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A reliability-aware chance-constrained battery sizing method for island microgrid

ENERGY 2022年第0期251卷 1页

作者： Huo, Da Santos, Marcos Sarantakos, Ilias Resch, Markus Wade, Neal Greenwood, David Cran field Univ Sch Water Energy & Environm Cran field MK43 0AL England Newcastle Univ Sch Engn Newcastle Upon Tyne NE1 7RU Northumberland England Wirtschaftsagentur Burgenland Forsch und Innovat G Europastrasse 1 A-7540 Gussing Austria

Island Microgrids can coordinate local energy resources, provide post-fault reliability improvements for local customers, and aggregate local power and energy resources to offer services to the wider system. A crucial component of an Island Microgrid is the battery energy storage system, which can manage local imbalances, alleviate constraints, and improve reliability by enabling post-fault islanding. A planning and sizing method is required to quantify and maximize the benefits of battery energy storage while avoiding over-investment and under-utilization. This paper combines comprehensive reliability assessment with chance-constrained convex optimization, via second-order cone programming, to optimally size energy storage within an Island Microgrid. chance constraints are applied to the battery state-of-charge to avoid sizing the energy storage to accommodate extreme cases of uncertainty, avoiding uneconomic investment. The probability of reaching a state-of-charge constraint also indicates the likelihood that the battery energy storage system will be unable to facilitate island operation in the event of an outage, which affects the Island Microgrid reliability. The method is demonstrated on a real Austrian distribution network as part of the MERLON project. Results illustrate that an optimal trade-off can be identified between system reliability and operating cost when the probability of violating the chance constraints is 4.8%.(c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Battery sizing BESS chance-constrained programming Distribution system Island microgrid Reliability Second-order cone programming

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Distributed optimal scheduling for coupled transmission-distribution integrated electricity-gas system

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ELECTRIC POWER SYSTEMS RESEARCH 2022年第0期206卷 107759-107759页

作者： Lan, Peng Wu, Gang Shen, Xiaodong Lv, Lin Liu, Junyong Zhao, Houxiang Jawad, Shafqat Liu, Jinyuan Sichuan Univ Coll Elect Engn Chengdu 610065 Peoples R China State Grid Sichuan Econ Res Inst Chengdu 610041 Peoples R China Power China Sichuan Elect Power Engn CO LTD Chengdu 610041 Peoples R China

The coordinated optimal scheduling of coupled transmission-distribution integrated electricity-gas system (CTD-IEGS) is of great significance to ensure the economy and security of the system operation. In this paper, an optimal scheduling model of CTD-IEGS is proposed. To deal with the influence of uncertainty on both trans-mission and distribution sides, a probabilistic reserve allocation model based on chance-constrained program-ming (CCP) method is proposed, and the discrete sequence transformation (DST) method is used to transform the CCP reserve constraints into an easy solvable deterministic form. Due to the interdependence between the transmission system operator (TSO) and distribution system operator (DSO), a distributed solution algorithm based on alternating direction method of multipliers (ADMM) is adopted to solve the optimal scheduling model of CTD-IEGS. Case studies show that the proposed distributed solution strategy can effectively realize the distributed scheduling and coordinated optimization of the transmission network and distribution network, and protect the information privacy of TSO and DSO. In addition, the CCP method can adjust the confidence level of reserve to meet the tolerance of transmission and distribution network decision-makers for different risks, and balance the operation economy and reliability of the system.

关键词： coupled transmission-distribution integrated electricity-gas system reserve allocation chance-constrained programming discrete sequence transformation distributed optimization

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Hybrid Chaotic Maps-Based Artificial Bee Colony for Solving Wind Energy-Integrated Power Dispatch Problem

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ENERGIES 2022年第13期15卷 4578页

作者： Alshammari, Motaeb Eid Ramli, Makbul A. M. Mehedi, Ibrahim M. King Abdulaziz Univ Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia King Abdulaziz Univ Ctr Excellence Intelligent Engn Syst CEIES Jeddah 21589 Saudi Arabia

A chance-constrained programming-based optimization model for the dynamic economic emission dispatch problem (DEED), consisting of both thermal units and wind turbines, is developed. In the proposed model, the probability of scheduled wind power (WP) is included in the set of problem-decision variables and it is determined based on the system spinning reserve and the system load at each hour of the horizon time. This new strategy avoids, on the one hand, the risk of insufficient WP at high system load demand and low spinning reserve and, on the other hand, the failure of the opportunity to properly exploit the WP at low power demand and high spinning reserve. The objective functions of the problem, which are the total production cost and emissions, are minimized using a new hybrid chaotic maps-based artificial bee colony (HCABC) under several operational constraints, such as generation capacity, system loss, ramp rate limits, and spinning reserve constraints. The effectiveness and feasibility of the suggested framework are validated on the 10-unit and 40-unit systems. Moreover, to test the robustness of the suggested HCABC algorithm, a comparative study is performed with various existing techniques.

