scholarly journals Efficient Economic and Resilience-Based Optimization for Disaster Recovery Management of Critical Infrastructures

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3418 ◽  
Author(s):  
Eng Lau ◽  
Kok Chai ◽  
Yue Chen ◽  
Jonathan Loo
Keyword(s):  
Disaster Recovery ◽  
Unit Commitment ◽  
Optimization Problems ◽  
Simplex Algorithm ◽  
Critical Infrastructures ◽  
Grid Optimization ◽  
Dual Simplex ◽  
Recovery Management ◽  
Grid Operation ◽  
The Cost

The traditional grid operation is unfortunately lacking the resilience and responsiveness in reacting to contingency events due to the poor utilization of available resources in mitigating the shortfalls. Such an unaddressed issue may affect the grid stability and the ultimate grid blackout. Therefore, this paper models a grid optimization module consisting of a mid and low (microgrid) voltage level grid component of an urban grid network for a disaster recovery. The model minimizes the cost of generation required to meet the demand through the economic dispatch in combination with the unit commitment. Two optimization problems are formulated that resemble the grid operation: normal (grid-connected) and islanded. A constrained-based linear programming optimization problem is used to solve the formulated problems, where the dual-simplex algorithm is used as the linear solver. The model ensures sufficient demand to be met during the outages through the N-1 contingency criterion for critical infrastructures. The simulation length is limited to 24 h and is solved using the MATLAB® R2017b software. Three different cases are established to evaluated the modelled grid resilience during the grid-connected or the islanding of operations subject to adversed events. The simulated results provide the economical outage recovery that will maintain the grid resilience across the grid.

A weak derivative approach to optimization of threshold parameters in a multicomponent maintenance system

2001 ◽  
Vol 38 (02) ◽  
pp. 386-406 ◽  
Author(s):  
Bernd Heidergott
Keyword(s):  
Optimization Problems ◽  
Estimation Algorithm ◽  
Replacement Policy ◽  
Cost Performance ◽  
Weak Derivative ◽  
Preventive Replacement ◽  
Maintenance System ◽  
Threshold Optimization ◽  
Age Replacement ◽  
The Cost

We consider a multicomponent maintenance system controlled by an age replacement policy: when one of the components fails, it is immediately replaced; all components older than a threshold age θ are preventively replaced. Costs are associated with each maintenance action, such as replacement after failure or preventive replacement. We derive a weak derivative estimator for the derivative of the cost performance with respect to θ. The technique is quite general and can be applied to many other threshold optimization problems in maintenance. The estimator is easy to implement and considerably increases the efficiency of a Robbins-Monro type of stochastic approximation algorithm. The paper is self-contained in the sense that it includes a proof of the correctness of the weak derivative estimation algorithm.

An improved proximal method with quasi-distance for nonconvex multiobjective optimization problem

2022 ◽  
Vol 0 (0) ◽  
Author(s):  
Fouzia Amir ◽  
Ali Farajzadeh ◽  
Jehad Alzabut
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Convergence Result ◽  
Proximal Method ◽  
Pareto Solution ◽  
Multiobjective Optimization Problems ◽  
Locally Lipschitz ◽  
The Cost ◽  
Constrained Multiobjective Optimization

Abstract Multiobjective optimization is the optimization with several conflicting objective functions. However, it is generally tough to find an optimal solution that satisfies all objectives from a mathematical frame of reference. The main objective of this article is to present an improved proximal method involving quasi-distance for constrained multiobjective optimization problems under the locally Lipschitz condition of the cost function. An instigation to study the proximal method with quasi distances is due to its widespread applications of the quasi distances in computer theory. To study the convergence result, Fritz John’s necessary optimality condition for weak Pareto solution is used. The suitable conditions to guarantee that the cluster points of the generated sequences are Pareto–Clarke critical points are provided.

