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robust Energy and Reserve Scheduling Under Wind Uncertainty Considering Fast-Acting Generators

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2019年第4期10卷 2142-2151页

作者： Cobos, Noemi G. Arroyo, Jose M. Alguacil, Natalia Street, Alexandre Univ Castilla La Mancha ETSI Ind Dept Ingn Elect Elect Automat & Comunicac E-13071 Ciudad Real Spain Pontifical Catholic Univ Rio de Janeiro Elect Engn Dept BR-22451045 Rio De Janeiro Brazil

The large-scale integration of wind power generation has significantly boosted the role played by conventional fast-acting generating units in power system operation. Due to their short response time, these relatively expensive devices are particularly suited to cope with the intermittency and volatility featured by uncertain wind-based generation. Within the context of network-constrained generation scheduling underwind-related uncertainty, this paper presents a new robust approach wherein the operation of both slow-and fast-acting generating units is precisely characterized in a day-ahead co-optimized energy and reserve market. As salient modeling features, the ramping-related effect of dispatch nonanticipativity and nonspinning reserve offers are both accounted for. The resulting robust counterpart is formulated as a challenging trilevel program with lower-level binary variables that is effectively solved by an exact nested column-and-constraint generation algorithm. Numerical results including a practical out-of-sample validation procedure demonstrate the operational advantages and the computational effectiveness of the proposed approach.

关键词： Energy and reserve scheduling fast-acting generating units nonanticipativity nonspinning reserve offers ramping capability two-stage robust optimization wind uncertainty

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A robust operation-based scheduling optimization for smart distribution networks with multi-microgrids

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APPLIED ENERGY 2018年 228卷 130-140页

作者： Liu, Yixin Guo, Li Wang, Chengshan Tianjin Univ Minist Educ Key Lab Smart Grid Tianjin Peoples R China

This paper proposes a two-level interactive mechanism to address the day-ahead scheduling optimization of smart distribution networks with multi-microgrids. Each microgrid realizes the economic dispatch in the lower level interactive model, where the uncertainties caused by renewable energy integration and loads as well as the coordination of controllable distributed generator, energy storage and demand response are handled through a two-stage robust model. In the upper level interactive model, the distribution network operator (DNO) guarantees the operational quality by considering the power flow constraints and decides the transaction price and quantity with each microgrid based on the solutions of the lower level problem, to minimize the operation cost. The interaction process is formulated as a Stackelberg game, in which the trading strategies of coupled micro grids, acted as followers, are influenced by price incentive mechanism of DNO and affect the decision of DNO in turn. A collaborative strategy promoting the internal power trading among microgrids is also presented to deal with the market power of DNO, such that the participating microgrids can benefit from the internal trading and further reduce the cost. Numerical cases on an improved IEEE 33-bus system show the effectiveness of the proposed model and solution strategy.

关键词： Smart distribution networks Multi-microgrids Interactive mechanism two-stage robust optimization Price incentive

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robust Transmission Network Expansion Planning Under Correlated Uncertainty

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IEEE TRANSACTIONS ON POWER SYSTEMS 2019年第3期34卷 2071-2082页

作者： Roldan, Cristina Minguez, Roberto Garcia-Bertrand, Raquel Arroyo, Jose M. Univ Castilla La Mancha Dept Ingn Elect Elect Automat & Comunicac ETSI Ind E-13071 Ciudad Real Spain

This paper addresses the transmission network expansion planning problem under uncertain demand and generation capacity. A two-stage adaptive robust optimization framework is adopted whereby the worst-case operating cost is accounted for under a given user-defined uncertainty set. This paper differs from previously reported robust solutions in two respects. First, the typically disregarded correlation of uncertainty sources is explicitly considered through an ellipsoidal uncertainty set relying on their variance-covariance matrix. In addition, we describe the analogy between the corresponding second-stage problem and a certain class of mathematical programs arising in structural reliability. This analogy gives rise to a relevant probabilistic interpretation of the second stage, thereby revealing an undisclosed feature of the worst-case setting characterizing robust optimization with ellipsoidal uncertainty sets. More importantly, a novel nested decomposition approach based on results fromstructural reliability is devised to solve the proposed robust counterpart, which is cast as an instance of mixed-integer trilevel programming. Numerical results from several case studies demonstrate that the effect of correlated uncertainty can be captured by the proposed robust approach.

