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Optimization of Local Voltage Control With Coordinating Droop Functions Under High PV Penetration

IEEE TRANSACTIONS ON POWER SYSTEMS 2024年第5期39卷 6776-6779页

作者： Zhang, Cuo Xu, Ruipeng Yang, Linfeng Univ Sydney Sch Elect & Comp Engn Sydney NSW 2006 Australia Univ New South Wales Sch Elect Engn & Telecommun Kensington NSW 2052 Australia Guangxi Univ Guangxi Key Lab Multimedia Commun Network Technol Sch Comp & Elect Informat Nanning 530004 Peoples R China

PV power generation has significantly penetrated distribution networks, and inverter based local voltage control has been applied in practice. However, volt-var and volt-watt droop control functions are not optimized or coordinated. This letter proposes an effective optimization model for inverter based local voltage control, systematically optimizing both volt-var and volt-watt droop functions under uncertainties and adopts a new solution method. The simulation results show high efficiency of the optimized droop control functions in minimizing power losses and PV power curtailment and addressing the overvoltage issue.

关键词： Voltage control Inverters Real-time systems stochastic processes Uncertainty programming Reactive power Distribution networks local voltage control mixed integer programming stochastic programming

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Enhancing Automatic Generation Control Performance Under Uncertainty and Congestion Through Detailed Transmission Modeling and Strategic Selection of Units

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IEEE TRANSACTIONS ON POWER SYSTEMS 2025年第3期40卷 2389-2400页

作者： Hinojosa, Victor H. Bolbaran, Daniel Gil, Esteban Gutierrez-Alcaraz, Guillermo Univ Tecn Feder Santa Maria Dept Elect Engn Valparaiso 23901223 Chile Univ Tecn Federico Santa Maria Dept Elect Engn Valparaiso Chile Univ Tecn Federico Santa Maria Adv Ctr Elect & Elect Engn Valparaiso Chile Tecnol Nacl Mexico Inst Tecnol Morelia ITM Programa Grad Invest Ingn Electr PGIIE Morelia 58120 Mich Mexico

Considering the automatic generation control (AGC) system as an intermediary between the economic dispatch problem and synchronous generator dynamics, this study introduces a novel formulation for the AGC system. The proposed scheduling framework is based on a stochastic $N-1$ lossy network-constrained economic dispatch problem formulated as a mixed-integer linear programming instance. Transmission losses are represented through piecewise linear expressions. After the primary frequency control finishes, the proposed scheduling methodology selects the generation units that will be activated and determines their regulation participation factors to minimize the activation and operational costs in a two-stage stochastic problem, incorporating the load-voltage dependency phenomenon, and modeling transmission power flow and power losses using linear lossy shift-factors. The proposed formulation also considers the distinctive possibility of AGC units to both increase and decrease power for addressing under-frequency events economically, offering an effective generation capability that co-optimizes energy and reserves, providing a more flexible and efficient control strategy compared to traditional AGC systems. The effectiveness of the proposed methodology is demonstrated in a 50-bus electrical system, evaluating its performance with diverse operational conditions and contingency generation events using DIgSILENT PowerFactory. Extensive computational RMS experiments are conducted to assess the frequency stability in the electrical power system.

关键词： Automatic generation control Biological system modeling Propagation losses Power system dynamics Frequency control Load flow Power system stability Load modeling Uncertainty stochastic processes DC lossy formulation DIgSILENT PowerFactory scheduling methodology shift-factors stochastic programming transmission constraints

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Bounds for probabilistic programming with application to a blend planning problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第3期297卷 964-976页

作者： Peng, Shen Maggioni, Francesca Lisser, Abdel KTH Royal Inst Technol Dept Math Optimizat & Syst Theory Lindstedtsvagen 25 SE-10044 Stockholm Sweden Univ Bergamo Dept Management Informat & Prod Engn Via Marconi 5 I-24127 Dalmine Bg Italy Univ Paris Saclay L2S Cent Supelec Bat Breguet3 Rue Joliot Curie F-91190 Gif Sur Yvette France

