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Optimal Scheduling of an Isolated Wind-Diesel-Energy Storage System Considering Fast Frequency Response and Forecast Error

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ENERGIES 2019年第5期12卷

作者： Nhung Nguyen Hong Nakanishi, Yosuke Waseda Univ Grad Sch Environm & Energy Engn Tokyo 1698050 Japan

Nowadays, the hybrid wind-diesel system is widely used on small islands. However, the operation of these systems faces a major challenge in frequency control due to their small inertia. Furthermore, it is also difficult to maintain the power balance when both wind power and load are uncertain. To solve these problems, energy storage systems (ESS) are usually installed. This paper demonstrates the effectiveness of using ESS to provide Fast Frequency Response (FFR) to ensure that the frequency criteria are met after the sudden loss of a generator. An optimal day-ahead scheduling problem is implemented to simultaneously minimize the operating cost of the system, take full advantage of the available wind power, and ensure that the ESS has enough energy to provide FFR when the wind power and demand are uncertain. The optimization problem is formulated in terms of two-stage chance-constrained programming, and solved using a Modified Sample Average Approximation (MSAA) algorithma combination of the traditional Sample Average Approximation (SAA) algorithm and the k-means approach. The proposed method is tested with a realistic islanded power system, and the effects of the ESS size and its response time is analyzed. Results indicate that the proposed model should perform well under real-world conditions.

关键词： chance-constrained programming day-ahead scheduling energy storage system fast frequency response wind power

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

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Energy Procedia 2019年 158卷 934-939页

作者： Chengsi Yong Xiangyu Kong Ying Chen Quan Xu Li Yu Key Laboratory of Smart Grid of Ministry of Education Tianjin University Tianjin China Electric Power Research Institute of China Southern Power Grid Guangzhou 510080 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.

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

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Multi-objective capacitated transportation problem with mixed constraint: a case study of certain and uncertain environment

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OPSEARCH 2018年第2期55卷 447-477页

作者： Gupta, Srikant Ali, Irfan Ahmed, Aquil Aligarh Muslim Univ Dept Stat & Operat Res Aligarh Uttar Pradesh India

In this paper, we have formulated a new model of multi-objective capacitated transportation problem (MOCTP) with mixed constraints. In this model, some objective functions are linear and some are fractional and are of conflicting in nature with each other. The main objective of this paper is to decide the optimum order of the product quantity which is to be shipped from source to the destination subject to the capacitated restriction on each route. Here the two situations have been discussed for the MOCTP model. In the first situation, we have considered that all the input information of the MOCTP model is exactly known and therefore a fuzzy goal programming approach have been directly used for obtaining the optimum order quantity of the product. While in the second situation the input information of the MOCTP model are uncertain in nature and this uncertainty have been studied and handled by the suitable approaches like trapezoidal fuzzy numbers, multi-choices, and probabilistic random variables respectively. Due to the presence of all these uncertainties and conflicting natures of objectives functions, we cannot solve this MOCTP directly. Therefore firstly we converted all these uncertainties into deterministic forms by using the appropriate transformation techniques. For this, the vagueness in MOCTP defined by trapezoidal fuzzy numbers has been converted into its crisp form by using the ranking function approach. Multichoices in input information parameters have been converted into its exact form by the binary variable transformation technique. Randomness in input information is defined by the Pareto probability distribution, and for conversion into deterministic form chance constrained programming has been used. After doing all these transformations, we have applied fuzzy goal programming approach for solving this resultant MOCTP model for obtaining the optimum order quantity. A case study has been done to illustrate the computational procedure.

关键词： Capacitated transportation problem Multi objective linear programming Multi-objective fractional programming Fuzzy goal programming Mixed constraints Multi-choice programming chance-constrained programming Pareto distribution

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A novel model of load supply capability evaluation in active distribution network 3

A novel model of load supply capability evaluation in active...

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3rd Asia Conference on Power and Electrical Engineering (ACPEE)

作者： Mu, Dayong Sun, Liwei Li, Tianyu Luan, Jingzhao Sun, Ting Zhou, Wei State Grid Dalian Elect Power Co Ltd Dalian 116001 Peoples R China Dalian Univ Technol Sch Elect Engn Dalian 116024 Peoples R China

The output of the controllable distributed generation (DG) is determined by the economic dispatch strategy of the active distribution network (ADN). When we calculate the load supply capability (LSC) of ADN, the influence of the economic dispatch results must be taken into account. This paper proposes a bi-level optimization model for maximum power supply capacity of ADN based on chance constrained programming. The upper level model is used to calculate the LSC, and the lower level considers the economic dispatch of controllable DGs. Based on the improved IEEE-33 distribution system, the results show that the model can get the maximum power supply capacity that meets the economic operation requirements of ADN.

