Abstract
Microgrid configurations provide a reliable and sustainable energy supply to off-grid settlements. Various energy sources, including renewable and non-renewables, are currently used to power the microgrids. Scheduling these energy resources and managing the microgrids can be challenging due to various constraints. Data envelopment analysis (DEA) is one of the well-known solution methodologies that can be effectively used for energy management studies. This chapter applies a multi-objective DEA to the energy management of a typical rural, remote micro-grid supplied by a solar plant and diesel generator sets. The methodology utilizes the DEA algorithm to identify microgrid optimal configurations by considering technical, environmental, and economic factors. The Tsumkwe rural region of Namibia is considered for studying the DEA application and analysis. The study evaluated the energy system's performance under varying efficiencies, fuel consumptions, and generator capacities.
TopBackground
It is always challenging to supply electricity to remote areas due to various technical and socio-economic reasons. Several researchers have reported on the challenges associated with rural electrification (Matheus & Musti, 2023; Chiguvare, 2019). Demand in the rural areas can be too low and thus utilities may not be able to invest in the standard power grid infrastructure due to lesser prospects of financial returns (Sastry, 2023). Small scale generating units are typically used to power the microgrids as the overall demand is generally less (Musti, 2020). Typically, solar PV plants and diesel generator sets are popular choices. Solar energy costs have been drastically reducing over the years and diesel generator sets are very simple to manage and are generally reliable. However, solar energy is usually available for 5 or 6 hours only during the day and thus diesel generator sets, and battery banks need to be used for the remaining duration.
On the other hand, users in remote areas are now encouraged through the green energy policies in various parts of the world to use solar water heaters (Sastry, 2023; Musti & Kapali 2021). Such changes in energy usages and the usual load changes result in dynamically varying energy demand profiles (Sastry, 2020). Thus, scheduling of the generation resources can be challenging due to varying operating conditions. Over the years, several authors have studied the problem of generator scheduling problems to meet the varying load conditions. DEA method is a non-parametric method that can be used empirically to determine the best possible solutions to a problem that has different constraints. One of the major steps in DEA methodology is to determine the DMU. Then the relative efficiency of each of the DMUs is determined by calculating the ratio of weighted outputs to weighted inputs, while ensuring the same ratios for all DMUs.
Key Terms in this Chapter
Rural Electrification: The process of providing electrical power to remote or sparsely populated areas that are often located far from centralized power generation facilities. The goal of rural electrification is to improve the quality of life and promote economic development in underserved rural communities by ensuring access to electricity.
Energy Mix: The combination of different energy sources, such as solar, wind, grid power, and fossil fuels, used to meet energy demands efficiently and sustainably.
Sustainable Energy Systems: Energy systems that prioritize the utilization of renewable and eco-friendly energy sources to meet present energy requirements while ensuring the capacity of future generations to fulfill their energy needs. Sustainable energy systems aim to lower emissions of greenhouse gases and mitigate other ecological consequences.
Efficiency Rating: A numerical measure or ratio that evaluates how effectively a particular configuration of a Decision Making Unit (DMU) transforms inputs into desired outputs. Efficiency scores vary between 0 and 1, with higher values signifying superior performance.
Data Envelopment Analysis (DEA): A mathematical technique for assessing the relative efficiency of multiple decision-making units (DMUs) by comparing their input and output performance. The best performing DMUs can be identified and a benchmark for inefficient units is set to improve their performance.
Green Energy: Energy derived from renewable and environmentally friendly sources, such as solar, wind, hydroelectric, and geothermal power. Green energy is often characterized by its reduced carbon footprint and lower environmental impact compared to fossil fuels, making it a key component of sustainable energy systems.