The reasons why the transmission network is a potentially attractive target for deliberate outages are twofold: (i) its crucial importance as a critical infrastructure for the society welfare, and (ii) its high level ...
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The reasons why the transmission network is a potentially attractive target for deliberate outages are twofold: (i) its crucial importance as a critical infrastructure for the society welfare, and (ii) its high level of vulnerability due to the current operation close to its static and dynamic limits. This new context where destructive agents come into play has been recognized by several agencies in Europe and North America, and various initiatives have been launched worldwide in order to assess and mitigate the vulnerability of transmission. Within this framework, this paper proposes the reinforcement and expansion of the transmission network as a way of mitigating the impact of increasingly plausible deliberate outages. The network planner selects the new lines to be built accounting not only for economic issues, as traditionally done, but also for the vulnerability of the transmission network against a set of credible intentional outages. The resulting vulnerability- and economic-constrained transmission expansion planning problem is formulated as a mixed-integerlinear program. A number of case studies numerically illustrate the tradeoff between economic- and vulnerability-related issues and its impact on the expansion plans. In addition, we compare the results with those achieved by a traditional expansion planning model based on cost minimization. (C) 2009 Elsevier Ltd. All rights reserved.
This article deals with the problem of optimal system energy management for combined heat and power production (CHP) with a heat pump. To generate steam and electric power by applying internal sources, an analysed ene...
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This article deals with the problem of optimal system energy management for combined heat and power production (CHP) with a heat pump. To generate steam and electric power by applying internal sources, an analysed energy system inside its power plant uses a steam boiler, an extraction steam turbine, and a heat pump. Electric power is also purchased from external sources, such as a grid. This leads to the decision problem of determining the economically optimum energy-mix for a given set of conditions. In this article, to select the energy inputs, an optimization method of mixed-integer linear programming is used. Depending on the unit cost of different types of energy the optimal cost-region diagram for the system can be found. This diagram presents a recommendation for the use of an optimum energy consumption of particular composition yielding the minimum energy-operating costs for a CHP system with a heat pump.
Herein we present a case of production planning in a woodturning company. The company wishes to plan the turning of various types of products of different radii in a set of parallel machines (lathes) and with the foll...
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Herein we present a case of production planning in a woodturning company. The company wishes to plan the turning of various types of products of different radii in a set of parallel machines (lathes) and with the following principal conditions: for each type of product there is a minimum production lot size;some lathes cannot manufacture every type of product: the production capacity of a lathe depends on the lathe itself and the type of product to be manufactured;the products are classified into families according to radius: and there is an intra-family setup time (for manufacturing different products that have the same radius) and an inter-family setup time (for consecutively manufacturing products that have different radii), which is longer;part of the production can be subcontracted: each type of product can be manufactured on different lathes and/or subcontracted;and the operators can work overtime, during which additional time they can simultaneously operate Multiple lathes. The goal is to meet the demand at minimum cost, which includes the cost of any overtime plus that of any subcontracting. The problem was modelled and solved by mixed-integer linear programming (MILP). The company considers the results to be satisfactory. (C) 2008 Elsevier Ltd. All rights reserved.
This paper presents an efficient mixed-integerlinear formulation for long term maintenance schedule of overhead lines. The proposed formulation is based on risk management approach and utilizes the model of decoupled...
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This paper presents an efficient mixed-integerlinear formulation for long term maintenance schedule of overhead lines. The proposed formulation is based on risk management approach and utilizes the model of decoupled risk factors. The proposed methodology yields a significant computational saving comparing to the previously reported dynamic programming. Risk management approach enables the asset managers to consider the actual condition of electrical equipment and expected consequence of their failures. Furthermore, the decoupled risk strategy in conjunction with the mixed-integer linear programming formulation establishes a precise description of time-dependent deterioration failure rate and provides the ability to determine the most cost-effective maintenance scenario while satisfying the reliability constraints. The proposed approach is tested on the Roy Billinton Test System distribution feeders and a typical real size case study. The results presented show the accuracy and efficiency of the proposed approach.
In this article we present a computational study for solving the distance-dependent rearrangement clustering problem using mixed-integer linear programming (MILP). To address sparse data sets, we present an objective ...
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In this article we present a computational study for solving the distance-dependent rearrangement clustering problem using mixed-integer linear programming (MILP). To address sparse data sets, we present an objective function for evaluating the pair-wise interactions between two elements as a function of the distance between them in the final ordering. The physical permutations of the rows and columns of the data matrix can be modeled using mixed-integer linear programming and we present three models based on (1) the relative ordering of elements, (2) the assignment of elements to a final position, and (3) the assignment of a distance between a pair of elements. These models can be augmented with the use of cutting planes and heuristic methods to increase computational efficiency. The performance of the models is compared for three distinct re-ordering problems corresponding to glass transition temperature data for polymers and two drug inhibition data matrices. The results of the comparative study suggest that the assignment model is the most effective for identifying the optimal re-ordering of rows and columns of sparse data matrices.
