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Joint chance-constrained Unit Commitment: Statistically Feasible Robust Optimization With Learning-to-Optimize Acceleration

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IEEE TRANSACTIONS ON POWER SYSTEMS 2024年第5期39卷 6508-6521页

作者： Liang, Jinhao Jiang, Wenqian Lu, Chenbei Wu, Chenye Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Peoples R China Tsinghua Univ Inst Interdisciplinary Informat Sci Beijing 100084 Peoples R China

Renewable energy penetration increases the power grid's operational uncertainty, threatening the economic effectiveness and reliability of the grid. In this article, we examine how uncertainty affects unit commitment (UC), a classical electricity market procedure. Stochastic programming has helped handle uncertainty for UC and performed well with distribution knowledge, but the lack of such information in practice deteriorates the effectiveness. Such a dilemma becomes more pronounced when dealing with joint chance constraints solely based on samples. To address this issue, we introduce statistical feasibility into UC and develop robust sample-based algorithms employing appropriate uncertainty sets to hedge uncertainty without distribution dependence. We also propose a learn-to-optimize acceleration method to convexify UC. Furthermore, we construct an optimization kernel to boost computational efficiency.

关键词： Uncertainty Optimization Renewable energy sources Generators Stochastic processes Kernel Computational efficiency chance-constrained programming robust optimization statistical feasibility unit commitment

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Transient Stability Preventive Control of Wind Farm Connected Power System Considering the Uncertainty

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Energy Engineering 2024年第6期121卷 1637-1656页

作者： Yuping Bian Xiu Wan Xiaoyu Zhou School of Electric Power Engineering Nanjing Institute of TechnologyNanjing211167China

To address uncertainty as well as transient stability constraints simultaneously in the preventive control of windfarm systems, a novel three-stage optimization strategy is established in this paper. In the first stage, the probabilisticmulti-objective particle swarm optimization based on the point estimate method is employed to cope with thestochastic factors. The transient security region of the system is accurately ensured by the interior point methodin the second stage. Finally, the verification of the final optimal objectives and satisfied constraints are enforcedin the last stage. Furthermore, the proposed strategy is a general framework that can combine other optimizationalgorithms. The proposed methodology is tested on the modified WSCC 9-bus system and the New England 39-bussystem. The results verify the feasibility of the method.

关键词： Transient preventive control chance-constrained programming multi-objective PSO TSCOPF wind farm

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Percentile optimization in multi-armed bandit problems

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ANNALS OF OPERATIONS RESEARCH 2024年第2-3期340卷 837-862页

作者： Ghatrani, Zahra Ghate, Archis Univ Washington Ind & Syst Engn Seattle WA 98195 USA

A multi-armed bandit (MAB) problem is described as follows. At each time-step, a decision-maker selects one arm from a finite set. A reward is earned from this arm and the state of that arm evolves stochastically. The goal is to determine an arm-pulling policy that maximizes expected total discounted reward over an infinite horizon. We study MAB problems where the rewards are multivariate Gaussian, to account for data-driven estimation errors. We employ a percentile optimization approach, wherein the goal is to find an arm-pulling policy that maximizes the sum of percentiles of expected total discounted rewards earned from individual arms. The idea is motivated by recent work on percentile optimization in Markov decision processes. We demonstrate that, when applied to MABs, this yields an intractable second-order cone program (SOCP) whose size is exponential in the number of arms. We use Lagrangian relaxation to break the resulting curse-of-dimensionality. Specifically, we show that the relaxed problem can be reformulated as an SOCP with size linear in the number of arms. We propose three approaches to recover feasible arm-pulling decisions during run-time from an off-line optimal solution of this SOCP. Our numerical experiments suggest that one of these three method appears to be more effective than the other two.

关键词： Dynamic programming Lagrangian relaxation chance-constrained programming

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An Uncertain Optimal Energy Flow Model for CCHP Campus Microgrid Using Parameterized Probability Boxes

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Journal of Modern Power Systems and Clean Energy 2023年第5期11卷 1618-1633页

作者： Shunjiang Lin Xuan Sheng Yuquan Xie Yanghua Liu Mingbo Liu IEEE School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China Digital Grid Research Institute China Southern Power GridGuangzhou 510700China School of Electromechanical Engineering Guangdong Polytechnic Normal UniversityGuangzhouChina

Due to the uncertain fluctuations of renewable energy and load power, the state variables such as bus voltages and pipeline mass flows in the combined cooling, heating, and power campus microgrid(CCHP-CMG) may exceed the secure operation limits. In this paper, an optimal energy flow(OEF) model for a CCHP-CMG using parameterized probability boxes(p-boxes) is proposed to describe the higher-order uncertainty of renewables and loads. In the model, chance constraints are used to describe the secure operation limits of the state variable p-boxes, and variance constraints are introduced to reduce their random fluctuation ranges. To solve this model, the chance and variance constraints are transformed into the constraints of interval cumulants(ICs) of state variables based on the p-efficient point theory and interval Cornish-Fisher expansion. With the relationship between the ICs of state variables and node power, and using the affine interval arithmetic method, the original optimization model is finally transformed into a deterministic nonlinear programming model. It can be solved by the CONOPT solver in GAMS software to obtain the optimal operation point of a CCHP-CMG that satisfies the secure operation requirements considering the higher-order uncertainty of renewables and loads. Case study on a CCHP-CMG demonstrates the correctness and effectiveness of the proposed OEF model.

