This paper addresses multistage distribution network expansion planning (DNEP) incorporating energy storage systems (ESSs). The ESSs are utilized to shave the peak demand and to reduce the planning cost. Annual and da...
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This paper addresses multistage distribution network expansion planning (DNEP) incorporating energy storage systems (ESSs). The ESSs are utilized to shave the peak demand and to reduce the planning cost. Annual and daily load duration curves are considered to evaluate the impacts of the ESSs on the planning. The proposed planning is carried out based on the AC power flow including active power, reactive power, and network loss. The problem is formulated as a constrained, mixed-integer, and nonlinearprogramming (MINLP) and solved by using particle swarm optimization (PSO) algorithm. A 11 kV and 30-bus radial distribution network is considered as case study and the typical ESSs are also regarded to install on the network. Simulation results demonstrate the effectiveness and viability of the proposed method to consider the ESSs in DNEP. The results indicate that integrating the ESSs in DNEP reduces the planning cost significantly, as well improves the technical parameters of the network such as bus voltages and line loading. (C) 2015 Elsevier Ltd. All rights reserved.
Plug-in electric vehicles (PEV) present a promising solution to mitigate greenhouse gas emissions but on the other hand, their increased penetration can impact power system operation, particularly so in an isolated mi...
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ISBN:
(纸本)9781479976645
Plug-in electric vehicles (PEV) present a promising solution to mitigate greenhouse gas emissions but on the other hand, their increased penetration can impact power system operation, particularly so in an isolated microgrid. Similarly, demand response (DR) has the potential to provide significant flexibility in the operation of an isolated microgrid, with limited generation capacity, by altering the demand and introducing an elasticity effect. This paper examines uncontrolled and controlled PEV charging strategies, in the presence of DR and battery energy storage systems (BESS), using an optimal energy management model for isolated microgrids. The model develops energy management strategies considering power flow constraints and different objective functions from the perspective of the microgrid operator and the owners of PEVs.
In response to the challenges brought by generation scheduling with hybrid AC/DC transmission system, this paper presents a hybrid scheduling mode for coordinated optimization of unit commitment and DC transmission po...
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ISBN:
(纸本)9781467371063
In response to the challenges brought by generation scheduling with hybrid AC/DC transmission system, this paper presents a hybrid scheduling mode for coordinated optimization of unit commitment and DC transmission power scheduling. Considering the characteristics of both units and DC transmission system, the optimal unit commitment and operation state of DC transmission system are acquired to achieve the best system economic profit, while AC/DC transmission constraints arc also delicate formulated in the model to ensure the security of scheduling results. Since this coordinated optimization model is a large scale mixedintegernonlinear optimization problem, the Benders decomposition method is employed to tackle the computational difficulties. The coordinated optimization model is decomposed into a master problem to optimize unit commitment and DC transmission power without considering AC/DC transmission constraints, and hourly security check sub-problems which check the power flow violations caused by inappropriate results of unit commitment and DC transmission power schedule obtained by the master problem. Corresponding solution procedure and iteration algorithm are proposed to enhance convergence and computation efficiency, in which parallel computation technique is implemented to accelerate the convergence. Numerical results based on the CEPRI 22-bus system validate the effectiveness of our proposed method.
With increasing concerns on customer needs in today's competitive market, the issue of incorporating customer requirements into product design arises the interest of both researchers and practitioners. Quality Fun...
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With increasing concerns on customer needs in today's competitive market, the issue of incorporating customer requirements into product design arises the interest of both researchers and practitioners. Quality Function Deployment (QFD) is a well-known methodology for customer-driven product design. However, conventionally, QFD analysis has a major challenge in understanding customer needs accurately. Kano's model, which studies the nature of customer needs, provides a way for a better classification of customer needs. However, seldom research contributions are found in terms of integrating Kano's model with QFD quantitatively. In this research, a novel integration approach is proposed. At first, Kano's model is quantified by identifying relationship between customer needs and customer satisfaction (CS). Next, both qualitative and quantitative results from Kano's model are integrated into QFD. Finally, a mixednon-linearintegerprogramming model is formulated to maximise CS under cost and technical constraints. In this research, an illustrative example associated with the design of notebook computers is also presented to demonstrate the availability of the proposed approach.
Current business environment compels manufacturers to produce high-quality products at low cost with the shortest possible delivery time. Cellular Manufacturing Systems (CMSs), utilised to equip the producers to deal ...
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Current business environment compels manufacturers to produce high-quality products at low cost with the shortest possible delivery time. Cellular Manufacturing Systems (CMSs), utilised to equip the producers to deal with this predicament, have been a point of attraction to both researchers and practitioners. Machine reliability is one of key performance measures of a CMS and the reason is that high machine failure rates lead to due date collision and loss of customers. Therefore, this paper presents a bi-objective mathematical model for a CMS considering the sequence data, alternative process plans, candidate locations for machines, maximum capacity for each machine and variable failure rate of each machine. In the proposed model, the variable failure rate is considered as a dependent variable of 'number of setups' and 'total processing time' in a regression equation. The first objective of this model is minimising the purchase cost of machines, intra-cellular movements (forward and backward) and the inter-cellular movement costs of materials while the second one is to minimise the total repair time for failed machines. To illustrate the performance of the proposed model, a numerical example is solved in the Generalized Algebraic Modeling Systems software using augmented epsilon-constraint method. The results of the numerical examples show that the proposed approach is promising.
