The effort to continuously improve and innovate smart appliances (SA) energy management requires an experimental research and development environment which integrates widely differing tools and resources seamlessly. T...
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The effort to continuously improve and innovate smart appliances (SA) energy management requires an experimental research and development environment which integrates widely differing tools and resources seamlessly. To this end, this paper proposes a novel Direct Load Control (DLC) testbed, aiming to conveniently support the research community, as well as analyzing and comparing their designs in a laboratory environment. Based on the LabVIEW computing platform, this original testbed enables access to knowledge of major components such as online weather forecasting information, distributed energy resources (e.g., energy storage, solar photovoltaic), dynamic electricity tariff from utilities and demand response (DR) providers together with different mathematical optimization features given by General Algebraic Modelling System (GAMS). This intercommunication is possible thanks to the different applications programming interfaces (API) incorporated into the system and to intermediate agents specially developed for this case. Different basic case studies have been presented to envision the possibilities of this system in the future and more complex scenarios, to actively support the DLC strategies. These measures will offer enough flexibility to minimize the impact on user comfort combined with support for multiple DR programs. Thus, given the successful results, this platform can lead to a solution towards more efficient use of energy in the residential environment.
The increasing penetration of distributed generations (DGs) and variable loads introduces significant power fluctuations to distribution networks, rendering conventional reconfiguration strategies ineffective. In the ...
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The increasing penetration of distributed generations (DGs) and variable loads introduces significant power fluctuations to distribution networks, rendering conventional reconfiguration strategies ineffective. In the context of an active distribution network (ADN), remote control switches can be operated in real time through a centralized control scheme. Therein the distribution network topology can be configured in a flexible and dynamic manner capable of adapting to time-varying load demand and DG output. This paper presents a dynamic reconfiguration approach for a three-phase unbalanced distribution network. The ADN topology is optimized for the look-ahead time periods and is adaptive to the time-varying load demand and DG output while minimizing the daily power loss costs. To improve the calculation efficiency, several linearization methods are introduced to formulate the dynamic reconfiguration as a mixed-integer linear programming problem, which can be effectively solved using off-the-shelf solvers. The effectiveness of the proposed approach is verified by the test results obtained on a modified IEEE 34 node test feeder.
We develop optimization approaches to the graph-clear problem, a pursuit-evasion problem where mobile robots must clear a facility of intruders. The objective is to minimize the number of robots required. We contribut...
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We develop optimization approaches to the graph-clear problem, a pursuit-evasion problem where mobile robots must clear a facility of intruders. The objective is to minimize the number of robots required. We contribute new formal results on progressive and contiguous assumptions and their impact on algorithm completeness. We present mixed-integer linear programming and constraint programming models, as well as new heuristic variants for the problem, comparing them to previously proposed heuristics. Our empirical work indicates that our heuristic variants improve on those from the literature, that constraint programming finds better solutions than the heuristics in run-times reasonable for the application, and that mixed-integer linear programming is superior for proving optimality. Given their performance and the appeal of the model-and-solve framework, we conclude that the proposed optimization methods are currently the most suitable for the graph-clear problem.
This paper presents a global optimization approach for solving signomial geometric programming (SGP) problems. We employ an accelerated extended cutting plane (ECP) approach integrated with piecewise linear (PWL) appr...
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This paper presents a global optimization approach for solving signomial geometric programming (SGP) problems. We employ an accelerated extended cutting plane (ECP) approach integrated with piecewise linear (PWL) approximations to solve the global optimization of SGP problems. In this approach, we separate the feasible regions determined by the constraints into convex and nonconvex ones in the logarithmic domain. In the nonconvex feasible regions, the corresponding constraint functions are converted into mixedintegerlinear constraints using PWL approximations, while the other constraints with convex feasible regions are handled by the ECP method. We also use pre-processed initial cuts and batched cuts to accelerate the proposed algorithm. Numerical results show that the proposed approach can solve the global optimization of SGP problems efficiently and effectively.
This paper presents a mixed-integerlinear robust multiobjective model for the expansion planning of an electric power system. An information-gap decision theory-based framework is proposed to take into account the un...
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This paper presents a mixed-integerlinear robust multiobjective model for the expansion planning of an electric power system. An information-gap decision theory-based framework is proposed to take into account the uncertainties in electrical demand and new power system elements prices. The model is intended to increase the power system resistance against the uncertainties caused by forecast errors. The normal boundary intersection method is used to obtain the Pareto front of the multiobjective problem. Since the planning problem is a large-scale problem, the model is kept linear using the Big M linearization technique that is able to significantly decrease the computational burden. The fuel transportation and availability constraints are taken into account. The model also enables the system planner to build new fuel transportation routes whenever it is necessary. The generating units' retirement is also incorporated into the model, and the simulation results are showed to the advantages of incorporating units' retirement in the power system expansion planning model instead of considering it separately. The proposed multiobjective method is applied to the Garver 6-bus, IEEE 24-bus, and IEEE 118-bus test systems, and the results are compared with the well-known epsilon-constraint method.