In this paper, we derive deterministic inner approximations for single and joint independent or dependent probabilistic constraints based on classical inequalities from probability theory such as the onesided Chebyshev inequality, Bernstein inequality, Chernoff inequality and Hoeffding inequality (see Pinter, 1989). The dependent case has been modelled via copulas. New assumptions under which the bounds based approximations are convex allowing to solve the problem efficiently are derived. When the convexity condition can not hold, an efficient sequential convex approximation approach is further proposed to solve the approximated problem. Piecewise linear and tangent approximations are also provided for Chernoff and Hoeffding inequalities allowing to reduce the computational complexity of the associated optimization problem. Extensive numerical results on a blend planning problem under uncertainty are finally provided allowing to compare the proposed bounds with the Second Order Cone (SOCP) formulation and Sample Average Approximation (SAA). (c) 2021 Elsevier B.V. All rights reserved.

关键词： stochastic programming Joint chance-constraints Bounds Copulas Blending problem

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Optimal sizing and allocation of PV-DG and DSTATCOM in the distribution network with uncertainty in consumption and generation

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DISCOVER APPLIED SCIENCES 2025年第5期7卷 1-25页

作者： Ebrahimi, Ahad Moradlou, Majid Bigdeli, Mehdi Mashhadi, Mostafa Rajabi Islamic Azad Univ Dept Elect Engn Zanjan Branch Zanjan Iran Sadjad Univ Technol Dept Elect Engn Mashhad Iran

The use of distributed generation resources and Flexible AC transmission (FACT) devices to improve technical constraints and reduce dependence on the upstream network eliminates the need to build new power plants. Maximizing line capacity utilization is a priority for the electricity industry. The advantages of using FACT devices include increasing line throughput and preventing line and bus congestion, improving bus voltage profiles, reducing line losses, preventing sub synchronous resonance, and so on. This study examined optimal sizing and allocation of photovoltaic distributed generation (PV-DG) and DSTATCOM. To solve the optimization problem, teaching-learning-based optimization (TLBO) was employed. The algorithm was run in the IEEE 33-bus standard test system. Because of the random nature of the consumption load and the random production nature of renewable DG units, the uncertainty of consumption and production was examined through stochastic programming methods. Moreover, to choose the scenario with the highest probability, the Monte Carlo method was employed. The scenarios included load certainty-PV generation uncertainty, load-PV generation uncertainty, and load-PV generation certainty. Injecting reactive and active power of PV-DG and DSTATCOM improved voltage stability index (VSI) to 0.9745 p.u (36.8%) and reduced the amount of power loss to 10.95 kW (94.8%). Furthermore, comparing TLBO's results with other algorithms verified its accuracy and minimum solution time.

关键词： DSTATCOM DG Uncertainty TLBO Monte Carlo method stochastic programming

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Simultaneous stochastic optimisation of mining complexes: Integrating progressive reclamation and waste management with contextual bandits

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INTERNATIONAL JOURNAL OF MINING RECLAMATION AND ENVIRONMENT 2024年第9期38卷 701-721页

作者： Levinson, Zachary Dimitrakopoulos, Roussos McGill Univ Dept Min & Mat Engn COSMO Stochast Mine Planning Lab FDA Bldg 3450 Univ St Montreal PQ H3A 0E8 Canada

Managing environmental performance of waste dump facilities in mining complexes is an integral part of long-term production planning. Sustainable long-term production scheduling solutions are desired to mitigate risk and return the environment to a productive post-mining state. A simultaneous stochastic optimisation framework for long-term production scheduling in mining complexes is developed that integrates waste management and progressive reclamation. The waste dump placement schedule is jointly optimised with the extraction sequence, destination policy, and stockpiling decisions in a single stochastic mathematical programming framework. This includes the timing of progressive reclamation activities in parallel with production to enhance waste dump rehabilitation. Uncertainty related to the production of acid rock drainage is quantified by simulating geochemical properties of waste and managing the blending of uncertain waste properties within the optimisation framework. With respect to the framework for simultaneous stochastic optimisation, contextual bandits are explored to improve the metaheuristic solution approach and solve the corresponding large-scale optimisation model. The framework is tested in a multi-mine copper-gold mining complex leading to improved environmental performance. Risk of acid rock drainage is decreased by 52.5% in the waste dump facilities. Reclamation planning activities for meeting environmental requirements are scheduled prior to closure. The solution approach more effectively improves the objective function with contextual bandits leading to a 24% improvement in the study presented.

