Fostering Energy Resilience in the Rural Thai Power System—A Case Study in Nakhon Phanom

Abstract

With rising electricity demand, heavy reliance on imports, and recent economic downturns due to the negative impact of the COVID-19 pandemic, supply chain bottlenecks, and the Russian invasion of Ukraine, Thailand is suffering severely from energy resilience risks. The government has therefore set a goal of decentralizing energy production through small-scale distributed renewable energy systems. To support their design and the planning process, we simulate multiple scenarios with wind turbines, photovoltaic systems, and battery storage for a model community in rural Nakhon Phanom, Thailand. Using the software NESSI4D, we evaluate and discuss their impact on energy resilience by considering environmental sustainability, economic attractiveness, and independence from the central power grid. To fill the gap of missing data on energy demand, we synthesize high-resolution load profiles from the Thailand Vietnam Socio-Economic Panel. We conclude that distributed photovoltaic systems with additional battery storage are only suitable to promote energy resilience if the government provides appropriate financial incentives. Considering temporal variations and local conditions, as well as a participatory decision-making process, are crucial for the long-term success of energy projects. Our advice to decision-makers is to design policies and regulatory support that are aligned with the preferences and needs of target communities.

Accessible decision support for sustainable energy systems in developing countries

“…In summary, we met our requirements of expanding the model of the software towards the unique energy system circumstances in developing countries, including new energy-producing, -consuming, and -storing components, and enabling the simulation of neighborhoods. We validated and cyclically improved our software through an extensive applicability check and various sensitivity analyses in rural Vietnam and Thailand, see, e.g., Hart and Breitner (2022).…”