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A Hybrid Method for Optimal Scheduling of Short-Term Electric Power Generation of Cascaded Hydroelectric Plants Based on Particle Swarm Optimization and chance-constrained programming

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IEEE TRANSACTIONS ON POWER SYSTEMS 2008年第4期23卷 1570-1579页

作者： Wu Jiekang Zhu Jianquan Chen Guotong Zhang Hongliang Guangxi Univ Sch Elect Engn Nanning 530004 Guangxi Peoples R China Guangxi Elect Net Co Wuzhou Power Supply Bur Wuzhou 543002 Peoples R China Guangxi Fangyuan Elect Co Ltd Nanning 530028 Peoples R China

A novel strategy for optimal scheduling of short-term electric power generation of cascaded hydroelectric plants based on particle swarm optimization (PSO) and chance-constrained programming is presented to maximize the expected profit at a given risk level in this paper. Based on chance-constrained programming, in which some specified probability are given to simulate some uncertainties, such as water inflows, electricity prices, unit status, and so on. This paper proposes a model for short-term scheduling optimization of cascaded hydro plants, which includes uncertainties, spatial-temporal constraints among cascaded reservoirs, etc. A hybrid particle swarm optimization (HPSO), which is embedded with evolutionary algorithms, is presented to use for the solution of global optimization problems. Catastrophe theory, which is concerned with natural evolutionary or survival-of-the-fittest, is utilized as an indication of the premature converge of PSO, and the positions of particles are further adjusted in the search space according to chaos optimization. In this way, each particle competes and cooperates with its neighbors. The proof shows that HPSO is guaranteed to converge to the global optimization solution with probability one. The model presented is solved by a combination method of HPSO and Monte Carlo simulation. Finally, a numerical example is served for demonstrating the feasibility of the method developed.

关键词： Cascaded hydroelectric plants chance-constrained programming optimal scheduling particle swarm optimization short-term electric power generation

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A chance-constrained programming based Approach to Optimal Hydro Energy Allocation

A Chance-Constrained Programming based Approach to Optimal H...

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2nd International Power and Energy Conference (PECon 2008)

作者： Liu, Guozhong Wen, Fushuan S China Univ Technol Dept Elect Engn Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781424424047

For the purpose of bringing into play its role as a peak load regulator, an on-grid electricity price shaver, and a reserve in a hydrothermal system, hydro energy available in each period is divided into two parts: the part to regulate peak loads and the other to serve as a system reserve, to cover insufficient energy supply due to the forced outage of thermal units. A recursive formula for the outage capacity models of thermal units is developed, and the expected energy loss is calculated with the probabilistic method. With the framework of chance-constrained programming, a mathematical model is proposed for determining the critical load point in hydro energy distribution optimization, On the basis of solution of the model with the Monte Carlo simulation algorithm, detailed illustration is given to the proposed model and the algorithm, with the IEEE reliability test system used as an example.

关键词： electricity market hydro energy allocation capacity model chance-constrained programming

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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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IEEE/PES Transmission and Distribution Conference and Exposition

作者： Wu, Jun Li, Guojie Sun, Yuanzhang Tsinghua Univ State Key Lab Power Syst Beijing 100084 Peoples R China

ISBN: (纸本)9781424419036

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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Healthcare Product Procurement in Dual Supplied Systems

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IFAC Proceedings Volumes 2013年第9期46卷 1650-1655页

作者： Cigdem Z. Gurgur Doermer School of Business and Management Sciences Indiana - Purdue University Fort Wayne IN 46805 USA

In this study we consider supplier selection and quantity allocation decisions for a health care provider/hospital that may purchase new products, as well as refurbished products. New products are more expensive than the used ones, but can be delivered any time and in any quantity. Refurbished products have the same quality, but they are sold in bundles. If the hospital accepts to purchase a specific bundle, it purchases the whole package, which may involve products it does not use or the quantities exceeding its need. The solution of the problem suggests that if the cost of excess inventory is high relative to the cost of understocking, the firm tends to rely on purchases through spot market. Which products are bundled at what quantity would also determine the attractiveness of the bundle. If some bundles involve items with positively correlated demand rate, this would make these bundles more attractive to the hospital, since it reduces the cost of over/under stocking.

