integrated energy system that involves wind energy, photovoltaic energy, hydrogen energy and energy storage in the sustainable port. The multiple energy sources are used to generate electricity to support container loading and unloading in vessels. The realistic container loads are unknown to the port because of the uncertain arrival information, which affect the specific integrated energy scheduling. A twostage stochastic programming model is proposed to incorporate uncertain demand, multi-energy supply, electricity storage and sales. The vessel delay costs and the related energy costs that are generated from electricity consumption, storage and sales are minimized when allocating the integrated energy to serve berthing vessels. A metaheuristic algorithm based on the adaptive large neighborhood search (ALNS) framework is proposed for solving the model. The proposed metaheuristic algorithm fixes the decision variable values of the first-stage problem and allows transfers to solve sub-problems under all uncertain scenarios. The effectiveness of the proposed algorithm is demonstrated through small-scale, medium-scale, and large-scale numerical experiments in terms of solution quality and computation time. Some experiments are further conducted to analyze the impact of renewable energy generation, renewable energy sources, berthing vessel types, and vessel delay tolerances. Managerial insights can be obtained for optimizing the integrated energy scheduling schemes in sustainable ports. The findings can also provide implications for ports with different scales when optimizing the configurations of renewable energy supply.

关键词： Renewable energy Uncertain container loads stochastic programming Metaheuristic algorithm Sustainable development

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INEXACT CUTS IN stochastic DUAL DYNAMIC programming

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SIAM JOURNAL ON OPTIMIZATION 2020年第1期30卷 407-438页

作者： Guigues, Vincent FGV Appl Math BR-22250900 Rio De Janeiro Brazil

We introduce an extension of stochastic dual dynamic programming (SDDP) to solve stochastic convex dynamic programming equations. This extension applies when some or all primal and dual subproblems to be solved along the forward and backward passes of the method are solved with bounded errors (inexactly). This inexact variant of SDDP is described for both linear problems (the corresponding variant being denoted by ISDDP-LP) and nonlinear problems (the corresponding variant being denoted by ISDDP-NLP). We prove convergence theorems for ISDDP-LP and ISDDP-NLP for both bounded and asymptotically vanishing errors. Finally, we present the results of numerical experiments comparing SDDP and ISDDP-LP on a portfolio problem with direct transaction costs modeled as a multistage stochastic linear optimization problem. In these experiments, ISDDP-LP allows us to strike a different balance between policy quality and computing time, trading off the former for the latter.

关键词： stochastic programming inexact cuts for value functions bounding epsilon-optimal dual solutions SDDP inexact SDDP

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