The Optimization Model of Wind Power and Thermal Power Jointly Run under the TOU Price

2013 ◽  
Vol 772 ◽  
pp. 705-710
Author(s):  
Li Wei Ju ◽  
Zhong Fu Tan ◽  
He Yin ◽  
Zhi Hong Chen
Keyword(s):  
Wind Power ◽  
Optimization Model ◽  
Unit Commitment ◽  
Thermal Power ◽  
Peak Load ◽  
Electricity Demand ◽  
Environmental Benefits ◽  
Power Company ◽  
The Cost ◽  
Pollution Emissions

In order to be able to absorb the abandoned wind, increasing wind-connect amount, the paper study the way of wind power, thermal power joint run and puts forward wind power, thermal power joint run optimization model based on the energy-saving generation dispatching way under the environment of TOU price and the target of minimizing the cost of coal-fired cost, unit commitment and pollution emissions. The numerical example finds, the TOU price can realize the goal of peak load shifting, increasing the electricity demand in the low load and reducing electricity demand in the peak load. The model can increase the amount of wind-connect grid, absorb the abandoned wind, reduce the use of coal-fired units under the environment, increase the average electricity sales price and profit of Power Company. Therefore, the model has significant economical environmental benefits

scholarly journals Integrated Offline and Online Decision Making under Uncertainty

2021 ◽  
Vol 70 ◽  
pp. 77-117
Author(s):  
Allegra De Filippo ◽  
Michele Lombardi ◽  
Michela Milano
Keyword(s):  
Optimization Problems ◽  
Greedy Heuristic ◽  
Management Problem ◽  
Quality Improvements ◽  
Solution Quality ◽  
Convex Optimization Problem ◽  
Routing Problem ◽  
Multi Stage ◽  
The Cost ◽  
Parametric Convex Optimization

This paper considers multi-stage optimization problems under uncertainty that involve distinct offline and online phases. In particular it addresses the issue of integrating these phases to show how the two are often interrelated in real-world applications. Our methods are applicable under two (fairly general) conditions: 1) the uncertainty is exogenous; 2) it is possible to define a greedy heuristic for the online phase that can be modeled as a parametric convex optimization problem. We start with a baseline composed by a two-stage offline approach paired with the online greedy heuristic. We then propose multiple methods to tighten the offline/online integration, leading to significant quality improvements, at the cost of an increased computation effort either in the offline or the online phase. Overall, our methods provide multiple options to balance the solution quality/time trade-off, suiting a variety of practical application scenarios. To test our methods, we ground our approaches on two real cases studies with both offline and online decisions: an energy management problem with uncertain renewable generation and demand, and a vehicle routing problem with uncertain travel times. The application domains feature respectively continuous and discrete decisions. An extensive analysis of the experimental results shows that indeed offline/online integration may lead to substantial benefits.

Shape Sensitivity Analysis Applied to Exterior Acoustics

2001 ◽  
Author(s):  
Gonzalo R. Feijóo ◽  
Assad A. Oberai ◽  
Peter M. Pinsky
Keyword(s):  
Optimization Problems ◽  
Auxiliary Problem ◽  
Adjoint Problem ◽  
The Body ◽  
Structural Acoustics ◽  
Shape Sensitivity ◽  
Shape Sensitivity Analysis ◽  
Primal Problem ◽  
Acoustic Media ◽  
The Cost

Abstract We present a method to calculate the derivative of a functional that depends on the shape of a body immersed in an acoustic media. The functional depends implicitly on the shape through the solution of an exterior acoustic problem. The derivative is calculated in terms of the solution of the primal problem and an auxiliary problem, the adjoint problem. An important aspect of this method is that the cost of calculating the derivative is independent of the number of parameters used to represent the shape of the body. This allows for efficient solution of optimization problems in structural acoustics.