关键词： Combined cooling heating and power campus microgrid(CCHP-CMG) chance-constrained programming higher-order uncertainty optimal energy flow parameterized probability box

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Allocating Cost of Uncertainties From Renewable Generation in Stochastic Electricity Market: General Mechanism and Analytical Solution

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IEEE TRANSACTIONS ON POWER SYSTEMS 2024年第2期39卷 4224-4239页

作者： Yang, Yue Wu, Wenchuan Xu, Shuwei Lin, Chenhui Wang, Bin Hefei Univ Technol Anhui Prov Lab Renewable Energy Utilizat & Energy Hefei 230009 Peoples R China Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing 100084 Peoples R China

Uncertainties of the volatile renewable generations require more flexible reserve capacity and incur additional operational cost named as "cost of uncertainties". The cost of uncertainties should be charged among responsible participants in electricity market appropriately. This article proposes a general mechanism to allocate the cost of uncertainties among multiple renewable generation sources in a chance-constrained electricity market framework. The proposed mechanism ensures coherency and fairness of allocation rigorously based on cost allocation theory and is applicable to arbitrary probability model of uncertainties. Furthermore, we develop an analytical algorithm to implement the cost allocation mechanism efficiently by leveraging different probability models of renewable generations. Numerical tests demonstrate that the proposed cost allocation mechanism formulates effective price signals to penalize renewable generation sources with more variability and conforms to cost causation principle.

关键词： Renewable generation chance-constrained programming power market cost allocation

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Leveraging Transformer-Based Non-Parametric Probabilistic Prediction Model for Distributed Energy Storage System Dispatch

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PROCESSES 2024年第4期12卷 779页

作者： Chen, Xinyi Ge, Yufan Zhang, Yuanshi Qian, Tao Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Southeast Univ Jiangsu Prov Key Lab Smart Grid Technol & Equipmen Nanjing 210096 Peoples R China

In low-voltage distribution networks, distributed energy storage systems (DESSs) are widely used to manage load uncertainty and voltage stability. Accurate modeling and estimation of voltage fluctuations are crucial to informed DESS dispatch decisions. However, existing parametric probabilistic approaches have limitations in handling complex uncertainties, since they always rely on predefined distributions and complex inference processes. To address this, we integrate the patch time series Transformer model with the non-parametric Huberized composite quantile regression method to reliably predict voltage fluctuation without distribution assumptions. Comparative simulations on the IEEE 33-bus distribution network show that the proposed model reduces the DESS dispatch cost by 6.23% compared to state-of-the-art parametric models.

关键词： low-voltage distribution networks distributed energy storage system chance-constrained programming PatchTST composite quantile regression non-parametric probabilistic prediction

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Best-case-aware planning of photovoltaic-battery systems for multi-mode charging stations

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RENEWABLE ENERGY 2024年 225卷

作者： Tostado-Veliz, Marcos Jordehi, Ahmad Rezaee Zhou, Yuekuan Mansouri, Seyed Amir Jurado, Francisco Univ Jaen Dept Elect Engn Linares 23700 Spain Islamic Azad Univ Dept Elect Engn Rasht Branch Rasht Iran HKUST Shenzhen Hong Kong Collaborat Innovat Res In Shenzhen 518048 Peoples R China Hong Kong Univ Sci & Technol Guangzhou Sustainable Energy & Environm Thrust Funct Hub Guangzhou 511400 Guangdong Peoples R China Hong Kong Univ Sci & Technol Dept Mech & Aerosp Engn Clear Water Bay Hong Kong Peoples R China Hong Kong Univ Sci & Technol Div Emerging Interdisciplinary Areas Clear Water Bay Hong Kong Peoples R China Comillas Pontif Univ Inst Res Technol IIT ICAI Sch Engn Madrid 28015 Spain

The proliferation of charging stations entails multiple challenges for power systems. In this regard, the installation of photovoltaic -battery systems may help to mitigate the negative effects of charging points. However, such assets should be carefully planned, paying attention to economic aspects, principally. Most of existing works optimize the photovoltaic -battery system in charging infrastructures taking a representative -space of the involved variables (e.g. photovoltaic potential, charging demand or energy prices). However, this approach tends to ignore low -probable scenarios. Thus, the best -case scenario for charging demand (i.e. that for which the highest charging profit is accessible) may not be included in the analysis and therefore such demand could be not attended properly, thus losing this monetary opportunity. This paper focuses on this issue and questions if considering the best -case scenario into planning photovoltaic -battery systems for charging stations is worthwhile or not. To this end, a novel best -case -aware planning tool is developed, including the best -case scenario through a novel chance -constrained formulation. The overall problem is then decomposed into a master -slave structure by which the economy of the system is optimized together with the number of scenarios for which the best -case profile can be attended. A case study serves to validate the developed tool and shed light on the questions arisen in this work. In particular, it is checked that considering the best -case scenario into planning tools is questionable from a monetary point of view. Nevertheless, its inclusion unlocks some collateral advantages such as incrementing the users ' satisfaction or reducing the grid -dependency.

