Multicriteria Decisions in Urban Energy System Planning: A Review

Abstract

Urban energy system planning (UESP) is a topic of growing concern for cities in deregulated energy markets, which plan to decrease energy demand, reduce their dependency on fossil fuels, and increase the share of renewable energy sources. UESP being a highly multisectoral and multi-actor task, multicriteria decision analysis (MCDA) methods are frequently used in the decision processes. These methods may provide support in organizing and identifying solutions to problems with conflicting objectives. However, knowing which method to use is generally not straightforward, as the appropriateness of a method or combination of methods depends on the decision problem's context. Therefore, this article reviewed scientific papers to characterize and analyze MCDA problems and methods in the context of UESP. The review systematically explores issues such as the scope of the problems, the alternatives and criteria considered, the expected decision outcomes, the decision analysis methods and the rationales for selecting and combining them, and the role of values in driving the decision problems. The final outcome is a synthesis of the data and insights obtained, which may help potential users identify appropriate decision analysis methods based on given problem characteristics.

Interactive Optimization With Parallel Coordinates: Exploring Multidimensional Spaces for Decision Support

“…Psychological studies have emphasized the limited ability of human decision makers in balancing multiple conflicting criteria, even between a limited number of alternatives (French, 1984;Larichev et al, 1987;Jaszkiewicz and Słowiński, 1999). Here an aggregative multiattribute decision analysis (MADA) method is proposed to facilitate this task, by revealing the best rank or score of each alternative relative to the others (Cajot et al, 2017a). Each solution is attributed a score that is displayed as an additional axis in the parallel coordinates, and that is used as additional decision criterion.…”

“…As pointed out in the introduction (Hwang and Masud, 1979), multiobjective decision analysis (MODA) methods are designed for the generation of alternatives, while the strength of MADA lies in the evaluation and comparison of predetermined alternatives. Many methods could be adopted, and there is a wide body of literature comparing the similarities, pros and cons of these methods (Zanakis et al, 1998;Cajot et al, 2017a). The often implicit assumption with most MADA methods is that the criteria can compensate each other.…”

“…The resulting score is not intended to replace the DM's decision , but rather to focus their attention on a limited number of alternatives, and stimulate questions and learning (e.g., discovering what characterizes a topor low-ranked alternative). As reviewed in Cajot et al (2017a), the application of MADA as a way to support decisions on precalculated non-dominated sets generated with MODA is fairly common (e.g., Aydin et al, 2014;Ribau et al, 2015;Fonseca et al, 2016;Carli et al, 2017). However, these applications are typically performed a posteriori.…”

“…To avoid burdening the user with further methodological aspects, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is adopted for its most intuitive and understandable principle, limited need for inputs and ability to handle many criteria and solutions (Zanakis et al, 1998;Cajot et al, 2017a). The method ranks each alternative according to its proximity to an ideal solution-which would present the best value in every criterion-and respectively according to its distance from a negative ideal solution-which would present all worst values (Hwang and Yoon, 1981).…”

Optimal Design of Heat-Integrated Water Allocation Networks

“…They further allow practitioners to understand why they are selecting a specific solution and how this solution's performance is compared to the rest. Among these tools, parallel coordinates visualization tool [32] is a powerful and practical way for evaluation and decision-making in high-dimensional spaces with applications in urban planning [33,34] and process integration [15]. This tool has been selected to visualize and further select promising alternatives similar to the approach presented by Kermani et al [15].…”