Map-based Consumer Selection

For the spatial optimization of the electric grid, the geolocation of the consumers must be provided. TFor this, a map from OpenStreetMap is integrated into the tool. The determination of the geocoordinates of the consumers can be done in three ways:

1. Automatic detection of buildings within areas marked using drawing tools.

2. Manual selection of consumer locations by placing markers on the map.

3. Manual input of the geocoordinates using input fields.

Demand Estimation

PeopleSuN's project includes a demand modeling tool which utilizes both quantitative and qualitative survey data collected from various non-urban Nigerian villages. With an extensive dataset that includes responses from thousands of households and enterprises, it models appliance ownership, electricity consumption trends, and available expenditures for electricity services, and determines affordable monthly costs. The tool uses statistical models in conjunction with stochastic algorithms to generate likely distributions of electricity demand and expenditures. Initially, this is applied to the surveyed areas and then extrapolated to cover all non-urban regions in Nigeria. The system leverages geolocation to customize the demand modeling for each village. Furthermore, it generates different consumer categories/profiles based on geographical zones and wealth categories, aiding in a more nuanced and accurate prediction of electricity demand.

Spatial Optimization of the Distribution Grid

The spatial grid optimization tool operates through a sequence of steps that ensure efficient energy system design. The tool identifies consumers, sorts them based on proximity to the load center, and meticulously calculates the optimal setup required to connect all consumers within the grid, adhering to constraints on maximum connections per consumer and maximum distance between consumers. The tool employs a clustering algorithm to determine optimal pole locations, constructs a minimum spanning tree (MST) for efficient pole interconnectivity, and connects each consumer to their nearest pole. If needed, the number of poles is increased, ensuring all constraints are met. Furthermore, long links exceeding the maximum allowed distance are segmented into smaller ones with additional poles. Thus, the tool offers a comprehensive solution for optimal grid design.

Design Optimization of the Generation System

This tool excels in the design and optimization of off-grid energy systems, integrating different types of energy converters including photovoltaic (PV) systems and diesel generators, along with components like battery systems, inverters, and rectifiers. The tool uses ERA5 satellite data and PVLIB for the modeling of solar potentials in PV systems. The design optimization of the tool revolves around minimizing the Levelized Cost of Energy (LCOE), a method that considers both capital and operational costs. This results in an optimal configuration that satisfies consumer demands, modeled as a mixed-integer linear model. Leveraging the open-source modeling framework OEMOF and the high-efficiency Gurobi solver, the tool offers an effective solution for complex optimization challenges. The insights generated by the tool - including installed capacities, time series of system operations, investment costs, CO₂ emissions, and fuel requirements - provide stakeholders with invaluable information for strategic decision-making.