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Reduced-Order Decomposition and Coordination Approach for Markov-Based stochastic unit commitment With Distributed Wind Farms

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IEEE ACCESS 2025年 13卷 65403-65419页

作者： Raghunathan, Niranjan Wang, Zongjie Bragin, Mikhail A. Luh, Peter B. Yan, Bing Zhao, Tianqiao Yue, Meng Univ Connecticut Dept Elect & Comp Engn Storrs CT 06268 USA Southern Calif Edison Energy Mkt Anal Advisor Rosemead CA 91770 USA Rochester Inst Technol Dept Elect & Microelect Engn Rochester NY 14623 USA Brookhaven Natl Lab Interdisciplinary Sci Dept Upton NY 11973 USA

States in the US increasingly integrate renewable energies to facilitate the transition to carbon neutrality. However, there are still many operational challenges to their efficient integration. This paper presents an innovative approach to solve the day-ahead Markov-based stochastic unit commitment (MSUC) problem with distributed wind generation. The MSUC utilizes a Markovian uncertainty set to represent the uncertainties in wind and other distributed intermittent renewable energies. While the Markovian uncertainty set intricately captures the stochasticity via states and state transitions, the set grows exponentially under the multi-dimensional uncertainties of distributed intermittent resources, thus rendering the MSUC intractable for systems with five or more such resources. In order to reduce the MSUC's complexity, the problem is decomposed into approximate area problems (AAP), where each AAP utilizes a reduced, area-centric uncertainty set created using principal component analysis. Furthermore, the AAPs consider the binary commitment variables of only the local units, thus rendering much reduced complexity than the original MSUC. The AAPs are iteratively solved, and their solutions are coordinated in a novel Surrogate Absolute-Value Lagrangian Relaxation-based framework. Numerical testing is performed on test cases based on the IEEE 118-bus system with up to ten distributed wind farms, and solutions are validated via Monte Carlo simulations with typical and extreme wind generation scenarios. The simulation results demonstrate the ability of our method to obtain robust and cost-effective solutions to the MSUC in a constrained time frame.

关键词： Generators Renewable energy sources Uncertainty Wind farms stochastic processes Optimization Costs Principal component analysis Complexity theory Wind Auxiliary problem principle decomposition and coordination distributed wind Lagrange relaxation Markov processes Markov-based stochastic unit commitment PCA multi-dimensional stochastic models SAVLR stochastic optimization stochastic unit commitment

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Sizing of energy storage for spinning reserve and efficiency increase in isolated power systems within a data-driven stochastic unit commitment framework

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JOURNAL OF ENERGY STORAGE 2025年 111卷

作者： Adeyemo, Ayotunde A. Marra, Francesco Tedeschi, Elisabetta Norwegian Univ Sci & Technol NTNU Dept Elect Energy N-7034 Trondheim Norway Equinor Res Ctr Arkitekt Ebbells Veg 10 N-7053 Ranheim Norway Univ Trento Dept Ind Engn Via Sommar 9 I-38123 Trento Italy

This paper proposes a data-driven stochastic unit commitment (SUC) framework for sizing battery energy storage system (BESS) for spinning reserve and efficiency increase in isolated power system. BESS is used to provide spinning reserve to manage the uncertainties in the power system. Additionally, load-shifting demand response (DR) is incorporated in the data-driven SUC model to reduce the size of the BESS required. Also, battery degradation is modelled in the data-driven SUC formulation to make the results as accurate as possible. Furthermore, to reduce the computational burden a novel scenario reduction method based on the Kantorovich distance metric is proposed for selecting scenarios (from the high number of generated scenarios for load and renewable sources) that describe the problem with limited loss of information. The case study is based on an offshore oil and gas platform in the North Sea which has four identical gas turbine generators and is assumed to integrate offshore wind turbines and an offshore solar farm, thus having three sources of uncertainty (load, wind and solar). Five test cases were analyzed: a baseline case without BESS and flexible load (DR), and other cases including degrading BESS and flexible load, either separately or jointly. First, the selected scenarios were analyzed and seen to retain most of the information in the original historical dataset, which validates the accuracy of the proposed scenario reduction method. The results showed that the methodology proposed in this work can be used to get an accurate optimal BESS capacity and that the inclusion of load-shifting DR can help reduce the optimal BESS capacity. Cases where an optimally sized BESS was considered provided cost reductions in the range 2.54 % to 3.68 % and carbon emissions reductions in the range 3.88 % to 4.5 % compared to the baseline case. Finally, the result also showed that battery degradation modelling is important as the cost reduction increased from 2.54 % to 3