关键词： Waste management reclamation reinforcement learning stochastic programming contextual bandits long-term mine planning

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On conditional cuts for stochastic dual dynamic programming

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EURO JOURNAL ON COMPUTATIONAL OPTIMIZATION 2020年第2期8卷 173-199页

作者： van Ackooij, W. Warin, X. EDF R&D OSIRIS 7 Blvd Gaspard Monge F-91120 Palaiseau France

Multistage stochastic programs arise in many applications from engineering whenever a set of inventories or stocks has to be valued. Such is the case in seasonal storage valuation of a set of cascaded reservoir chains in hydro management. A popular method is stochastic dual dynamic programming (SDDP), especially when the dimensionality of the problem is large and dynamic programming is no longer an option. The usual assumption of SDDP is that uncertainty is stage-wise independent, which is highly restrictive from a practical viewpoint. When possible, the usual remedy is to increase the state-space to account for some degree of dependency. In applications, this may not be possible or it may increase the state-space by too much. In this paper, we present an alternative based on keeping a functional dependency in the SDDP-cuts related to the conditional expectations in the dynamic programming equations. Our method is based on popular methodology in mathematical finance, where it has progressively replaced scenario trees due to superior numerical performance. We demonstrate the interest of combining this way of handling dependency in uncertainty and SDDP on a set of numerical examples. Our method is readily available in the open-source software package StOpt.

关键词： stochastic programming SDDP algorithm Nonsmooth optimization Conditional expectations

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Subproblem sampling vs. scenario reduction: efficacy comparison for stochastic programs in power systems applications

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ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2025年第1期16卷 111-139页

作者： Sakhavand, Nahal Gangammanavar, Harsha Univ Texas Arlington Dept Ind Mfg & Syst Engn Arlington TX 76010 USA Southern Methodist Univ Dept Operat Res & Engn Management Dallas TX 75275 USA

In this paper, we present a two-stage stochastic programming and simulation-based framework for tackling large-scale planning and operational problems that arise in power systems with significant renewable generation. Traditional algorithms (the L-shaped method, for example) used to solve the sample average approximation of the true problem suffer from computational difficulties when the number of scenarios or the size of the subproblem increases. To address this, we develop a cutting plane method that uses sampling internally within optimization to select only a random subset of subproblems to solve in any iteration. We analyze the convergence property of the subproblem sampling-based method and demonstrate its computational advantages on two alternative formulations of the stochastic unit commitment-economic dispatch problem. We conduct the numerical experiments on modified IEEE-30 and IEEE-118 test systems. We also present detailed steps for assessing the quality of solutions obtained from sampling-based stochastic programming methods and determining a solution to prescribe to the system operators.

关键词： stochastic programming Unit commitment Economic dispatch Sampling-based cutting-plane methods Scenario reduction

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Enhancing disaster resilience: optimizing debris management for end-of-life buildings amidst uncertainty

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2025年第4期42卷 406-421页

作者： Nayeem, Moddassir Khan Abbaas, Omar Univ Texas San Antonio Dept Mech Aerosp & Ind Engn 1 UTSA CircleEB 3-04-54 San Antonio TX 78249 USA

This study develops a stochastic Mixed Integer Linear programming (SMILP) model to optimize the reverse logistics network for debris generated from proactive demolition of end-of-life buildings. Unlike most existing research, this study addresses proactive strategies for the Mitigation stage of disaster management. Our model identifies the optimal number of landfilling areas and sorting facilities, factoring in uncertainties in debris quantity and material quality. It incorporates environmental constraints, such as mandatory sorting processes and recycling thresholds. Multiple scenarios are considered, each with unique parameter values and occurrence probabilities, with the overall objective of minimizing net costs across all scenarios. A realistic case study is used to illustrate the model, demonstrating its capacity to reduce post-disaster recovery costs, improve operational efficiency, and balance financial and environmental considerations. This study offers insights for decision-makers, advocating proactive end-of-life buildings' management as a disaster preparedness and sustainable practices.