关键词： Health Care Supply Chains Heuristic Procedure Stochastic Inventory Control chance-constrained programming Simulated Annealing

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The Routing Optimization for Multi-modal Transport with Carbon Emission Consideration under Uncertainty

The Routing Optimization for Multi-modal Transport with Carb...

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32nd Chinese Control Conference (CCC)

作者： Sun Bin Chen Qiushuang Nankai Univ Coll Informat Tech Sci Tianjin 300071 Peoples R China

ISBN: (纸本)9789881563835

To deal with the uncertainty and environmental pollution of multi-modal transport, a chance-constrained programming model for transportation decision of multi-modal transport is proposed. The activity-based approach is adopted to estimate the carbon emission, and the chance constraint is used to describe the influence of uncertain transport time on on-time delivery. To improve the solving speed, the deterministic equivalent form of chance-constraint is obtained. An algorithm based on network deformation and improved genetic algorithm is designed to solve the model. The simulation experiments show that the model considering carbon emission can effectively reduce carbon emissions and the deterministic equivalent form can efficiently accelerate the convergent speed.

关键词： Multi-modal transport Uncertainty Carbon emission chance-constrained programming

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Dynamic Traffic Assignment under Uncertainty: A Distributional Robust chance-constrained Approach

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NETWORKS & SPATIAL ECONOMICS 2012年第1期12卷 167-181页

作者： Chung, Byung Do Yao, Tao Zhang, Bo Penn State Univ Dept Ind & Mfg Engn University Pk PA 16802 USA

This paper provides a chance-constrained programming approach for transportation planning and operations under uncertainty. The major contribution of this paper is to approximate a joint chance-constrained Cell Transmission Model based System Optimum Dynamic Traffic Assignment with only partial distributional information about uncertainty as a linear program which is computationally efficient. Numerical experiments have been conducted to show the performance of the proposed approach compared with other two workable approaches based on a cumulative distribution function and a sampling method. This new approach can be used as a pragmatic tool for system optimum dynamic traffic control and management.

关键词： Dynamic traffic assignment Transportation planning chance-constrained programming Joint chance constraint Data uncertainty

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Optimal Bidding Strategies for Wind Power Producers in the Day-ahead Electricity Market

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ENERGIES 2012年第11期5卷 4804-4823页

作者： Zhang, Haifeng Gao, Feng Wu, Jiang Liu, Kun Liu, Xiaolin Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Syst Engn Inst Xian 710049 Peoples R China

Wind Power Producers (WPPs) seek to maximize profit and minimize the imbalance costs when bidding into the day-ahead market, but uncertainties in the hourly available wind and forecasting errors make the bidding risky. This paper assumes that hourly wind power output given by the forecast follows a normal distribution, and proposes three different bidding strategies, i.e., the expected profit-maximization strategy (EPS), the chance-constrained programming-based strategy (CPS) and the multi-objective bidding strategy (ECPS). Analytical solutions under the three strategies are obtained. Comparisons among the three strategies are conducted on a hypothetical wind farm which follows the Spanish market rules. Results show that bid under the EPS is highly dependent on market clearing price, imbalance prices, and also the mean value and standard deviation of wind forecast, and that bid under the CPS is largely driven by risk parameters and the mean value and standard deviation of the wind forecast. The ECPS combining both EPS and CPS tends to choose a compromise bid. Furthermore, the ECPS can effectively control the tradeoff between expected profit and target profit for WPPs operating in volatile electricity markets.

关键词： wind power bidding day-ahead electricity market risk chance-constrained programming multi-objective optimization

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LINEAR MATRIX INEQUALITIES WITH STOCHASTICALLY DEPENDENT PERTURBATIONS AND APPLICATIONS TO chance-constrained SEMIDEFINITE OPTIMIZATION

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SIAM JOURNAL ON OPTIMIZATION 2012年第4期22卷 1394-1430页

