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Optimal design of energy conversion units and envelopes for residential building retrofits using a comprehensive MILP model

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APPLIED ENERGY 2017年第Part1期185卷 1-15页

作者： Schuetz, Thomas Schiffer, Lutz Harb, Hassan Fuchs, Marcus Mueller, Dirk Rhein Westfal TH Aachen EON Energy Res Ctr Inst Energy Efficient Bldg & Indoor Climate Mathieustr 10 Aachen Germany

The optimal design of buildings is a complex task involving energy systems as well as construction measures. Typically, in exact optimization models, only energy systems are considered, whereas envelope components are neglected. When considering both, heuristics are commonly used, which do not guarantee optimal or close to optimal results. Thus, this paper presents the governing equations, validation and exemplary usage of a building model suitable for exact optimization problems. The developed model simultaneously considers energy systems and building envelopes. It is based on ISO 13790 and validated according to ASHRAE 140 and further compared to a more detailed model. The findings show that the developed model largely complies with the ASHRAE requirements and is able to assess buildings' dynamic behavior regarding indoor air temperatures as well as hourly, peak load, and annual heating loads. The simultaneous optimization of energy system and envelope is further demonstrated analyzing retrofitting options of a residential building. We consider solely installing additional PV units, modernizing the building envelope according to German regulations and an optimization without constraints regarding building envelope and energy system. The results indicate that installing additional PV units can moderately reduce total costs and CO2 emissions. The envelope modernization according to governmental regulations leads to largely increased costs at lower emissions, whereas the unconstrained optimization is able to simultaneously achieve significant cost and CO2 emission advantages. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Building energy systems Building envelopes German Energy Saving Ordinance ISO 13790 mixed-integer linear programming Optimal design Retrofitting

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Adaptive Scheme for Caching YouTube Content in a Cellular Network: Machine Learning Approach

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IEEE ACCESS 2017年 5卷 5870-5881页

作者： Tanzil, S. M. Shahrear Hoiles, William Krishnamurthy, Vikram Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada Cornell Tech Dept Elect & Comp Engn New York NY 10011 USA Cornell Univ New York NY 10011 USA

Content caching at base stations is a promising solution to address the large demands for mobile data services over cellular networks. Content caching is a challenging problem as it requires predicting the future popularity of the content and the operating characteristics of the cellular networks. In this paper, we focus on constructing an algorithm that improves the users' quality of experience (QoE) and reduces network traffic. The algorithm accounts for users' behavior and properties of the cellular network (e.g. cache size, bandwidth, and load). The constructed content and network aware adaptive caching scheme uses an extreme-learning machine neural network to estimate the popularity of content, and mixed-integer linear programming to compute where to place the content and select the physical cache sizes in the network. The proposed caching scheme simultaneously performs efficient cache deployment and content caching. Additionally, a simultaneous perturbation stochastic approximation method is developed to reduce the number of neurons in the extreme-learning machine method while ensuring a sufficient predictive performance is maintained. Using real-world data from YouTube and a NS-3 simulator, we demonstrate how the caching scheme improves the QoE of users and network performance compared with industry standard caching schemes.

关键词： Content caching cellular network extreme learning machines mixed-integer linear programming popularity prediction feature selection YouTube

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Comparison of optimization solvers in the model predictive control of a PV-battery-heat pump system

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Energy Procedia 2018年 155卷 524-535页

作者： Ronny Gelleschus Michael Böttiger Peter Stange Thilo Bocklisch Chair of Energy Storage Systems Technische Universität Dresden Helmholtzstr. 9 01069 Dresden Germany Chair of Building Energy Systems and Heat Supply Technische Universität Dresden Helmholtzstr. 14 01069 Dresden Germany

This paper presents a model predictive control approach for a home energy system with a heat pump, a thermal storage, a photovoltaic system and a battery. The modeling of the system in the mixed-integer linear programming framework is demonstrated and results of a one-year simulation with real measured PV and electric load data are shown. Different solution strategies for the underlying optimization problem are presented. The strategies are compared with respect to their performance and reliability. In this rather complex case branch & cut-based algorithms performed best in solving the optimization problem.

关键词： Energy Management System Model Predivtive Control Energy Storage Sector Coupling mixed-integer linear programming Genetic Algorithm Dynamic programming

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Corporates' control strategies using network optimization

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2017年第5期24卷 1041-1059页

作者： Martins, Pedro Neves, Elisabete Polytech Inst Coimbra ISCAC Coimbra Portugal Univ Lisbon Ctr Math Fundamental Applicat & Operat Res CMAF CIO Lisbon Portugal Univ Tras Os Montes & Alto Douro Ctr Estudos Transdisciplinares Desenvolvimento CE Vila Real Portugal

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.

