Artificial Intelligence in rural off-grid Polygeneration Systems: A Case Study with RVE.Sol focusing on Electricity Supply & Demand Balancing

Abstract

Growing data generation and increasing computational power accelerate the advance of machine learning (ML) as a subsection of artificial intelligence in various sectors, while in Sub-Saharan Africa (SSA) electrification cannot keep up with the pace of population growth. Hence, this study aims to determine how ML can support rural polygeneration minigrids and thus assisting the electrification efforts in SSA in cooperation with the company RVE.Sol. This study focuses on electricity supply and demand balancing, but also discusses other application areas and non-rural context. Within the (micro)grid and energy area, main application areas studied in academia are identified as power and load forecasting, scheduling and sizing. Building on existing works, this thesis proposes a concept aimed at improving the supply and demand mismatch, while discussing further ML applications and generating knowledge transfer to general, non-rural polygeneration systems. The load and generation mismatch and the impact of possible demand response (DR) implementation are quantified, followed by an expert questionnaire to back up machine learning knowledge in the discussed context. Moreover, GHI and PV power predictions are performed to obtain indications about promising features and algorithms. Finally, considering the previous steps a concept for ML supported generation and load matching by DR is proposed. Results indicate that DR could improve the significant mismatch of load and power generation in RVE.Sol’s grids. According to the proposed model, a 30% acceptance rate to the DR scheme results in 56% operational expenditure (OPEX) and approximately 60% CO2 and particulate matter (PM) emissions decline. A sensitivity analysis indicates that acceptance is a critical success factor for a DR scheme. Hence, a DR concept is proposed where load and PV power are forecasted by ML to set 4 different tariff periods 24 h in advance to improve acceptance. The tariff prices could possibly be derived by reinforcement learning. Preliminary PV power forecasting indicates that a random forest algorithm for regression with weather and time related input features is promising due to high accuracy and short training time compared to other algorithms including neural networks. While the proposed scheme has advantages within all three pillars of sustainability, the lack of data as well as small system and load sizes/low complexities remain as two major impediments for ML in rural polygeneration systems. Thus, ML likely bares better applicability in the urban and developed context, where data availability is higher and loads are more diverse.