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An investigation of reactive power planning based on chance constrained programming

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2007年第9期29卷 650-656页

作者： Yang, Ning Yu, Cw. Wen, Fushuan Chung, C. Y. Hong Kong Polytech Univ Dept Elect Engn Hong Kong Hong Kong Peoples R China Zhejiang Univ Dept Elect Engn Hangzhou 310027 Peoples R China

Deregulation in the electricity supply industry has brought many new challenges to the problem of reactive power planning. Although the problem has been extensively studied, available standard optimization models and methods do not offer good solutions to this problem, especially in a competitive electricity market environment where many factors are uncertain. Given this background, a novel method for reactive power planning based on chance constrained programming is presented in this paper, with uncertain factors taken into account. A stochastic optimization model is first formulated under the presumption that the generator outputs and load demands can be modeled as specified probability distributions. A method is then presented for solving the optimization problem using the Monte Carlo simulation method and genetic algorithm. Finally, a case study is used to illustrate the validity and essential features of the proposed model and methodology. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： reactive power planning uncertainties chance constrained programming Monte Carlo simulation genetic algorithm

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Stochastic scheduling with minimizing the number of tardy jobs using chance constrained programming

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MATHEMATICAL AND COMPUTER MODELLING 2013年第5-6期57卷 1154-1164页

作者： Elyasi, Ali Salmasi, Nasser Sharif Univ Technol Dept Ind Engn Tehran Iran

In this research, two scheduling problems i.e., single machine scheduling problem with minimizing the number of tardy jobs (1 parallel to Sigma U-j) and two machine flow shop scheduling problem with a common due date and minimizing the number of tardy jobs (F-2|d(j) = d| Sigma U-j) are investigated in a stochastic setting in the class of non-preemptive static list policies. It is assumed that the processing times of jobs are independent random variables. The stochastic problems are solved based on chance constrained programming. An equivalent deterministic problem is generated for each stochastic problem by linearization of the chance constraints. Then, the generated deterministic problems are solved using efficient algorithms, which have been developed for the deterministic version of the problems. Several numerical examples are presented to illustrate the solution methods. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Stochastic scheduling Single machine scheduling Flow shop scheduling Tardy jobs chance constrained programming

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On solving chance constrained programming problems involving uniform distribution with fuzzy parameters

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2013年第2期7卷 151-159页

作者： Biswas, Animesh Modak, Nilkanta Univ Kalyani Dept Math Kalyani 741235 W Bengal India

As a special field of mathematical programming, fuzzy chance constrained programming is now emerging as a promising area of study from the view point of its ability to capture fuzziness and randomness simultaneously. In this paper a fuzzy goal programming method has been presented for solving multiobjective chance constrained programming problems in which the right sided parameters associated with the system constraints are uniformly distributed fuzzy random variables. In the proposed approach the fuzzy chance constrained programming problem is converted first into its equivalent fuzzy programming form by using the concept of alpha-cuts. Then the problem is decomposed on the basis of tolerance ranges of fuzzy parameters associated with the system constraints. Next by setting imprecise aspiration level to each of the individual objectives, the membership function is defined to measure the degree of achievements of goal levels of the objectives. Afterwards a fuzzy goal programming model is developed to achieve the highest degree of each of the defined membership goals to the extent possible by minimizing the group regrets consisting of under deviational variables of the fuzzy goals in the decision making context. To explore the potentiality of the proposed approach, an illustrative example is solved and the solution is compared with other technique.

关键词： Multiobjective programming chance constrained programming uniform distribution fuzzy numbers fuzzy random variables fuzzy goal programming

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A chance constrained programming model and an improved large neighborhood search algorithm for the electric vehicle routing problem with stochastic travel times

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EVOLUTIONARY INTELLIGENCE 2023年第1期16卷 153-168页

作者： Messaoud, Elhassania Hassania Sch Publ Works Logist Transport & Rd Safety Res Team Casablanca Morocco

The Electric Vehicle Routing Problem (EVRP) is an extension to the well-known Vehicle Routing Problem (VRP) where the fleet consists of electric vehicles, which may need to visit recharging stations while servicing the customers due to their battery capacities. This paper solves the Electric Vehicle Routing Problem with Stochastic Travel Times (EVRPSTT) by proposing a chance constrained programming (CCP) Model, as well as a new scheme based on an Improved Large Neighborhood Search (ILS) algorithm and a Monte Carlo Sampling (MCS) procedure. The proposed approach is firstly tested in the deterministic environment using the EVRP benchmark data set, where the numerical results show that this approach is able to provide EVRP optimal solutions, within a very short computational time, for 39 out of 48 used benchmark instances with 20, 50, 75 and 100 customers. Thereafter, to show the efficiency of the proposed approach for solving the CCP model of the EVRPSTT, others tests are performed on the same set of instances, while taking into consideration a large number of scenarios.