关键词： corporate governance stock shareholding network network optimization mixed-integer linear programming

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Dual Pricing Algorithm in ISO Markets

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IEEE TRANSACTIONS ON POWER SYSTEMS 2017年第4期32卷 3308-3310页

作者： O'Neill, Richard P. Castillo, Anya Eldridge, Brent Hytowitz, Robin Broder Fed Energy Regulatory Commiss Washington DC 20426 USA Sandia Natl Labs POB 5800 Albuquerque NM 87185 USA Johns Hopkins Univ Baltimore MD 21218 USA

The challenge to create efficient market clearing prices in centralized day-ahead electricity markets arises from inherent nonconvexities in unit commitment problems. When this aspect is ignored, marginal prices may result in economic losses to market participants who are part of the welfare maximizing solution. In this essay, we present an axiomatic approach to efficient prices and cost allocation for a revenue neutral and nonconfiscatory day-ahead market. Current cost allocation practices do not adequately attribute costs based on transparent cost causation criteria. Instead we propose an ex post multipart pricing scheme, which we refer to as the dual pricing algorithm. Our approach can be incorporated into current day-ahead markets without altering the market equilibrium.

关键词： Centralized day-ahead electricity market mixed-integer linear programming non-convex pricing power system economics

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On optimizing the deployment cost of visual sensor networks

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JOURNAL OF HIGH SPEED NETWORKS 2017年第1期24卷 17-30页

作者： Mehdi, Rouan-Serik Mejdi, Kaddour Univ Oran 1 Lab LITIO Ahmed Ben Bella Algeria

We study in this paper the problem of minimizing the number and the locations of deployed cameras in visual sensor networks where to objective is to monitor a set of targets with a predefined quality level. To this end, we first propose a mathematical programming formulation, based on mixed-integer linear programming (MILP), which is designed to provide optimal solutions in case where the deployment area is represented through a set of discrete potential locations of the cameras. Due to the combinatorial nature of such problems, finding exact solutions entails a tremendous computational cost. Consequently, we introduce various suboptimal solution approaches, based on a number of well-known metaheuristics, such as particle swarm optimization (PSO) and genetic algorithms. Numerical results show PSO succeeds to find the best solutions in the majority of considered scenarios. Furthermore, even for large instances, it provides better feasible solutions than those returned the MILPs after a significant amount of time.

关键词： Visual sensor networks deployment problem mixed-integer linear programming particle swarm optimization genetic algorithms greedy algorithms simulated annealing target coverage

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Frequency constrained unit commitment

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ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2017年第1期8卷 31-56页

作者： Cardozo, Carmen Capely, Laurent Dessante, Philippe UPMC Univ Paris 06 Sorbonne Univ Univ Paris SudCentraleSupelec GeePs Grp Elect Engn Paris UMR CNRS 8507 3-11 Rue Joliot Curie F-91192 Gif Sur Yvette France EFESE EDF R&D 1 Ave Gen Gaulle F-92141 Clamart France

The unit commitment (UC) problem deals with the short-term schedule of the electrical generation to meet the power demand. The main objective is to minimize the production cost, while respecting technical and security constraints. In addition to the system load, a specific amount of spare capacity is committed to cope with uncertainties, such as forecasting errors and unit outages;this is called reserve and it has been traditionally specified following a static reliability criterion. In a system with a conventional generation mix, this security constraint allows one to obtain UC solutions that naturally provide an acceptable transient response. However, the increasing penetration of variable generation sources, such as wind and solar, can lead to UC solutions that no longer ensure system security. Thus, enhanced security constraints have been proposed to consider the power system dynamics when optimising the dayahead generation schedule. Some published works are focused on the formulation of these constraints in amixed-integer linear programming structure to apply classic optimisation techniques. Nevertheless, power system dynamics is a non-linear problem, and, to the authors' knowledge, the limits of these linear approximations have not been discussed in literature. This work examines the ability of different UC models to produce secure schedules when facing unit outages, through the implementation of a set of primary reserve and energy co-optimisation models. These models are built based on linear approximations of dynamic constraints that are available in recent literature. Then, dynamic simulations are performed for every conceivable outage to observe the transient response of a test system and to quantify the risk of under frequency load shedding. It is shown that the system's need for frequency regulation depends on the operating point and that enforcing a fixed reserve, inertia or maximal frequency slope constraint is not cost/ effective to limit the dynamic

关键词： Unit commitment Primary reserve optimisation Under frequency load shedding mixed-integer linear programming Variable generation

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A mixed-integer programming model for integrated production and maintenance

A mixed-integer programming model for integrated production ...

