One of the most critical goals in the operation and planning of distribution networks is the creation of networks with sufficient reliability. Existing models are often simulation-based or try to introduce topology-in...
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One of the most critical goals in the operation and planning of distribution networks is the creation of networks with sufficient reliability. Existing models are often simulation-based or try to introduce topology-independent algebraic reliability measures with simplifications or extensive computations. This paper presents new and efficient topology-variable-based linear expressions that can evaluate the reliability indices of practical radial distribution networks. Furthermore, the model is extended to consider the inclusion of renewable distributed generation (DG) units to restore part of restorable loads in the islanded mode of operation. Also, the stochastic nature of renewable generation, as well as load demand, is considered. Therefore, the proposed model can be readily used in various optimization models to operate and plan distribution networks with reliability concerns. The application of the proposed method to several small- to large-scale test cases ranging from standard 37-node up to practical 1080-node benchmarks shows the proposed model's accuracy and computational effectiveness compared to the state-of-the-art conventional simulation-based topology-variable-based approaches. The impact of the system's islanded operation on the reliability indices is also evaluated on the modified DG-enhanced 37node test system.
Traditional methods for flexible capacity allocation do not take into account the actual operation status of resources, and this can lead to redundancy of allocation results in a high renewable penetration power syste...
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Traditional methods for flexible capacity allocation do not take into account the actual operation status of resources, and this can lead to redundancy of allocation results in a high renewable penetration power system. Using collaborative optimization during the flexibility resource planning stage can significantly improve the overall economics and flexibility. Therefore, a bilevel operation-planning joint optimization model for flexible capacity allocation is proposed in this paper. The aim is to optimize the annual total cost and flexibility of the system. The upper planning level introduces the economic costs, flexibility resource capacity, and flexibility index which are used as the evaluation index of system flexibility, while in the lower operation level, a morphological clustering algorithm based on the multiscale and entropy weight method is proposed for obtaining typical scenarios of flexibility demand. On this basis, the lower level simulates production to estimate daily operating costs. In addition, the model is solved iteratively using the nondominated sorting genetic algorithm-II (NSGA-II) and the linear programming method to obtain the Pareto solutions. Case studies are carried out based on a practical town area, and the results verify the validity and rationality of the proposed bilevel capacity allocation model.
In works of R. Gabasov and his colleagues the adaptive method for solving the interval linear programming problems is proposed. This approach has wide applications in optimal control of dynamic objects. The implementa...
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In works of R. Gabasov and his colleagues the adaptive method for solving the interval linear programming problems is proposed. This approach has wide applications in optimal control of dynamic objects. The implementation of this method for particular situations requires specific modifications of the basic algorithm. In this paper an algorithm for solving of an optimal control problem in real time for a technical object a type of multiple-input is offered.
The aim of this paper is to show a novel floorplanner based on Mixed-Integer linear programming (MILP), providing a suitable formulation that makes the problem tractable using state-of-the-art solvers. The proposed me...
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The aim of this paper is to show a novel floorplanner based on Mixed-Integer linear programming (MILP), providing a suitable formulation that makes the problem tractable using state-of-the-art solvers. The proposed method takes into account an accurate description of heterogeneous resources and partially reconfigurable constraints of recent FPGAs. A global optimum can be found for small instances in a small amount of time. For large instances, with a time limited search, a 20% average improvement can be achieved over floorplanners based on simulated annealing. Our approach allows the designer to customize the objective function to be minimized, so that different weights can be assigned to a linear combination of metrics such as total wire length, aspect ratio and area occupancy.
Hydropower generated from dams has significant economic value, however, that value is achieved at the cost of native ecosystem devastation. Here, we have estimated loss in hydropower revenue due to inclusion of the st...
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Hydropower generated from dams has significant economic value, however, that value is achieved at the cost of native ecosystem devastation. Here, we have estimated loss in hydropower revenue due to inclusion of the steady low flow days –Bug Flow Experiments. We developed a linear optimization model and constraint method that restrict the number of steady low flow days while maximizes the hydropower revenue generation. The results suggested that increase in release volume will benefit both the objectives (win-win scenario), energy price differential between on-and off-peak periods controls the position and shape of tradeoff curves, and offset release does not have impact on the tradeoffs. Monthly results of the model helped us devise a program where hydropower producers are compensated for the steady low flow days. The program allocates funds and provides opportunities for ecosystem managers to pay hydropower producers revenue loss from the steady low flow days (escape from the win-lose scenario). In other words, the ecosystem managers are empowered to make decision about when and how many steady low flows days to buy against compensating the hydropower producers. This study is an initial effort and next steps would include a) improve results by adding information from the GTMax SL model used by the Western Area Power Authority and b) engagement with more organizations: National Park Service, Bureau of Reclamation, and Glen Canyon Dam Adaptive Management program.
