This paper addresses the coordinated operation between wind farms and reservoir hydropower plants in networks with weak transmission capacity. The joint operation of the energy sources is captured by a Virtual Power P...
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This paper addresses the coordinated operation between wind farms and reservoir hydropower plants in networks with weak transmission capacity. The joint operation of the energy sources is captured by a Virtual Power Plant (VPP) representation. With the aim of maximising renewable energy penetration while ensuring a suitable VPP integration, a supervisory controller for the energy dispatch layer is proposed. To deal with the variable nature of both resources, the controller has been designed into a model predictive control framework including mixed-integer quadratic programming (given the hybrid considered model) for solving the related optimisation problem, and complementary techniques as constraints softening and time-varying weighting. The results indicate that the predictive control approach achieves a better Independent System Operator (ISO) reference tracking that, together with reservoir management, are the most important factors to ensure a suitable VPP incorporation to the main grid. As a case study, a sub-network of the Argentinian power system is tackled. The controller performance is quantitatively and qualitatively assessed under uncertain conditions and compared with other approaches over a nine-year period. By means of the proposed power management policy, the ISO's effort to balance the sub-network is reduced 56% with regard to other approaches without any water spillage. (C) 2020 Elsevier Ltd. All rights reserved.
Battery storage system design has become a crucial task for nanogrids and microgrids planning, as it strongly determines the techno-economic viability of the project. Despite that, most of developed methodologies for ...
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Battery storage system design has become a crucial task for nanogrids and microgrids planning, as it strongly determines the techno-economic viability of the project. Despite that, most of developed methodologies for optimally planning this kind of systems still present some important issues like high computational burden or insufficient results. This paper develops a novel methodology for battery storage system planning in nanogrids and microgrdis, which aims at overcoming the main issues presented by other methodologies. To achieve this goal, our proposal originally combines different software, clustering techniques and optimization tools. As salient features of the developed approach, it is worth remarking its efficiency, versatility, ability to manage with different time horizons and comprehensiveness. A prospective nanogrid in the region of Cuenca, Ecuador, serves as illustrative case study to show the capabilities, efficiency and effectiveness of the proposed approach as providing sufficient guidelines for its universal applicability. Among other relevant results, our proposal is able to determine that, for the studied grid, the daily operating cost can be reduced up to 17% by using Nickel-Cadmium batteries, however, the usage of Lead-Acid and Sodium-Sulfur technologies resulted more attractive through the project lifetime due to their longer lifetimes and relatively low capital costs.
With the springing up of data centers (DCs) worldwide, their huge energy consumption presents a daunting challenge to the energy system. This paper considers DC's integration, especially DC's dynamic voltage f...
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ISBN:
(纸本)9781728171951
With the springing up of data centers (DCs) worldwide, their huge energy consumption presents a daunting challenge to the energy system. This paper considers DC's integration, especially DC's dynamic voltage frequency scaling (DVFS) and thermal inertia, proposing a novel optimal sizing method for the energy station in the multi-energy system (MES), which can realize the co-optimization of energy flow and information flow. Firstly, the MES integrated with DC is modeled. Based on DVFS and thermal inertia, the deep coupling relationship of DC's computing workload, electricity load and cooling load is delved. Then, the optimal sizing model aimed to minimize the total cost is established. Constraints include power balance, DC's computing workload balance and thermal balance. The problem is simplified to a mixed-integer quadratic programming one. Case studies based on a test system illustrate that the co-optimization considering DC's integration can reduce the total cost of the energy station by 7.2%.
Binary classification is a fundamental task in machine learning. It consists of learning a relationship between observable features of a set of training objects and their observable membership to either of two classes...
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ISBN:
(纸本)9781538672204
Binary classification is a fundamental task in machine learning. It consists of learning a relationship between observable features of a set of training objects and their observable membership to either of two classes to predict as accurately as possible the class membership of new test objects whose features are observable but whose class membership is unknown. One of the most successful methods for binary classification is the support vector machine classifier that aims at finding a hyperplane in the feature space separating the training objects of the two classes. However, the accuracy of this classifier in predicting the correct classes strongly depends on the features selected for determining the hyperplane. In this paper, we propose the first exact approach, which is based on mixed-integer quadratic programming and delayed constraint generation, to identify an optimal set of relevant features for determining the hyperplane. The results of a computational experiment demonstrate that the proposed approach is able to successfully select an optimal set of relevant features in a short running time even for classification tasks with over 10,000 objects and 100 features.
Reported dynamic multi-satellite scheduling approaches for Earth observations show many limitations when operating in time-varying uncertain environment. They largely run over predetermined time periods and often offl...