In designing energy supply systems, designers should consider the robustness in performance criteria against the uncertainty in energy demands. In this paper, a robust optimal design method of energy supply systems un...
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In designing energy supply systems, designers should consider the robustness in performance criteria against the uncertainty in energy demands. In this paper, a robust optimal design method of energy supply systems under uncertain energy demands is proposed using a mixed-integerlinear model so that it can consider discrete characteristics for selection and on/off status of operation and piecewise linear approximations for nonlinear performance characteristics of constituent equipment. First, a robust optimal design problem is formulated as a three-level min-max-min optimization one by expressing uncertain energy demands by intervals based on the interval programming, evaluating the robustness in a performance criterion based on the minimax regret criterion, and considering hierarchical relationships among design variables, uncertain energy demands, and operation variables. Then, a special solution method of the problem is proposed especially in consideration of the existence of integer operation variables. In a case study, the proposed method is applied to the robust optimal design of a cogeneration system with a simple configuration. Through the study, the validity and effectiveness of the method is ascertained, and some features of the obtained solutions are clarified. (C) 2018 Elsevier Ltd. All rights reserved.
Alarm identification refers to selecting a set of measurements to be configured to the alarm system. Contrary to prior literature which uses qualitative cause-effect based techniques, the present work incorporates qua...
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Alarm identification refers to selecting a set of measurements to be configured to the alarm system. Contrary to prior literature which uses qualitative cause-effect based techniques, the present work incorporates quantitative aspects such as the time taken by measurements for deviation, to make alarm identification more reliable. The present work proposes a systematic approach to alarm identification through an optimization formulation, as a mixed-integer linear programming (MILP) problem, for the time. The proposed formulation maximizes the time available for operators to respond to faults while keeping the number of alarms triggered at a minimum. Subsequently, a linear multi-objective optimization formulation reduces the number of optimal solutions taking into account additional criteria, such as order of priority of potential faults. The proposed formulation is applied to the Tennessee Eastman (TE) Challenge problem. A closed-loop simulator was used for fault propagation, to obtain quantitative information required to apply the formulation, and CPLEX solver in GAMS was used to solve this case study problem.
A mixed-integer linear programming model is proposed to determine the optimal number, location and capacity of the warehouses required to support a long-term forecast with seasonal demand. Discrete transportation cost...
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A mixed-integer linear programming model is proposed to determine the optimal number, location and capacity of the warehouses required to support a long-term forecast with seasonal demand. Discrete transportation costs, dynamic warehouse contracting, and the handling of safety stock are the three main distinctive features of the problem. Four alternatives for addressing discrete transportation costs are compared. The most efficient formulation is obtained using integer variables to account for the number of units used of each transportation mode. Contracting policies constraints are derived to ensure use of warehouses for continuous periods. Similar constraints are included for the case when a warehouse is closed. Safety stock with risk-pooling effect is considered using a piecewise-linear representation. To solve large-scale problems, tightening constraints, and simplified formulations are proposed. These formulations are based on single-sourcing assumptions and yield near-optimal results with large reduction in the solution time. (c) 2017 Elsevier Ltd. All rights reserved.
Large-scale mixed-integer linear programming (MILP) problems, such as those from two-stage stochastic programming, usually have a decomposable structure that can be exploited to design efficient optimization methods. ...
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Large-scale mixed-integer linear programming (MILP) problems, such as those from two-stage stochastic programming, usually have a decomposable structure that can be exploited to design efficient optimization methods. Classical Benders decomposition can solve MILPs with weak linking constraints (which are decomposable when linking variables are fixed) but not strong linking constraints (which are not decomposable even when linking variables are fixed). In this paper, we first propose a new rigorous bilevel decomposition strategy for solving MILPs with strong and weak linking constraints, then extend a recently developed cross decomposition method based on this strategy. We also show how to apply the extended cross decomposition method to two-stage stochastic programming problems with conditional-value-at-risk (CVaR) constraints. In the case studies, we demonstrate the significant computational advantage of the proposed extended cross decomposition method as well as the benefit of including CVaR constraints in stochastic programming. (C) 2018 Elsevier Ltd. All rights reserved.
This paper studies the cyclic jobshop hoist scheduling with multi-capacity reentrant tanks and time-window constraints. Parts of different types are processed in a series of tanks with bounded processing times. Multi ...
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This paper studies the cyclic jobshop hoist scheduling with multi-capacity reentrant tanks and time-window constraints. Parts of different types are processed in a series of tanks with bounded processing times. Multi capacity tanks are used to handle stages with long processing times. Tanks can be reentrant so that a part visits them more than once. A hoist is responsible for the transportation of parts between tanks. We consider the cyclic scheduling where multiple parts enter and leave the production line during a cycle. The difficulty to deal with the problem lies in how to effectively handle the constraints related to multi-capacity reentrant tanks and their relations with time windows. To this end, a mixed-integer linear programming model is developed by addressing the time-window constraints and tank capacity constraints in a novel way. Computational experiments are conducted to demonstrate the effectiveness of the proposed model.
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