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A stochastic optimal scheduling of multi-microgrid systems considering emissions: A chance constrained model

JOURNAL OF CLEANER PRODUCTION 2020年 275卷

作者： Aghdam, Farid Hamzeh Kalantari, Navid Taghizadegan Mohammadi-Ivatloo, Behnam Azarbaijan Shahid Madani Univ Dept Elect Engn Tabriz Iran Univ Tabriz Fac Elect & Comp Engn Tabriz Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam

With increasing demand in electrical energy and the development of distributed generation technologies, microgrids are formed to avoid the consequences of these changes. Furthermore, this leads to the arrival of multiple microgrids in the distribution network, better known as multi-microgrid systems. Renewable-based generators are indisputable parts of modern electrical energy systems, which might insert uncertainty to the mathematical modeling of the entities for the scheduling purposes. Thus, in this paper, chance-constrained programming is employed for the day-ahead scheduling of a multi-microgrid system in an uncertain environment. Alongside the renewable-based generation units, conventional units are being used, which may have environmental problems such as higher greenhouse gas emissions. Given the global policy for reducing pollutants, a framework for energy management of the multi-microgrid system aiming at decreasing emissions alongside other financial goals is proposed. Finally, a modified version of the IEEE 33-bus test system with multiple microgrids is selected for verification of the proposed methodology. The obtained results in various case studies indicated that considering the emission in the objective function, significantly affects the amount of greenhouse gas emission. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Multi-microgrid Day-ahead scheduling chance-constrained programming Emissions

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Decentralized day-ahead scheduling of multi-area integrated electricity and natural gas systems considering reserve optimization

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ENERGY 2020年 198卷

作者： Wu, Gang Xiang, Yue Liu, Junyong Gou, Jing Shen, Xiaodong Huang, Yuan Jawad, Shafqat Sichuan Univ Coll Elect Engn Chengdu 610065 Peoples R China State Grid Sichuan Econ Res Inst Chengdu 610041 Peoples R China

To maximize the benefits of multi-area cooperation and guarantee system reliability, a day-ahead scheduling model for multi-area integrated electricity and natural gas systems (IEGS) considering reserve optimization is proposed in this paper. The reserve scheduling is integrated into the multi-area optimal dispatch model to hedge against the risk caused by the uncertainties in electricity and gas systems. The reserve constraints in electricity and natural gas systems are modeled as probabilistic forms with a predefined confidence level based on chance-constrained programming (CCP). In order to protect the information privacy of each area, the original centralized energy-reserve co-optimization problems are decomposed into several area sub-problems that can be solved independently. Furthermore, the alternating direction method of multipliers (ADMM) with self-adaptive penalty parameters is adopted to solve the proposed model in a decentralized manner. The feasibility and effectiveness of the proposed models and methods are verified in a three-area IEGS. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Multi-area integrated electricity and natural gas systems Reserve optimization Uncertainties ADMM chance-constrained programming

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Optimal Power Flow Under Uncertainty: An Extensive Out -of-Sample Analysis

Optimal Power Flow Under Uncertainty: An Extensive Out -of-S...

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IEEE PES Innovative Smart Grid Technologies Europe (IEEE ISGT-Europe)

作者： Arrigo, Adriano Ordoudis, Christos Kazempour, Jalal De Greve, Zacharie Toubeau, Jean-Francois Vallee, Francois Univ Mons Elect Power Engn Unit Mons Belgium Tech Univ Denmark Dept Elect Engn Lyngby Denmark

ISBN: (纸本)9781538682180

The uncertainty induced by high penetration of stochastic generation in power systems requires to be properly taken into account within Optimal Power Flow (OPF) problems to make informed day-ahead decisions that minimize the social cost in view of potential balancing actions. This ends up in a two-stage OPF problem that is usually solved using two-stage stochastic programming or adaptive robust optimization. Another alternative is the use of chance-constrained programming that allows to control the conservativeness of the decisions. In this paper, we aim at defining a fair basis for assessing the performance of these three techniques, using an extensive out-of-sample evaluation. Considering a common wind power database, each technique leads to optimal day-ahead decisions that are a posteriori assessed through the real-time stage on unseen realizations of the uncertainty. Our main conclusion is that undertaking conservative decisions results in lower standard deviations of the cost, but at the expense of higher expected cost.

关键词： Stochastic programming adaptive robust optimization chance-constrained programming optimal power flow out-of-sample analysis.

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A Mixed-Integer Distributionally Robust chance-constrained Model for Optimal Topology Control in Power Grids with Uncertain Renewables

A Mixed-Integer Distributionally Robust Chance-Constrained M...

