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Showing papers on "Integer programming published in 2018"


Proceedings Article
27 Sep 2018
TL;DR: Verification of piecewise-linear neural networks as a mixed integer program that is able to certify more samples than the state-of-the-art and find more adversarial examples than a strong first-order attack for every network.
Abstract: Neural networks have demonstrated considerable success on a wide variety of real-world problems. However, neural networks can be fooled by adversarial examples – slightly perturbed inputs that are misclassified with high confidence. Verification of networks enables us to gauge their vulnerability to such adversarial examples. We formulate verification of piecewise-linear neural networks as a mixed integer program. Our verifier finds minimum adversarial distortions two to three orders of magnitude more quickly than the state-of-the-art. We achieve this via tight formulations for non-linearities, as well as a novel presolve algorithm that makes full use of all information available. The computational speedup enables us to verify properties on convolutional networks with an order of magnitude more ReLUs than had been previously verified by any complete verifier, and we determine for the first time the exact adversarial accuracy of an MNIST classifier to perturbations with bounded l[infinity] norm e = 0:1. On this network, we find an adversarial example for 4.38% of samples, and a certificate of robustness for the remainder. Across a variety of robust training procedures, we are able to certify more samples than the state-of-the-art and find more adversarial examples than a strong first-order attack for every network.

600 citations


Journal ArticleDOI
TL;DR: In this article, an improved real-coded genetic algorithm and an enhanced mixed integer linear programming (MILP) based method have been developed to schedule the unit commitment and economic dispatch of microgrid units.

288 citations


Journal ArticleDOI
TL;DR: This paper introduces a new concept of task caching, and proposes efficient algorithm, called task caching and offloading (TCO), based on alternating iterative algorithm, which outperforms others in terms of less energy cost.
Abstract: While augment reality applications are becoming popular, more and more data-hungry and computation-intensive tasks are delay-sensitive. Mobile edge computing is expected to an effective solution to meet the low latency demand. In contrast to previous work on mobile edge computing, which mainly focus on computation offloading, this paper introduces a new concept of task caching. Task caching refers to the caching of completed task application and their related data in edge cloud. Then, we investigate the problem of joint optimization of task caching and offloading on edge cloud with the computing and storage resource constraint. We formulate this problem as mixed integer programming which is hard to solve. To solve the problem, we propose efficient algorithm, called task caching and offloading (TCO), based on alternating iterative algorithm. Finally, the simulation experimental results show that our proposed TCO algorithm outperforms others in terms of less energy cost.

219 citations


Journal ArticleDOI
TL;DR: This paper proposes a new optimal scheduling mode for minimizing the operating costs of an isolated microgrid (MG) by using chance-constrained programming and significantly exceeds the commonly used hybrid intelligent algorithm with much better and more stable optimization results and significantly reduced calculation times.
Abstract: By modeling the uncertainty of spinning reserves provided by energy storage with probabilistic constraints, a new optimal scheduling mode is proposed for minimizing the operating costs of an isolated microgrid (MG) by using chance-constrained programming. The model is transformed into a readily solvable mixed integer linear programming (MILP) formulation in GAMS via a proposed discretized step transformation (DST) approach and finally solved by applying the CPLEX solver. By properly setting the confidence levels of the spinning reserve probability constraints, the MG operation can be achieved a trade-off between reliability and economy. The test results on the modified ORNL DECC lab MG test system reveal that the proposal significantly exceeds the commonly used hybrid intelligent algorithm with much better and more stable optimization results and significantly reduced calculation times.

197 citations


Journal ArticleDOI
TL;DR: These extensions that were added to the constraint integer programming framework SCIP to enable it to solve convex and nonconvex mixed-integer nonlinear programs (MINLPs) to global optimality are described and insights into the performance impact of individual MINLP solver components are provided.
Abstract: This paper describes the extensions that were added to the constraint integer programming framework SCIP in order to enable it to solve convex and nonconvex mixed-integer nonlinear programs (MINLPs) to global optimality. SCIP implements a spatial branch-and-bound algorithm based on a linear outer-approximation, which is computed by convex over- and underestimation of nonconvex functions. An expression graph representation of nonlinear constraints allows for bound tightening, structure analysis, and reformulation. Primal heuristics are employed throughout the solving process to find feasible solutions early. We provide insights into the performance impact of individual MINLP solver components via a detailed computational study over a large and heterogeneous test set.

