Despite ongoing efforts to expand rural electrification in developing countries, many communities still lack reliable and affordable electricity. Low population densities, limited economic activity, and modest energy demand make grid extension costly and impractical in remote areas. As a result, renewable energy microgrids are increasingly viewed as viable alternatives for supporting local development. While prior research highlights their technical feasibility, limited attention has been given to how these systems operate as integrated socio-technical interventions that connect energy access with agricultural productivity and broader community outcomes. In South Africa, persistent challenges such as load shedding, unstable grid supply, and limited access to modern energy services continue to disrupt farming activities and constrain education and healthcare delivery. Although renewable energy initiatives have been introduced, evidence of their long-term performance, institutional sustainability, and contribution to livelihoods remains fragmented. In particular, there is limited empirical understanding of how financial, technical, and institutional factors interact to influence microgrid success. This paper addresses this gap by examining the design and implementation of a renewable energy microgrid at the Masia Development Centre in Limpopo Province using a systems thinking approach. It analyses the interactions between technical performance, community participation, agricultural needs, and environmental conditions. By moving beyond purely technical assessments, the study provides context-specific insights into how microgrids can be designed and governed to enhance sustainability, resilience, and rural development outcomes.

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A high penetration of wind and solar generation within the generation mix will result in an increased supply risk due to the random variations inherent in renewable generation. This document proposes a new methodology for calculating system marginal costs as part of generation fleet expansion planning that accounts for the impact of these random variations. This new methodology incorporates an additional cost into the marginal cost calculation, reflecting the risk of supply deficits during periods of scarcity. This allows for the inclusion in the planning process of generation units that contribute to the electricity system firm capacity, thereby enhancing the security of supply to meet demand. As an example, this document analyzes the economic feasibility of a generation project that utilizes hydrogen (H₂) as fuel. The project combines H₂ production for industrial use, powered by renewable generation (the GH₂ project), with the provision of firm capacity supplied by a gas turbine (TG) generator fueled by H₂ (the hybrid project). The economic feasibility of this hybrid project stems from two key factors: i) intra-annual energy arbitrage—purchasing energy from the electricity market during periods of the year characterized by low marginal costs, and selling energy during periods of scarcity with high marginal costs; and ii) maximizing the electrolyzer's capacity factor by purchasing energy in the market during hours when the electrolyzer possesses residual capacity (i.e., capacity not currently utilized by the GH₂ project). The project incorporates H₂ storage within salt domes.

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This study examined how carbon pricing and compliance costs affect corporate trading behaviour in carbon markets. It addressed a gap in understanding how market-oriented climate policies shape corporate conduct at the micro level. The study used a two-stage quantitative methodology and analysed 5,000 firm-level records from four sectors (cement, energy, manufacturing, steel) and multiple fuel types. Multiple linear regression identified determinants of carbon prices and compliance costs. Logistic regression assessed whether these factors could predict firms’ trading decisions—whether to purchase or sell allowances. Sensitivity and scenario analyses tested model robustness under theoretical conditions: high emissions, policy tightening, demand shock, and price escalation. Results showed very weak links between operational variables and market outcomes. The carbon price regression had an R² of 0.0019, and the compliance cost regression had an R² of 0.0007, indicating that firm-level factors explain less than 0.2% of price variation. This is not a statistical failure—it’s the key finding and the novel contribution of this study: treating near-zero explanatory power as substantive evidence that carbon pricing and trading are driven mainly by external policy frameworks, regulatory uncertainty, and strategic anticipation—not current emissions or compliance costs. The logistic regression’s accuracy was 0.50 and ROC-AUC 0.48, consistent with random classification, further supporting the conclusion. The results show that effective carbon markets need more than price signals. Clear regulatory rules, stable allowance mechanisms, and supportive institutional infrastructure are crucial for aligning firm incentives with real emission-reduction objectives.

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