Description of the Spatial Grid Optimization Tool

The Spatial Grid Optimization model strategically identifies optimal locations for poles, distribution cables, and connection cables to build the distribution grid. It achieves this by first clustering consumers and then connecting these clusters in a way that results in the lowest total distance. This approach minimizes investment and operational costs while ensuring comprehensive coverage and adherence to connection and distance constraints. Additionally, the model calculates annualized costs for assessing the Levelized Cost of Energy (LCOE).

The model provides the option to identify consumers with particularly high connection costs and exclude them from being connected to the grid. Instead, these consumers should be equipped with a solar home system. To achieve this, the costs for poles, connection, and distribution cables are distributed among consumers along each branch and its sub-branches, starting from the load center of the grid, which is the pole closest to the median longitude and latitude values. For each branch and its sub-branches, the costs are allocated from the load center, with consumers only responsible for the costs of poles and cables connecting them towards the load center. Costs for poles and cables beyond a consumer are borne by subsequent consumers.

The user can set a threshold value called Max Specific Grid Cost. If a consumer exceeds this cost, they are excluded from the grid. Starting from the ends of the branches, consumers with excessive costs are identified, and when consumers are excluded, the cost distribution is recalculated iteratively. The process continues until the consumer at the end of a branch falls below the threshold. Once this happens, the branch is no longer evaluated. The process stops when all consumers at the ends of branches have costs below the threshold.

1. The starting point for the optimization are the respective geolocations of the consumers.
   
2. Clustering of consumers by constrained Kmeans algorithm (size of clusters are limited).
   
3. The centroids of the clusters are taken as the geolocations of the poles.
   
4. Each consumer is connected to its cluster pole.
   
5. A minimum spanning tree (MST) is then constructed from this graph, which connects all the poles with minimum total distance (Kruskal's algorithm).
   
6. Addition of intermediate poles to prevent unacceptable cable spans.

Description of the Energy System Optimization Tool

This tool is a comprehensive solution for the design and optimization of off-grid energy systems. It integrates a variety of energy sources, including photovoltaic (PV) systems and diesel generators, and supports key components such as battery systems, inverters, hydrogen components and rectifiers. The tool's core functionality lies in its ability to design and optimize these systems to meet specific energy demands in the most cost-efficient way. It uses advanced data modeling techniques, including ERA5 satellite data and PVLIB, to accurately simulate solar potentials, ensuring that PV systems are optimized for the specific environmental conditions of the installation site.

A central aspect of the tool is its focus on minimizing the total annualized costs. Annualized costs account for both the capital expenditures (CapEx) associated with the installation of energy systems and the operational expenditures (OpEx) incurred throughout their lifetime. By optimizing the balance between these costs, the tool generates a configuration that not only meets consumer energy demands but does so in the most financially sustainable manner. This optimization is achieved through mixed-integer linear programming (MILP), a mathematical modeling approach that enables the tool to explore multiple possible configurations and select the one that offers the lowest total annualized cost.

The tool's optimization relies on the OEMOF (Open Energy Modeling Framework) and its Solph library, which specialize in energy system optimization. OEMOF Solph offers a flexible, open-source platform for modeling various energy converters, storage solutions, and energy flows. Using mixed-integer linear programming (MILP), the tool addresses complex tasks like investment planning and unit commitment.

Investment optimization using MILP offers an efficient approach to system design by minimizing total annualized costs, which include both initial capital investments and ongoing operational expenses. MILP enables the tool to handle discrete decisions, such as selecting additional PV arrays or generators, as well as continuous decisions like determining their optimal capacities. By focusing on a minimal formulation and foregoing the global optimality guarantees of nonlinear programming (NLP), MILP can be solved more efficiently. However, this efficiency comes with the trade-off of accepting some inaccuracies due to the linearization of nonlinear relationships. Nonetheless, MILP provides a practical balance between solution quality and computational performance, making it well-suited for optimizing energy technologies.

Unit commitment refers to the tool's ability to determine which energy units (such as generators or battery systems) should be activated at any given time to meet energy demands efficiently. By using MILP, the tool ensures that these decisions are made in a way that minimizes operational costs, such as fuel consumption and wear-and-tear on equipment, while adhering to technical constraints such as storage capacity and generation limits.

Data-Driven Results for Strategic Decision-Making

The tool generates detailed results regarding the energy system's performance and financial metrics. Key outputs include installed capacities, operational time series, investment costs, CO₂ emissions, and fuel requirements. These results provide stakeholders with a comprehensive overview of the system's operational efficiency and its long-term economic and environmental impacts. This information supports strategic planning for off-grid energy systems across various scales, from small rural electrification projects to larger industrial applications.