193 citations


Journal ArticleDOI
TL;DR: In this article, the authors proposed a novel optimal planning method for a community level MES that jointly determines the optimal generation, conversion and delivery of electricity, heat, cooling, and other services.

190 citations


Journal ArticleDOI
TL;DR: In this paper, the authors discuss the peculiarity of this kind of 0-1 MILP models, and describe an effective bound-tightening technique intended to ease its solution.
Abstract: Deep Neural Networks (DNNs) are very popular these days, and are the subject of a very intense investigation. A DNN is made up of layers of internal units (or neurons), each of which computes an affine combination of the output of the units in the previous layer, applies a nonlinear operator, and outputs the corresponding value (also known as activation). A commonly-used nonlinear operator is the so-called rectified linear unit (ReLU), whose output is just the maximum between its input value and zero. In this (and other similar cases like max pooling, where the max operation involves more than one input value), for fixed parameters one can model the DNN as a 0-1 Mixed Integer Linear Program (0-1 MILP) where the continuous variables correspond to the output values of each unit, and a binary variable is associated with each ReLU to model its yes/no nature. In this paper we discuss the peculiarity of this kind of 0-1 MILP models, and describe an effective bound-tightening technique intended to ease its solution. We also present possible applications of the 0-1 MILP model arising in feature visualization and in the construction of adversarial examples. Computational results are reported, aimed at investigating (on small DNNs) the computational performance of a state-of-the-art MILP solver when applied to a known test case, namely, hand-written digit recognition.

174 citations


Journal ArticleDOI
TL;DR: Simulations show that this approach is able to dramatically enhance the scalability of task admission at a marginal cost of extra energy, as compared with the optimal branch and bound method, and can be efficiently implemented for online programming.
Abstract: Task admission is critical to delay-sensitive applications in mobile edge computing, but is technically challenging due to its combinatorial mixed nature and consequently limited scalability. We propose an asymptotically optimal task admission approach which is able to guarantee task delays and achieve $(1-\epsilon)$ -approximation of the computationally prohibitive maximum energy saving at a time-complexity linearly scaling with devices. $\epsilon $ is linear to the quantization interval of energy. The key idea is to transform the mixed integer programming of task admission to an integer programming (IP) problem with the optimal substructure by pre-admitting resource-restrained devices. Another important aspect is a new quantized dynamic programming algorithm which we develop to exploit the optimal substructure and solve the IP. The quantization interval of energy is optimized to achieve an $[\mathcal {O}(\epsilon),\mathcal {O}(1/\epsilon)]$ -tradeoff between the optimality loss and time complexity of the algorithm. Simulations show that our approach is able to dramatically enhance the scalability of task admission at a marginal cost of extra energy, as compared with the optimal branch and bound method, and can be efficiently implemented for online programming.

163 citations


Journal ArticleDOI
01 Jun 2018
TL;DR: An intersection control server processes data streams from approaching vehicles, periodically solves an optimization problem, and assigns to each vehicle an optimal arrival time that ensures safety while significantly reducing number of stops and intersection delays.
Abstract: We propose an urban traffic management scheme for an all connected vehicle environment. If all the vehicles are autonomous, for example, in smart city projects or future's dense city centers, then such an environment does not need a physical traffic signal. Instead, an intersection control server processes data streams from approaching vehicles, periodically solves an optimization problem, and assigns to each vehicle an optimal arrival time that ensures safety while significantly reducing number of stops and intersection delays. The scheduling problem is formulated as a mixed-integer linear program (MILP), and is solved by IBM CPLEX optimization package. The optimization outputs (scheduled access/arrival times) are sent to all approaching vehicles. The autonomous vehicles adjust their speed accordingly by a proposed trajectory planning algorithm with the aim of accessing the intersection at their scheduled times. A customized traffic microsimulation environment is developed to determine the potentials of the proposed solution in comparison to two baseline scenarios. In addition, the proposed MILP-based intersection control scheme is modified and simulated for a mixed traffic consisting of autonomous and human-controlled vehicles, all connected through a wireless communication to the intersection controller of a signalized intersection.

