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International Conference on Sustainable Power Generation and Supply

作者： Han Xiaoqing Yan Yong Taiyuan Univ Technol Coll Elect & Power Engn Taiyuan 030024 Shanxi Peoples R China

ISBN: (纸本)9781424449347

The calculation of penetration limits of wind farm is an important issue on the planning of wind farm, Basing on reliability modeling of wind farm, this paper proposes a method to calculate penetration limits of wind farm by taking reliability index as restriction. Since load demand and output of wind farm are of randomicity, and the restriction is expressed as form of reliability, Monte Carlo method and genetic algorithm are used on test system. By programming on MATLAB, two cases with different average wind speeds and different confidence levels are considered. The results show that it is very essential to take into account stochastic characteristics of wind speed on calculating penetration limits.

关键词： chance constrained programming Genetic algorithm MATLAB Monte Carlo simulation Penetration Power system Reliability index Reliability modeling Wind farm Wind power generation

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Pool & Discard Algorithm for chance constrained Optimization Problems

Pool & Discard Algorithm for Chance Constrained Optimization...

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作者： Kůdela, Jakub Brno University of Technology

In this paper, we describe an effective algorithm for handling chance constrained optimization problems, called the Pool & Discard algorithm. The algorithm utilizes the scenario approximation framework for chance constrained optimization problems, and the warm-start and problem modification features of modern solvers. The exploitation of the problem structure and efficient implementation allows us to considerably speed up the computations, especially for large instances, when compared with conventional methods.

关键词： chance constrained programming scenario approximation P&D algorithm stochastic programming constraint removal

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chance-constrained performance of power-to-gas system incorporating gas-fired power plant and wind power producer with CO2 recycling

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JOURNAL OF CLEANER PRODUCTION 2022年 367卷

作者： Yang, Guihua Ma, Junchi Liu, Yonghao Gao, Yukai Yang, Dongmei Daqing Normal Univ Sch Elect & Mech Engn Daqing 163712 Heilongjiang Peoples R China Ctr Dis Control & Prevent Xinjiang Prod & Construct Corps Urumqi 830002 Xinjiang Peoples R China

The power-to-gas (P2G) technology is one of the primary solutions to the global warming crisis due to its rapid technological development. The environment is under the devastating influence of greenhouse gases such as CO2, leading to the integration of multiple systems and components on the supply and demand side, such as P2G, in coordination with renewable energy (RE) to tackle this problem. P2G can efficiently perform alongside gas-or coal-fired power plant to accomplish the carbon recycling process. The primary energy source for P2G is surplus RE which is easily accessible. In this article, the proposed model uses an on-site carbon capture, which has the advantage of preventing CO2 from being transported to distant areas. The recycling process of the carbon is fulfilled through water carbon capture, water electrolyzer and methanation facilities. The whole goal of this system is to use the surplus wind energy to convert CO2 to CH4, resulting in excessive natural gas as fuel and less carbon emission. The gas flow in mole value for input/output is considered to model the energy conversion and relations between devices alongside power and heat flows. The mathematical formulation of all devices' oper-ating range is considered to model the proposed energy system realistically. This model has the ability to reduce carbon emission and wind curtailment significantly. Moreover, in the case of measuring risk, chance-constrained programming (CCP) is implemented in the model to enhance the decision-making strategies. Various confidence levels control the CCP;decreasing it will result in a more risk-taker approach and less conservatism. According to the results, the CO2 will be decreased to 32% when using P2G. Implementing P2G and two gas storages will reduce the carbon emission to 39%. Moreover, decreasing confidence level from 100% to 75% results in 34% carbon reduction for a single power plant.

关键词： Power-to-gas Gas-fires power plant Renewable energy Emission reduction chance constrained programming

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A chance-constrained Multistage Planning Method for Active Distribution Networks

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ENERGIES 2019年第21期12卷 4154-4154页

作者： Koutsoukis, Nikolaos Georgilakis, Pavlos Natl Tech Univ Athens Sch Elect & Comp Engn Zografos 15780 Greece

This paper introduces a multistage planning method for active distribution networks (ADNs) considering multiple alternatives. The uncertainties of load, wind and solar generation are taken into account and a chance constrained programming (CCP) model is developed to handle these uncertainties in the planning procedure. A method based on a k-means clustering technique is employed for the modelling of renewable generation and load demand. The proposed solution methodology, which is based on a genetic algorithm, considers multiple planning alternatives, such as the reinforcement of substations and distribution lines, the addition of new lines, and the placement of capacitors and it aims at minimizing the net present value of the total operation cost plus the total investment cost of the reinforcement and expansion plan. The active network management is incorporated into planning method in order to exploit the control capabilities of the output power of the distributed generation units. To validate its effectiveness and performance, the proposed method is applied to a 24-bus distribution system.

