Combined heat and power (CHP) production is universally accepted as one of the most energy-efficient technologies to produce energy with lower fuel consumption and fewer emissions. In CHP technology, heat and power ge...
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Combined heat and power (CHP) production is universally accepted as one of the most energy-efficient technologies to produce energy with lower fuel consumption and fewer emissions. In CHP technology, heat and power generation follow a joint characteristic. Traditional CHP production is usually applied in backpressure plants, where the joint characteristic can often be represented by a convex model. Advanced CHP production technologies such as backpressure plants with condensing and auxiliary cooling options, gas turbines, and combined gas and steam cycles may require non-convex models. Cost-efficient operation of a CHP system can be planned using an optimization model based on forecasts for heat load and power price. A long-term planning model decomposes into thousands of single-period models, which can be formulated in the convex case as linear programming (LP) problems, and in the non-convex case as mixed integer programming (MIP) problems. In this paper, we introduce EBB algorithm, for solving the non-convex single-period CHP models of a long-term planning problem under the deregulated power market. EBB is based on the Branch and Bound (B&B) algorithm where tight lower bounds are computed analytically for pruning the search tree and the LP sub-problems are solved through an efficient envelope-based dual algorithm. We compare the performance of EBB with realistic models against the ILOG CPLEX 9.0 MIP solver and the Power Simplex (PS)-based B&B algorithm (PBB). PS is an efficient specialized primal-based Simplex, algorithm developed for convex CHP planning problems. EBB is from 661 to 955 times (with average 785) faster than CPLEX and from 11 to 31 times (with average 24) faster than PBB. (c) 2006 Elsevier B.V. All rights reserved.
We address a multi-item capacitated lot-sizing problem with setup times that arises in real-world production planning contexts. Demand cannot be backlogged, but can be totally or partially lost. Safety stock is an obj...
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We address a multi-item capacitated lot-sizing problem with setup times that arises in real-world production planning contexts. Demand cannot be backlogged, but can be totally or partially lost. Safety stock is an objective to reach rather than an industrial constraint to respect. The problem is NP-hard. We propose mixed integer programming heuristics based on a planning horizon decomposition strategy to find a feasible solution. The planning horizon is partitioned into several sub-horizons over which a freezing or a relaxation strategy is applied. Some experimental results showing the effectiveness of the approach on real-world instances are presented. A sensitivity analysis on the parameters of the heuristics is reported.
The decision tree (DT) induction process has two major phases: the growth phase and the pruning phase. The pruning phase aims to generalize the DT that was generated in the growth phase by generating a sub-tree that a...
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The decision tree (DT) induction process has two major phases: the growth phase and the pruning phase. The pruning phase aims to generalize the DT that was generated in the growth phase by generating a sub-tree that avoids over-fitting to the training data. Most post-pruning methods essentially address post-pruning as if it were a single objective problem (i.e. maximize validation accuracy), and address the issue of simplicity (in terms of the number of leaves) only in the case of a tie. However, it is well known that apart from accuracy there are other performance measures (e.g. stability, simplicity, interpretability) that are important for evaluating DT quality. In this paper, we propose that multi-objective evaluation be done during the post-pruning phase in order to select the best sub-tree, and propose a procedure for obtaining the optimal sub-tree based on user provided preference and value function information. (C) 2006 Elsevier Ltd. All rights reserved.
We propose an ISO model for coordinating transmission expansion planning with competitive generation capacity planning in electricity markets. The purpose of the model is a holistic simulation of generation and transm...
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We propose an ISO model for coordinating transmission expansion planning with competitive generation capacity planning in electricity markets. The purpose of the model is a holistic simulation of generation and transmission capacity expansion in the market environment. The solution represents an iterative process for simulating the interactions among GENCOs, TRANSCOs, and the ISO, which might be terminated by the ISO based on a pre-specified stopping criterion. The proposed model adopts a joint energy and transmission auction market and a capacity mechanism. The joint auction market enables competition to occur among generation and transmission resources. The capacity mechanism offers incentives for market participant investments that reflect the locational values of additional capacity. Transmission capacity expansion decisions are made by merchant transmission lines that recover their capacity investments through transmission marginal pricing and capacity payments. Transmission network security is reflected in the proposed competitive resource planning model. The examples illustrate a coordinated planning of generation and transmission in restructured power systems.
We propose minimum volume ellipsoids (MVE) clustering as an alternative clustering technique to k-means for data clusters with ellipsoidal shapes and explore its value and practicality. MVE clustering allocates data p...
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We propose minimum volume ellipsoids (MVE) clustering as an alternative clustering technique to k-means for data clusters with ellipsoidal shapes and explore its value and practicality. MVE clustering allocates data points into clusters in a way that minimizes the geometric mean of the volumes of each cluster's covering ellipsoids. Motivations for this approach include its scale-invariance, its ability to handle asymmetric and unequal clusters, and our ability to formulate it as a mixed-integer semidefinite programming problem that can be solved to global optimality. We present some preliminary empirical results that illustrate MVE clustering as an appropriate method for clustering data from mixtures of "ellipsoidal" distributions and compare its performance with the k-means clustering algorithm as well as the MCLUST algorithm (which is based on a maximum likelihood EM algorithm) available in the statistical package R.
