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A Hybrid Method for Transient Stability-Constrained Optimal Power Flow Computation

IEEE TRANSACTIONS ON POWER SYSTEMS 2012年第4期27卷 1769-1777页

作者： Xu, Yan Dong, Zhao Yang Meng, Ke Zhao, Jun Hua Wong, Kit Po Univ Newcastle Ctr Intelligent Elect Networks CIEN Callaghan NSW 2308 Australia Hong Kong Polytech Univ Kowloon Hong Kong Peoples R China Univ Western Australia Sch Elect Elect & Comp Engn Perth WA 6009 Australia

A hybrid method combining classical deterministic programming technique and evolutionary algorithm (EA)-enhanced stochastic optimization strategy is proposed for more effective and flexible computation of transient stability-constrained optimal power flow (TSC-OPF). The method consists of searching the maximum TSC-feasible solution region with EA and deterministically optimizing an operating point in the found region via a conventional OPF solution. Stability constraints are exactly treated by a rigorous transient stability assessment (TSA) procedure, where complex system model and multi-contingency can be readily considered and stability margin is applied as the transient stability index (TSI) to avoid over-stabilizing. Benefiting from both the EA and the deterministic programming, the proposed method can continuously approach the global optima in a robust, reliable and fast-convergent way. The method is verified on the New England test system and a dynamic equivalent system of a real-world large power grid. Comparing with existing methods, it has provided more economic solutions and better adaptability to multi-swing instability and multi-contingency stabilizing. It can also be a unified approach for solving other stability constrained-OPFs.

关键词： deterministic programming evolutionary algorithm optimal power flow stability margin transient stability

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Long term nurse scheduling via a decision support system based on linear integer programming: A case study at the University Hospital in Cagliari

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COMPUTERS & INDUSTRIAL ENGINEERING 2018年 126卷 337-347页

作者： Zanda, Simone Zuddas, Paola Seatzu, Carla Univ Cagliari Dept Elect & Elect Engn Cagliari Italy Univ Cagliari Dept Math & Comp Sci Cagliari Italy

This paper proposes a long term nurse scheduling approach based on linear integer programming. The main contribution consists in the derivation of a mathematical model that characterizes in a given time horizon, the set of feasible scheduling that satisfy a series of constraints related to contractual rules, specific local requirements, and different working conditions of nurses. When performing optimization, different goals may be imposed. Motivated by a real case study in Italy, special attention is devoted to the following three main issues: reduce the requirement of nurses from other departments, keep the number of working hours of each nurse as close as possible to a given value that is proportional to the length of the considered time horizon, and assign not isolated free days. To address the issues related to computational complexity in the case of very long time horizons and even more, to keep into account sudden variations in the availability of nurses, the proposed optimization model has been included in a Decision Support System (DSS) which allows to partition the whole time interval in several time intervals, where the scheduling is performed taking into account a series of input data that are continuously updated. A real case study is considered, namely the surgery department of the University Hospital in Cagliari, Italy. The scheduling resulting from the proposed approach is compared with that adopted by the hospital planner who currently computes it manually. It is also shown that in most of the cases, the proposed method finds out solutions not considered by the hospital planner, greatly reducing redundancies or weakness in the staff, and satisfying all the required constraints.

关键词： Nurse scheduling Optimization Healthcare Integer programming deterministic programming

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New approaches for optimizing standby redundant systems with fuzzy lifetimes

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COMPUTERS & INDUSTRIAL ENGINEERING 2018年 123卷 263-277页

作者： Zhou, Jian Han, Yulin Liu, Jing Pantelous, Athanasios A. Shanghai Univ Sch Management Shanghai 200444 Peoples R China Monash Univ Monash Business Sch Dept Econometr & Business Stat 20 Chancellors WalkWellington Rd Clayton Vic 3800 Australia

Standby redundancy is one of the most popular ways to optimize systems reliability with several applications in modern engineering. Under real-world conditions, the system lifetime can be modeled as a fuzzy interval due to parameter uncertainty and data inaccuracy. In this paper, several fuzzy standby redundancy optimization models are proposed utilizing three distinct types of decision criteria, namely as the expected value model, the chance-constrained and the dependent-chance programming. Thereafter, two solution processes are proposed. The first one utilizes a novel algorithm with lower computational complexity for a specific fuzzy objective which is embedded into intelligent optimization algorithms to derive a hybrid algorithm. While the other approach relies upon the transformation of the fuzzy model into its deterministic counterpart employing a family of fuzzy operational laws, then the solution derives in a straightforward manner using classical optimization algorithms or some well-developed software packages. Finally, several diverse numerical examples are considered for demonstrating the superiority of our treatment. It is shown that the herein developed solution approaches for the fuzzy standby redundancy optimization problems exhibit higher efficiency and satisfactory accuracy as compared with standard solution methods.