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robust planning-operation co-optimization of energy hub considering precise model of batteries’ economic efficiency

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Energy Procedia 2019年 158卷 6496-6501页

作者： Cong Chen Xinwei Shen Qinglai Guo Hongbin Sun Tsinghua-Berkeley Shenzhen Institute Tsinghua University Shenzhen People’s Republic of China The Department of Electrical Engineering State Key Laboratory of Power System Tsinghua University Beijing People’s Republic of China

The increasing penetration of renewable energy in integrated energy system (IES) makes it necessary to consider the uncertainty and an appropriate investment and operation for energy storage systems (ESS). In this paper, we propose a robust planning-operation co-optimization method for Energy Hub (EH) considering the sizing problem and precise model of batteries’ economic efficiency, in which batteries’ total cost and the lifetime loss cost is mainly considered. The problem is formulated as a two-stage robust optimization model, in which the second-stage problem has integer variables. To solve the problem, dual norms are used to transform the uncertain constraints into robust counterparts, then the problem is rearranged and can be solved by Benders Decomposition. Through case studies, the benefits on the flexibility and economy of EH brought by precise ESS model is analyzed. And the consideration of lifetime factor and robust optimization strengthen the economy and reliability of EH, respectively.

关键词： Energy hubs energy storage systems two-stage robust optimization lifetime of batteries planning-operation co-optimization

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Optimized operation method of small and medium-sized integrated energy system for P2G equipment under strong uncertainty

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ENERGY 2020年 199卷 117269-117269页

作者： Zhou, Suyang Sun, Kaiyu Wu, Zhi Gu, Wei Wu, Gaoxiang Li, Zhe Li, Junjie Southeast Univ Sch Elect Engn Nanjing Peoples R China Elect Power Res Inst State Grid Chongqing Elect P Chongqing Peoples R China

This paper studies an Integrated Energy System (IES) and the application of Hydrogen Compressed Natural Gas (HCNG) and Power to Gas (P2G) equipment in IES. Based on the C&CG algorithm, this paper establishes a two-stage robust optimization(TRO) model of the system operating in uncertain environments. Introducing HCNG and P2G equipment into IES can improve the system's ability to accommodate renewable energy, reduce system operation cost and reduce interaction fluctuation between the system and the main grid. robust optimization can effectively reduce the risk of system operation in uncertain environments and improve the stability of system operation. Under the compensation of the real-time market, the financial performance of the robust optimization model is better than the deterministic optimization model according to the numerical simulation. This study is helpful to the study of the operation and scheduling of IES in remote areas with weak power system infrastructure and abundant renewable energy resources. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Integrated energy system Uncertainty HCNG Optimal dispatch two-stage robust optimization

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robust transmission expansion planning with uncertain generations and loads using full probabilistic information

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ELECTRIC POWER SYSTEMS RESEARCH 2020年 189卷 106793-106793页

作者： Roldan, Cristina Garcia-Bertrand, Raquel Minguez, Roberto Univ Castilla La Mancha Dept Elect Engn Ciudad Real Spain ESCISSION Co Madrid Spain

Recent advances in Transmission Network Expansion Planning (TNEP) have demonstrated that two-stage adaptive robust optimization (ARO) renders the expansion planning problem tractable for real systems and, at the same time, constitutes a relevant approach to deal with uncertain demand and generation capacity in the TNEP problem. However, the use of cardinality and polyhedral uncertainty sets does not consider the correlation of uncertain parameters and makes the interpretation of final results difficult from a probabilistic view-point. The recent use of level II structural-reliability and decomposition techniques allows the incorporation of ellipsoidal uncertainty sets, which permits the use of expected values and the variance-covariance matrix of uncertain parameters (First-Order Second-Moment, FOSM). However, it also constitutes an approximation. This paper presents an iterative method which, using FOSM and appropriately modifying the reliability index associated with the desired objective function quantile, transforms the model into a level III structural-reliability method that allows the consideration of the complete probabilistic structure of the uncertain parameters. Numerical results from a realistic case study are presented and discussed.

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A two-stage robust Reactive Power optimization Considering Uncertain Wind Power Integration in Active Distribution Networks

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2016年第1期7卷 301-311页

作者： Ding, Tao Liu, Shiyu Yuan, Wei Bie, Zhaohong Zeng, Bo Xi An Jiao Tong Univ Dept Elect Engn State Key Lab Elect Insulat & Power Equipment Xian 710049 Peoples R China Univ S Florida Dept Ind & Management Syst Engn Tampa FL 33613 USA

Traditional reactive power optimization aims to minimize the total transmission losses by control reactive power compensators and transformer tap ratios, while guaranteeing the physical and operating constraints, such as voltage magnitudes and branch currents to be within their reasonable range. However, large amounts of renewable resources coming into power systems bring about great challenges to traditional planning and operation due to the stochastic nature. In most of the practical cases from China, the wind farms are centrally integrated into active distribution networks. By the use of conic relaxation based branch flow formulation, the reactive optimization problem in active distribution networks can be formulated as a mixed integer convex programming model that can be tractably dealt with. Furthermore, to address the uncertainties of wind power output, a two-stage robust optimization model is proposed to coordinate the discrete and continuous reactive power compensators and find a robust optimal solution that can hedge against any possible realization within the uncertain wind power output. Moreover, the second order cone programming-based column-and-constraint generation algorithm is employed to solve the proposed two-stage robust reactive power optimization model. Numerical results on 33-, 69- and 123-bus systems and comparison with the deterministic approach demonstrate the effectiveness of the proposed method.