148 citations


Journal ArticleDOI
TL;DR: A rigorous integrated integer linear programming model is formulated to minimize the total passenger waiting time at all of involved stations, in which the train timetable provides a service-oriented operation plan and optimal passenger flow control is imposed to avoid congestion on platforms within the transportation capacities.
Abstract: With the drastic increase of travel demands in urban areas, more and more metro lines are nowadays suffering from oversaturated situations, leading to the accumulation of passengers on platforms with potential accident risks. To further improve the service quality and reduce accident risks, this paper proposes an effective method for collaboratively optimizing the train timetable and accurate passenger flow control strategies on an oversaturated metro line. Through considering the dynamic characteristics of passenger flow, a rigorous integrated integer linear programming model is firstly formulated to minimize the total passenger waiting time at all of involved stations, in which the train timetable provides a service-oriented operation plan and optimal passenger flow control is imposed to avoid congestion on platforms within the transportation capacities. To solve the problem of interest efficiently, a hybrid algorithm, which combines an improved local search and CPLEX solver, is designed to search for high-quality solutions. Finally, two sets of numerical experiments, including a small-scale case and a real-world instance with operation data of the Beijing metro system, are implemented to demonstrate the performance and effectiveness of the proposed approaches.

133 citations


Journal ArticleDOI
TL;DR: This paper addresses the virtual network function (VNF) placement problem in cloud datacenter considering users’ service function chain requests (SFCRs) and designs a Two-StAge heurisTic solution (T-SAT) designed to solve the ILP.
Abstract: Network function virtualization (NFV) brings great conveniences and benefits for the enterprises to outsource their network functions to the cloud datacenter. In this paper, we address the virtual network function (VNF) placement problem in cloud datacenter considering users’ service function chain requests (SFCRs). To optimize the resource utilization, we take two less-considered factors into consideration, which are the time-varying workloads, and the basic resource consumptions (BRCs) when instantiating VNFs in physical machines (PMs). Then the VNF placement problem is formulated as an integer linear programming (ILP) model with the aim of minimizing the number of used PMs. Afterwards, a Two-StAge heurisTic solution (T-SAT) is designed to solve the ILP. T-SAT consists of a correlation-based greedy algorithm for SFCR mapping (first stage) and a further adjustment algorithm for virtual network function requests (VNFRs) in each SFCR (second stage). Finally, we evaluate T-SAT with the artificial data we compose with Gaussian function and trace data derived from Google's datacenters. The simulation results demonstrate that the number of used PMs derived by T-SAT is near to the optimal results and much smaller than the benchmarks. Besides, it improves the network resource utilization significantly.

Journal ArticleDOI
TL;DR: A Mixed Integer Linear Programming (MILP) formulation for derivation of persistent UAV delivery schedules is proposed and a Receding Horizon Task Assignment (RHTA) heuristic is developed and tested with numerical examples for island-area delivery.

Journal ArticleDOI
TL;DR: The paper describes BARON's dynamic strategy for deciding under what conditions to activate integer programming relaxations in the course of branch-and-bound, and describes cutting plane and probing techniques that originate from the literature of integer linear programming and have been adapted in BARON to solve nonlinear problems.
Abstract: In this paper, we present recent developments in the global optimization software BARON to address problems with integer variables. A primary development was the addition of mixed-integer linear programming relaxations to BARON's portfolio of linear and nonlinear programming relaxations, aiming to improve dual bounds and offer good starting points for primal heuristics. Since such relaxations necessitate the solution of NP-hard problems, their introduction to a branch-and-bound algorithm raises many practical issues regarding their effective implementation. In addition to describing BARON's dynamic strategy for deciding under what conditions to activate integer programming relaxations in the course of branch-and-bound, the paper also describes cutting plane and probing techniques that originate from the literature of integer linear programming and have been adapted in BARON to solve nonlinear problems. Finally, we describe BARON's primal heuristics for finding good solutions of mixed-integer nonlinear pro...