关键词： active distribution network load supply capability economic dispatch bi-level optimization chance-constrained programming

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On Controllability of Demand Response Resources & Aggregators' Bidding Strategies 20

On Controllability of Demand Response Resources & Aggregator...

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Power Systems Computation Conference (PSCC)

作者： Bruninx, K. Pandzic, H. Le Cadre, H. Delarue, E. Katholieke Univ Leuven Dept Mech Engn Leuven Belgium EnergyVille Genk Belgium VITO Mol Belgium Univ Zagreb Fac Elect Engn & Comp Zagreb Croatia

ISBN: (纸本)9781910963104

European legislation encourages prosumers to generate, consume and sell self-generated electricity in local and large-scale electricity markets. Since the capacity of residential prosumers is typically insufficient to take part in wholesale markets, aggregators act as mediators. This allows prosumers to monetize the flexibility of their loads through demand response (DR). This paper analyzes the strategic behavior of a DR aggregator, responsible for the optimization of its DR providers' bids in a wholesale market. The DR resource consists of an aggregation of residential electric heating systems, governed by a physical load model and user-specified comfort constraints. The interaction between the aggregator and the DR providers is modelled via Nash Bargaining Theory. Possible deviations from the scheduled heating patterns are accounted for via chance constraints. Results show that limitedly controllable DR resources and a risk-averse aggregator may significantly reduce the monetary benefits of DR.

关键词： Aggregator Demand Response chance-constrained programming Nash Bargaining Theory

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Environmentally sustainable stochastic procurement model

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MANAGEMENT OF ENVIRONMENTAL QUALITY 2018年第3期29卷 472-498页

作者： Kaur, Harpreet Singh, Surya Prakash Birla Inst Management Technol Greater Noida Noida India Indian Inst Technol Delhi Dept Management Studies New Delhi India

Purpose Procurement planning has always been a huge and challenging activity for business firms, especially in manufacturing. With government legislations about global concern over carbon emissions, the manufacturing firms are enforced to regulate and reduce the emissions caused throughout the supply chain. It is observed that procurement and logistics activities in manufacturing firms contribute heavily toward carbon emissions. Moreover, highly dynamic and uncertain business environment with uncertainty in parameters such as demand, supplier and carrier capacity adds to the complexity in procurement planning. The paper aims to discuss these issues. Design/methodology/approach This paper is a novel attempt to model environmentally sustainable stochastic procurement (ESSP) problem as a mixed-integer non-linear program. The ESSP optimizes the procurement plan of the firm including lot-sizing, supplier and carrier selection by addressing uncertainty and environmental sustainability. The model applies chance-constrained-based approach to address the uncertain parameters. Findings The proposed ESSP model is solved optimally for 30 data sets to validate the proposed ESSP and is further demonstrated using three illustrations solved optimally in LINGO 10. Originality/value The ESSP model simultaneously minimizes total procurement cost and carbon emissions over the entire planning horizon considering uncertain demand, supplier and carrier capacity.

关键词： Supplier selection Carrier selection Carbon emissions trading chance-constrained programming Environmentally sustainable Mixed-integer non-linear program (MINLP) Stochastic procurement

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Stochastic programming-Based Fault Diagnosis in Power Systems Under Imperfect and Incomplete Information

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ENERGIES 2018年第10期11卷

作者： Song, Huizhong Dong, Ming Han, Rongjie Wen, Fushuan Salam, Md. Abdus Chen, Xiaogang Fan, Hua Ye, Jian State Grid Hangzhou Xiaoshan Power Supply Co Beiganshan Rd 12 Hangzhou 311201 Zhejiang Peoples R China Zhejiang Univ Sch Elect Engn 38 Zheda Rd Hangzhou 310027 Zhejiang Peoples R China State Grid Dalian Power Supply Co Zhongshan Rd 102 Dalian 116000 Peoples R China Ton Duc Thang Univ Dept Management Sci & Technol Dev Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Elect & Elect Engn Ho Chi Minh City Vietnam Univ Teknol Brunei Dept Elect & Elect Engn BE-1410 Bandar Seri Begawan Brunei

When a fault occurs in a section or a component of a given power system, the malfunctioning of protective relays (PRs) and circuit breakers (CBs), and the false and missing alarms, may manifestly complicate the fault diagnosis procedure. It is necessary to develop a methodologically appropriate framework for this application. As a branch of stochastic programming, the well-developed chance-constrained programming approach provides an efficient way to solve programming problems fraught with uncertainties. In this work, a novel fault diagnosis analytic model is developed with the ability of accommodating the malfunctioning of PRs and CBs, as well as the false and/or missing alarms. The genetic algorithm combined with Monte Carlo simulations are then employed to solve the optimization model. The feasibility and efficiency of the developed model and method are verified by a real fault scenario in an actual power system. In addition, it is demonstrated by simulation results that the computation speed of the developed method meets the requirements for the on-line fault diagnosis of actual power systems.