Multiobjective optimization design of hybrid composite laminates for vibration using sequential permutation table method

2021 ◽  
pp. 095440622110504
Author(s):  
Zhao Jing ◽  
Qin Sun ◽  
Yongjie Zhang ◽  
Ke Liang
Keyword(s):  
Multiobjective Optimization ◽  
Fundamental Frequency ◽  
Hybrid Composite ◽  
Composite Laminates ◽  
Optimization Design ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Design Flexibility ◽  
The Cost ◽  
Ply Orientation

Due to the large variable design space in optimization problems of composite laminates, it remains one of the challenging tasks to develop efficient optimization design methods to improve the design flexibility and efficiency. This work presents a sequential permutation table (SPT) method for the multiobjective optimization design of two-material hybrid composite laminates with simply supported boundary conditions, which maximizes the fundamental frequency and minimizes the cost/weight. Based on the vibration analysis of hybrid composite laminates, the approximate linear regularity of the square of fundamental frequency is derived, and two best ply orientations for the two materials are identified, respectively. By assigning one best ply orientation with maximum fundamental frequency at respective stacking positions, and using another best ply orientation to replace plies in the stacking sequence from the mid-plane to the outermost can lead to the optimum. Two multiobjective optimization problems are employed to verify the SPT method, results are compared with those obtained by heuristic algorithms. The obtained better solutions demonstrate the effectiveness and efficiency of the SPT method and its potentials for optimal design of hybrid composite laminates.

Sensitivity Analysis on Linear Programming Problems with Trapezoidal Fuzzy Variables

2013 ◽  
pp. 64-82
Author(s):  
Seyed Hadi Nasseri ◽  
Ali Ebrahimnejad
Keyword(s):  
Linear Programming ◽  
Simplex Algorithm ◽  
Programming Models ◽  
Fuzzy Data ◽  
Fuzzy Variables ◽  
Fuzzy Environment ◽  
Dual Simplex ◽  
Linear Programming Problems ◽  
Simplex Tableau ◽  
Dual Simplex Algorithm

In the real word, there are many problems which have linear programming models and sometimes it is necessary to formulate these models with parameters of uncertainty. Many numbers from these problems are linear programming problems with fuzzy variables. Some authors considered these problems and have developed various methods for solving these problems. Recently, Mahdavi-Amiri and Nasseri (2007) considered linear programming problems with trapezoidal fuzzy data and/or variables and stated a fuzzy simplex algorithm to solve these problems. Moreover, they developed the duality results in fuzzy environment and presented a dual simplex algorithm for solving linear programming problems with trapezoidal fuzzy variables. Here, the authors show that this presented dual simplex algorithm directly using the primal simplex tableau algorithm tenders the capability for sensitivity (or post optimality) analysis using primal simplex tableaus.

Location and Provisioning Problems in Cloud Computing Networks

2013 ◽  
pp. 22-45 ◽  
Author(s):  
Federico Larumbe ◽  
Brunilde Sansò
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Data Centers ◽  
Optimization Problems ◽  
Virtual Machine Placement ◽  
Future Research ◽  
Network Efficiency ◽  
Placement Problem ◽  
The Cost

This chapter addresses a set of optimization problems that arise in cloud computing regarding the location and resource allocation of the cloud computing entities: the data centers, servers, software components, and virtual machines. The first problem is the location of new data centers and the selection of current ones since those decisions have a major impact on the network efficiency, energy consumption, Capital Expenditures (CAPEX), Operational Expenditures (OPEX), and pollution. The chapter also addresses the Virtual Machine Placement Problem: which server should host which virtual machine. The number of servers used, the cost, and energy consumption depend strongly on those decisions. Network traffic between VMs and users, and between VMs themselves, is also an important factor in the Virtual Machine Placement Problem. The third problem presented in this chapter is the dynamic provisioning of VMs to clusters, or auto scaling, to minimize the cost and energy consumption while satisfying the Service Level Agreements (SLAs). This important feature of cloud computing requires predictive models that precisely anticipate workload dimensions. For each problem, the authors describe and analyze models that have been proposed in the literature and in the industry, explain advantages and disadvantages, and present challenging future research directions.

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