关键词： Electric vehicle routing problem Stochastic travel times chance constrained programming Large neighborhood search Monte carlo sampling

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A quantum krill herd algorithm based chance constrained programming for surfactant flooding 32

A quantum krill herd algorithm based chance constrained prog...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Liu, Zhe Li, Shurong Beijing Univ Posts & Telecommun Automat Sch Beijing 100876 Peoples R China

ISBN: (纸本)9781728158556

This paper presents a chance constrained programming (CCP) based on quantum krill herd algorithm (QKH) to solve an engineering optimization for surfactant flooding in oil development. First, an optimal control model describing surfactant flooding mechanism is established. In this model, the fluctuant oil price is the uncertainty needs to be addressed, the injection concentration of surfactant is the control variable and the net present value is the objective function. The chance constrained programming (CCP) is adopted to transform the uncertain model into a deterministic nonlinear programming model. Then a quantum krill herd algorithm (QKH) is presented to solve the model. Compared with the standard krill herd algorithm (KH), QKH has a superior global searching ability and computational efficiency owing to the quantum representation. At last, an actual optimization model for surfactant flooding is solved by the proposed method and the optimal injection strategy is obtained.

关键词： Quantum krill herd algorithm chance constrained programming Surfactant flooding

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Multi-objective chance constrained programming Model for Operational-Planning of V2G Integrated Microgrid 5

Multi-objective Chance Constrained Programming Model for Ope...

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5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT)

作者： Waqar, Asad Wang, Shaorong Khalid, Muhammad S. Shi, Xiaohan Huazhong Univ Sci & Technol Wuhan Hubei Peoples R China Shandong Univ Jinan Shandong Peoples R China

ISBN: (纸本)9781467371063

This paper presents a novel chance constrained programming model for the operational-planning of a microgrid containing renewable energy sources like wind and solar, and vehicle to grid (V2G) storage. The model investigates the economic implications of the V2G storage on the microgrid operation. The microgrids consisting of intermittent renewable energy sources always need additional backup to mitigate the fluctuating power. This additional backup results in increased investment and operational costs. The V2G storage in such a situation can act as energy buffer and can supply the microgrid with balancing services at low costs. However the availability of the power from the V2G storage is also intermittent in nature because of the random plug-in pattern of electric vehicles. In this paper a microgrid containing a wind, PV and diesel generation and energy storage (V2G/fixed) is connected to an external grid. The external grid has the problem of capacity shortage. Because of the intermittent energy sources, a novel multi-objective stochastic chance constrained programming model is developed for the operational planning of microgrid. The multiple objectives include the minimization the total net present cost and the operating hours of the diesel generation subject to constraints. The time series data for wind speed, solar radiation, V2G plug-in pattern and load fluctuation is estimated by using the Metropolis-Hastings algorithm, which is a Markov chain Monte Carlo Method. Due to the conflicting objectives, the non-dominant solutions are sorted out and presented using the Pareto front. The simulation results show confidence levels of provision of power by V2G storage and renewable energy sources. The results further show substantial economic implications of V2G storage on microgrid operation in terms of minimization of the total net present cost.

关键词： V2G operational-planning chance constrained programming Metropolis-Hastings algorithm Markov chain Monte Carlo microgrid renewable energy

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AN INSURANCE AND INVESTMENT PORTFOLIO MODEL USING chance-constrained programming

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 1995年第5期23卷 577-585页

作者： LI, SX ADELPHI UNIV SCH BUSINESS & MANAGEMENTGARDEN CITYNY 11530 USA

An insurance and investment portfolio model is here formulated in terms of the LP-Models' of chance constrained programming, which is then related to the 'satisficing concepts' of Simon. For a given insurers' aspiration level of return on equity and risk levels of violating minimum requirements on return and on rash and Liquid assets, rye propose a method to maximize the insurers' probability of achieving their aspiration level, subject to two chance constraints and other regulatory and institutional constraints. An empirical example is given, based on the industry's aggregated data for a twenty year period.

