Multi-objective problem based operation and emission cots for heat and power hub model through peak load management in large scale users

2018 ◽  
Vol 171 ◽  
pp. 411-426 ◽  
Author(s):  
Pengcheng Wei ◽  
Fangcheng He ◽  
Li Li ◽  
Xi Shi ◽  
Rolando Simoes
Keyword(s):  
Large Scale ◽  
Peak Load ◽  
Load Management ◽  
Multi Objective ◽  
Hub Model

scholarly journals Distributed multi-objective scheduling of power consumption for smart buildings

2019 ◽  
Vol 2 (S1) ◽  
Author(s):  
Marvin Nebel-Wenner ◽  
Christian Reinhold ◽  
Farina Wille ◽  
Astrid Nieße ◽  
Michael Sonnenschein
Keyword(s):  
Power Consumption ◽  
Smart Grids ◽  
Residential Buildings ◽  
Cost Optimization ◽  
Peak Load ◽  
Load Management ◽  
Management Technique ◽  
Smart Buildings ◽  
Multi Objective ◽  
The Cost

Abstract Load management of electrical devices in residential buildings can be applied with different goals in the power grid, such as the cost optimization regarding variable electricity prices, peak load reduction or the minimization of behavioral efforts for users due to load shifting. A cooperative multi-objective optimization of consumers and generators of power has the potential to solve the simultaneity problem of power consumption and optimize the power supply from the superposed grid regarding different goals. In this paper, we present a multi-criteria extension of a distributed cooperative load management technique in smart grids based on a multi-agent framework. As a data basis, we use feasible power consumption and production schedules of buildings, which have been derived from simulations of a building model and have already been optimized with regard to self-consumption. We show that the flexibilities of smart buildings can be used to pursue different targets and display the advantage of integrating various goals into one optimization process.

scholarly journals Multi-Objective Optimization of Integrated Civilian-Military Scheduling of Medical Supplies for Epidemic Prevention and Control

Healthcare ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 126
Author(s):  
Hai-Feng Ling ◽  
Zheng-Lian Su ◽  
Xun-Lin Jiang ◽  
Yu-Jun Zheng
Keyword(s):  
Prevention And Control ◽  
Large Scale ◽  
Medical Services ◽  
The Novel ◽  
Multi Objective ◽  
Medical Supplies ◽  
Problem Instances ◽  
Novel Coronavirus ◽  
And Control ◽  
Minimum Ratio

In a large-scale epidemic, such as the novel coronavirus pneumonia (COVID-19), there is huge demand for a variety of medical supplies, such as medical masks, ventilators, and sickbeds. Resources from civilian medical services are often not sufficient for fully satisfying all of these demands. Resources from military medical services, which are normally reserved for military use, can be an effective supplement to these demands. In this paper, we formulate a problem of integrated civilian-military scheduling of medical supplies for epidemic prevention and control, the aim of which is to simultaneously maximize the overall satisfaction rate of the medical supplies and minimize the total scheduling cost, while keeping a minimum ratio of medical supplies reservation for military use. We propose a multi-objective water wave optimization (WWO) algorithm in order to efficiently solve this problem. Computational results on a set of problem instances constructed based on real COVID-19 data demonstrate the effectiveness of the proposed method.

scholarly journals A novel encoding for separable large-scale multi-objective problems and its application to the optimisation of housing stock improvements

2020 ◽  
Vol 96 ◽  
pp. 106650
Author(s):  
Alexander E.I. Brownlee ◽  
Jonathan A. Wright ◽  
Miaomiao He ◽  
Timothy Lee ◽  
Paul McMenemy
Keyword(s):  
Large Scale ◽  
Housing Stock ◽  
Multi Objective

A multi-objective sparse evolutionary framework for large-scale weapon target assignment based on a reward strategy

2021 ◽  
Vol 40 (5) ◽  
pp. 10043-10061
Author(s):  
Xiaoping Shi ◽  
Shiqi Zou ◽  
Shenmin Song ◽  
Rui Guo
Keyword(s):  
Large Scale ◽  
The Novel ◽  
Genetic Operators ◽  
Multi Objective ◽  
Target Assignment ◽  
Solution Selection ◽  
Battlefield Environment ◽  
Sparse Problems ◽  
Novel Model ◽  
Error Parameter

 The asset-based weapon target assignment (ABWTA) problem is one of the important branches of the weapon target assignment (WTA) problem. Due to the current large-scale battlefield environment, the ABWTA problem is a multi-objective optimization problem (MOP) with strong constraints, large-scale and sparse properties. The novel model of the ABWTA problem with the operation error parameter is established. An evolutionary algorithm for large-scale sparse problems (SparseEA) is introduced as the main framework for solving large-scale sparse ABWTA problem. The proposed framework (SparseEA-ABWTA) mainly addresses the issue that problem-specific initialization method and genetic operators with a reward strategy can generate solutions efficiently considering the sparsity of variables and an improved non-dominated solution selection method is presented to handle the constraints. Under the premise of constructing large-scale cases by the specific case generator, two numerical experiments on four outstanding multi-objective evolutionary algorithms (MOEAs) show Runtime of SparseEA-ABWTA is faster nearly 50% than others under the same convergence and the gap between MOEAs improved by the mechanism of SparseEA-ABWTA and SparseEA-ABWTA is reduced to nearly 20% in the convergence and distribution.

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