Multi-criteria decision analysis (MCDA) methods have become increasingly popular in decision-making for sustainable energy because of the multi-dimensionality of the sustainability goal and the complexity of socio-economic and biophysical systems. This article reviewed the corresponding methods in different stages of multi-criteria decision-making for sustainable energy, i.e., criteria selection, criteria weighting, evaluation, and final aggregation. The criteria of energy supply systems are summarized from technical, economic, environmental and social aspects. The weighting methods of criteria are classified into three categories: subjective weighting, objective weighting and combination weighting methods. Several methods based on weighted sum, priority setting, outranking, fuzzy set methodology and their combinations are employed for energy decision-making. It is observed that the investment cost locates the first place in all evaluation criteria and CO2 emission follows closely because of more focuses on environment protection, equal criteria weights are still the most popular weighting method, analytical hierarchy process is the most popular comprehensive MCDA method, and the aggregation methods are helpful to get the rational result in sustainable energy decision-making.

Review on multi-criteria decision analysis aid in sustainable energy decision-making

Energy is a vital input for social and economic development. As a result of the generalization of agricultural, industrial and domestic activities the demand for energy has increased remarkably, especially in emergent countries. This has meant rapid grower in the level of greenhouse gas emissions and the increase in fuel prices, which are the main driving forces behind efforts to utilize renewable energy sources more effectively, i.e. energy which comes from natural resources and is also naturally replenished. Despite the obvious advantages of renewable energy, it presents important drawbacks, such as the discontinuity of generation, as most renewable energy resources depend on the climate, which is why their use requires complex design, planning and control optimization methods. Fortunately, the continuous advances in computer hardware and software are allowing researchers to deal with these optimization problems using computational resources, as can be seen in the large number of optimization methods that have been applied to the renewable and sustainable energy field. This paper presents a review of the current state of the art in computational optimization methods applied to renewable and sustainable energy, offering a clear vision of the latest research advances in this field.

Optimization methods applied to renewable and sustainable energy: A review

MES (multi-energy systems): An overview of concepts and evaluation models

A review on simulation-based optimization methods applied to building performance analysis

Thermal comfort and building energy consumption implications - A review

Interactive quantitative modeling and cost optimization of regional sustainable development based on water-energy-food nexus

The performances of combined cooling, heating and power (CCHP) system are greatly dependent on its design, operation strategy and thermal and electric demands. This paper illustrates how the use of a genetic algorithm can provide speedy optimization, by applying it to two styles of buildings operated in different operation strategies. The primary energy consumptions of CCHP system following electric demand management (EDM) and thermal demand management (TDM) are firstly analyzed respectively. Then, sixteen hypothetical buildings are constructed to represent various energy demands. Primary energy saving (PES), annual total cost saving (ATCS), and CO2 emission reduction (CO2ER), are weighted to evaluate the integrated performances of CCHP system in comparison to separation production system. Finally, the optimized CCHP system for sixteen scenarios using GA are compared.Practical application: This paper provides an optimization design method for CCHP system. The performance analysis of CCHP systems running d...

An illustration of the optimization of combined cooling heating and power systems using genetic algorithm

This paper studied the application of neural network PID in the central air-conditioning system. From the control viewpoint, the central air-conditioning system has chrematistic as large inertia, delay and nonlinear and uncertain disturbance factor, for example the indoor and outdoor. In order to acquire better performance in the central air-conditioning system, the neural network PID control was studied through comparing the traditional PID control in simulation and experiments. The output of system was predicted through the algorithm of nonlinear, at last based on the mathematical model of air-conditioning space, the simulation and the experiment in a studio of TV station have found that neural network PID controller has the capability of self-studying and self-adapting and obtain faster response and better performance.

Jiangjiang Wang

Papers

Interactive quantitative modeling and cost optimization of regional sustainable development based on water-energy-food nexus

An illustration of the optimization of combined cooling heating and power systems using genetic algorithm

Simulation and Experiment Study of Neural Network PID Controller in Central Air-conditioning System

A Combined Power and Steam System Integrated with Solar Photovoltaic/Thermal Collector: Thermodynamic Characteristics and Cost-Benefit Analyses

Optimal design of a novel hybrid renewable energy CCHP system considering long and short-term benefits