关键词： power dispatch spinning reserve chance-constrained programming chaotic maps optimization

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On the Interaction Between Aggregators, Electricity Markets and Residential Demand Response Providers

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IEEE TRANSACTIONS ON POWER SYSTEMS 2020年第2期35卷 840-853页

作者： Bruninx, Kenneth Pandzic, Hrvoje Le Cadre, Helene Delarue, Erik Katholieke Univ Leuven Dept Mech Engn B-3001 Leuven Belgium Flemish Inst Technol Res VITO B-2400 Mol Belgium EnergyVille B-3600 Genk Belgium Univ Zagreb Fac Elect Engn & Comp Zagreb 10000 Croatia VITO B-2400 Mol Belgium

To decarbonize the heating sector, residential consumers may install heat pumps. Coupled with heating loads with high thermal inertia, these thermostatically controlled loads may provide a significant source of demand side flexibility. Since the capacity of residential consumers is typically insufficient to take part in the day-ahead electricity market (DAM), aggregators act as mediators that monetize the flexibility of these loads through demand response (DR). In this paper, we study the strategic interactions between an aggregator, its consumers and the DAM using a bilevel optimization framework. The aggregator-consumer interaction is captured either as a Stackelberg or a Nash Bargaining Game, leveraging chance-constrained programming to model limited controllability of residential DR loads. The aggregator takes strategic positions in the DAM, considering the uncertainty on the market outcome, represented as a stochastic Stackelberg Game. Results show that the DR provider-aggregator cooperation may yield significant monetary benefits. The aggregator cost-effectively manages the uncertainty on the DAM outcome and the limited controllability of its consumers. The presented methodology may be used to assess the value of DR in a deregulated power system or may be directly integrated in the daily routine of DR aggregators.

关键词： Aggregator chance-constrained programming Nash bargaining game Stackelberg game demand response thermostatically controlled loads

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An advanced day-ahead bidding strategy for wind power producers considering confidence level on the real-time reserve provision

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APPLIED ENERGY 2020年 280卷 115973-115973页

作者： Hosseini, Seyyed Ahmad Toubeau, Jean-Francois De Greve, Zacharie Vallee, Francois Univ Mons Power Syst & Market Res Grp Elect Power Engn Unit EPEU B-7000 Mons Belgium

The current evolutions in electricity market policies combined with the technical progress in wind farm control encourage Wind Power Producers (WPPs) to participate in the joint day-ahead energy and reserve market (JERM). In this paper, an advanced bidding strategy dedicated to optimal dispatch of the WPP in the JERM is proposed. The suggested strategy exploits a novel bi-objective two-stage chance-constrained stochastic model in which various revenue streams, stemming from both day-ahead and real-time stages, are fully accounted for. The first objective of the presented model is to allocate the optimal share of the power assigned to each market floor in the day-ahead stage so as to maximize the WPP's profit. Then, the formulation considers the confidence level of delivering the contracted reserve power in real-time through an additional competing objective. Meanwhile, the presented method allows us to also illustrate the effect of reserve availability as a probabilistic measure on WPP's profit. In this regard, the wind speed and system frequency uncertainties are introduced as stochastic inputs in the proposed framework. The obtained revenue streams regarding each market floor as well as the total revenue of the WPP are then evaluated in a Monte Carlo out-of-sample analysis. The ex-post samples contain system frequency data from the Belgian market along with wind speed data representing the wind turbulence intensity level. Outcomes reveal not only the effectiveness and flexibility of the proposed model for enhancing WPPs' revenues, but also the importance of properly considering the uncertain real-time delivery of the contracted reserve power.