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ISBN:
(纸本)9789897583964
Reported dynamic multi-satellite scheduling approaches for Earth observations show many limitations when operating in time-varying uncertain environment. They largely run over predetermined time periods and often offline, assume negligible execution time, improperly account for the passage of time during planning, remain myopic or fail to show "anytime" behavior. A novel approach to solving the dynamic multi-satellite scheduling problem is proposed. The open-loop with feedback DynaQUEST approach includes an event-driven controller monitoring dynamic situation evolution while supervising a co-evolving episodic scheduling problem solver. Reactive to real-time and delayed information feedback, the controller timely enables the problem-solver to stay responsive, interruptible and adaptive, taking advantage of emerging opportunities to timely improve solution quality. The problem-solver continually solves a new static problem shaped by dynamic changes and constrained by current resource commitments to adaptively expand the emergent solution. Problem model formulation is based on network flow optimization using mathematical programming. Departing from mainstream approaches widely promoting an exact objective function coupled with a heuristic problem-solving method, the proposed approach alternatively combines an approximate objective function and an exact algorithm. The approach embraces an extended time horizon relaxing myopic planning. Computational results prove the approach to be cost-effective and to outperform alternate baseline heuristics.
We devise a model predictive control algorithm for impulsive linear systems with autonomous flow dynamics and controlled jumps. Thereby the moments of jumps are not fixed, but rather considered as decision variables. ...
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We devise a model predictive control algorithm for impulsive linear systems with autonomous flow dynamics and controlled jumps. Thereby the moments of jumps are not fixed, but rather considered as decision variables. To this end, the complete system dynamics is formulated as a mixed-logical dynamical system after an appropriate discretization step. The resulting optimization problem contains both discrete and continuous decision variables, giving rise to a mixed-integerprogramming problem. The objective of the optimization is to steer the states into a target set. The stability is addressed through an appropriate cost function together with invariance conditions, as well as by introducing terminal constraints which are only enforced within a certain distance to the target set, thus, providing a trade-off between guaranteed convergence to the target set and computational complexity. Copyright (C) 2020 The Authors.
This paper presents a real-world application of a mixed-integer receding horizon control in an onshore oil gathering network. The objective is to stabilize the operation of the gathering network by coordinating the au...
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This paper presents a real-world application of a mixed-integer receding horizon control in an onshore oil gathering network. The objective is to stabilize the operation of the gathering network by coordinating the automatic switching of pumps to avoid abrupt flow variations at the central station, minimizing pump switching and, at the same time, maintaining the level of fluid in the satellite stations under control. The applied technological solution easily integrates different systems, optimization tools and heuristic rules, allowing in-house development of complex applications.
Undertakings for Collective Investments in Transferable Securities (UCITS) are investment funds that are regulated by the European Union. UCITS have become increasingly popular, resulting in a total corresponding amou...
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Undertakings for Collective Investments in Transferable Securities (UCITS) are investment funds that are regulated by the European Union. UCITS have become increasingly popular, resulting in a total corresponding amount of assets under management of (sic) 8.5 trillion by the end of 2016. We present a two-stage approach to the problem of how to construct a portfolio of assets for a UCITS that aims to replicate the returns of a financial index subject to the constraints imposed by the UCITS regulations. In the first stage, we apply a genetic algorithm that treats subsets of the index constituents as individuals to construct a good feasible solution in a short CPU time. In this genetic algorithm, we use a new representation of subsets, which is the first to exhibit all of the following four desirable properties: feasibility, efficiency, locality, and heritability. In the second stage, we apply local branching based on a new mixed-integer quadratic programming formulation to improve the best solution obtained in the first stage. In a numerical experiment on real-world data, the approach yields very good feasible solutions in a short CPU time. (C) 2018 Elsevier Ltd. All rights reserved.
This article studies the problem of obtaining equilibrium clearing prices for markets with non-convexities when it is relevant to account for convex quadratic deliverability costs and constraints. In a general market,...
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This article studies the problem of obtaining equilibrium clearing prices for markets with non-convexities when it is relevant to account for convex quadratic deliverability costs and constraints. In a general market, such a situation arises when quadratic commodity or transactions costs are relevant. In the particular case of electricity markets, there is a mix of resources including dispatchable and renewable energy sources, leading to the presence of integer variables and quadratic costs reflecting ramping needs. To illustrate our results, we compute and analyze the equilibrium clearing prices of the Scarfs classical market problem with the addition of ramping costs. (C) 2019 Elsevier B.V. All rights reserved.
Dictionary learning for sparse representations is generally conducted in two alternating steps-sparse coding and dictionary updating. In this paper, a new approach to solve the sparse coding step is proposed. Because ...
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Dictionary learning for sparse representations is generally conducted in two alternating steps-sparse coding and dictionary updating. In this paper, a new approach to solve the sparse coding step is proposed. Because this step involves an l(0)-norm, most, if not all, existing solutions only provide a local or approximate solution. Instead, a real l(0) optimization is considered for the sparse coding problem providing a global solution. The proposed method reformulates the optimization problem as a mixed-integerquadratic program (MIQP), allowing then to obtain the global optimal solution by using an off-the-shelf optimization software. Because computing time is the main disadvantage of this approach, two techniques are proposed to improve its computational speed. One is to add suitable constraints and the other to use an appropriate initialization. The results obtained on an image denoising task demonstrate the feasibility of the MIQP approach for processing real images while achieving good performance compared to the most advanced methods.
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