关键词： stochastic unit commitment Mixed integer linear programming Lithium Iron Phosphate Battery Battery energy storage system Scenario reduction Multiple uncertainties

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stochastic unit commitment With Topology Control Recourse for Power Systems With Large-Scale Renewable Integration

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IEEE TRANSACTIONS ON POWER SYSTEMS 2018年第3期33卷 3315-3324页

作者： Shi, Jiaying Oren, Shmuel S. Univ Calif Berkeley Dept Ind Engn & Operat Res Berkeley CA 94720 USA Univ Calif Berkeley Tsinghua Berkeley Shenzhen Inst Berkeley CA 94720 USA

In this paper, we model topology control through transmission switching as a recourse action in the day-ahead operation of power systems with large-scale renewable generation resources. We prove that transmission switching could only reduce the linear objective value of direct current optimal power flow when congestion exists. However, we also show, through a simple example, that unit commitment cost could be reduced by transmission switching even in the absence of congestion. To solve the stochastic unit commitment with topology control within reasonable computational time, we proposed a heuristic that first decomposes a practical system into zones and then solves the problem for each zone in parallel. The benefit of topology control recourse is demonstrated on a network representing the Central European System. We compare the costs of the network with different loading and renewable generation conditions. The cost reduction of the test system can reach 3.34% with heavy load and large-scale renewable generation while in a single zone the cost reduction can be above 7%.

关键词： Renewable generation integration stochastic unit commitment topology control transmission switching

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stochastic unit commitment Incorporating Demand Side Management and Optimal Storage Capacity

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2019年第1期43卷 559-571页

作者： Hamdy, Mohamed Elshahed, Mostafa Khalil, Doaa El-zahab, Essam El-din Abo Cairo Univ Fac Engn Elect Power Dept Giza 12613 Egypt

High penetration of wind energy imposes several operational challenges due to its uncertainty and intermittent nature. Flexible energy resources represent key solutions to compensate for power mismatch associated with wind power (WP) uncertainty and intermittency. This paper proposes a new stochastic unit commitment (SUC) problem formulation including high penetration of wind energy, energy storage system (ESS), and demand side management. Firstly, the Latin hypercube sampling is combined with Cholesky decomposition method to generate different WP scenarios. The simulated scenarios are then reduced using the fast forward selection algorithm. Finally, a novel SUC formulation implements these reduced scenarios to size the ESS optimally, considering its cost and benefit maximization of wind energy. To validate the proposed approach, a nine-unit test system is used to demonstrate the reduction in the operational cost and the increase in the utilized wind energy under different operational conditions.

关键词： Demand side management Energy storage system Latin hypercube sampling stochastic unit commitment

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stochastic unit commitment Performance Considering Monte Carlo Wind Power Scenarios

Stochastic Unit Commitment Performance Considering Monte Car...