In the context of learning theory many efforts have been devoted to developing classification algorithms able to scale up with massive data problems. In this paper the complementary issue is addressed, aimed at derivi...
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In the context of learning theory many efforts have been devoted to developing classification algorithms able to scale up with massive data problems. In this paper the complementary issue is addressed, aimed at deriving powerful classification rules by accurately learning from few data. This task is accomplished by solving a new mixed integer programming model that extends the notion of discrete support vector machines, in order to derive an optimal set of separating hyperplanes for binary classification problems. According to the cardinality of the set of hyperplanes, the classification region may take the form of a convex polyhedron or a polytope in the original space where the examples are defined. Computational tests on benchmark datasets highlight the effectiveness of the proposed model, that yields the greatest accuracy when compared to other classification approaches.
Models for integrated production and demand planning decisions can serve to improve a producer's ability to effectively match demand requirements with production capabilities. In contexts with price-sensitive dema...
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Models for integrated production and demand planning decisions can serve to improve a producer's ability to effectively match demand requirements with production capabilities. In contexts with price-sensitive demands, economies of scale in production, and multiple capacity options, such integrated planning problems can quickly become complex. To address these complexities, this paper provides profit-maximizing production planning models for determining optimal demand and internal production capacity levels under price-sensitive deterministic demands, with subcontracting and overtime options. The models determine a producer's optimal price, production, inventory, subcontracting, overtime, and internal capacity levels, while accounting for production economies of scale and capacity costs through concave cost functions. We use polyhedral properties and dynamic programming techniques to provide polynomial-time solution approaches for obtaining an optimal solution for this class of problems when the internal capacity level is time-invariant. (C) 2007 Wiley Periodicals, Inc.
The need for local energy planning is not reduced after liberalization. Both integrated energy companies and local governments have to consider alternative solutions across traditional supply and demand sectors and ma...
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The need for local energy planning is not reduced after liberalization. Both integrated energy companies and local governments have to consider alternative solutions across traditional supply and demand sectors and make plans for the total integrated energy infrastructure. This situation has created a need for new improved methodologies and tools for system planning and operation that include multiple energy carriers and sufficient topological details. In this paper, a novel optimisation model 'eTransport' is presented that takes into account both the topology of multiple energy infrastructures and the technical and economic properties of different investment alternatives. The model minimises total energy system cost (investments, operation and emissions) of meeting predefined energy demands of electricity, gas, space heating and tap water heating within a geographical area over a given planning horizon, including alternative supply infrastructures for multiple energy carriers. The model employs a nested optimisation, calculating both the optimal diurnal operation of the energy system and the optimal expansion plan typically 20-30 years into the future. The model is tested on a number of real case studies, and a full graphical user interface has been implemented. A sample case study is included to demonstrate the use of the model. (c) 2007 Elsevier Ltd. All rights reserved.
This paper develops optimal bidding strategies based on hourly unit commitment in a generation company (GENCO) that participates in energy and ancillary services markets. The price-based unit commitment problem with u...
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This paper develops optimal bidding strategies based on hourly unit commitment in a generation company (GENCO) that participates in energy and ancillary services markets. The price-based unit commitment problem with uncertain market prices is modeled as a stochastic mixedinteger linear program. The market price uncertainty is modeled using the scenario approach, Monte Carlo simulation is applied to generate scenarios, scenario reduction techniques are applied to reduce the size of the stochastic price-based unit commitment problem, and postprocessing is applied based on marginal cost of committed units to refine bidding curves. The financial risk associated with market price uncertainty is modeled using expected downside risk, which is incorporated explicitly as a constraint in the problem. Accordingly, the proposed method provides a closed-loop solution to devising specific strategies for risk-based bidding in a GENCO. Illustrative examples show the impact of market price uncertainty on GENCO's hourly commitment schedule and discuss the way GENCOs could decrease financial risks by managing expected payoffs.
We study the deployment planning problem (DPP) that may roughly be defined as the problem of the planning of the physical movement of military units, stationed at geographically dispersed locations, from their home ba...
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We study the deployment planning problem (DPP) that may roughly be defined as the problem of the planning of the physical movement of military units, stationed at geographically dispersed locations, from their home bases to their designated destinations while obeying constraints on scheduling and routing issues as well as on the availability and use of various types of transportation assets that operate on a multimodal transportation network. The DPP is a large-scale real-world problem for which analytical models do not exist. We propose a model for solving the problem and develop a solution methodology which involves an effective use of relaxation and restriction that significantly speeds up a CPLEX-based branch-and-bound. The solution times for intermediate-sized problems are around 1 h at maximum, whereas it takes about a week in the Turkish Armed Forces to produce a suboptimal feasible solution based on trial-and-error methods. The proposed model can be used to evaluate and assess investment decisions in transportation infrastructure and transportation assets as well as to plan and execute cost-effective deployment operations at different levels of planning. (c) 2005 Elsevier Ltd. All rights reserved.
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