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Two-stage interval stochastic chance-constrained robust programming and its application in flood management

JOURNAL OF CLEANER PRODUCTION 2017年 167卷 908-918页

作者： Ding, Xiaowen Hua, Dongxu Jiang, Guihong Bao, Zhengfeng Yu, Lei North China Elect Power Univ Minist Educ Key Lab Reg Energy & Environm Syst Optimizat Beijing 102206 Peoples R China Univ Regina Inst Energy Environm & Sustainable Communities Regina SK S4S 7H9 Canada Three Gorges Cascade Dispatch & Commun Ctr Yichang 443133 Peoples R China

Today, flood diversion is a useful strategy of flood management that is used to mitigate damage from flooding throughout the world. This study developed two-stage interval stochastic chance-constrained robust programming for flood diversion of flood management under multiple uncertainties by integrating chance-constrained programming and robust programming into two-stage stochastic programming. The new model can resolve problems of system uncertainties and difficulties of trade-offs between the system economy as well as system stability. The proposed approach is especially useful for obtaining an optimum flood mitigation scheme with the minimum cost and an acceptable risk under complex uncertainties. In addition, the advanced method was also used to test a hypothetical case study of flood diversion planning. The results indicate that desired flood diversion schemes with lower costs and acceptable risks under complex uncertainties could be achieved. In conclusion, this study proposed a new method for flood management and meanwhile it could efficiently handle problems of independent uncertainty as well as trade-offs between economy and stability in flood management system which were hardly deal with by current methods. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Flood diversion Robust programming chance-constrained programming Uncertainty Trade-off

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A chance constrained programming method to determine optimal capital structure for privatized infrastructure

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CONSTRUCTION MANAGEMENT AND ECONOMICS 2021年第7期39卷 579-594页

作者： Wang, Xiuqin Wang, Bing Xu, Yantao Shi, Lanmin Tianjin Univ Coll Management & Econ Tianjin Peoples R China Zhejiang Univ Technol Coll Management Hangzhou 310023 Peoples R China

Capital structure optimization is an important aspect to ensure the success of public-private partnership (PPP) financing. Existing optimization methods fail to provide certain confidence level of the optimization result of PPP capital structure. The aim of this paper to optimize the capital structure of PPP projects by developing a chance-constrained programming (CCP) method with a certain confidence level under project risks. The results show that compared with existing methods, CCP method yields a greater optimal equity share, smaller NPV value, and higher confidence level depending on private sector's risk preference. Besides, the optimal equity share is mainly determined by banks' requirement and government regulation on commitment and it shrinks as banks' risk preference grows. Theoretical calculation and simulation technique were adopted to make the result more convincing. The paper can help the private sector to more reasonably and reliably determine the optimal capital structure according to their risk preference.

关键词： PPP Projects Capital Structure chance-constrained programming

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Gamma distribution approach in chance-constrained stochastic programming model

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JOURNAL OF INEQUALITIES AND APPLICATIONS 2011年第1期2011卷 1-13页

作者： Atalay, Kumru D. Apaydin, Aysen Fac Med Deptment Med Educ TR-06490 Ankara Turkey Fac Sci Dept Stat TR-06100 Ankara Turkey

In this article, a method is developed to transform the chance-constrained programming problem into a deterministic problem. We have considered a chance-constrained programming problem under the assumption that the random variables a(ij) are independent with Gamma distributions. This new method uses estimation of the distance between distribution of sum of these independent random variables having Gamma distribution and normal distribution, probabilistic constraint obtained via Essen inequality has been made deterministic using the approach suggested by Polya. The model studied on in practice stage has been solved under the assumption of both Gamma and normal distributions and the obtained results have been compared.

关键词： chance-constrained programming Essen inequality Gamma distribution

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Fuzzy chance-constrained geometric programming: the possibility, necessity and credibility approaches

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OPERATIONAL RESEARCH 2017年第1期17卷 67-97页

作者： Shiraz, Rashed Khanjani Tavana, Madjid Fukuyama, Hirofumi Di Caprio, Debora Univ Tabriz Sch Math Sci Tabriz Iran La Salle Univ Business Syst & Analyt Dept Business Analyt Philadelphia PA 19141 USA Univ Paderborn Fac Business Adm & Econ Business Informat Syst Dept D-33098 Paderborn Germany Fukuoka Univ Fac Commerce Fukuoka Japan York Univ Dept Math & Stat Toronto ON M3J 1P3 Canada Polo Tecnol IISS G Galilei Via Cadorna 14 I-39100 Bolzano Italy

Geometric programming (GP) is a powerful tool for solving a variety of optimization problems. Most GP problems involve precise parameters. However, the observed values of the parameters in real-life GP problems are often imprecise or vague and, consequently, the optimization process and the related decisions take place in the face of uncertainty. The uncertainty associated with the coefficients of GP problems can be formalized using fuzzy variables. In this paper, we propose chance-constrained GP to deal with the impreciseness and the ambiguity inherent to real-life GP problems. Given a fuzzy GP model, we formulate three variants of chance-constrained GP based on the possibility, necessity and credibility approaches and show how they can be transformed into equivalent deterministic GP problems to be solved via the duality algorithm. We demonstrate the applicability of the proposed models and the efficacy of the introduced procedures with two numerical examples.