关键词： MODELING OPTIMIZATION PORTFOLIO ANALYSIS chance constrained programming

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Optimal Scheduling for Home Energy Based on chance constrained programming

Optimal Scheduling for Home Energy Based on Chance Constrain...

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China International Conference on Electricity Distribution (CICED)

作者： Fu, Zhixin Wu, Ruiqian Yuan, Yue Hohai Univ Coll Energy & Elect Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538667750

Aiming at the optimal scheduling for home energy, on the load side, this paper classifies the loads according to the urgency, and establishes their satisfaction models respectively, then minimizes the customer's cost while guarantees the inhabitants' satisfaction. On the power side, an optimal scheduling model based on the chance constrained programming (CCP) algorithm is proposed to optimize the energy scheduling problem of the home energy management system(HEMS). This model can considerably decrease the consumers' cost by introducing real-time pricing(RTP), photovoltaic(PV) and energy storage system(ESS). Considering that the short-term distribution of PV output is approximately normal distribution, the PV output is expressed as the sum of predicted output and prediction error. The distribution characteristics of error are applied to optimize the scheduling of ESS. The experimental studies reveal that the proposed work is able to satisfy the consumer and achieves better results than previous related researches.

关键词： Home energy management satisfaction chance constrained programming real-time pricing electricity cost

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A Unified Approach for Multiobjective Fuzzy chance constrained programming with Joint Normal Distribution

A Unified Approach for Multiobjective Fuzzy Chance Constrain...

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World Congress on Engineering and Computer Science

作者： Biswas, Animesh Modak, Nilkanta Univ Kalyani Dept Math Kalyani 741235 W Bengal India

ISBN: (纸本)9789881925237

This paper describes some mathematical techniques and modeling aspects for solving fuzzy multiobjective probabilistic decision making problems in which the constraints are jointly distributed and the right sided parameters of the constraints are normally distributed fuzzy random variables. The probabilistic model is first converted into equivalent fuzzy programming model by using incomplete gamma function described in a fuzzy decision making environment. Then independent optimal solution of each objective are determined under the decomposed set of system constraints which are obtained by considering fuzzy nature of parameters involved with them. The tolerance membership function for measuring the degree of satisfaction of the decision maker with the achievement of objective values is defined. The membership functions are then converted into fuzzy goals by assigning unity as aspiration level. Finally a weighted fuzzy goal programming technique is used to achieve the highest degree of each of the defined membership goal to the extent possible by minimizing under deviational variables and thereby obtaining most satisfactory solution in the decision making context which leads to an efficient as well as optimal compromise solution. A numerical example is solved to illustrate the proposed methodology and the solution is compared with some other technique developed earlier.

关键词： chance constrained programming Incomplete gamma function Fuzzy random variable Fuzzy number Fuzzy goal programming

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Transmission system expansion planning based on stochastic chance constrained programming with security constraints

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3rd International Conference on Electric Utility Deregulation, Restructuring and Power Technologies

作者： Fan, Hong Cheng, Haozhong Ying, Zhiwei Jiang, Fengqing Shi, Fangdi Shanghai Jiao Tong Univ Shanghai 200240 Co Peoples R China

ISBN: (纸本)9787564112493

Transmission system expansion planning faces a great challenge from more uncertainties under electric market environment. A new transmission system expansion planning with security constraints is proposed based on chance constrained programming in the paper. This model has two merits 1) it introduces the investment risk and security risk from uncertainties based on chance constraints, the planning operator may be given more information from the optimal strategies and make right decisions. 2) it coordinates the conflict between security risk and investment cost in transmission planning. Niche genetic algorithm based stochastic simulation method is proposed to solve the planning model and successfully realize the task Simulation results obtained from modified 18-bus system and comparison analysis prove that the proposed methodology is valid.

关键词： chance constrained programming electric power system niche genetic algorithm transmission system expansion planning uncertainty

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