关键词： active distribution network chance constrained programming distribution network planning metaheuristic optimization

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Bridging chance-constrained and stochastic optimization for risk estimation of virtual energy hubs dominated by hybrid vehicles under diverse uncertainties: To improve economic sustainability

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SUSTAINABLE CITIES AND SOCIETY 2024年 111卷

作者： Han, Lu Wang, Dongxuan Liu, Yu Hebei Agr Univ Coll Econ & Management Baoding 071000 Hebei Peoples R China Hebei Agr Univ Coll Sci & Technol Huanghua 061100 Hebei Peoples R China

Virtual energy hubs (VRHBs) combine dispersed energy resources to enhance the efficient use of various energy sources, grid flexibility, and renewable energy adoption. This paper investigates the adoption of VRHBs with power-to-gas (P2G) facilities and hybrid electric-compressed natural gas vehicles (HNGEVs) in the presence of renewable resources to effectively tackle carbon emissions and increase the flexibility of VRHBs. The proposed P2G facility is constructed from carbon capture to collect carbon pollution from fossil fuel resources and HNGEVs to recycle into synthesis gas in a methanation plant. Furthermore, due to severe uncertainties due to renewable resources and energy consumption intensity, the proposed framework utilizes stochastic-driven chance-constrained optimization, which enables decision-makers in VRHBs to account for the probabilistic nature of uncertainties. chance-constrained optimization explicitly considers uncertainty through probabilistic constraints, providing a more robust and reliable solution compared to deterministic methods. By modeling uncertainties using chance-constrained programming, VRHBs can manage the economic risks under unexpected worst realizations in uncertainties. Based on the results, the P2G facility leads to a significant reduction in the daily emissions by 63.06 % and 42.77 % observed under no-risk and full-risk conditions, respectively. In addition, the suggested chance-constrained method resulted in a major decrease of 69 % in overall operational costs.

关键词： chance constrained programming Virtual energy hub Plug-in hybrid compressed natural gas-electric vehicles Power-to-gas technology Carbon reduction

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Multi-objective optimal short-term planning of renewable distributed generations and capacitor banks in power system considering different uncertainties including plug-in electric vehicles

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2020年 119卷 105885-000页

作者： Zeynali, Saeed Rostami, Naghi Feyzi, M. R. Univ Tabriz Fac Elect & Comp Engn Tabriz Iran

The increasing penetration of solar distributed generations (SDGs) and wind distributed generations (WDGs) together with plug-in electric vehicles (PEVs) will lead to a promising amount of reduction in greenhouse gas emissions. Nevertheless, they bring about adversities such as uncertainty in production-load sides, augmented power loss, and voltage instability in the power system, which should be carefully addressed to increase the reliability. In this concern, this paper proposes a multi-objective optimization methodology for sizing and siting of SDGs, WDGs, and capacitor banks (CBs) in the power system considering uncertainties stemmed from PEVs load demand, solar irradiance, wind speed, and the conventional load. The understudy objectives are the voltage stability index, green-house gas emissions, and the total cost. An unconventional point estimate method (PEM) is used to handle the related uncertainties, and chance-constrained programming method is deployed to deal with smooth constraints. The corresponding probability distribution functions of output variables are estimated by the maximum entropy method. Furthermore, robustness analysis is made by Monte Carlo simulation (MCS). The proposed methodology is applied to a typical radial distribution network. The results show that the presence of PEV's significantly increases the load demand, which results in voltage collapse in the distribution system without the presence of distributed generations. However, the proposed probabilistic method ensures the safe operation of the distribution system with the optimal allocation of renewable distributed generations and CBs. Moreover, the results of deterministic and probabilistic cases are compared under different penetration levels of PEVs. The best tradeoff solution of the Pareto front is selected by the fuzzy satisfying method.

关键词： Renewable distributed generation Point estimate method Sizing and siting Multi-objective optimization chance constrained programming

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Selection of waste dump sites using a tabu search algorithm

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JOURNAL OF THE SOUTHERN AFRICAN INSTITUTE OF MINING AND METALLURGY 2008年第1期108卷 9-13页

作者： Kumral, M. Dimitrakopoulos, R. McGill Univ COSMO Stochast Mine Planning Lab Dept Min Met & Mat Engn Montreal PQ Canada

The selection of optimal site (s) for waste dumps is a significant problem of surface mines. A number of financial, environmental and safety requirements must be simultaneously considered to avoid potential losses. in this paper, the site selection problem is solved by the so-called tabu search, which is a local or neighborhood search procedure moving iteratively from one possible solution to another until a predefined stopping criterion is met. The application of the method requires a preliminary qualitative analysis for the selection of possible locations given environmental considerations and safety requirements. The method then focuses on minimization of dump and roadways construction and transportation costs such that dump capacities and blending requirements will not be exceeded. Some model data such as sulphur and nitrogen contents of each waste material are treated as random data, and chance constrained programming, a stochastic programming approach, is used to deal with the random characteristics of these data. The method proposed is demonstrated for six neighbouring mines varying extraction rates of wastes and five possible dumps varying with construction costs, The results show that tabu search is a very effective method in selecting optimal sites of mine waste dumps.

