scholarly journals Multi-Objective Optimization of a Microgrid Considering the Uncertainty of Supply and Demand

2021 ◽  
Vol 13 (3) ◽  
pp. 1320
Author(s):  
Shiping Geng ◽  
Gengqi Wu ◽  
Caixia Tan ◽  
Dongxiao Niu ◽  
Xiaopeng Guo
Keyword(s):  
Environmental Protection ◽  
Energy Demand ◽  
Demand Uncertainty ◽  
Flow Direction ◽  
Supply And Demand ◽  
Fuzzy Model ◽  
Copula Function ◽  
Scenario Generation ◽  
Particle Swarm Algorithm ◽  
Multi Objective

Starting from the perspective of the uncertainty of supply and demand, using the Copula function and fuzzy numbers a scenario generation method, considering the uncertainty of scenery, and a random fuzzy model of energy demand uncertainty are proposed. Then, through the energy flow direction and the energy supply, production, conversion, storage, and demand, a multi-objective model considering the economic and environmental protection of a park is constructed. Here, the park refers to a microgrid that gathers distributed energy such as wind and photovoltaics and has requirements for cooling, heat, and electricity at the same time. Next, combining the constraints of each link, the particle swarm algorithm is used to solve the model. Finally, an example is analyzed in a certain park. The results of the example show that, on the one hand, the proposed scenario generation method and fuzzy number method can reduce the uncertainty of supply and demand, effectively fitting the wind and photovoltaic output and various energy demands. On the other hand, considering the economy and environmental protection of the park at the same time, the configuration of energy storage equipment can not only improve the economy of the park, but also promote the consumption of renewable energy.

scholarly journals Impact of Social Welfare Metrics on Energy Allocation in Multi-Objective Optimization

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2961
Author(s):  
Anders Clausen ◽  
Aisha Umair ◽  
Yves Demazeau ◽  
Bo Nørregaard Jørgensen
Keyword(s):  
Resource Allocation ◽  
Social Welfare ◽  
Energy Demand ◽  
Optimal Allocation ◽  
Supply And Demand ◽  
Multi Objective Optimization ◽  
Multi Agent Systems ◽  
Multi Objective ◽  
Pareto Sets ◽  
The Impact

Resource allocation problems are at the core of the smart grid where energy supply and demand must match. Multi-objective optimization can be applied in such cases to find the optimal allocation of energy resources among consumers considering energy domain factors such as variable and intermittent production, market prices, or demand response events. In this regard, this paper considers consumer energy demand and system-wide energy constraints to be individual objectives and optimization variables to be the allocation of energy over time to each of the consumers. This paper considers a case in which multi-objective optimization is used to generate Pareto sets of solutions containing possible allocations for multiple energy intensive consumers constituted by commercial greenhouse growers. We consider the problem of selecting a final solution from these Pareto sets, one of maximizing the social welfare between objectives. Social welfare is a set of metrics often applied to multi-agent systems to evaluate the overall system performance. We introduce and apply social welfare ordering using different social welfare metrics to select solutions from these sets to investigate the impact of the type of social welfare metric on the optimization outcome. The results of our experiments indicate how different social welfare metrics affect the optimization outcome and how that translates to general resource allocation strategies.

scholarly journals Research on multi-objective planning and optimization of integrated energy system based on economy and environmental protection

2021 ◽  
Vol 245 ◽  
pp. 01039
Author(s):  
Xiaohui Wang ◽  
Lu Xue ◽  
Yongli Wang ◽  
Yuli Zhang ◽  
Yijiang Liu
Keyword(s):  
Environmental Protection ◽  
Energy System ◽  
Energy Utilization ◽  
Particle Swarm Algorithm ◽  
Renewable Energy Consumption ◽  
System Planning ◽  
Multi Objective ◽  
Planning Optimization ◽  
Integrated Energy System ◽  
Energy System Planning

The development of a regional integrated energy system is an important way to improve energy utilization, alleviate the problem of renewable energy consumption and improve the environment. Aiming at the problems of single planning technology and fewer resource types in the existing integrated energy system planning, various types of equipment in the integrated energy system were built, and the integrated energy system was established with the goal of optimal economy and certain environmental protection. The system multi-objective planning optimization model, and then the particle swarm algorithm is used to solve the example problem to verify the feasibility and economy of the model and the optimization algorithm.

Optimal Allocation of Water Resources Based on Multi-Objective Particle Swarm Algorithm and Information Entropy

2014 ◽  
Vol 641-642 ◽  
pp. 75-79 ◽  
Author(s):  
Wei Lin Liu ◽  
Li Na Liu
Keyword(s):  
Information Entropy ◽  
Water Allocation ◽  
Optimal Allocation ◽  
Supply And Demand ◽  
Particle Swarm ◽  
Complex Problem ◽  
Particle Swarm Algorithm ◽  
Multi Objective ◽  
Decision Making Problem ◽  
North Water

Water allocation is a very complex problem, involving social, economic, environmental, and political factors. Consequently, it is a multi-objective decision-making problem. This paper presents a multi-objective model for the optimal allocation on multisource water for multiuser under sufficiently considering the harmonious development among economy, society and environment. A multi-objective particle swarm optimization (MOPSO) algorithm is employed to generate a set of Pareto-optimal solutions. At the same time, to facilitate easy implementation for the water allocation operator, information entropy theory is adopted to sort the decision results according to the magnitude of the superiority degrees. As a case study the proposed approach has been applied to the reasonable allocation of water supply and demand in the water-receiving areas of the South-to-North Water Transfer Project in China, in which the maximal benefit of economy, society and environment was regarded as the multi-objectives. The results show that the proposed approach is able to offer the quantifiable benefits or costs among different objectives for the water managers, and is highly professional in making decisions for allocating water among use sectors and different areas.