关键词： Active distribution network column-and-constraint generation algorithm reactive power optimization two-stage robust optimization wind power

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robust Transmission Network Expansion Planning Problem Considering Storage Units 2

Robust Transmission Network Expansion Planning Problem Consi...

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2nd International Conference on Smart Energy Systems and Technologies (SEST)

作者： Garcia-Cerezo, Alvaro Baringo, Luis Garcia-Bertrand, Raquel Univ Castilla La Mancha Dept Elect Engn Ciudad Real Spain

ISBN: (纸本)9781728111568

This paper addresses the transmission network expansion planning problem considering storage units under uncertain demand and generation capacity. A two-stage adaptive robust optimization framework is adopted whereby short- and long-term uncertainties are accounted for. This work differs from previously reported solutions in an important aspect, namely, we include binary recourse variables to avoid the simultaneous charging and discharging of storage units once uncertainty is revealed. two-stage robust optimization with discrete recourse problems is a challenging task, so we propose using a nested column-and-constraint generation algorithm to solve the resulting problem. This algorithm guarantees convergence to the global optimum in a finite number of iterations. The performance of the proposed algorithm is illustrated using the Garver's test system.

关键词： Storage units transmission network expansion planning two-stage robust optimization uncertainty

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Risk-Averse Generation Maintenance Scheduling With Microgrid Aggregators

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IEEE TRANSACTIONS ON SMART GRID 2018年第6期9卷 6470-6479页

作者： Manshadi, Saeed D. Khodayar, Mohammad E. Southern Methodist Univ Dept Elect Engn Dallas TX 75205 USA

This paper presents risk-averse long-term generation maintenance scheduling in the power systems with a considerable installed capacity of microgrids. Microgrid aggregators facilitate the participation of microgrids in the wholesale market. In this paper, the effect of microgrids as controllable demand entities on the generation maintenance scheduling practices in the power system is investigated. The uncertainties in the marginal cost of generation in microgrids, the generation capacity installed within the microgrids, and the system electricity demand are captured using respective nominal values and uncertainty intervals. Moreover, the contingencies in transmission network are addressed by introducing additional variables. A two-stage robust optimization problem is formulated to determine a trade-off among the performance and conservativeness of the procured solution in the long-term operation horizon. The problem is formulated as a mixed integer linear programming problem and column-and-constraint generation procedure is used to solve the problem. The master problem minimizes the maintenance cost of the generation units subjected to generation units' constraints in the long-term operation horizon and the sub-problems determine the worst realization of the uncertainties and generate additional constraints in the master problem. The proposed methodology is applied to two case studies for a 6-bus and IEEE 118-bus power systems.

关键词： Maintenance scheduling outage scheduling microgrids column-and-constraint generation uncertainty two-stage robust optimization

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A two-stage robust approach for the reliable logistics network design problem

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2018年 111卷 185-202页

作者： Cheng, Chun Qi, Mingyao Zhang, Ying Rousseau, Louis-Martin Polytech Montreal Dept Math & Ind Engn Montreal PQ H3C 3A7 Canada CIRRELT Montreal PQ H3C 3A7 Canada Tsinghua Univ Grad Sch Shenzhen Res Ctr Modern Logist Shenzhen 518055 Peoples R China Zhejiang Cainiao Supply Chain Management Co Ltd Hangzhou 310000 Zhejiang Peoples R China

This paper examines a three-echelon logistics network in which all supply and transshipment nodes are subject to disruption. We use uncertainty sets to describe the possible scenarios without depending on probabilistic information. We adopt a two-stage robust optimization approach where location decisions are made before and recourse decisions are made after the disruptions are known. We construct three two-stage robust models, which are solved exactly by a column-and-constraint-generation algorithm. Numerical tests demonstrate that the proposed algorithm outperforms the Benders decomposition method in both solution quality and computational time, and that the system's reliability can be improved with only a slight increase in the normal cost. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Reliable logistics network design Facility disruption two-stage robust optimization Column-and-constraint-generation algorithm

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