A novel network-based framework for the short-term scheduling of multi-purpose batch processes is presented. The novelty of the proposed approach lies in five key concepts. First, it is based on a new continuous-time ...
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A novel network-based framework for the short-term scheduling of multi-purpose batch processes is presented. The novelty of the proposed approach lies in five key concepts. First, it is based on a new continuous-time representation that does not require tasks to start (end) exactly at a time point;thus reducing the number of time points needed to represent a solution. Second, processing units are modeled as being in different activity states to allow storage of input/output materials. Third, time variables for "idle" and "storage" periods of a unit are introduced to enable the matching between tasks and time points without big-M constraints. Fourth, material transfer variables are introduced to explicitly account for unit connectivity. Fifth, inventory variables for storage in processing units are incorporated to model non-simultaneous and partial material transfers. The proposed representation leads to MILP formulations which address limitations of existing scheduling methods. (C) 2009 Elsevier Ltd. All rights reserved.
The operational planning of a multisite production and distribution network, which entails making operational-level decisions for efficient production facility utilitization and customer-order fulfillment over a time ...
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The operational planning of a multisite production and distribution network, which entails making operational-level decisions for efficient production facility utilitization and customer-order fulfillment over a time horizon of several months, is of great importance but has received considerably less attention compared to operational planning approaches for a single production site. The inherent complexities of simultaneously optimizing the allocation of production tasks at each facility, as well as the interplay between several production and distribution centers make operational planning of a multisite production and distribution network challenging especially when addressing large-scale, industrial applications. The proposed multisite planning with production disaggregation model (Multisite-PPDM) has been formulated in order to address industrially relevant supply chains and determine both the production and shipment profiles for the supply chain of interest. (C) 2008 Elsevier Ltd. All rights reserved.
A mixed-integer linear programming model is presented for the optimum planning of multi-plant, multi-supplier, and multi-grade petrochemical production. In the production of multiple grades of a given petrochemical pr...
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A mixed-integer linear programming model is presented for the optimum planning of multi-plant, multi-supplier, and multi-grade petrochemical production. In the production of multiple grades of a given petrochemical product, the amount of transitional off-spec production depends on the sequencing of different grades. For each time period, the discrete-time model determines the optimum mix of petrochemical grades for each plant, the quantity to produce of each selected grade, and the optimum production sequence of different grades. In addition, assuming limited raw-material availability, the model determines the quantity of each raw material to purchase from each supplier. The model incorporates demand, capacity, raw-material availability, and sequencing constraints in order to maximize total profitability. The model is applied to real-life data from multi-grade polypropylene production in a large petrochemical company.
Arm at problems for waste recycle and treatment, the article construct system of remanufacturing industry reverse logistics networks, and build MILP(mixed-integer linear programming) model in order to minimize operati...
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ISBN:
(纸本)9781424435197
Arm at problems for waste recycle and treatment, the article construct system of remanufacturing industry reverse logistics networks, and build MILP(mixed-integer linear programming) model in order to minimize operating cost of reverse logistics from two reverse logistics handling methods of remanufacturing and raw materials recycle. Then the article ascertain the number and location of all kind of recycle center and handling establishment local and assign optimizing logistics amount at logistics route consist of net nodes. Finally, the article validates model validity and feasibility.
Security-constrained generation scheduling (SCGS) is an indispensable tool for independent system operators (ISOS) to plan a secure and economical hourly generation schedule in the daily/weekly ahead markets. Power ou...
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ISBN:
(纸本)9781424442409
Security-constrained generation scheduling (SCGS) is an indispensable tool for independent system operators (ISOS) to plan a secure and economical hourly generation schedule in the daily/weekly ahead markets. Power outputs of thermal units are usually formulated as step functions in almost all literature on SCGS, i.e., the generation level schedule is taken as an energy delivery schedule. The ramp-rate constraints are also simplified as limits on the differences of generation levels in consecutive time periods. However, it has been found in our previous work that the energy delivery schedule obtained from this formulation may not be realizable. The schedule obtained is generally different from what is implemented. In this paper, feasible energy delivery is embedded in SCGS model and power outputs of thermal units are formulated as piece-wise linear functions rather than step functions. The schedule obtained from the new formulation is deliverable in terms of energy and precise ramping process for implementing the energy schedule is also obtainable. A mixed-integer linear programming (MILP) method is applied to solve the problem with the new formulation and numerical testing is performed for a six-bus system and an IEEE 118-bus system. The testing results and primary analysis show that the new formulation is valid.
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