Fuzzy multi-objective decision making approach for nuclear power plant installation

2020 ◽  
Vol 39 (5) ◽  
pp. 6339-6350
Author(s):  
Esra Çakır ◽  
Ziya Ulukan
Keyword(s):  
Linear Programming ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Energy Supply ◽  
Energy Demand ◽  
Nuclear Power ◽  
Power Plants ◽  
Total Duration ◽  
Objective Functions ◽  
Multi Objective

Due to the increase in energy demand, many countries suffer from energy poverty because of insufficient and expensive energy supply. Plans to use alternative power like nuclear power for electricity generation are being revived among developing countries. Decisions for installation of power plants need to be based on careful assessment of future energy supply and demand, economic and financial implications and requirements for technology transfer. Since the problem involves many vague parameters, a fuzzy model should be an appropriate approach for dealing with this problem. This study develops a Fuzzy Multi-Objective Linear Programming (FMOLP) model for solving the nuclear power plant installation problem in fuzzy environment. FMOLP approach is recommended for cases where the objective functions are imprecise and can only be stated within a certain threshold level. The proposed model attempts to minimize total duration time, total cost and maximize the total crash time of the installation project. By using FMOLP, the weighted additive technique can also be applied in order to transform the model into Fuzzy Multiple Weighted-Objective Linear Programming (FMWOLP) to control the objective values such that all decision makers target on each criterion can be met. The optimum solution with the achievement level for both of the models (FMOLP and FMWOLP) are compared with each other. FMWOLP results in better performance as the overall degree of satisfaction depends on the weight given to the objective functions. A numerical example demonstrates the feasibility of applying the proposed models to nuclear power plant installation problem.

scholarly journals A socio-psychological modal choice approach to modelling mobility and energy demand for electric vehicles

2020 ◽  
Vol 3 (S1) ◽  
Author(s):  
Khoa Nguyen ◽  
René Schumann
Keyword(s):  
Energy Demand ◽  
Supply And Demand ◽  
Stochastic Approach ◽  
Social Characteristics ◽  
Limited Information ◽  
Demand Model ◽  
Modal Choice ◽  
Charging Infrastructure ◽  
Human Decision ◽  
Electro Mobility

Abstract The development of efficient electric vehicle (EV) charging infrastructure requires modelling of consumer demand at an appropriate level of detail. Since only limited information about real customers is available, most simulations employ a stochastic approach by combining known or estimated business features (e.g. arrival and departure time, requested amount of energy) with random variations. However, these models in many cases do not include factors that deal with the social characteristics of EV users, while others do not emphasise on the economic elements. In this work, we introduced a more detailed demand model employing a modal choice simulation framework based on Triandis’ Theory of Interpersonal Behaviour, which can be calibrated by empirical data and is capable of combining a diverse number of determinants in human decision-making. By applying this model on Switzerland mobility domain, an analysis on three of the most popular EV incentives from both supply and demand sides is provided, which aims for a better understanding of electro-mobility systems by relating its causes and effects.

scholarly journals Healthcare Facility Location-Allocation Optimization for China’s Developing Cities Utilizing a Multi-Objective Decision Support Approach

2018 ◽  
Vol 10 (12) ◽  
pp. 4580 ◽  
Author(s):  
Li Wang ◽  
Huan Shi ◽  
Lu Gan
Keyword(s):  
Heuristic Algorithms ◽  
Programming Model ◽  
Rapid Development ◽  
Supply And Demand ◽  
Healthcare Facility ◽  
Allocation Model ◽  
Location Allocation ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Allocation Problems

With rapid development of the healthcare network, the location-allocation problems of public facilities under increased integration and aggregation needs have been widely researched in China’s developing cites. Since strategic formulation involves multiple conflicting objectives and stakeholders, this paper presents a practicable hierarchical location-allocation model from the perspective of supply and demand to characterize the trade-off between social, economical and environmental factors. Due to the difficulties of rationally describing and the efficient calculation of location-allocation problems as a typical Non-deterministic Polynomial-Hard (NP-hard) problem with uncertainty, there are three crucial challenges for this study: (1) combining continuous location model with discrete potential positions; (2) introducing reasonable multiple conflicting objectives; (3) adapting and modifying appropriate meta-heuristic algorithms. First, we set up a hierarchical programming model, which incorporates four objective functions based on the actual backgrounds. Second, a bi-level multi-objective particle swarm optimization (BLMOPSO) algorithm is designed to deal with the binary location decision and capacity adjustment simultaneously. Finally, a realistic case study contains sixteen patient points with maximum of six open treatment units is tested to validate the availability and applicability of the whole approach. The results demonstrate that the proposed model is suitable to be applied as an extensive planning tool for decision makers (DMs) to generate policies and strategies in healthcare and design other facility projects.

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