The ownership of a quoted company is usually spread among various shareholders. This dispersion can be characterized by an oriented network, whose nodes represent the companies, and an arc between companies i and j, w...
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The ownership of a quoted company is usually spread among various shareholders. This dispersion can be characterized by an oriented network, whose nodes represent the companies, and an arc between companies i and j, with weight s(ij), indicates that company i owns s(ij) percent of company j, being denoted by stock shareholding network (SSN). Given two sets of companies T and C-0 from a SSN, we say that the companies in C-0 control those in T if they own or imply ownership of at least aj percent of companies' T shares. So, given a targeting set T, our problem is to find a set of companies C-0 in which we invest such that the entire investment cost is the lowest. This problem has relevant applications in corporate governance and it can be modeled within network optimization. We discuss its applicability using a broad set of companies in the European stock market.
Floods as natural phenomena exist and will continue to occur. No manmade project can stop a flood from happening, but there are several effective methods to reduce its risk, aftermath and consequences. This paper cons...
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Floods as natural phenomena exist and will continue to occur. No manmade project can stop a flood from happening, but there are several effective methods to reduce its risk, aftermath and consequences. This paper considers a novel formulation of the network interdiction problem with application on controlling floods. The aim of this work is to identify those arcs prone for flood, and implementing flood-control projects on these arcs, while required budget for deriving all these operations is bounded. the model is tested on a real-world case in Tabriz-Iran, and results are visualized.
This paper deals with the distribution network reconfiguration problem in a multi-objective scope, aiming to determine the optimal radial configuration by means of minimizing the active power losses and a set of commo...
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This paper deals with the distribution network reconfiguration problem in a multi-objective scope, aiming to determine the optimal radial configuration by means of minimizing the active power losses and a set of commonly used reliability indices formulated with reference to the number of customers. The indices are developed in a way consistent with a mixed-integer linear programming (MILP) approach. A key contribution of the paper is the efficient implementation of the epsilon-constraint method using lexicographic optimization in order to solve the multi-objective optimization problem. After the Pareto efficient solution set is generated, the resulting configurations are evaluated using a backward/forward sweep load-flow algorithm to verify that the solutions obtained are both non-dominated and feasible. Since the epsilon-constraint method generates the Pareto front but does not incorporate decision maker (DM) preferences, a multi-attribute decision making procedure, namely, the technique for order preference by similarity to ideal solution (TOPSIS) method, is used in order to rank the obtained solutions according to the DM preferences, facilitating the final selection. The applicability of the proposed method is assessed on a classical test system and on a practical distribution system.
In wastewater systems, the efficiency of the treatment process is strongly related to the composition of its influent. When the treatment is overloaded (in volume and/or pollutants), its efficiency decreases and the e...
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In wastewater systems, the efficiency of the treatment process is strongly related to the composition of its influent. When the treatment is overloaded (in volume and/or pollutants), its efficiency decreases and the effluent cannot attain the quality required by the receiving waters. This work considers the problem of mixing multiple wastewater streams, with multiple contaminants, into a single stream (the influent) on which various specifications are imposed. The problem has recently been solved by probabilistic methods that can achieve a nearly optimal solution. In this paper, an exact technique is proposed to find the optimal solution with a mixed-integer linear programming solver for the first time. The procedure is applied to a case study with different industrial effluents whose discharges will compose the influent to a treatment plant with constrained capacity (both in volume and pollutant loads). The optimal utility solution achieved describes the discharges that satisfy all constraints. This proposal constitutes an efficient way to manage treatment influents while reducing the computational time required by two orders of magnitude compared to probabilistic methods.
Optimizing the scheduling of liquid drug product manufacturing is paramount for pharmaceutical companies in their increasingly competitive environment and requires the modelling of industry-specific constraints. Such ...
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Optimizing the scheduling of liquid drug product manufacturing is paramount for pharmaceutical companies in their increasingly competitive environment and requires the modelling of industry-specific constraints. Such constraints include: (i) changing sequence-dependent setup times;(ii) maintaining a sterile production environment (e.g., through sterile holding times);(iii) periods with limited or no plant activity (e.g., no workforce during weekends);and (iv) demand timing (i.e., delivery deadline and release date constraints). In this work, an immediate precedence model is formulated to optimize the scheduling of liquid drug product manufacturing, considering the industry-specific constraints. The primary objective is to minimize the production makespan. Four case studies comprising up to 38 batches from a real multi-product facility illustrate the performance of the rigorous optimization approach. The makespan could be reduced by up to 7.9% compared to expert schedules. (C) 2016 Elsevier Ltd. All rights reserved.
In this paper, we derive a strengthened MILP formulation for certain gas turbine unit commitment problems, in which the ramping rates are no smaller than the minimum generation amounts. This type of gas turbines can u...