02 Jul 2018
TL;DR: The SCIP Optimization Suite as discussed by the authors provides a collection of software packages for mathematical optimization centered around the constraint integer programming framework SCIP, which includes the MIP and MINLP core with new primal heuristics and a new selection criterion for cutting planes.
Abstract: The SCIP Optimization Suite provides a collection of software packages for mathematical optimization centered around the constraint integer programming framework SCIP. This paper discusses enhancements and extensions contained in version 6.0 of the SCIP Optimization Suite. Besides performance improvements of the MIP and MINLP core achieved by new primal heuristics and a new selection criterion for cutting planes, one focus of this release are decomposition algorithms. Both SCIP and the automatic decomposition solver GCG now include advanced functionality for performing Benders’ decomposition in a generic framework. GCG’s detection loop for structured matrices and the coordination of pricing routines for Dantzig-Wolfe decomposition has been significantly revised for greater flexibility. Two SCIP extensions have been added to solve the recursive circle packing problem by a problem-specific column generation scheme and to demonstrate the use of the new Benders’ framework for stochastic capacitated facility location. Last, not least, the report presents updates and additions to the other components and extensions of the SCIP Optimization Suite: the LP solver SoPlex, the modeling language Zimpl, the parallelization framework UG, the Steiner tree solver SCIP-Jack, and the mixed-integer semidefinite programming solver SCIP-SDP.

Journal ArticleDOI
TL;DR: In this paper, the authors present an implementation of a memetic algorithm based on mixed integer programming, which is especially suited for practical broadband optimization of layered thin-film materials, and optimize the spectra of a radiative cooling device and an incandescent light bulb filter.
Abstract: Multilayer optical films have been extensively used in optical technology, but the design of multilayer structures for broadband applications is often challenging due to the need to incorporate material dispersion. Here, we present an implementation of a memetic algorithm based on mixed integer programming, which is especially suited for practical broadband optimization of layered thin-film materials. In our implementation, the optimization variables consist of a list of discrete variables that represents different dielectric materials, along with a list of continuous variables that represents the thicknesses of each material. As a set of concrete demonstrations, we optimize the spectra of a radiative cooling device and an incandescent light bulb filter. The resulting structures from the optimization can, by using more materials, achieve better performance than their counterparts in the literature while using fewer numbers of material layers.

Journal ArticleDOI
TL;DR: This paper proposes a distributed deep learning-based offloading (DDLO) algorithm for MEC networks, where multiple parallel DNNs are used to generate offloading decisions, and adopts a shared replay memory to store newly generated offload decisions.
Abstract: This paper studies mobile edge computing (MEC) networks where multiple wireless devices (WDs) choose to offload their computation tasks to an edge server. To conserve energy and maintain quality of service for WDs, the optimization of joint offloading decision and bandwidth allocation is formulated as a mixed integer programming problem. However, the problem is computationally limited by the curse of dimensionality, which cannot be solved by general optimization tools in an effective and efficient way, especially for large-scale WDs. In this paper, we propose a distributed deep learning-based offloading (DDLO) algorithm for MEC networks, where multiple parallel DNNs are used to generate offloading decisions. We adopt a shared replay memory to store newly generated offloading decisions which are further to train and improve all DNNs. Extensive numerical results show that the proposed DDLO algorithm can generate near-optimal offloading decisions in less than one second.

Journal ArticleDOI
TL;DR: Three solution approaches are proposed to solve the resulting multi-objective mixed-integer nonlinear programming (MINLP) problem to deliver both an irregular train schedule and a rolling stock circulation plan simultaneously and an approximated MILP approach is proposed to reduce the number of constraints introduced by passenger demand.
Abstract: We study the integration of train scheduling and rolling stock circulation planning under time-varying passenger demand for an urban rail transit line, where the practical train operation constraints, e.g., the capacity of trains, the number of available rolling stocks, and the entering/exiting depot operations, are considered. Three solution approaches are proposed to solve the resulting multi-objective mixed-integer nonlinear programming (MINLP) problem to deliver both an irregular train schedule (i.e., departure and arrival times of all train services) and a rolling stock circulation plan (including entering/exiting depot operations of rolling stocks and connections between train services) simultaneously. We first present an iterative nonlinear programming (INP) approach, where the solutions of the original MINLP problem are obtained by solving a nonlinear programming problem and a mixed integer linear programming (MILP) problem iteratively. Moreover, an equivalent MILP formulation of the original MINLP model is developed and an approximated MILP approach is proposed to reduce the number of constraints introduced by passenger demand. A case study is conducted based on the practical data of the Beijing Yizhuang line, where the three proposed approaches are compared with a state-of-the-art approach and a practical method used by the traffic planners. This comparison shows the effectiveness and efficiency of the three proposed approaches.