We study a home healthcare planning problem under the demand uncertainty, where the service type (authorization) and capacity are the first-stage decision and the homecare resource allocation is the second -stage deci...
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We study a home healthcare planning problem under the demand uncertainty, where the service type (authorization) and capacity are the first-stage decision and the homecare resource allocation is the second -stage decision which adapts to the demand realizations. We model the problem using an adaptive robust optimization technique where we construct a budget uncertainty set of demand using the well-known Mahalanobis Distance. We analyze the impact of the authorization, capacity decisions as well as the budget (robustness level) of the Mahalanobis uncertainty set onto the worst-case revenue. To solve the model, we develop a Benders decomposition algorithm that solves a pair of a mixed-integer second-order cone program (MISOCP) and a mixed integer linear program (MILP) in each iteration, both can be handled by off-the-shelf MIP solvers, with finite-step convergence. We also develop an affine approximation approach that directly solves one instance of MISOCP. Finally, sufficient numerical studies demonstrate the effectiveness of our model and the solution approaches.
In this work the application of Mixed Integer linear programming (MILP) techniques is proposed for choosing the optimal topology of an air compressor room, while (i) focusing on energy efficiency and (ii) keeping the ...
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In this work the application of Mixed Integer linear programming (MILP) techniques is proposed for choosing the optimal topology of an air compressor room, while (i) focusing on energy efficiency and (ii) keeping the input power to the lowest value. Usually the proposal of the best configuration (type of machines, size and number) is driven by the experience of the personnel performing the assessment, according to his own knowledge of the available products/machines on the market. The paper proposes the formulation of two optimization problems which enable (i) to perform the best configuration of a compressor room, according with an available machines database; (ii) to ensure that the best choice implies the minimization of the input power for the compressor room, thus, the minimum consumption for the end-user. The presented formulations are tested using both simulated and real case studies, showing how a higher level of knowledge of the flow profile can help in the reduction of the energy consumption. It will be also demonstrated that important energy savings improvements can be obtained if we compare the choices derived from our methodology with real compressor rooms, whose choice was done without applying such method, as it may happen on the market.
This article presents a framework for the optimal control of boundary flows on transportation networks. The state of the system is modeled by a first order scalar conservation law (Lighthill-Whitham-Richards PDE). Bas...
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ISBN:
(纸本)9781479947287
This article presents a framework for the optimal control of boundary flows on transportation networks. The state of the system is modeled by a first order scalar conservation law (Lighthill-Whitham-Richards PDE). Based on an equivalent formulation of the Hamilton-Jacobi PDE, the problem of controlling the state of the system on a network link in a finite horizon can be posed as a linear Program. Assuming all intersections in the network are controllable, we show that the optimization approach can be extended to an arbitrary transportation network, preserving linear constraints. Unlike previously investigated transportation network control schemes, this framework leverages the intrinsic properties of the Halmilton-Jacobi equation, and does not require any discretization or boolean variables on the link. Hence this framework is very computational efficient and provides the globally optimal solution. The feasibility of this framework is illustrated by an on-ramp metering control example.
Planning in Artificial Intelligence is a problem of finding a sequence of actions that transform given initial state of the problem to desired goal situation. In this work we consider computational difficulty of so ca...
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Planning in Artificial Intelligence is a problem of finding a sequence of actions that transform given initial state of the problem to desired goal situation. In this work we consider computational difficulty of so called conditional planning. Conditional planning is a problem of searching for plans that depend on sensory information and succeed no matter which of the possible initial states the world was actually in. Finding a plan of such problems is computationally difficult. To avoid this difficulty a transformation to linear programming Problem, illustrated by an example, is proposed.
As a highly complex multi-input and multi-output system, blast furnace plays an important role in industrial development. Although much research has been done in the past few decades, there still exist many problems, ...
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ISBN:
(纸本)9781479914821
As a highly complex multi-input and multi-output system, blast furnace plays an important role in industrial development. Although much research has been done in the past few decades, there still exist many problems, such as the modeling and control problems. In view of these reasons, this paper is concerned with developing a Wiener model to predict the silicon content of blast furnace. Unlike traditional Wiener model, this paper avoids the optimization of high number of model parameters. The Wiener model here is composed of a basis filter filter expansion named Laguerre filter and a linear programming support vector regression (LP-SVR). They are used to represent the linear dynamic component and the nonlinear static element. Take the advantages that Laguerre filter can approximate linear systems with a lower model and order and LP-SVR can achieve a sparse solution, the proposed Wiener model not only improves the prediction accuracy but also reduces the computation complexity. Simulation results show that this Wiener model is suitable for the prediction of blast furnace silicon content.
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