关键词： Standby redundancy optimization Fuzzy lifetime Fuzzy simulation deterministic programming LR fuzzy interval

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Synergistic integration of control equipment and renewable resources for improved self-healing in smart distribution networks

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ELECTRICAL ENGINEERING 2024年 1-21页

作者： Gilanifar, Milad Shahabi, Majid Babol Noshirvani Univ Technol Fac Elect & Comp Engn Babol Iran

This paper presents a novel deterministic model for the optimal placement of remotely controllable switches, manual switches, and distributed generation units. This model aims to bolster the self-healing capability of smart radial distribution networks by exploring the synergistic impact of integrating renewable resources and switches. The model uses climate and load data from a specific geographical region and accurately reflects the stochastic nature of wind and solar radiation. By formulating the problem as a mixed-integer nonlinear programming model and employing heuristic methods, including particle swarm optimization and artificial bee colony, we aim to minimize energy interruptions and operational costs while enhancing network reliability. The introduction of remotely controlled switches enables various restoration strategies, while renewable units in faulted zones can function as local islands, ensuring a continuous power supply. Implementation of the proposed model on the IEEE 33-bus test network shows considerable reductions in expected energy not supplied and planning costs. The results highlight that the optimal integration of renewable resources and control devices results in a more reliable and self-healing distribution network, offering a significant advantage over traditional approaches.

关键词： Remotely controllable switches Self-healing Smart grid Wind turbine Photovoltaic deterministic programming

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Note on Solving Probabilistic programming Problems Involving Multi-choice Parameters

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JOURNAL OF INTERDISCIPLINARY MATHEMATICS 2015年第5期18卷 617-627页

作者： Hung, Kuo-Chen Lin, Scott Shu-Cheng Chu, Chun-Hsiao Hungkuang Univ Dept Comp Sci & Informat Management Taichung Taiwan Chungyu Inst Technol Dept Tourism & Travel Management Keelung Taiwan Aletheia Univ Dept Tourism New Taipei Taiwan

We study the probabilistic programming problems with multi-choice parameters proposed by Acharya and Biswal (2011) that was published in Opsearch. We point out that the multi-choice parameters can be simplified to avoid the complicated solution procedure proposed by Acharya and Biswal (2011). We use the same numerical example of Acharya and Biswal (2011) to demonstrate that our simplification is effective to derive better minimum.

关键词： Probabilistic programming multi-choice programming non-linear programming deterministic programming

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Optimal Storage Battery Scheduling for Energy-Efficient Buildings in a Microgrid 27

Optimal Storage Battery Scheduling for Energy-Efficient Buil...

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27th Chinese Control and Decision Conference (CCDC)

作者： Zhang, Yuanming Jia, Qing-Shan Tsinghua Univ Ctr Intelligent & Networked Syst Beijing 100084 Peoples R China

ISBN: (纸本)9781479970162

Residential and commercial buildings consume more than 40% of electrical power and contribute a large percentage of CO2 and SO2. In order to alleviate energy crises and environment deterioration, improving energy efficiency of buildings has become significant and important. This paper considers the storage battery scheduling problem in a networked building microgrid with decentralized solar power and storage battery. First, we formulate an optimization model with minimizing overall electricity cost as the objective. Then we use a linearization technique to simplify the model and reformulate the optimization problem as a deterministic programming and further a stochastic programming with scenario tree being used to handle the uncertainties of solar power and building load. At last, test results of a toy model verify the effectiveness of the proposed method and provide insight on power scheduling in a networked microgrid for improving building energy efficiency.