In this paper, a new method is presented in optimization of hydrogen network. The mixed integer non-linear programming (MINLP) and non-linearprogramming (NLP) problems have been solved with two methods, simultaneousl...
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In this paper, a new method is presented in optimization of hydrogen network. The mixed integer non-linear programming (MINLP) and non-linearprogramming (NLP) problems have been solved with two methods, simultaneously. The linearization technique for non-linearprogramming models which proposed by McCormick (1976) and also a new method proposed by Faria and Bagajewicz (2011) have been used to solve these problems. Application of this new method is presented in global optimization of MINLP/NLP, and hydrogen network problem. Copyright (C) 2014, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.
We consider a multi-period problem of fair transfer prices and inventory holding policies in two enterprise supply chains. This problem was formulated as a mixedintegernon-linear program by Gjerdrum et al. (Eur J Op...
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We consider a multi-period problem of fair transfer prices and inventory holding policies in two enterprise supply chains. This problem was formulated as a mixedintegernon-linear program by Gjerdrum et al. (Eur J Oper Res 143:582-599, 2002). Existing global optimization methods to solve this problem are computationally expensive. We propose a continuous approach based on difference of convex functions (DC) programming and DC Algorithms (DCA) for solving this combinatorial optimization problem. The problem is first reformulated as a DC program via an exact penalty technique. Afterward, DCA, an efficient local approach in non-convex programming framework, is investigated to solve the resulting problem. For globally solving this problem, we investigate a combined DCA-Branch and Bound algorithm. DCA is applied to get lower bounds while upper bounds are computed from a relaxation problem. The numerical results on several test problems show that the proposed algorithms are efficient: DCA provides a good integer solution in a short CPU time although it works on a continuous domain, and the combined DCA-Branch and Bound finds an -optimal solution for large-scale problems in a reasonable time.
A coupled optimization of the electricity and gas systems is presented in this paper. The electricity problem involves a unit commitment with co-optimization of energy and reserves under a power pool, considering all ...
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ISBN:
(纸本)9781479960958
A coupled optimization of the electricity and gas systems is presented in this paper. The electricity problem involves a unit commitment with co-optimization of energy and reserves under a power pool, considering all system operational and unit technical constraints. The gas problem involves a large-scale highly non-convex and non-linear problem structure, which is modeled as a mixed integer non-linear programming model. The decomposition of the overall problem is based on the Augmented Lagrangian method. An iterative process is implemented, coordinating the two interdependent systems using an alternating minimization method, in which the Lagrange multipliers are updated using a subgradient method. The solution algorithm is evaluated using the Greek power and gas system, employing thirteen gas-fired units and fifty-three gas network nodes. The test results indicate the strong interdependence of the two systems, and demonstrate the efficiency of the presented algorithm in coordinating them.
We consider a multi-period problem of fair transfer prices and inventory holding policies in two enterprise supply chains. This problem was formulated as a mixedintegernon-linear program by Gjerdrum et al. (Eur J Op...
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We consider a multi-period problem of fair transfer prices and inventory holding policies in two enterprise supply chains. This problem was formulated as a mixedintegernon-linear program by Gjerdrum et al. (Eur J Oper Res 143:582-599, 2002). Existing global optimization methods to solve this problem are computationally expensive. We propose a continuous approach based on difference of convex functions (DC) programming and DC Algorithms (DCA) for solving this combinatorial optimization problem. The problem is first reformulated as a DC program via an exact penalty technique. Afterward, DCA, an efficient local approach in non-convex programming framework, is investigated to solve the resulting problem. For globally solving this problem, we investigate a combined DCA-Branch and Bound algorithm. DCA is applied to get lower bounds while upper bounds are computed from a relaxation problem. The numerical results on several test problems show that the proposed algorithms are efficient: DCA provides a good integer solution in a short CPU time although it works on a continuous domain, and the combined DCA-Branch and Bound finds an -optimal solution for large-scale problems in a reasonable time.
We consider a class of optimal power flow (OPF) applications where some loads offer a modulation service in exchange for an activation fee. These applications can be modeled as multi-period formulations of the OPF wit...
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ISBN:
(纸本)9788393580132
We consider a class of optimal power flow (OPF) applications where some loads offer a modulation service in exchange for an activation fee. These applications can be modeled as multi-period formulations of the OPF with discrete variables that define mixed-integernon-convex mathematical programs. We propose two types of relaxations to tackle these problems. One is based on a Lagrangian relaxation and the other is based on a network flow relaxation. Both relaxations are tested on several benchmarks and, although they provide a comparable dual bound, it appears that the constraints in the solutions derived from the network flow relaxation are significantly less violated.
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