Integrated sites are tightly interconnected networks of large-scale chemical processes. Given the large-scale network structure of these sites, disruptions in any of its nodes, or individual chemical processes, can pr...
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Integrated sites are tightly interconnected networks of large-scale chemical processes. Given the large-scale network structure of these sites, disruptions in any of its nodes, or individual chemical processes, can propagate and disrupt the operation of the whole network. Random process failures that reduce or shut down production capacity are among the most common disruptions. The impact of such disruptive events can be mitigated by adding parallel units and/or intermediate storage. In this paper, we address the design of large-scale, integrated sites considering random process failures. In a previous work (Terrazas-Moreno et al., 2010), we proposed a novel mixed-integer linear programming (MILP) model to maximize the average production capacity of an integrated site while minimizing the required capital investment. The present work deals with the solution of large-scale problem instances for which a strategy is proposed that consists of two elements. On one hand, we use Benders decomposition to overcome the combinatorial complexity of the MILP model. On the other hand, we exploit discrete-rate simulation tools to obtain a relevant reduced sample of failure scenarios or states. We first illustrate this strategy in a small example. Next, we address an industrial case study where we use a detailed simulation model to assess the quality of the design obtained from the MILP model. (C) 2011 Elsevier Ltd. All rights reserved.
This paper addresses the problem of the self-scheduling of a power company with a dominant role in both the production and retail sectors of an electricity market. An integrated 0/1 mixedintegerlinearprogramming (M...
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This paper addresses the problem of the self-scheduling of a power company with a dominant role in both the production and retail sectors of an electricity market. An integrated 0/1 mixedintegerlinearprogramming (MILP) formulation is provided, which combines both thermal and hydro subsystems in a single portfolio for a dominant power company through a detailed modeling of the operating constraints of thermal units and hydroplants. Residual demand curves for energy and reserves are used to model the effect of the power company's interactions with its competitors. Test results on a medium-scale real test system address the effect that the power company's forward commitments and the market rules have on its daily self-scheduling and profits as well as on the resulting energy and reserve market clearing prices. (C) 2012 Elsevier Ltd. All rights reserved.
Transmission switching can improve the economic benefits of a power system through changing its topology during operations. However, the switching operation itself represents a step change in power systems, which is, ...
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Transmission switching can improve the economic benefits of a power system through changing its topology during operations. However, the switching operation itself represents a step change in power systems, which is, to some extent, similar to a contingency that can bring disturbances into systems. This paper proposes a new model for multi-period, static-security-constrained transmission switching. Because the power flow on the network will be redistributed instantaneously after the switching operations, the new model involves using disjunctive programming, which considers two sets of power flow equations under possibly different topologies before/after switching. Each switchable transmission element is modeled into four actions or disjunctions. An action transition diagram coupling of four actions in different hours is used to represent feasible paths of instantaneous changes in the element status. Disjunctive formulations are transformed into mixed-integerprogramming problems. We compare the difference of the previous transmission switching model and the proposed one by using several numerical tests and verify the effectiveness of our solution methodology in the six-bus and RTS-96 systems.
This paper presents a methodology for optimization of technological operations in a CHP plant and for simultaneous planning of electricity trading with profit maximization being the objective. A general modelling fram...
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This paper presents a methodology for optimization of technological operations in a CHP plant and for simultaneous planning of electricity trading with profit maximization being the objective. A general modelling framework is developed, which is aimed at rapid CHP plant models prototyping using an object-oriented modelling language. The framework consists of two main parts - first-principle models of technological components and a model of trading with standardized power products on power markets. The complexity of models is chosen considering their further implementation within a mixed-integerlinear optimization problem. The choice of linear and piece-wise linear problem formulation results from the need of its applicability for practical problem instances, while non-linear descriptions usually involve unacceptable computational times. Using the proposed methodology and general-purpose solver Gurobi, optimal solution for short-term problems (24-48 h) are found within few minutes. In the case of medium- and long-term problems (weeks to months), near optimal solutions (with an error usually under 0.5% and 1.0% respectively) are found within 2 h. (C) 2011 Elsevier Ltd. All rights reserved.
Mathematical models of complex biological networks are valuable to make predictions on system properties and to identify therapeutic targets. However, development, validation and analysis of predictive models is often...
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Mathematical models of complex biological networks are valuable to make predictions on system properties and to identify therapeutic targets. However, development, validation and analysis of predictive models is often hampered as absolute and quantitative measurement data are rarely available. Instead, the data are typically uncertain with respect to the measured variable or time, relational due to normalization, or data are given as conditional if-then observations. Many common approaches for model development and validation cannot deal with such semi-quantitative data and qualitative information. We present a framework for the guaranteed invalidation and parameter estimation of dynamical models using such data. For this purpose, the data are formally expressed by sets of equalities and inequalities containing binary variables. Then a mixed-integer nonlinear feasibility problem is constructed, which is subsequently relaxed into a mixed-integerlinear program that can be solved efficiently. A model can be proved inconsistent, that is, invalid with the uncertain and semi-quantitative/qualitative data, if the solution set of the mixed-integerlinear program is empty. To exemplify the approach, we analyze different models whether they can show adaptation to a step-input. First, we invalidate all but one model and, second, derive outer-bounds for those regions in the parameter space of the non-invalidated model that contain parameterizations for which it is consistent with the data. Copyright (c) 2012 John Wiley & Sons, Ltd.