Journal ArticleDOI
TL;DR: A novel distributed model predictive control (MPC) is proposed for the power dispatching optimization of the microgrid in this paper, where there is a local MPC for each of the following entities: DGs, storage batteries, and shiftable loads.
Abstract: This paper considers the energy dispatching optimization of a grid-connected microgrid in a park in the city of Shanghai in a distributed framework, in order to improve its economic and environment-friendly performance. The microgrid is composed of distributed generations (DGs), energy storage systems, and shiftable loads. Some properties of the microgrid make the dispatching problem difficult. For example, the disturbance in renewable energy is unpredictable; the operation of shiftable loads is discrete and the relationship between the generated power and the total operation cost is nonlinear. A novel distributed model predictive control (MPC) is proposed for the power dispatching optimization of the microgrid in this paper, where there is a local MPC for each of the following entities: DGs, storage batteries, and shiftable loads. In this method, the centralized mix-integer programming problem of microgrid energy dispatching is converted into several interacted nonlinear programming problems and integer programming problems, and subsystem-based MPCs coordinate with each other via iteratively minimizing the cost over the entire system. In this way, the realization of plug-and-play property becomes easier and the computational load is reduced. The numerical results show the effectiveness of the proposed method.

Journal ArticleDOI
TL;DR: Simulation results from a 49-subregion or 7-region network shows a great potential of the proposed scheme in achieving coordination and efficient use of network capacity, leading to increased mobility.
Abstract: High level of detail renders microscopic traffic models impractical for control purposes and local control schemes cannot coordinate actions over large scale heterogeneously congested urban networks. Developing efficient models and control methods for large-scale urban road networks is, therefore, an important research challenge. Alleviating congestion via manipulation of traffic flows or assignment of vehicles to specific paths has a great potential in achieving efficient network usage. Motivated by this fact, this paper proposes a hierarchical traffic management system. The upper-level route guidance scheme builds a model predictive control (MPC) scheme and optimizes network performance based on actuation via regional split ratios, whereas the lower-level path assignment mechanism develops an integer linear programming (ILP) formulation and recommends subregional paths for vehicles to follow, satisfying the regional split ratios in order to achieve said performance. Simulation results from a 49-subregion or 7-region network shows a great potential of the proposed scheme in achieving coordination and efficient use of network capacity, leading to increased mobility.

Journal ArticleDOI
TL;DR: The memetic algorithm is efficient whether the problem is studied with hard or soft time window and synchronization constraints, various caregivers qualification or several home health care offices.
Abstract: This work addresses a home health care routing and scheduling problem with time window and synchronization constraints. Each patient is associated with a period of availability according to their preferences while some visits may require the presence of two staff members simultaneously, which requires the synchronization of two visits. In this paper, the problem is studied with hard and soft patients time window and synchronization constraints. We developed a mixed integer programming model and a memetic algorithm featuring two original crossover operators. Experiments are conducted on benchmark instances from the literature as well as new instances based on real life data from a home health care provider in France. The results highlight the efficiency of the memetic algorithm since it provides great results while being flexible to the instance type. Indeed, the memetic algorithm is efficient whether the problem is studied with hard or soft time window and synchronization constraints, various caregivers qualification or several home health care offices.

Journal ArticleDOI
TL;DR: Pyomo.dae is an open source Python-based modeling framework that enables high-level abstract specification of optimization problems with differential and algebraic equations, providing a high degree of modeling flexibility and the ability to express constraints that cannot be easily specified in other modeling frameworks.
Abstract: We describe pyomo.dae, an open source Python-based modeling framework that enables high-level abstract specification of optimization problems with differential and algebraic equations. The pyomo.dae framework is integrated with the Pyomo open source algebraic modeling language, and is available at http://www.pyomo.org . One key feature of pyomo.dae is that it does not restrict users to standard, predefined forms of differential equations, providing a high degree of modeling flexibility and the ability to express constraints that cannot be easily specified in other modeling frameworks. Other key features of pyomo.dae are the ability to specify optimization problems with high-order differential equations and partial differential equations, defined on restricted domain types, and the ability to automatically transform high-level abstract models into finite-dimensional algebraic problems that can be solved with off-the-shelf solvers. Moreover, pyomo.dae users can leverage existing capabilities of Pyomo to embed differential equation models within stochastic and integer programming models and mathematical programs with equilibrium constraint formulations. Collectively, these features enable the exploration of new modeling concepts, discretization schemes, and the benchmarking of state-of-the-art optimization solvers.