关键词： Disaster management end-of-life building stochastic programming recycling reverse logistics

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Optimal Risk Management in Grid-Connected Photovoltaic System Design Under Environmental and Economic Changes: A Case Study in Saudi Arabia's Climate

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PROCESS INTEGRATION AND OPTIMIZATION FOR SUSTAINABILITY 2025年第2期9卷 553-574页

作者： Attia, Ahmed M. Darghouth, Mohamed N. Mohammed, Awsan Abdel-Aal, Mohammed A. M. King Fahd Univ Petr & Minerals KFUPM Dept Ind & Syst Engn Dhahran 31261 Saudi Arabia Laval Univ Dept Mech Engn Quebec City PQ G1V0A6 Canada King Fahd Univ Petr & Minerals KFUPM Dept Construct Engn & Management Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Smart Mobil & Logist IRC Dhahran 31261 Saudi Arabia Interuniv Res Ctr Enterprise Networks Logist & Tra Montreal PQ Canada

Energy plays a pivotal role in addressing the climate crisis, with fossil fuel combustion being a significant source of greenhouse gas emissions. To combat this, industrial nations are urged to transition to renewable energy sources like solar photovoltaic systems. However, optimizing photovoltaic systems requires consideration of technical, economic, and environmental factors. In this study, we devised a two-phase stochastic programming method for optimizing a photovoltaic system connected to a power grid. Our focus is on incorporating economic and environmental uncertainties into risk scenarios. The aim is to ascertain the optimal number of photovoltaic array installations, accounting for importing energy from the grid and exporting surplus energy to external utilities. Environmental factors and market change parameters are dynamically determined during decision-making, while model parameters are deterministic. The initial optimization stage determines the necessary number of photovoltaic modules. Given the scenario-driven nature of photovoltaic module selection, energy allocation to clients, external utilities, and grid interactions vary accordingly. Our proposed approach employs risk metrics to address environmental, economic, and reliability considerations in decision-making. A case study for sizing a photovoltaic system to power a housing area at King Fahd University of Petroleum and Minerals demonstrates the effectiveness of our approach. Sensitivity analysis highlights key trade-offs, showing the proposed system can break even at $1.79 million per year, generate 388.53 megawatt-hours of electricity annually, and reduce carbon dioxide emissions by 50.52 kg per year. Our study underscores the importance of integrating renewable energy solutions into the global energy mix to mitigate climate change impacts.

关键词： PV system optimization Environmental and economic uncertainties Risk management stochastic programming Scenario-based optimization

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A risk analytics model for strategic workforce planning: readiness of enlisted military personnel

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ANNALS OF OPERATIONS RESEARCH 2024年第1期338卷 513-533页

作者： MacDonald, Leo Paul, Jomon Aliyas Kennesaw State Univ Dept Econ Finance & Quantitat Anal 560 Parliament Garden Way Kennesaw GA 30144 USA

We develop a dynamic stochastic model of military workforce planning that incorporates uncertainties about personnel gains and losses across ranks. We then apply it to determine the probability of not meeting required targets as well as the resulting shortages and overages in the short, medium, and long terms along with the evaluation of policies to mitigate these risks. Our model allows decision makers to adjust recruiting and training practices to minimize the risk of not meeting target personnel levels as well as to value retention and reenlistment policies by calculating the expected marginal value of retaining additional service members. Moreover, it allows us to create a penalty function to optimize recruiting and training levels. The outcome is a tool to evaluate and ensure comprehensive force readiness.

关键词： stochastic programming Workforce planning Military Risk analysis

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