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

The PROMENTHEE II method is employed to evaluate five combined cooling, heat and power (CCHP) system systems using four aspects such as technology, economy, environment, and society. The CCHP systems with dynamical sources respectively are gas engine, gas turbine, Stiring engine, solid oxide fuel cell (SOFC) and a separate plant. The prime of the multi criteria evaluation method is the selection of preference function and calculation of the net flow. The criterion with linear preference function is applied to compare the five systems. The result shows that the SOFC mode is the optimal scheme in terms of the net flow. To verify the robustness of the PROMENTHEE II, the sensitivity analysis is done by exchanging the criteria weight. The result shows that PROMENTHEE II method is practical and convenient and it can provide a reliable evaluation result for CCHP systems.Copyright © 2009 by ASME

Using the Multi Criteria Analysis Method PROMENTHEE II to Select the Optimal CCHP System: A Case Study

Energy storage is widely recognized as an effective alternative to match the instantaneous imbalance between distributed renewable energy sources and loads. However, suitable installation sites and capacities are necessarily determined in integrated energy networks. This paper constructs a two-layer optimization model of integrated power and heat networks to obtain the optimal installation nodes and capacities of electric energy storage (EES) and thermal energy storage (TES) units. In the optimization model, the interchange of power to heat with storage units is collaborated to unitize distributed renewable energy sources and decrease comprehensive costs, including investment cost of storage units and operation cost. A chance-constrained optimization model is proposed to transform the uncertain variables of electric and heat loads into a deterministic optimization problem. The impacts of confidence levels involved in load probability distributions on the optimal nodes and capacities are analyzed. The results demonstrate that the optimal installation nodes are determined by the distributions of distributed power sources and load ratios in networks. The increased confidence level globally raises the optimal installation capacities of EES and TES. When the confidence level increases from 0.50 to 0.99, the utilization rate of wind power increases by 3.91 %, whereas the comprehensive cost increases by 22.34 %. The hybrid integrations of EES and TES consume more uncertain renewable power and improve the economic performances by the interchange of power to heat. 

Chance-constrained optimization of distributed power and heat storage in integrated energy networks

With the development of economy in China, the energy saving is more and more important. Energy consumption for heating in the residential sector is one of the main parts of the total energy consumption in northern China. Based on the characteristic of grey system theory, which is an effective method to solve uncertain problems, used grey incidence analytic into optimization and comparison of heating methods. The comparison of heating sources includes coal-fired boiler, oil-fired boiler and gas-fired boiler in a residential area. A heating optimization framework based on grey incidence degree was built based on grey incidence analytic method, which considers six indexes to reflecting the heating method. Finally, the grey incidence degree of coal-fired boiler, oil-fired boiler and gas-fired boiler are respectively 0.800, 0.806 and 0.886 when the indexes are coordinately important, which shows the gas-fired boiler is optimization strategy as heating for the discussed project in northern China. The comparison shows that the oil-fired boiler is sensitive to the energy price. In addition the results show that the method is simple and practical; the model provides an effective method to optimization and comparison of heating methods.

Jiangjiang Wang

Papers

Using the Multi Criteria Analysis Method PROMENTHEE II to Select the Optimal CCHP System: A Case Study

Chance-constrained optimization of distributed power and heat storage in integrated energy networks

Grey incidence analysis on optimization and comparison of heating methods in Northern China

An integrated optimization framework for regional energy planning with a sustainability assessment model

Energy, Exergy, and Exergoeconomic Analysis of Solar-Driven Solid Oxide Electrolyzer System Integrated with Waste Heat Recovery for Syngas Production