Journal ArticleDOI
TL;DR: This paper proposes a decomposition algorithm based on Nested Benders Decomposition for multi-period MILP problems to allow the solution of larger instances and adapts previous nested Benders methods by handling integer and continuous state variables, although at the expense of losing its finite convergence property.

Journal ArticleDOI
TL;DR: This paper proposes a model and algorithm for optimally designing DWC electric vehicle (EV) systems, particularly those operating in multiple-route environments, and applies a particle swarm optimization algorithm to solve the given multi-route DWC-EV system optimization problem.
Abstract: Dynamic wireless charging (DWC) technology, a novel way of supplying vehicles with electric energy, allows the vehicle battery to be recharged remotely while it is moving over power tracks, which are charging infrastructures installed beneath the road. DWC systems mitigate the range limitation of electric vehicles by using power tracks as additional sources of electric energy. This paper proposes a model and algorithm for optimally designing DWC electric vehicle (EV) systems, particularly those operating in multiple-route environments. Multi-route system comprises several single routes that share common road segments, and the vehicles operating on a specific route are equipped with identical batteries. We build a general model to optimally allocate power tracks and determine the vehicle battery size for each route. Then, we apply a particle swarm optimization algorithm to solve the given multi-route DWC-EV system optimization problem. A numerical example is solved to illustrate the characteristics of the multi-route model, and we show that the proposed modeling approach and algorithm are effective, compared with a mixed integer programming-based exact solution approach. We also conduct a sensitivity analysis to examine the solution behavior of the problem.

Journal ArticleDOI
TL;DR: This paper forms the RSCA problem using a nodearc- based integer linear programming (ILP) method in which the numbers of both variables and constraints are greatly reduced compared with previous ILP methods, thereby leading to a significant improvement in convergence efficiency.
Abstract: In this paper, we focus on the static routing, spectrum, and core assignment (RSCA) problem in spacedivision multiplexing (SDM)-based elastic optical networks (EONs) with multi-core fiber (MCF). In RSCA problems, it is a challenging task to control the inter-core interference, called inter-core crosstalk (XT), within an acceptable level and simultaneously maximize the spectrum utilization. We first consider XT in a worst interference scenario (i.e., XTunaware), which can simplify the RSCA problem. In this scenario, we formulate the RSCA problem using a nodearc- based integer linear programming (ILP) method in which the numbers of both variables and constraints are greatly reduced compared with previous ILP methods, thereby leading to a significant improvement in convergence efficiency. Then, we consider the XT strictly (i.e., XT-aware) and formulate the problem using a mixed integer linear programming (MILP) method, which is an extension of the above node-arc-based ILP method. It is more suitable for different XT thresholds and/or geographically large networks, in that it has a higher degree of generalizability. Finally, we propose an XT-aware-based heuristic algorithm. The simulation results demonstrate that our heuristic algorithm achieves higher spectrum efficiency, higher degree of generalizability, and higher computational efficiency than the existing heuristic algorithm(s).

Book ChapterDOI
01 Jan 2018
TL;DR: In this article, a stochastic integer programming (SIP) approach is used to optimise open pit mine design and production scheduling, where the objectives are to maximize the total net present value (NPV) and to minimize unsatisfied demand for processed ore.
Abstract: Conventional approaches to optimising open pit mine design and production scheduling are based on a single estimated orebody model, which does not account for geological variability. Conditional simulation can be employed to quantitatively address the resulting grade uncertainty. Multiple simulated orebody models provide a suitable input for stochastic integer programming (SIP), a type of mathematical programming that generates the optimal result for a defined set of objectives under uncertainty. In the case of production scheduling, the objectives are to maximise the total net present value (NPV) and to minimise unsatisfied demand for processed ore. Using a set of multiple simulated orebody models as input into an SIP model allows for the integration of in situ deposit variability and uncertainty directly into the production scheduling optimisation process.