关键词： Bidding strategy Energy and reserve market chance-constrained programming Stochastic optimization Wind turbulence intensity

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chance-constrained co-optimization for day-ahead generation and reserve scheduling of cascade hydropower-variable renewable energy hybrid systems

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APPLIED ENERGY 2022年 324卷

作者： Zhang, Juntao Cheng, Chuntian Yu, Shen Su, Huaying Dalian Univ Technol Inst Hydropower & Hydroinformat Dalian 116024 Peoples R China Dalian Univ Technol Key Lab Ocean Energy Utilizat & Energy Conservat Minist Educ Dalian 116023 Peoples R China Guizhou Elect Power Dispatching & Control Ctr Guiyang 550002 Peoples R China

The rapid development of variable renewable energy (VRE), such as wind and solar energy, has stimulated the complementary operation of VRE with flexible cascade hydropower stations in China. Due to the uncertainties of VRE power generation and complex constraints of cascade hydropower stations, formulating reliable day-ahead generation and reserve scheduling plans is a real challenge in the actual operation stage of the cascade hydropower-VRE hybrid systems (CHVHS). In this paper, we propose a tractable chance-constrained co-optimization model for day-ahead generation and reserve scheduling of a CHVHES. First, compared with existing models, the relationship between the hydropower reserve capacities and water-electricity conversion efficiency is finely modeled. Accordingly, the economic allocation of total VRE reserve requirements among cascade hydropower stations is for the first time considered in our proposed model. This can save more water resources for cascade hydropower stations when compensating for VRE, further improving hydro-VRE complementary profits. Second, we propose a solution approach for chance constraints by incorporating the nonparametric probabilistic forecasting of VRE power based on quantile regression into the chance-constrained model, ensuring that the stochastic dependence between VRE power output and its point forecast can be effectively captured. Importantly, this solution approach does not require prior knowledge or any probability distribution assumptions of VRE power and does not introduce any additional computational burden. With the help of three-dimensional interpolation technology for nonlinear constraints, the proposed scheduling model is finally cast as a mixed-integer linear programming model that is computationally tractable. Numerical tests implemented on a real CHVHES located in Southwest China verify the effectiveness and advantages of the proposed methods.

关键词： chance-constrained programming Day-ahead generation and reserve scheduling Cascade hydropower Variable renewable energy Nonparametric probabilistic forecasting

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chance-constrained Scheduling of Underground Pumped Hydro Energy Storage in Presence of Model Uncertainties

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2020年第3期11卷 1516-1527页

作者： Toubeau, Jean-Francois De Greve, Zacharie Goderniaux, Pascal Vallee, Francois Bruninx, Kenneth Univ Mons Elect Power Engn Unit B-7000 Mons Belgium Univ Mons Geol & Appl Geol Unit B-7000 Mons Belgium Univ Leuven KU Leuven Energy Inst TME BranchEnergy Convers B-3001 Leuven Belgium Flemish Inst Technol Res B-2400 Mol Belgium Energy Ville B-3600 Genk Belgium

Abandoned underground quarries or mines may be rehabilitated as natural reservoirs for underground pumped hydro energy storage (UPHES). In addition to the inherent modeling inaccuracies of the traditional PHES that arise from, e.g., approximating the nonlinear pump/turbine head-dependent performance curves, the optimal operation of these underground plants is also affected by endogenous model uncertainties. The latter typically arise from a limited knowledge of the physical characteristics of the system such as the geometry and hydraulic properties of the underground cavity. In this paper, chance-constrained programming is leveraged to immunize the day-ahead scheduling of an UPHES owner against both these model uncertainties and the modeling approximations. The proposed method is tested on a fictitious UPHES system using an existing underground quarry as lower reservoir. Results demonstrate that the methodology allows finding a compromise between conservativeness and economic performance, while being computationally efficient. This model may thus be integrated in the daily scheduling routine of UPHES owners, or may help regulators and system operators to better estimate the available flexibility of such resources.