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8th IFAC Conference on Manufacturing Modelling, Management and Control (MIM)

作者： Hnaien, F. Yalaoui, F. Mhadhbi, A. Nourelfath, M. Univ Technol Troyes ICD LOSI UMR CNRS 6281 12 Rue Marie Curie F-10010 Troyes France Finaxys 27 Rue Poissonniers F-92200 Neuilly Sur Seine France Univ Laval Dept Mech Engn Interuniv Res Ctr Enterprise Networks Logist & Tr Quebec City PQ G1V 0A6 Canada

This paper deals with the production planning and preventive maintenance scheduling on a single machine multi-product capacitated lot-sizing problem (CLSP). The machine is assumed to be subject to random failures. Preventive maintenances at the beginning of each production period to reduce the risk of failure and minimal repairs at failure is considered. The aim is to minimize the sum of the total production and maintenance costs related to inventory, backorder, production, set-up, preventive maintenance (PM), and minimal repair (MR) under demand satisfaction and machine capacities constraints over the entire horizon. Using the Weibull model, we present a mixed-integer linear programming (MIP) model to solve the problem. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： capacitated lot-sizing problem production planning preventive maintenance minimal repair mixed-integer linear programming

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A multi-objective framework for cost-unavailability optimisation of residential distributed energy system design

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2017年 9卷 104-117页

作者： Wouters, Carmen Fraga, Eric S. James, Adrian M. Univ Coll London Sch Energy & Resources Adelaide SA 5000 Australia UCL Dept Chem Engn Ctr Proc Syst Engn London WC1E 6BT England

Future energy systems are expected to include distributed energy systems (DES) and microgrids (MG) at the distribution level. These energy efficient environments enable participating consumers to locally generate and share both electrical and thermal energy. Apart from the potential for a more cost-efficient energy system design, improved system availability is also increasingly put forward as a major advantage of MGs. This paper proposes a mixed-integer linear programming (MILP) approach for the design of a neighbourhood-based energy system, considering the trade-off between total annualised cost and electrical system unavailability. System design is optimised to meet the yearly neighbourhood energy demands by selecting technologies and interactions from a pool of dispatchable and renewable polygeneration and storage alternatives. The availability implementation employs a Markov chain approach combined with logic-gate integer programming. The Pareto trade-off sets of on-and off-grid MG modes are obtained using a weighted-sum approach. The developed model is subsequently applied to an Australian case-study. The sought after trade-off "knee" points for each Pareto curve are hereby identified. Additionally, through comparing on-and off-grid design trade-offs, the need for component redundancy for systems with islanding capabilities is analysed. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： z Dependability Distributed generation Logic-gate integer programming Markov state space analysis Microgrid mixed-integer linear programming

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RECIFE-SAT: A MILP-based algorithm for the railway saturation problem

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JOURNAL OF RAIL TRANSPORT PLANNING & MANAGEMENT 2017年第1-2期7卷 19-32页

作者： Pellegrini, Paola Marliere, Gregory Rodriguez, Joaquin Univ Lille Nord France F-59000 Lille France IFSTTAR LEOST COSYS F-59650 Villeneuve Dascq France IFSTTAR ESTAS COSYS F-59650 Villeneuve Dascq France

Measuring capacity of railway infrastructures is a problem even in its definition. In this paper, we propose RECIFE-SAT, a MILP-based algorithm to quantify capacity by solving the saturation problem. This problem consists of saturating an infrastructure by adding as many trains as possible to an existing (possibly empty) timetable. Specifically, RECIFE-SAT considers a large set of potentially interesting saturation trains and integrates them in the timetable whenever possible. This integration is feasible only when it does not imply the emergence of any conflict with other trains. Thanks to a novel approach to microscopically represent the infrastructure, RECIFE-SAT guarantees the absence of conflicts based on the actual interlocking system deployed in reality. Hence, it can really quantify the actual capacity of the infrastructure considered. The presented version of RECIFE-SAT has two objective functions, namely it maximizes the number of saturation trains scheduled and the number of freight ones. In an experimental analysis performed in collaboration with the French infrastructure manager, we show the promising performance of RECIFE-SAT. To the best of our knowledge, RECIFE-SAT is the first algorithm which is shown to be capable of saturating rather large railway networks considering a microscopic infrastructure representation. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Railway capacity Saturation problem mixed-integer linear programming Microscopic representation

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