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

Cost-Based Site and Capacity Optimization of Multi-Energy Storage System in the Regional Integrated Energy Networks

A novel emergy-based optimization model of a building cooling, heating and power system

The uncertainties of cooling load significantly impact the operations performance of building energy systems. A novel interval prediction method is proposed to quantify the uncertainties of cooling load. Due to the cooling load having a similar changing trend from 1:00 to 24:00, a cooling load classification method based on the time of the day is proposed to analyze the uncertainty. Firstly, the gated recurrent unit algorithm is used to construct the point prediction model. Secondly, the kernel density estimation method is employed for probability statistics of prediction error. Moreover, the percentile method constructs the upper and lower bounds of the cooling load prediction interval with given confidence levels. Finally, a commonly used interval prediction method is compared to the proposed method, and a real cooling load dataset is used to assess the prediction method. The results demonstrate that the point prediction model has a mean absolute percentage error of 10.19% in the interval prediction dataset. The interval prediction results illustrate that the prediction interval coverage probability is 91.75%, and the prediction interval normalized average width is 20.83% in the confidence level of 90%. Compared with the commonly used method, the proposed method has a narrower interval. 

An interval prediction method for quantifying the uncertainties of cooling load based on time classification

Quantitative sustainability evaluations of hybrid combined cooling, heating, and power schemes integrated with solar technologies

Heating systems are used in homes and apartments worldwide. The variable flow system is advantaged to the energy saving while the control in variable flow system is difficult. This paper describes the typical variable flow heating system and presents the coupling mathematical model of heat exchanger. Based on the mathematical model, a new multivariable PID decoupling controller is proposed. This controller consists of diagonally matrix method-based decomposition compensatory unit and PID controller. To obtain better performance in the control system, an improved genetic algorithm is adopted to optimize the PID parameters in floating point representations. The population mutates before crossover in the computing process of the improved genetic algorithm, which is benefit to the convergence. To solve the slow convergence during the later evolution, the micromutation algorithm is adopted. The simulation results show that the decoupling control system is effective and feasible. The method can overcome the strong coupling features of the system and has better performance.

Jiangjiang Wang

Papers

Cost-Based Site and Capacity Optimization of Multi-Energy Storage System in the Regional Integrated Energy Networks

A novel emergy-based optimization model of a building cooling, heating and power system

An interval prediction method for quantifying the uncertainties of cooling load based on time classification

Quantitative sustainability evaluations of hybrid combined cooling, heating, and power schemes integrated with solar technologies

Genetic optimization algorithm on PID decoupling controller for variable flow heating system