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IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Rachunok, Benjamin Staid, Andrea Watson, Jean-Paul Woodruff, David L. Yang, Dominic Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA Sandia Natl Labs Discrete Math & Optimizat POB 5800 Albuquerque NM 87185 USA Univ Calif Davis Grad Sch Management Davis CA 95616 USA

ISBN: (纸本)9781538635964

stochastic versions of the unit commitment problem have been advocated for addressing the uncertainty presented by high levels of wind power penetration. However, little work has been done to study trade-offs between computational complexity and the quality of solutions obtained as the number of probabilistic scenarios is varied. Here, we describe extensive experiments using real publicly available wind power data from the Bonneville Power Administration. Solution quality is measured by re-enacting day-ahead reliability unit commitment (which selects the thermal units that will be used each hour of the next day) and real-time economic dispatch (which determines generation levels) for an enhanced WECC-240 test system in the context of a production cost model simulator;outputs from the simulation, including cost, reliability, and computational performance metrics, are then analyzed. Unsurprisingly, we find that both solution quality and computational difficulty increase with the number of probabilistic scenarios considered. However, we find unexpected transitions in computational difficulty at a specific threshold in the number of scenarios, and report on key trends in solution performance characteristics. Our findings are novel in that we examine these tradeoffs using real-world wind power data in the context of an out-of-sample production cost model simulation, and are relevant for both practitioners interested in deploying and researchers interested in developing scalable solvers for stochastic unit commitment.

关键词： Renewables Probabilistic Scenarios stochastic unit commitment Wind Power

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stochastic unit commitment basec on ARIMA Scenario Generation and Reduction

Stochastic Unit Commitment basec on ARIMA Scenario Generatio...

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IEEE/PES Transmission and Distribution Conference and Exposition (T&D)

作者： Zhang, Guangyuan Li, Wanning Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA

ISBN: (纸本)9781509021574

Under high penetration of wind energy, the power system is facing the challenge to maintain the balance between generation and load due to the uncertainty and variability of wind. The traditional unit commitment only consider the deterministic load and wind output in next day and use the operating reserve to handle the net load uncertainty. In this paper, the stochastic unit commitment is developed to address the uncertainty of load and wind output. The time series model Autoregressive Integrated Moving Average (ARIMA) is applied to provide the point estimation and prediction interval for next 24 hours based on the historical load and wind data. The Monte-Carlo simulation and scenario reduction is applied to generate the scenarios and reduce the scenario number. The Benders Decomposition is used to solve the large scale stochastic programming problem in a more efficient manner. A 6 bus system is simulated and studied for the proposed model and algorithm.

关键词： stochastic unit commitment Benders Decomposition Time Series Scenario Generation Scenario Reduction

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stochastic unit commitment Problem Incorporating Renewable Energy Power 5th

Stochastic Unit Commitment Problem Incorporating Renewable E...

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5th International Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO)

作者： Ramya, N. M. Babu, M. Ramesh Arunachalam, S. Karpagam Coll Engn Dept EEE Coimbatore Tamil Nadu India St Josephs Coll Engn Dept EEE Madras Tamil Nadu India

ISBN: (纸本)9783319202945;9783319202938

It is necessary to incorporate wind and pumped storage plants in classical unit commitment problem due to the increase in use of renewable energy sources. The cost of power generation will be reduced due to inclusion of the renewable energy resources. In this work a Weibull probability density function is used to predict the wind speed. The proposed unit commitment (UC) problem includes the factors account for both overestimation and underestimation of available wind power. Pumped storage hydro plants are also included in the scheduling process to balance the uncertainties in the wind power generation. Premature convergence and high computation time are the main drawbacks of the conventional PSO algorithm to solve the optimization problems. In this work a Modified PSO (MPSO) algorithm is proposed to remove the drawbacks of the conventional PSO to solve the proposed stochastic unit commitment problem (SUC).

关键词： stochastic unit commitment Particle swarm optimization (PSO) Reserve cost Penalty cost

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stochastic unit commitment via Progressive Hedging - Extensive Analysis of Solution Methods

Stochastic Unit Commitment via Progressive Hedging - Extensi...