This paper addresses the problem of the self-scheduling of thermal generating units during commissioning. A 0/1 mixed-integerlinear formulation is presented, which allows an accurate and realistic modeling for the sc...
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This paper addresses the problem of the self-scheduling of thermal generating units during commissioning. A 0/1 mixed-integerlinear formulation is presented, which allows an accurate and realistic modeling for the scheduling of the commissioning tests that should be performed once the construction of the thermal unit has been completed and prior to entering its commercial operation. A flexible contract between the producer and the contractor regarding the performing period of the commissioning tests is proposed. The model presented can be used by a producer with thermal units in commissioning, who acts either as a price-taker or a price-maker in the day-ahead energy market. Test results on a medium-scale real test system address the effect that the implementation of the proposed model has on the producer profits as well as on the day-ahead market clearing prices.
Recently, the mixed-model assembly line (MMAL) has been widely studied by many researchers. In fact, there are two basic problems, namely balancing and sequencing problems, which have been investigated in a lot of stu...
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Recently, the mixed-model assembly line (MMAL) has been widely studied by many researchers. In fact, there are two basic problems, namely balancing and sequencing problems, which have been investigated in a lot of studies separately, but few researchers have solved both problems simultaneously. Regarding this, the best results in minimising total utility work have been gained by developing a co-evolutionary genetic algorithm (Co-GA) so far. This paper provides a mixed-integer linear programming (MILP) model to jointly solve the problems. Because of NP-hardness, an evolution strategies (ES) algorithm is presented and evaluated by the same test problems in the literature. Two main hypotheses, namely simultaneous search and feasible search, are tested in the proposed algorithm to improve the quality of solutions. To calibrate the algorithm, a Taguchi design of experiments is employed. The proposed ES is compared with the modified version of Co-GA and the MILP model results. According to numerical experiments and statistical proving, the proposed ES outperformed the modified Co-GA from two points of view: the objective function and the computational time. Additionally, the meta-heuristic algorithms are examined in terms of other well-known criteria in MMAL. Finally, the contribution of each hypothesis in accounting for this superiority is analysed.
Multi-objective optimization (MOO) has recently attracted an increasing interest in environmental engineering. One major limitation of the existing solution methods for MOO is that their computational burden tends to ...
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Multi-objective optimization (MOO) has recently attracted an increasing interest in environmental engineering. One major limitation of the existing solution methods for MOO is that their computational burden tends to grow rapidly in size with the number of environmental objectives. In this paper, we study the use of Principal Component Analysis (PCA) to identify redundant environmental metrics in MOO that can be omitted without disturbing the main features of the problem, thereby reducing the associated complexity. We show that, besides its numerical usefulness, the use of PCA coupled with MOO provides valuable insights on the relationships between environmental indicators of concern for decision-makers. The capabilities of the proposed approach are illustrated through its application to the design of environmentally conscious chemical supply chains (SCs). (C) 2011 Elsevier Ltd. All rights reserved.
In this paper, we study a variant of the resource-constrained project scheduling problem in which resources are flexible, i.e., each resource has several skills. Each activity in the project may need several resources...
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In this paper, we study a variant of the resource-constrained project scheduling problem in which resources are flexible, i.e., each resource has several skills. Each activity in the project may need several resources for each required skill. We present a mixed-integer linear programming formulation for this problem. Several sets of additional inequalities are also proposed. Due to the fact that some of the above-mentioned inequalities require a valid upper bound to the problem, a heuristic procedure is proposed. Computational experience is reported based on randomly generated data, showing that for instances of reasonable size the proposed model enlarged with the additional inequalities can be solved efficiently.
In the current study, the costs and benefits of deploying energy storage system (ESS) are discussed, and the role of ESS in transmission expansion planning (TEP) is investigated. Based on the classical formulation of ...
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ISBN:
(纸本)9781467327299
In the current study, the costs and benefits of deploying energy storage system (ESS) are discussed, and the role of ESS in transmission expansion planning (TEP) is investigated. Based on the classical formulation of TEP, a new formulation is developed considering the simultaneous addition of new circuits and installation of ESS. The fictitious costs of ESS are set instead of actual costs in the proposed model, which can be used to determine the site and size of ESS for transmission investment reduction. The whole formulation is a mixed-integer linear programming problem and can be solved by the well-developed algorithms. The proposed TEP method, which considers the optimal deployment of ESS, has been simulated on three test systems. Test results show the effectiveness of the proposed method and illustrate the potential of installing ESS to reduce network investment costs.
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