关键词： Geometric programming chance-constrained programming Fuzzy logic Possibility Necessity Credibility

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Strategic retail pricing and demand bidding of retailers in electricity market: A data-driven chance-constrained programming

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ADVANCES IN APPLIED ENERGY 2022年 7卷

作者： Qiu, Dawei Dong, Zihang Ruan, Guangchun Zhong, Haiwang Strbac, Goran Kang, Chongqing Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing 100084 Peoples R China

This paper proposes a novel bi-level optimization model to study the strategic retail pricing and demand bidding problems of an electricity retailer that considers the interactions between demand response and market clearing process. In order to accurately forecast the day-ahead demand bids submitted by the retailer, a novel deep learning framework based on convolutional neural networks and long short-term memory is proposed that can capture both local trends and long-term dependency of the forecasting data. In addition, uncertainties about the retailer's served demand, rivals' demand bids, and wind power generation are incorporated using the data-driven uncertainty set constructed from data. We further propose chance-constrained programming that introduces a set of chance constraints to represent the operational risk associated with the market uncertainties. To solve this problem, we first reformulate chance-constrained programming as a tractable second-order conic programming and then convert it into a single-level mathematical program with equilibrium constraints by using its Karush Kuhn Tucker conditions. The scope of the examined case studies is four-fold. First, they evaluate the benefits of the proposed forecasting framework in terms of higher accuracy and expected profit compared to the conventional forecasting methods. Second, they demonstrate how demand flexibility affects the retailer's strategies and its business cases. Third, they highlight the added value of the proposed bi-level model capturing the market clearing process by comparing its outcomes against the state-of-the-art bi-level model with exogenous market prices. Finally, they analyze the retailer's strategies and business cases at different confidence levels regarding the imposed chance constraints.

关键词： Electricity retailer Demand response Deep learning Bi-level optimization problem chance-constrained programming

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Adaptive Sampling Detection Based Immune Optimization Approach and Its Application to chance constrained programming 8

Adaptive Sampling Detection Based Immune Optimization Approa...

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8th International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Yang, Kai Zhang, Zhuhong Guizhou Univ Coll Comp Sci Guiyang 550025 Peoples R China Guizhou Univ Coll Elect & Informat Guiyang 550025 Peoples R China

ISBN: (纸本)9783319122854

This work investigates a bio-inspired adaptive sampling immune optimization algorithm to solve linear or nonlinear chance-constrained optimization problems without any noisy information. In this optimizer, an efficient adaptive sampling detection scheme is developed to detect individual's feasibility, while those high-quality individuals in the current population can be decided based on the reported sample-allocation scheme;a clonal selection-based time-varying evolving mechanism is established to ensure the evolving population strong population diversity and noisy suppression as well as rapidly moving toward the desired region. The comparative experiments show that the proposed algorithm can effectively solve multi-modal chance-constrained programming problems with high efficiency.

关键词： chance-constrained programming immune optimization feasibility detection sample-allocation multimodality

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Wind power and thermal power turbines performance replacement optimization model based on chance constrained programming

Wind power and thermal power turbines performance replacemen...

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IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA)

作者： Wang, Wei Peng, Daoxin Tang, Zhongfu Qin, Chao North China Elect Power Univ Sch Humanities & Social Sci Beijing 102206 Peoples R China North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China

ISBN: (纸本)9781479969890

Energy shortage and growing environmental pressure let the power industry face increasingly tough energy conservation situation;therefore, the previous condition must be adjusted to optimize performance for power generation. Our optimization purposes can be reached by researching the power generation performance for Wind & Fire turbine and introducing chance-constrained programming. While solving chance constrained programming model, the previous model is converted to its equivalent and fuzzy satisfaction theory is introducing, which can obscure multi-objective optimization model. By converting multiple objectives into a single objective, ultimately, we get the optimum results of thermal power and wind turbine power performance scheduling model, which shows that we ultimately achieve optimal results by the power generation performance replacement of wind and fire turbine.