关键词： power system fault diagnosis analytic model chance-constrained programming

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Optimizing financial and physical assets with chance-constrained programming in the electrical industry

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OPTIMIZATION AND ENGINEERING 2011年第1-2期12卷 237-255页

作者： Zorgati, Riadh van Ackooij, Wim EDF R&D OSIRIS 1 F-92141 Clamart France

This paper deals with a chance-constrained programming formulation and approximate resolution of an offer-demand equilibrium problem in the context of electricity markets. First, we state the probabilistic model. Computing the coefficients of the problem matrix is easy for financial assets, but a challenging task for physical assets. By introducing maximal production capacities, the computation becomes tractable for thermal plants but still leads to a combinatorial problem for hydraulic production. The obtained problem matrix is sparse, large scale and with random coefficients describing underlying uncertainty factors affecting the available power of assets. Second, we suggest some ways for approximately solving the obtained combinatorial chance-constrained program, which is in fact a stochastic multi-knapsack problem. A formal link between joint and individual chance constraints is exhibited and may lead to a simplified processing of the problem. Finally we illustrate our approximate algorithm on a stylized example.

关键词： Unit commitment Hedging chance-constrained programming

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Determination of the optimal blending problem of organic-chemical fertilizer under uncertainty

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SOIL USE AND MANAGEMENT 2018年第4期34卷 449-460页

作者： Jareonkitpoolpol, A. Ongkunaruk, P. Janssens, G. K. Kasetsart Univ Fac Agroind Dept Agroind Technol Bangkok 10900 Thailand Hasselt Univ Fac Business Econ Res Grp Logist Hasselt Belgium

The advantages of organic-chemical fertilizers have been recognised by farmers, and accordingly, the demand for them has increased. An organic-chemical fertilizer, with an amount of nutrients less than that registered or as specified on the label, is considered a fake or a nonconforming fertilizer. It has been observed that nutrient compositions of samples of organic-chemical fertilizers are much greater than what is specified on the label. This research aimed to reduce the raw materials cost of organic-chemical fertilizers while increasing the conformance probability of the nutrient composition. Three models have been formulated to determine the optimal organic-chemical fertilizer blend: a linear programming model (LP), a chance-constrained programming model (CCP), and a simulation optimization model. A Monte Carlo simulation was developed to determine the probability that the blending formula, currently in commercial use, and blending formulas obtained from the three optimization models, would be out-of-specification. The current and three proposed models were compared in terms of total raw material cost and probability of nonconformance. Our blending formulas could save a material cost of at least 16.6% compared with the commercial formula. Also, the probability of producing nonconforming fertilizers is drastically reduced. On a macro level, the use of the models allows farmers to reduce chemical residues in the soil while Thailand could reduce the import volume of chemical substances. This research may have a significant impact on manufacturers, farmers and the Thai economy.

关键词： chance-constrained programming combining organic amendments blending problem Monte Carlo simulation linear programming

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An integrated chance-constrained stochastic model for efficient and sustainable supplier selection and order allocation

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2018年第21期56卷 6890-6916页

作者： Moheb-Alizadeh, Hadi Handfield, Robert North Carolina State Univ Grad Program Operat Res Raleigh NC USA North Carolina State Univ Poole Coll Management Dept Business Management Raleigh NC 27695 USA

Effective allocation of scarce resources across supply chain environments is an emerging issue, as enterprises face shortfalls in raw materials, human labour, budgetary resources, equipment, energy and capacity. We consider these related objectives in designing efficient and sustainable supply networks using a multi-objective mixed-integer non-linear programming (MINLP) model for efficient and sustainable supplier selection and order allocation with stochastic demand. Our approach considers sustainability dimensions including economic, environmental and social responsibility, but also seeks to design the most efficient supply network given constraints of the supply market. Enterprise efficiency is assessed using a bi-objective data envelopment analysis (DEA) whose inputs include raw materials, current expenses and labour force capacity. The resulting model is non-convex because of the presence of bilinear terms in DEA-related constraints, so we introduce a multi-stage solution procedure that first uses piecewise McCormick envelopes (PCM) to linearise the bilinear terms. Next, we introduce a set of valid inequalities in order to improve solution time of the problem whose dimension significantly increases after being linearised. We then exploit chance constrained programming approaches to deal with stochastic demand. Finally, a single aggregated objective function is derived using a fuzzy multi-objective programming approach. A manufacturing case study demonstrates the validity of the proposed approach, and its effectiveness in designing a supply network that addresses the 'triple bottom line' of people, profit and planet that comprises many sustainability initiatives in an efficient manner.

关键词： supplier selection order allocation non-linear programming valid inequalities chance-constrained programming data envelopment analysis (DEA)

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