关键词： building energy efficiency storage battery microgrid deterministic programming stochastic programming

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Stochastic and deterministic Unit Commitment Considering Uncertainty and Variability Reserves for High Renewable Integration

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ENERGIES 2017年第1期10卷 140页

作者： Marneris, Ilias G. Biskas, Pandelis N. Bakirtzis, Anastasios G. Aristotle Univ Thessaloniki Dept Elect & Comp Engn Thessaloniki 54124 Greece

The uncertain and variable nature of renewable energy sources in modern power systems raises significant challenges in achieving the dual objective of reliable and economically efficient system operation. To address these challenges, advanced scheduling strategies have evolved during the past years, including the co-optimization of energy and reserves under deterministic or stochastic Unit Commitment (UC) modeling frameworks. This paper presents different deterministic and stochastic day-ahead UC formulations, with focus on the determination, allocation and deployment of reserves. An explicit distinction is proposed between the uncertainty and the variability reserve, capturing the twofold nature of renewable generation. The concept of multi-timing scheduling is proposed and applied in all UC policies, which allows for the optimal procurement of such reserves based on intra-hourly (real-time) intervals, when concurrently optimizing energy and commitments over hourly intervals. The day-ahead scheduling results are tested against different real-time dispatch regimes, with none or limited look-ahead capability, or with the use of the variability reserve, utilizing a modified version of the Greek power system. The results demonstrate the enhanced reliability achieved by applying the multi-timing scheduling concept and explicitly considering the variability reserve, and certain features regarding the allocation and deployment of reserves are discussed.

关键词： deterministic programming multi-timing scheduling real-time dispatch stochastic programming uncertainty reserve variability reserve wind integration

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Optimal Storage Battery Scheduling for Energy-Efficient Buildings in a Microgrid

Optimal Storage Battery Scheduling for Energy-Efficient Buil...

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第27届中国控制与决策会议

作者： Yuanming Zhang Qing-Shan Jia Center for Intelligent and Networked Systems Tsinghua University

ISBN: (纸本)9781479970186

Residential and commercial buildings consume more than 40%of electrical power and contribute a large percentage of CO2 and *** order to alleviate energy crises and environment deterioration,improving energy efficiency of buildings has become significant and *** paper considers the storage battery scheduling problem in a networked building microgrid with decentralized solar power and storage ***,we formulate an optimization model with minimizing overall electricity cost as the *** we use a linearization technique to simplify the model and reformulate the optimization problem as a deterministic programming and further a stochastic programming with scenario tree being used to handle the uncertainties of solar power and building *** last,test results of a toy model verify the effectiveness of the proposed method and provide insight on power scheduling in a networked microgrid for improving building energy efficiency.

关键词： building energy efficiency storage battery microgrid deterministic programming stochastic programming

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A Comparison of two Hydro Scheduling Algorithms, SDDP and LpSim

A Comparison of two Hydro Scheduling Algorithms, SDDP and Lp...

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International Universities Power Engineering Conference

作者： Gunnar Geir Pétursson Kristján Jónasson Egill Benedikt Hreinsson úlfar Linnet University of Iceland Landsvirkjun Iceland

ISBN: (纸本)9781479932559

In this study we present the results of a comparison between the two methods implemented in two hydro scheduling algorithms, SDDP and LpSim. Both methods are carried out for a range of reservoir inflow series and variable load. The yardstick we use for the comparison of the methods is the amount of load each respective program can satisfy while fulfilling restrictions to maximum load shedding and minimum reservoir volumes. Iceland's isolated power system was selected for this case study. Its electricity production is pre-dominantly hydro (74%), the rest being geothermal (26%) with intermittent thermal generation options (~0.02% in production, 2.4% in installed capacity). The system is not connected with other countries. In January 2013, wind power officially became the third renewable source for electricity in Iceland. In the near future the possibility of a submarine power cable connecting Iceland to mainland Europe may further increase the dynamics of Iceland's currently isolated power system. If constructed, future power stations in the lower Thjórsá river basin will add more levels to the cascade of hydro plants there, decreasing the accuracy of hydro scheduling algorithms that rely on aggregate reservoir approximations. It is therefore imperative to investigate the performance of the currently used medium- and long-term hydro scheduling algorithm.

关键词： Comparison deterministic programming hydro scheduling SDDP stochastic programming water values

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