关键词： chance-constrained programming entropy theory fuzzy satisfaction theory

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Calculation of Maximum Injection Power of Wind Farms Based on chance-constrained programming

Calculation of Maximum Injection Power of Wind Farms Based o...

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2008 IEEE/PES Transmission & Distribution Conference & Exposition, part 3

作者： Jun Wu Guojie Li Yuanzhang Sun State Key Laboratory of Power Systems Tsinghua University BeiJing China

Due to the stochastic characteristics of wind farms outputs, this paper presents a method based on the stochastic programming theory in calculation of the maximum injection power of large-scale wind farms connected to power systems. Calculation of the maximum injection power can be turned into a chance-constrained programming problem under certain security, stability and power quality constraints by this method. It introduces random variables to describe active powers of wind farms and pays attention to realistic operating situations during short time periods. A hybrid intelligent algorithm combined with the stochastic simulation, neural networks and genetic algorithm is proposed to calculate the maximum injection power. The results for the IEEE 30-bus system with wind farms demonstrate the correctness and effectiveness of the proposed method. The method avoids fussy calculation of the dynamic simulation and conservative results computed by deterministic programming methods, and it ensures the optimization of results.

关键词： Maximum injection power Wind farm Stochastic programming chance-constrained programming Hybrid intelligent algorithm

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Two-Stage chance-constrained Fractional programming for Sustainable Water Quality Management under Uncertainty

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2015年第5期141卷 04014074-04014074页

作者： Zhou, X. Huang, G. H. Zhu, H. Yan, B. Univ Regina Inst Energy Environm & Sustainable Communities Regina SK S4S 0A2 Canada North China Elect Power Univ SC Energy & Environm Res Acad Beijing 102206 Peoples R China Xiamen Univ Technol Dept Environm Engn Xiamen 361024 Peoples R China

In this study, a two-stage chance-constrained fractional programming (TCFP) method is developed for dealing with water quality management problems associated with stochastic inputs. Two-stage chance-constrained fractional programming is a hybrid of stochastic linear fractional programming (SLFP) and two-stage stochastic programming (TSP) methods. It can not only balance objectives of two aspects through converting a bi-objective problem into a ratio one but can also analyze various policy scenarios when the promised production targets are violated. For demonstrating its advantages, the proposed TCFP method is applied to a case study of water quality management where managers have to consider conflicting objectives between economic development and environmental conservation, as well as stochastic features expressed as probability distributions. The obtained solutions under different significance levels can help managers to identify desired policies under various environmental, economic, and constraint-violation conditions.

关键词： Two-stage stochastic programming Linear fractional programming chance-constrained programming Uncertainty Water quality management

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A long-term intelligent operation and management model of cascade hydropower stations based on chance constrained programming under multi-market coupling

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ENVIRONMENTAL RESEARCH LETTERS 2021年第5期16卷

作者： Lu, Jia Li, Gang Cheng, Chuntian Liu, Benxi Dalian Univ Technol DUT Inst Hydropower Syst & Hydroinformat Dalian 2 Linggong Rd Dalian 116024 Liaoning Peoples R China

Under the medium- and long-term electric markets, cascaded hydropower stations face a series of practical challenges due to the uncertainty of inflow and market price. For long-term dispatch scheduling, the allocation of power generation in multimarkets is critical, including clean energy priority consumption market, inter provincial market and intra provincial market in order to maximize the operator's expected revenue and reduce the market operation risks. Based on the hydro-dominant electricity market structure and settlement rules, we propose a long-term optimal operation method for cascade hydropower stations considering the uncertainty of multiple variables. First, a seasonal autoregressive integrated moving average model is used to handle the time-varying and seasonal characteristics of inflow series by using a copula connect function to fit the joint distribution of the monthly inflow, the clearing price of the intra provincial market and the delivery volume of the inter provincial market. Then, uncertain chance-constrained programming is established. Finally, a developed particle swarm optimization algorithm embedded in a Monte Carlo simulation is solved for hydropower operation policies, and the maximum revenue, resource allocation and scheduling strategy are obtained under the corresponding risk tolerance. Taking the actual data of cascaded hydropower stations in Yunnan Province, China, as an example, a simulation analysis is carried out. The results show that the proposed method can reasonably describe the uncertainty and correlation between the variables, realizing the optimal allocation of resources among multimarkets, and provide references for the long-term optimal operation of cascade hydropower stations in a multimarket environment. The results also show that the decision strategies should be determined considering the decision-maker's risk preference.

关键词： cascade hydropower stations multimarket Copula-SARIMA chance-constrained programming

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