关键词： Uncertainty Turbines Hydraulic systems Schedules Magnetic heads Computational modeling Indexes chance-constrained programming day-ahead scheduling flexibility model uncertainties underground pumped hydro energy storage

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Optimal allocation model of the water resources in Harbin under representative concentration pathway scenarios

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WATER SUPPLY 2020年第7期20卷 2903-2914页

作者： Meng, Fanxiang Li, Linqi Li, Tianxiao Fu, Qiang Heilongjiang Univ Sch Water Resources & Elect Power Harbin 150080 Heilongjiang Peoples R China Northeast Agr Univ Sch Water Conservancy & Civil Engn Harbin 150030 Heilongjiang Peoples R China Northeast Agr Univ Key Lab Effect Utilizat Agr Water Resources Minist Agr Harbin 150030 Heilongjiang Peoples R China Northeast Agr Univ Heilongjiang Prov Key Lab Water Resources & Water Harbin 150030 Heilongjiang Peoples R China

Water conservation is a strategic choice for sustainable societal development. Rational planning and allocation of water resources is an effective way to improve the efficiency of water resource utilization. Taking Harbin as an example, this paper constructs a linear fractional programming model based on chance-constrained programming. The model can reflect the randomness of water inflow under different climatic conditions while taking into account the interests of different decision makers at the upper and lower levels and the two contradictory objectives of maximizing economic benefits and minimizing water demands to improve water distribution efficiency and reduce the complexity of water resource distribution systems. The results showed that under the three climatic scenarios of RCP4.5, RCP6.0, and RCP8.5, the agricultural water supply accounted for 90.04%, 87.66%, and 84.15% of the total regional water supply, respectively. In the process of building the model, considering the importance of sewage treatment in water safety evaluations, the cost of sewage treatment is included in the upper-level benefits of the model. The sustainable development of water resources should be guaranteed while rationally allocating water resources and pursuing economic benefits.

关键词： chance-constrained programming linear fractional programming water resource allocation

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Analytical Reformulation for Stochastic Unit Commitment Considering Wind Power Uncertainty With Gaussian Mixture Model

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IEEE TRANSACTIONS ON POWER SYSTEMS 2020年第4期35卷 2769-2782页

作者： Yang, Yue Wu, Wenchuan Wang, Bin Li, Mingjie Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing 100084 Peoples R China State Grid Corp China Natl Elect Power Control Ctr Beijing 100031 Peoples R China

To capture the stochastic characteristics of renewable energy generation output, chance-constrained unit commitment (CCUC) model is widely used. Conventionally, analytical reformulation for CCUC is usually based on simplified probability assumption or neglecting some operational constraints, otherwise scenario-based methods are used to approximate probability with heavy computational burden. In this paper, Gaussian mixture model (GMM) is employed to characterize the correlation between wind farms and probability distribution of their forecast errors. In our model, chance constraints including reserve sufficiency and branch power flow bounds are ensured to be satisfied with predetermined probability. To solve this CCUC problem, we propose a Newton method based procedure to acquire the quantiles and transform chance constraints into deterministic constraints. Therefore, the CCUC model is efficiently solved as a mixed-integer quadratic programming problem. Numerical tests are performed on several systems to illustrate efficiency and scalability of the proposed method.

关键词： Wind power generation Generators Wind farms Stochastic processes Optimization Mathematical model Wind forecasting chance-constrained programming unit commit-ment stochastic optimization

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The multi-level warehouse layout problem with uncertain information: uncertainty theory method

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INTERNATIONAL JOURNAL OF GENERAL SYSTEMS 2020年第5期49卷 497-520页

作者： He, Rongheng Li, Hui Zhang, Bo Chen, Mei Zhongnan Univ Econ & Law Sch Stat & Math Wuhan Peoples R China Columbia Univ Fu Fdn Sch Engn & Appl Sci New York NY USA Hubei Univ Educ Sch Math & Econ Wuhan Peoples R China

This paper investigates the multi-level warehouse layout problem with indeterminate factors, in which the monthly demands and horizontal transportation distances are described by uncertain variables. We first consider the distribution function of the total cost for transportation. Second, two uncertain models, namely, the chance-constrained programming model and the chance-maximum programming model, are developed to lay out the multi-level warehouse under uncertainty. Some properties of the models are discussed to solve the models. The properties point out that the optimal solution to the chance-constrained programming model is equivalent to a corresponding deterministic model. Additionally, we also discuss the relation between the chance-constrained programming model and the chance-maximum programming model, which leads to an effective approach to search for the optimal solution to the chance-maximum programming model. Finally, a numerical experiment is illustrated to show the ideas of the proposed models.

关键词： Warehouse layout problem chance-constrained programming chance-maximum programming uncertainty theory uncertain programming

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