Community-led efforts, by local government leaders and NGOs, are a critical piece of decarbonization and solving the climate crisis. The future buildout of community-oriented distributed (local) clean energy projects, including those coupled with microgrid/resiliency protections, will serve as the backbone of our national clean energy buildout, and our energy security and critical service infrastructure protection. We need to work to protect all of our citizens, and especially the most vulnerable, from the most serious adverse effects of climate change.

These projects, which are often complex and involve many technical, financial, and legal moving parts, require sophisticated modeling capabilities that are not typically available to local city/county engineers, planners, and the local NGOs, i.e., those that are best positioned to champion such projects. Local city/county planners and NGOs that want to pursue clean energy/resiliency projects are often not in a position budgetarily, or politically within the local/county governmental hierarchy, to hire private sector consultants to conduct such early stage analyses. This obstacle is further compounded by the fact that such renewable energy/resiliency projects often need the support and explicit cooperation of the local electric utility, which can be very difficult to secure without concrete modeling work to demonstrate the viability and benefits of the proposed project.

This transformative training support helps communities leverage their often limited clean energy/resiliency planning resources, and provides local government planners, indigenous nations and NGOs with in-house capabilities to identify, analyze and model - for their own planning purposes and at minimal cost - the technical and financial feasibility of potential local solar PV/resiliency projects. This modeling proficiency knowledge allows for the early visioning of projects prior to the outlay of capital to private consulting firms, and provides for a more informed and efficient contractor procurement process once early decision-making has occurred.