关键词： Battery energy storage chance-constrained programming Electric vehicle Photovoltaic energy Renewable energy

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An integrated multi-objective multi-product inventory managed production planning problem under uncertain environment

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ANNALS OF OPERATIONS RESEARCH 2024年第3期339卷 1679-1723页

作者： Modibbo, Umar Muhammad Gupta, Srikant Ahmed, Aquil Ali, Irfan Aligarh Muslim Univ Dept Stat & Operat Res Aligarh 202002 Uttar Pradesh India Jaipuria Inst Management Dept Decis Sci Jaipur Rajasthan India Modibbo Adama Univ Dept Stat & Operat Res PMB 2076 Yola Nigeria

Integrating inventory with production-distribution operations has critical importance realized by several companies' top management a long time before;however, challenging. The management has to decide where to shift products, which demand to fulfil, from which distribution centre (s), how much inventory to keep and a host of critical and complex decisions. As the organization increase in size and complexity, more robust optimization tools required to help analyze and comprehend these complex decisions. In this study, inventory, production and distribution problems with planned shortages, back-ordered quantity and delay in replenishment studied under uncertainty. A multi-objective multi-product model for minimizing the inventory, production and shipment costs and delivery time developed for the integrated system. The study considers the model's different input parameters under a fuzzy and stochastic environment. The fuzziness converted to deterministic using interval type-2 trapezoidal fuzzy number. The randomness follows the Maxwell-Boltzmann distribution in which the deterministic form obtained using chance-constrained programming. The developed integrated model illustrated a numerical example in a manufacturing firm and solved using three robust goal programming variants. The results indicated inventory, production and transshipment costs are 99% minimized with 100% delivery time achievement.

关键词： Inventory control Production-distribution Probabilistic distribution chance-constrained programming Multi-objective robust optimization Trapezoidal fuzzy number

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An optimization framework for scheduling multi-period multi-product oil pipeline systems under belief degree-based uncertain parameters

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JOURNAL OF APPLIED MATHEMATICS AND COMPUTING 2023年第1期69卷 37-68页

作者： Goudarzi, F. Khalili Maleki, H. R. Niroomand, S. Shiraz Univ Technol Dept Math Shiraz Fars Iran Firouzabad Inst Higher Educ Dept Ind Engn Firouzabad Fars Iran

Considering that one of the most significant challenges for oil industry planners is developing an effective schedule plan that reduces costs, increases profitability, and enhances customer satisfaction, this article improves a formulation for scheduling multi-product pipelines. The primary goal is to minimize operational expenses, which include inventory and shortages, interfaces, and labor. Consideration is given to batch sizing limits, inventory carrying costs, backlog, and settlement periods. Additionally, certain parameters have been classified as having the belief degree-based type of uncertainty due to a lack of relevant historical data. As solution strategies, three separate conversion approaches are used. The model's efficiency and dependability, as well as the proposed methodologies, are demonstrated numerically. Sensitivity analysis shows that objective function values are highly dependent on changes in confidence levels, so the best technique is implemented.

关键词： Belief degree-based uncertainty chance-constrained programming Multi-product oil pipeline scheduling Zigzag probability distribution

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A shared-mobility-based framework for evacuation planning and operations under forecast uncertainty

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IISE TRANSACTIONS 2023年第10期55卷 971-984页

作者： Moug, Kati Jia, Huiwen Shen, Siqian Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA

To meet evacuation needs from carless populations who need personalized assistance to evacuate safely, in this article we propose a ridesharing-based evacuation program that recruits volunteer drivers before a disaster strikes, and then matches volunteer drivers with evacuees once demand is realized. We optimize resource planning and evacuation operations under uncertain spatiotemporal demand, and construct a two-stage stochastic mixed-integer program to ensure high demand fulfillment rates. We consider three formulations to improve the number of evacuees served, by minimizing an expected penalty cost, imposing a probabilistic constraint, and enforcing a constraint on the conditional value at risk of the total number of unserved evacuees, respectively. We discuss the benefits and disadvantages of the different risk measures used in the three formulations, given certain carless population sizes and the variety of evacuation modes available. We also develop a heuristic approach to provide quick, dynamic and conservative solutions. We demonstrate the performance of our approaches using five different networks of varying sizes based on regions of Charleston County, South Carolina, an area that experienced a mandatory evacuation order during Hurricane Florence, and utilize real demographic data and hourly traffic count data to estimate the demand distribution.

关键词： Stochastic programming evacuation planning ridesharing chance-constrained programming conditional value at risk (CVaR)

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