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PowerTech, 2015 IEEE Eindhoven

作者： Ordoudis, Christos Pinson, Pierre Zugno, Marco Morales, Juan M. Tech Univ Denmark Dept Elect Engn Lyngby Denmark Tech Univ Denmark Dept Appl Math & Comp Sci Lyngby Denmark

ISBN: (纸本)9781479976959

Owing to the massive deployment of renewable power production units over the last couple of decades, the use of stochastic optimization methods to solve the unit commitment problem has gained increasing attention. Solving stochastic unit commitment problems in large-scale power systems requires high computational power, as stochastic models are dramatically more complex than their deterministic counterparts. This paper provides new insight into the potential of Progressive Hedging to decrease the solution time of the stochastic unit commitment problem with a relatively small trade-off in terms of the suboptimality of the solution. Computational studies show that the run-time is at most half of what is needed to solve the original extensive formulation of the problem, when more than ten wind power scenarios are utilized. These studies demonstrate great potential for solving real-world stochastic unit commitment problems using the Progressive Hedging algorithm.

关键词： Electricity market operations progressive hedging stochastic unit commitment wind power

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Two-stage stochastic unit commitment model including non-generation resources with conditional value-at-risk constraints

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ELECTRIC POWER SYSTEMS RESEARCH 2014年第Nov.期116卷 427-438页

作者： Huang, Yuping Zheng, Qipeng P. Wang, Jianhui Univ Cent Florida Dept Ind Engn & Management Syst Orlando FL 32816 USA Argonne Natl Lab Argonne IL 60439 USA

This paper presents a two-stage stochastic unit commitment (UC) model, which integrates non-generation resources such as demand response (DR) and energy storage (ES) while including risk constraints to balance between cost and system reliability due to the fluctuation of variable generation such as wind and solar power. This paper uses conditional value-at-risk (CVaR) measures to model risks associated with the decisions in a stochastic environment. In contrast to chance-constrained models requiring extra binary variables, risk constraints based on CVaR only involve linear constraints and continuous variables, making it more computationally attractive. The proposed models with risk constraints are able to avoid over-conservative solutions but still ensure system reliability represented by loss of loads. Then numerical experiments are conducted to study the effects of non-generation resources on generator schedules and the difference of total expected generation costs with risk consideration. Sensitivity analysis based on reliability parameters is also performed to test the decision preferences of confidence levels and load-shedding loss allowances on generation cost reduction. (c) 2014 Elsevier B.V. All rights reserved.

关键词： stochastic unit commitment Demand response Energy storage Conditional value-at-risk Benders decomposition Sensitivity analysis

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Tractable stochastic unit commitment for Large Systems During Predictable Hazards

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IEEE ACCESS 2020年 8卷 115078-115088页

作者： Mohammadi, Farshad Sahraei-Ardakani, Mostafa Univ Utah Dept Elect & Comp Engn Salt Lake City UT 84112 USA

Many foreseeable natural hazards, including extreme weather events, lead to an outage of multiple transmission lines. Although such outages can be predicted in advance, there is a great deal of uncertainty in these predictions. To appropriately use the failure estimations in power system scheduling, this paper formulates a stochastic unit commitment (SUC) problem with explicit modeling of the predicted outages. The formulated problem, however, is extremely computationally-demanding, as the uncertainty is placed on the binary status of transmission lines. This paper, then, develops a computationally efficient algorithm to solve the formulated SUC for large-scale systems. The algorithm employs generation shift factors to enable rapid calculation of power flows. Additionally, flow canceling transactions are used to model multiple line outages without having to recalculate shift factors. Finally, critical constraints are iteratively detected and added to the problem. This approach substantially reduces the size of the problem, which helps computational tractability. The effectiveness of the developed algorithm is demonstrated through simulation studies on the Texas 2000-bus test system. The algorithm is used to minimize the lost load during a hypothetical hurricane. The results show that the algorithm is computationally tractable and can effectively identify a preventive dispatch, leading to a substantial reduction in power outages.

关键词： Extreme weather Predictable natural hazards Preventive operation Power system reliability Power system resilience stochastic unit commitment Uncertainty management

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