Improving algorithms to help solar project developers avoid the NIMBY effect

A group of scientists from the Kansas State University and Cornell University, in the United States, is proposing to improve existing approaches and tools for multi-criteria decision analysis in the development and construction of large scale PV power plants by adding socio-demographic and socio-economic criteria.

The researchers summarized in a study, published in Applied Energy, all existing geographical information system (GIS) multi-criteria decision analysis (MCDA) methodologies and found that all of them are based exclusively on techno-economic considerations on project costs and expected growth in regional electricity demand.

In GIS-MCDA approaches, the site selection criteria are determined after a specific region is chosen for the potential construction of a solar park. The criteria are then split into exclusion criteria, including constraints or restrictive factors, and decision criteria, which the scientists describe as suitability criteria or preferences, or the ones which are optimized. “Then, by processing exclusion criteria in GIS, a feasible region is generated,” they further explained. “Following the creation of a feasibility surface, the relative importance of various suitability criteria is determined through the application of MCDA algorithms, which are then used to further classify the feasibility region into suitability tiers.”

The researchers stressed that the three most important factors that are considered in decision criteria in project planning are solar radiation, distance from the electricity network, and the slope of the land. Protected lands and legal restrictions, as well as the presence of farmland and open water and wetland, are the main factors that are taken into account for exclusion criteria.

The U.S. group identified four MCDA algorithms that are coupled with GIS and have been used alone or in combination with other algorithms in solar project planning: analytical hierarchy process (AHP), technique for order preference by similarity to ideal solution (TOPSIS), elimination and choice translating reality (ELECTRE), and weighted linear combination (WLC).

Although these techniques have proven to be, thus far, effective in terms of economic payback and technical requirements, they have not shown the same effectiveness in avoiding the “not-in-my-backyard” (NIMBY) response from local communities, due to the lack of socio-demographic and socio-economic factors in their respective models. The NIMBY effect consists of the opposition to the locating of something considered undesirable in one's neighborhood.

The scientists stressed all recent efforts made by the scientific communities to integrate social dynamics in the modeling and concluded that there is still much to be done to understand the degree to which measuring these variables proactively could help mitigate public opposition down the line on individual projects.

The academics also explained, public opposition from communities or groups of citizens should not automatically exclude an area from consideration for renewable energy facilities. Social variables can help identify areas with expected opposition and this information, however, should not necessarily be automatically assumed to be an exclusion criterion within the GIS-MCDA framework. As for the solar project developers, they should not simply try to avoid opposition from local communities but try to better understand and engage this opposition to improve facility designs.

In future research, the research team wants to further analyze the shortcomings of the GIS-MCDA approach when used in isolation, and test the analytical tool within an interactive and responsive siting process.