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IEEE Milan PowerTech Conference

作者： Nazemi, Mostafa Dehghanian, Payman Lejeune, Miguel George Washington Univ Washington DC 20052 USA

ISBN: (纸本)9781538647226

This paper proposes a distributionally robust chance-constrained (DRCC) optimization model for optimal topology control in power grids overwhelmed with significant renewable uncertainties. A novel moment-based ambiguity set is characterized to capture the renewable uncertainties with no knowledge on the probability distributions of the random parameters. A distributionally robust optimization (DRO) formulation is proposed to guarantee the robustness of the network topology control plans against all uncertainty distributions defined within the moment-based ambiguity set. The proposed model minimizes the system operation cost by co-optimizing dispatch of the lower-cost generating units and network topology-i.e., dynamically harnessing the way how electricity flows through the system. In order to solve the problem, the DRCC problem are reformulated into a tractable mixed-integer second order cone programming problem (MISOCP) which can be efficiently solved by off-the-shelf solvers. Numerical results on the IEEE 118-bus test system verify the effectiveness of the proposed network reconfiguration methodology under uncertainties.

关键词： chance-constrained programming distributionally robust optimization (DRO) optimal topology control renewable uncertainty characterization

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Stochastic supply chain, transportation models: implementations and benefits

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OPSEARCH 2019年第2期56卷 432-476页

作者： Baidya, Abhijit Natl Inst Technol Agartala Dept Math Jirania 799055 West Tripura India

To transport the commodities in minimum time with maximum safety, the difficulties arise due to mutiny, territory slide, bad road and crashed communication systems, etc. and to overcome these kind of problems, the stochastic solid transportation models with safety and time objective functions under essential constraints are formulated. Taking expected value criterion, chance-constrained programming technique, uniform distribution U(a,b), exponential distribution EXP(beta) and normal distribution N(mu, sigma(2)), four new de-randomization processes are proposed to handle the stochastic programming problem. Finally, the deterministic form of the model is solved using generalized reduced gradient techniques (LINGO.13.0 optimization software). Finally, an enlarge comparison of the proposed concept with the earlier concept are presented and the nature of the solutions is discussed.

关键词： Multi-stage solid transportation problem Stochastic variable chance-constrained programming Expected value model Safety factor Docking time Loading time Unloading time

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An optimization method of active distribution network considering uncertainties of renewable DGs 10

An optimization method of active distribution network consid...

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10th International Conference on Applied Energy (ICAE)

作者： Yong, Chengsi Kong, Xiangyu Chen, Ying Xu, Quan Yu, Li Tianjin Univ Minist Educ Key Lab Smart Grid Tianjin Peoples R China China Southern Power Gnat Elect Power Res Inst Guangzhou 510080 Guangdong Peoples R China

With the access of the renewable DGs such as wind turbines and photovoltaic generations, network operation state is uncertain due to the randomness of these renewable DGs. This paper proposes a novel optimal method of the active distribution network (ADN) considering the uncertain conditions and the coordination control of source-network-load. By optimizing the controllable distributed power output, controlling the network switches, and managing the demand-side load synchronously, the impact of distributed renewable energies can be reduced and the reliable operation of ADN can be ensured. The chance constrained programming is used to deal with the uncertainties. The proposed model is settled by the improved teaching-learning-based optimization algorithm (ITLBO) and the performance of the algorithm is verified by the comparison with the TLBO in the modified IEEE 33-bus distribution system. (C) 2019 The Authors. Published by Elsevier Ltd.

关键词： Active distribution network distributed renewable energy teaching-learning-based optimization chance-constrained programming

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Optimization of Radiation Therapy Treatment Planning Considering Setup Uncertainty and Radiobiological Effects

Optimization of Radiation Therapy Treatment Planning Conside...

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作者： Khabazian, Azin University of Houston

学位级别：Ph.D., Doctor of Philosophy

The clinical goal of radiation therapy is to maximize tumor cell killing while minimizing toxic effects on surrounding healthy tissues. A treatment protocol is used to decide on the treatment strategy and is a description of the desired radiation dose to the various regions of interest. Treatment planning then aims to find a plan as close to the treatment protocol as possible (Romeijn and Dempsey (2008)). Every step of radiation therapy is subject to some types of uncertainties (i.e., setup uncertainty, patient motion, and tumor shrinkage), which may compromise the quality of treatment. Basically, in treatment planning, a region of the patient where both tumor and organs at risk (OARs) are located with a certain probability is irradiated with a lower dose than the prescribed tumor dose. However, under uncertainty, the nearby healthy organs that should be irradiated by lower dose are always occupied by tumor voxels with a higher dose. Although the more ambitious goal is to damage the tumor cells so as to guarantee total tumor coverage for treatment, severe patient complications can occur when the surrounding healthy tissues receive an excessive amount of the radiation dose. Therefore, it is desired to develop an optimization approach to meet prescription requirements and tackle the uncertainties in radiation therapy treatments. The proposed research attempts to overcome these limitations and find optimal beamlet intensity that will deliver a dose distribution close to the prescribed dose lead to a better sparing of healthy tissues. First, to control the safety of the critical organs at risk during radiation as well as to provide sufficient tumor coverage, a chance constrained programming (CCP) (Charnes and Cooper (1959)) approach is presented to handle setup uncertainty in radiation treatment planning that allows constraint violation up to a certain degree as it is the case in practice. We assume the uncertain dose distribution is governed by a known probability func