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In this paper, we derive a strengthened MILP formulation for certain gas turbine unit commitment problems, in which the ramping rates are no smaller than the minimum generation amounts. This type of gas turbines can usually start-up faster and have a larger ramping rate, as compared to the traditional coalfired power plants. Recently, the number of this type of gas turbines increases significantly due to affordable gas prices and their scheduling flexibilities to accommodate intermittent renewable energy generation. In this study, several new families of strong valid inequalities are developed to help reduce the computational time to solve these types of problems. Meanwhile, the validity and facet-defining proofs are provided for certain inequalities. Finally, numerical experiments on a modified IEEE 118-bus system and the power system data based on recent studies verify the effectiveness of applying our formulation to model and solve this type of gas turbine unit commitment problems, including reducing the computational time to obtain an optimal solution or obtaining a much smaller optimality gap, as compared to the default CPLEX, when the time limit is reached with no optimal solutions obtained.
Symmetry is a natural phenomenon that appears when an electric system has identical generating units. As a core function in any electricity market, the unit commitment algorithm solves a very large-scale mixed-integer...
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Symmetry is a natural phenomenon that appears when an electric system has identical generating units. As a core function in any electricity market, the unit commitment algorithm solves a very large-scale mixed-integer based optimization problem. In this work, the symmetric properties that exist in a competitive electricity market are exploited in improving the performance of its unit commitment solutions. A static symmetry removal strategy based on hierarchical constraints is proposed. They establish priorities for the unit status variables. Extensive numerical examples are illustrated using a real-world, large-scale electricity market. Two available commercial solvers are used to compare the performance of each solver with and without our proposed method. Results show that there are opportunities to improve the unit commitment solutions if the symmetric properties of the market system are properly exploited. (C) 2016 Elsevier B.V. All rights reserved.
There is a trend for demand response (DR) market as a dedicated competitive environment for trading DR. In this market, aggregators participate as DR providers, while system operator, retailers and distributors are th...
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There is a trend for demand response (DR) market as a dedicated competitive environment for trading DR. In this market, aggregators participate as DR providers, while system operator, retailers and distributors are the DR buyers. Scheduling DR through the DR markets leads to a fair allocation of the benefits and payments across all participants. However, the integration of the DR markets into the existing power markets leads to technical and economic challenges. Those challenges associated with the integration of the DR markets into the energy/reserve markets are addressed in this study. To clear the DR markets jointly with the energy/reserve market, a bilevel approach is proposed in which the upper level belongs to energy/reserve market problem and the lower level includes DR market clearance. The proposed bilevel programming problem is then recast as a mixed-integer linear programming problem which can be solved using commercially available software. Finally, numerical results are provided to illustrate the performance of the proposed approach, demonstrating it brings about lower reserve price and higher social welfare compared with the existing markets.
This paper proposes a method of describing the load shifting ability of flexible electrical loads in a manner suitable for existing power system dispatch frameworks. The concept of an asymmetric block offer for flexib...
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This paper proposes a method of describing the load shifting ability of flexible electrical loads in a manner suitable for existing power system dispatch frameworks. The concept of an asymmetric block offer for flexible loads is introduced. This offer structure describes the ability of a flexible load to provide a response to the power system and the subsequent need to recover. The conventional system dispatch algorithm is altered to facilitate the dispatch of demand response units alongside generating units using the proposed offer structure. The value of demand response is assessed through case studies that dispatch flexible supermarket refrigeration loads for the provision of regulating power. The demand resource is described by a set of asymmetric blocks, and a set of four blocks offers is shown to offer cost savings for the procurement of regulating power in excess of 20%. For comparative purposes, the cost savings achievable with a fully observable and controllable demand response resource are evaluated, using a time series model of the refrigeration loads. The fully modeled resource offers greater savings;however the difference is small and potentially insufficient to justify the investment required to fully model and control individual flexible loads.
This paper presents a new mixed-integer linear programming (MILP) model to solve the multistage long-term expansion planning problem of electrical distribution systems (EDSs) considering the following alternatives: in...
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This paper presents a new mixed-integer linear programming (MILP) model to solve the multistage long-term expansion planning problem of electrical distribution systems (EDSs) considering the following alternatives: increasing the capacity of existing substations, constructing new substations, allocating capacitor banks and/or voltage regulators, constructing and/or reinforcing circuits, and modifying, if necessary, the system's topology. The aim is to minimize the investment and operation costs of the EDS over an established planning horizon. The proposed model uses a linearization technique and an approximation for transforming the original problem into an MILP model. The MILP model guarantees convergence to optimality by using existing classical optimization tools. In order to verify the efficiency of the proposed methodology, a 24-node test system was employed.
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