关键词： mine wastes site selection chance constrained programming tabu search

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Credibility-based fuzzy mathematical programming for bi-objective capacitated partial facility interdiction with fortification and demand outsourcing model

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SCIENTIA IRANICA 2017年第2期24卷 778-793页

作者： Azadeh, A. Kokabi, R. Hallaj, D. Univ Tehran Sch Ind & Syst Engn Coll Engn Tehran Iran Iran Univ Sci & Technol Dept Ind Engn Tehran Iran

The concepts of fortification and partial interdiction have not been considered concurrently in previous studies. In this paper, we added the fortification and partial interdiction concepts to interdiction problem for the first time;the reason is that in interdiction situations, defenders decide to protect some important facilities according to their budgets, and attackers like to destroy most unprotected facilities according to their resources, and therefore, to cripple the defenders' systems. Moreover, we use the advantages of credibility-based fuzzy mathematical programming and introduce an integrated model based on uncertainty contexts. In this bi-objective model, decision-maker gives satisfaction degrees for constraints, and then we use the interactive possibility model to solve the bi-objective model with varying confidence levels. These confidence levels specify the knowledge of attacker and defender about themselves. In addition, we propose Genetic Algorithm (GA) to solve the suggested model. In the experiments, we generate problem instances and solve them by Multi-Objective Mixed-Integer Non-Linear programming (MOMINLP) and the proposed genetic algorithm for various settings. (C) 2017 Sharif University of Technology. All rights reserved.

关键词： Facility interdiction Fortification Fuzzy mathematical programming chance constrained programming Multi-Objective Mixed-Integer Non Linear programming (MOMINLP) Genetic algorithm

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Hybrid fuzzy-stochastic approach to multi-product, multi-period, and multi-resource master production scheduling problem: Case of a polyethylene pipe and fitting manufacturer

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SCIENTIA IRANICA 2019年第3期26卷 1809-1823页

作者： Hajiagha, S. H. Razavi Hashemi, Sh Sadat Sadeghi, M. Khatam Univ Dept Management Tehran Iran Saramadan Andishe Avina Co Dept Management Tehran Iran Allameh Tabatabaei Univ Fac Management & Accounting Ind Management Grp Tehran Iran

Master production scheduling is an effective phase of production planning, which ends in production scheduling and magnitude of different products in a company. This problem requires investigating a wide range of parameters, regarding demand, manufacturing resource usage, and costs. Uncertainty is an intrinsic characteristic of these parameters. In this paper, a model is developed for master production scheduling under uncertainty, in which demands, as time-dependent variables, are considered as stochastic variables, while cost, and utilization parameters, with cognitive ambiguity, are expressed as fuzzy numbers. A hybrid approach is also proposed to solve the extended model. The application of the proposed method is examined in a practical problem of a polyethylene pipe and fitting Co. in Iran. The result showed a high degree of applicability. (C) 2019 Sharif University of Technology. All rights reserved.

关键词： Master production scheduling Stochastic demand chance constrained programming Fuzzy set theory

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Analyzing the Trade-Offs between Meeting Biorefinery Production Capacity and Feedstock Supply Cost: A chance constrained Approach

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ENERGIES 2021年第16期14卷 4763页

作者： Lambert, Lixia H. DeVuyst, Eric A. English, Burton C. Holcomb, Rodney Oklahoma State Univ Dept Agr Econ Stillwater OK 74078 USA Univ Tennessee Dept Agr & Resource Econ Knoxville TN 37996 USA Oklahoma State Univ Food & Ag Prod Ctr Stillwater OK 74078 USA

Commercial-scale switchgrass production for cellulosic biofuel remains absent in U.S. A well-recognized difficulty is the steady provision of high-quality feedstock to biorefineries. Switchgrass yield is random due to weather and growing conditions, with low yields during establishment years. Meeting biorefinery production capacity requirements 100% of the time or at any other frequency requires contracting sufficient amount of agricultural land areas to produce feedstock. Using chance-constrained programming, the trade-offs between the degree of certainty that refinery demand for feedstock and the cost of contracting production acreage is assessed. Varying the certainty from 60% to 95%, we find the costs of production, logistics and transportation ranged from 27% to 96% of the cost of 100% certainty. Investors and managers need to consider the cost of certainty of biomass acquisition when contracting for production acreage.

关键词： biofuel switchgrass (Panicum virgatum L ) uncertainty risk chance constrained programming

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