Multi-Criteria Decision-Making in Power Grid Sustainability

Multi-Criteria Decision-Making (MCDM) has emerged as a cornerstone for advancing sustainable power systems by enabling stakeholders to balance technical performance, economic viability, environmental impact and social considerations. In the context of modern grids, decision makers must accommodate fluctuating renewable generation, maintain stability and resilience, manage investment and operational risk, and satisfy regulatory and community objectives. MCDM frameworks typically structure problems into a hierarchy of criteria—such as reliability, cost, emissions, land use and social acceptance—assign relative weights and apply ranking or aggregation methods to derive an optimal solution. Recent advances integrate artificial intelligence, fuzzy logic and hybrid modelling to capture uncertainty, non-linearity and stakeholder preferences more faithfully. Application areas include the selection of grid-scale storage technologies, evaluation of construction and expansion projects, assessment of market-oriented business models and optimisation of grid-renewable coordination. By providing transparent, repeatable and quantitative support, MCDM enhances policy formulation, investment planning and operational strategy, ensuring that power networks can evolve sustainably in response to decarbonisation imperatives, digitalisation trends and evolving regulatory landscapes.

Research from Nature Portfolio

Recent studies have applied a strategic evaluation framework to assess the security of power systems with high shares of renewable energy. One investigation employed a SWOT analysis to identify internal strengths and weaknesses and external opportunities and threats associated with new-energy grid integration. Factor weights were determined through a fuzzy Analytic Hierarchy Process, while a fuzzy-Measurement Alternatives and Ranking according to Compromise Solution (MARCOS) method was used to prioritise improvement strategies. The resulting strategy ranking highlights the importance of technical advancement, local load development and policy incentives to reconcile large-scale renewables deployment with grid-operation security and sustainability goals.

Multi-Criteria Decision-Making in Power Grid Sustainability publication trend

The graph below shows the total number of articles in multi-criteria decision-making in power grid sustainability across all publications each year (not limited to Nature Index journals).

Technical terms

Multi-Criteria Decision-Making (MCDM): A set of methods for evaluating and ranking alternatives against multiple conflicting criteria.

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS): An algorithm that identifies options closest to an ideal solution and furthest from a negative ideal.

SWOT Analysis: A strategic tool for categorising internal strengths and weaknesses alongside external opportunities and threats.

Fuzzy Analytic Hierarchy Process (Fuzzy-AHP): A weighting technique that incorporates fuzzy logic into the hierarchical pairwise comparison of criteria.

Measurement Alternatives and Ranking according to Compromise Solution (MARCOS): A recent MCDM approach that ranks alternatives based on compromise distances to ideal and anti-ideal solutions.

Bayesian Best-Worst Method: A probabilistic extension of the best-worst pairwise weighting procedure accommodating uncertainty in expert judgements.

Matter-Element Extension: A mathematical framework for constructing and evaluating composite indices under uncertain or vague information.

References

1. New energy power system operation security evaluation based on the SWOT analysis. Scientific Reports (2022).
2. A Hybrid MCDM Model for Evaluating the Market-Oriented Business Regulatory Risk of Power Grid Enterprises Based on the Bayesian Best-Worst Method and MARCOS Approach. Energies (2022).
3. Comprehensive Evaluation of Coordination Development for Regional Power Grid and Renewable Energy Power Supply Based on Improved Matter Element Extension and TOPSIS Method for Sustainability. Sustainability (2016).
4. Application of BP Neural Network Based on Genetic Algorithm Optimization in Evaluation of Power Grid Investment Risk. IEEE Access (2019).