Journal ArticleDOI
TL;DR: In this paper, the authors validate the energy efficiency improvements in core networks obtained through mixed integer linear programming (MILP) optimization models as part of the GreenMeter study carried out by the GreenTouch consortium by developing closed form expressions and bounds for the power consumption of core networks.
Abstract: In this paper, we validate the energy efficiency improvements in core networks obtained through mixed integer linear programming (MILP) optimization models as part of the GreenMeter study carried out by the GreenTouch consortium by developing closed form expressions and bounds for the power consumption of core networks. We consider nonbypass, bypass, mixed line rates, and physical topology optimization energy efficiency schemes. In addition to validating the optimization model results by setting bounds on the power consumption, these bounds can predict network performance at operating conditions highly complex for the MILP models. The derivation of a single bound that includes all the measures proved intractable and therefore each measure is evaluated separately.

Journal ArticleDOI
TL;DR: Results demonstrate the ability of the proposed NILM algorithm to accurately identify and allocate individual energy signatures in a computationally efficient manner, which makes it suitable for inexpensive home energy management.
Abstract: This paper presents a nonintrusive load monitoring (NILM) algorithm based on mixed-integer linear programming. The formulation deals with the problem of multiple switching that arises when disaggregating individual appliance’s consumptions from a compound power measurement. Mixed-integer linear constraints are used to efficiently represent the load signatures of each appliance. Also, a window-based strategy is used to enhance the computational performance of the proposed NILM algorithm. The disaggregation can be made using only active power measurements at a low sampling rate, which is available in most energy meters. Moreover, if available, other signatures can be added to the model to improve its accuracy, such as reactive power signatures or harmonics. The performance of the algorithm is evaluated using three test cases from the almanac of minutely power dataset. The proposed method is also compared with a disaggregation method called aided linear integer programming. Results demonstrate the ability of the proposed method to accurately identify and allocate individual energy signatures in a computationally efficient manner, which makes it suitable for inexpensive home energy management.

Journal ArticleDOI
TL;DR: A novel decentralized on-line fault diagnosis approach based on the solution of some integer linear programming problems for discrete event systems in a Petri net framework and a sufficient and necessary condition under which the second presented protocol can successfully diagnose a fault in the decentralized architecture is proved.
Abstract: This paper proposes a novel decentralized on-line fault diagnosis approach based on the solution of some integer linear programming problems for discrete event systems in a Petri net framework. The decentralized architecture consists of a set of local sites communicating with a coordinator that decides whether the system behavior is normal or subject to some possible faults. To this aim, some results allow defining the rules applied by the coordinator and the local sites to provide the global diagnosis results. Moreover, two protocols for the detection and diagnosis of faults are proposed: they differ for the information exchanged between local sites and coordinator and the diagnostic capability. In addition, a sufficient and necessary condition under which the second presented protocol can successfully diagnose a fault in the decentralized architecture is proved. Finally, some examples are presented to show the efficiency of the proposed approach.

Journal ArticleDOI
TL;DR: In this article, a multi-objective optimization method was proposed to jointly optimize the planning and operation of a grid-tied microgrid with various distributed generation sources such as wind turbine and photovoltaic arrays with the assistance of demand side management.
Abstract: This paper presents a multiobjective optimization method to jointly optimize the planning and operation of a grid-tied microgrid (MG) with various distributed generation sources such as wind turbine and photovoltaic arrays with the assistance of demand side management. In order to explore the economy and demand variety, the problem is formulated as a double-objective optimization to minimize the total annual cost and to maximize the customer satisfaction. To solve the multiobjective optimization problem, a fuzzy satisfaction-maximizing method is adopted to convert the original problem into a single objective optimization problem and a mixed integer linear programming algorithm is then used to solve the problem. To verify the proposed solution, various case studies have been carried out and compared. The results show that the proposed method is effective in minimizing the cost of an MG without sacrificing the satisfaction of customers.

Journal ArticleDOI
TL;DR: An iterative procedure for the optimal design of a microgrid topology in active distribution networks, which applies graph partitioning, integer programming, and performance index for the ideal design is proposed.
Abstract: Loop-based microgrids are signified by their high reliability in islanded and grid-connected operations. This paper proposes an iterative procedure for the optimal design of a microgrid topology in active distribution networks, which applies graph partitioning, integer programming, and performance index for the optimal design. The proposed approach avoids infeasible and non-optimal designs of microgrid structures and provides remedial solutions for enhancing our previous topology design method. The numerical results for a microgrid test system show that the proposed designated steps can optimize a loop-based microgrid structure in an active distribution network.