Geo-Spatial resource analysis and optimization of investment strategies for renewable energy

Abstract

In this paper we describe a concept that brings geo-spatial data analysis together with optimal modeling of renewable energy planning and investment processes to aid in decision making (“when and where” to invest), a process that takes into account development cost, resource constraints and requirements for new infrastructure. This concept is implemented in a new tool named GSPEIS (Geo-Spatial Planner for Energy Investment Strategies). The GSPEIS system accomplishes these goals by bringing a powerful visualization framework that enables the user to understand and explore the problem space, together with genetic algorithm-based optimization engine that helps users interactively generate optimal solutions. We demonstrate here how our innovative approach with a heavy focus on user involvement enables analysts and decision makers to (1) configure the system and filter critical inputs, (2) run underlying models that annotate the visualization and configuration space with specific costs, statistics and constraints, and (3) optimize across the goal space for different objectives such as investment return, energy production, or revenue. Our approach provides visually controlled spatial optimization across resources and infrastructure while adhering to a diverse set of constraints.