作者： Cheung, Sin-Shuen So, Anthony Man-Cho Wang, Kuncheng Cornell Univ Sch Operat Res & Informat Engn Ithaca NY 14853 USA Chinese Univ Hong Kong Dept Syst Engn & Engn Shatin Hong Kong Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China Chinese Univ Hong Kong CUHK BGI Innovat Inst Trans Shatin Hong Kong Peoples R China Chinese Univ Hong Kong Dept Syst Engn & Engn Management Shatin Hong Kong Peoples R China

The wide applicability of chance-constrained programming, together with advances in convex optimization and probability theory, has created a surge of interest in finding efficient methods for processing chance constraints in recent years. One of the successes is the development of so-called safe tractable approximations of chance-constrained programs, where a chance constraint is replaced by a deterministic and efficiently computable inner approximation. Currently, such an approach applies mainly to chance-constrained linear inequalities, in which the data perturbations either are independent or define a known covariance matrix. However, its applicability to chance-constrained conic inequalities with dependent perturbations-which arises in finance, control, and signal processing applications-remains largely unexplored. In this paper, we develop safe tractable approximations of chance-constrained affinely perturbed linear matrix inequalities, in which the perturbations are not necessarily independent, and the only information available about the dependence structure is a list of independence relations. To achieve this, we establish new large deviation bounds for sums of dependent matrix-valued random variables, which are of independent interest. A nice feature of our approximations is that they can be expressed as systems of linear matrix inequalities, thus allowing them to be solved easily and efficiently by off-the-shelf solvers. We also provide a numerical illustration of our constructions through a problem in control theory.

关键词： linear matrix inequalities large deviation bounds chance-constrained programming stochastic programming semidefinite programming

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chance-constrained programming models for capital budgeting with NPV as fuzzy parameters

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2007年第1期198卷 149-159页

作者： Huang, Xiaoxia Univ Sci & Technol Beijing Sch Management Beijing 100083 Peoples R China

In an uncertain economic environment, experts' knowledge about outlays and cash inflows of available projects consists of much vagueness instead of randomness. Investment outlays and annual net cash flows of a project are usually predicted by using experts' knowledge. Fuzzy variables can overcome the difficulties in predicting these parameters. In this paper, capital budgeting problem with fuzzy investment outlays and fuzzy annual net cash flows is studied based on credibility measure. Net present value (NPV) method is employed, and two fuzzy chance-constrained programming models for capital budgeting problem are provided. A fuzzy simulation-based genetic algorithm is provided for solving the proposed model problems. Two numerical examples are also presented to illustrate the modelling idea and the effectiveness of the proposed algorithm. (c) 2005 Elsevier B.V. All rights reserved.

关键词： capital budgeting fuzzy programming chance-constrained programming NPV

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Risk and Energy Consumption Tradeoffs in Cloud Computing Service via Stochastic Optimization Models

Risk and Energy Consumption Tradeoffs in Cloud Computing Ser...

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5th IEEE/ACM International Conference on Utility and Cloud Computing (UCC)

作者： Wang, Jue Shen, Siqian Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA

ISBN: (纸本)9780769548623;9781467344326

Energy efficiency and computational reliability are two key concerns associated with modern computations that involve computational resource sharing and highly uncertain job arrivals from various sources (customers). In 2010, large-scale data center operations consume around 2% of the total energy use in the US. Meanwhile, the development of cloud computing in the IT industry possesses great potential for lowering energy consumption by partitioning and scheduling job requests among multiple computational servers. In this paper, we formulate stochastic integer programming models to minimize energy consumption of cloud computing servers over finite time periods, while maintaining a pre-specified quality of service (QoS) level for satisfying uncertain computational requests. The models dynamically monitor and predict customer requests for each period, and proactively switch servers on/off according to estimated customer requests. QoS levels are maintained by either enforcing zero unsatisfied requests, or imposing a joint chance constraint to bound possible failures in a backlogging model. When uncertain requests follow continuous distributions, we employ the Sampling Average Approximation for generating scenario-based requests. Such an approach transforms the original probabilistic model into deterministic mixed-integer linear programs. We further demonstrate computational results of all models by testing instances with different parameter combinations, and investigate how backlogging, unit penalty cost and QoS levels influence computational performances and optimal solutions.

关键词： Sustainability and stability of Cloud Computations chance-constrained programming mixed-integer programming sensitivity analysis

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