关键词： chance-constrained programming Radiation treatment planning Setup uncertainty

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Fuzzy Stochastic Linear Fractional programming based on Fuzzy Mathematical programming

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FUZZY INFORMATION AND ENGINEERING 2018年第3期10卷 324-338页

作者： Nasseri, S. H. Bavandi, S. Univ Mazandaran Dept Math Babol Sar Iran

In this paper, we consider a Fuzzy Stochastic Linear Fractional programming problem (FSLFP). In this problem, the coefficients and scalars in the objective function are the triangular fuzzy number and technological coefficients and the quantities on the right side of the constraints are fuzzy random variables with the specific distribution. Here we change an FSLFP problem to an equivalent deterministic Multi-objective Linear Fractional programming (MOLFP) problem. Then by using Fuzzy Mathematical programming approach transformed MOLFP problem is reduced single objective Linear programming (LP) problem. A numerical example is presented to demonstrate the effectiveness of the proposed method.

关键词： Trapezoidal fuzzy number linear fractional programming problem fuzzy Stochastic linear fractional programming problem chance-constrained programming fuzzy mathematical programming multi-objective linear fractional programming problem

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chance-constrained multi-objective approach for hazardous materials routing and scheduling under demand and service time uncertainty

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JOURNAL OF MULTI-CRITERIA DECISION ANALYSIS 2020年第5-6期27卷 318-336页

作者： Moghaddam, Kamran S. Azadian, Farshid Clayton State Univ Coll Business Dept Mkt & Supply Chain Management Morrow GA 30260 USA Embry Riddle Aeronaut Univ David OMaley Coll Business Dept Management Mkt & Operat Daytona Beach FL USA

The transportation of hazardous materials (hazmat) is a challenging problem that often requires a trade-off between conflicting objectives. In practice, the complexity of the problem is exacerbated due to the lack of sufficient and reliable historical data. In this research, a stochastic multi-objective optimization model for hazardous materials (hazmat) vehicle routing and scheduling problem is developed. The goal is to find optimal links and routes to obtain a trade-off between the safe and fast distribution of hazmat through a transport network under customers' demand and service time uncertainty. We utilized a hybrid game theory based compromise programming to develop a solution algorithm to determine the Pareto-optimal solutions, which are based on the total travel distance and total risk imposed on the transportation process. Computational results of a realistic numerical case study demonstrate the effectiveness of the proposed model and the solution algorithm in obtaining Pareto-optimal solutions.

关键词： chance-constrained programming demand uncertainty hazardous material multi-objective optimization service time uncertainty vehicle routing and scheduling problem

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Dealing with equality and benefit for water allocation in a lake watershed: A Gini-coefficient based stochastic optimization approach

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JOURNAL OF HYDROLOGY 2018年 561卷 322-334页

作者： Dai, C. Qin, X. S. Chen, Y. Guo, H. C. Nanyang Technol Univ Sch Civil & Environm Engn Singapore 639798 Singapore Peking Univ Coll Environm Sci & Engn Beijing 100871 Peoples R China

A Gini-coefficient based stochastic optimization (GBSO) model was developed by integrating the hydrological model, water balance model, Gini coefficient and chance-constrained programming (CCP) into a general multi-objective optimization modeling framework for supporting water resources allocation at a watershed scale. The framework was advantageous in reflecting the conflicting equity and benefit objectives for water allocation, maintaining the water balance of watershed, and dealing with system uncertainties. GBSO was solved by the non-dominated sorting Genetic Algorithms-II (NSGA-II), after the parameter uncertainties of the hydrological model have been quantified into the probability distribution of runoff as the inputs of CCP model, and the chance constraints were converted to the corresponding deterministic versions. The proposed model was applied to identify the Pareto optimal water allocation schemes in the Lake Dianchi watershed, China. The optimal Pareto-front results reflected the tradeoff between system benefit (alpha(SB)) and Gini coefficient (alpha(G)) under different significance levels (i.e. q) and different drought scenarios, which reveals the conflicting nature of equity and efficiency in water allocation problems. A lower q generally implies a lower risk of violating the system constraints and a worse drought intensity scenario corresponds to less available water resources, both of which would lead to a decreased system benefit and a less equitable water allocation scheme. Thus, the proposed modeling framework could help obtain the Pareto optimal schemes under complexity and ensure that the proposed water allocation solutions are effective for coping with drought conditions, with a proper tradeoff between system benefit and water allocation equity.

关键词： Gini coefficient chance-constrained programming GLUE NSGA-II Water allocation Lake Dianchi watershed

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