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The efficient uptake of decentralized solar rooftop photovoltaics (PV) is in some cases hindered by ineffective energy and political framework conditions. These may be based on inaccurate and uncertain potential assessments in the early development stage of the solar market. This paper develops a more accurate, cost-effective, and robust potential assessment for emerging and developing economies. Adjusting the module efficiency corresponding to regional and household conditions improves the output accuracy. The rooftop PV market changes are simulated regarding different input changes and policy designs, including changing the Feed-In Tariff (FIT), grid tariff, and technology development. In the case study, the market potential in Vietnam is estimated at 260–280 TWh/a and is clustered into six groups in priority order, in which Hanoi and Ho Chi Minh need the most policy focus. Changing the FIT from 8.83 to 9 Euro cent/kWh and using different regional FITs can activate an additional 16% of the market and lead to a possible 28 million Euro benefit. Increasing the grid tariff to 8.7 cents/kWh could activate the self-consumption model, and the self-sufficient market can be guaranteed in the case of CAPEX and OPEX being lower than 650 Euro/kWp. Future developments of the method should focus on combining this top-down method with detailed bottom-up approaches.

Purpose The connection between urbanization and energy consumption in the context of cross-country and cross-sector analyses is poorly understood, especially in the Association of South East Asian (ASEAN). This paper aims to present the first extensive multi-level analysis of the relationship between urbanization and energy consumption in ASEAN countries from 1995 to 2013. Design/methodology/approach The multi-level (across country and sector) index decomposition method is used to analyze urbanization, energy mix, energy intensity and activity effects on energy demand. Urbanization is measured by two representative factors, name the urban population and the number of non-agriculture workers. Findings Despite the decreasing rate of urbanization, its effect on energy consumption has played the most important role since 2000. Since then, the effect has continued to increase at the national and sectoral levels across the whole region. The strongest urbanization impacts are encountered in the residential sector, followed by transportation and industrial sectors with much weaker effects in the commercial sector. The way in which urbanization impacts energy consumption depends strongly on the income level of the country studied. Practical implications The results provide quantitative relationships between urbanization and energy demand. For example, if the urban population and the non-agriculture workers decreased by 0.1 per cent per year, this would reduce energy demand by 1.4 per cent and 2.6 per cent per year respectively. Originality/value This contribution provides detailed quantitative insights into the relationships between urbanization and energy demand at sectoral, national and international levels, which are invaluable for policymakers in the region.

Purpose With the growing deployment of variable renewable energy sources, such as wind and PV and the increasing interconnection of the power grid, multi-regional energy system models (ESMs) are increasingly challenged by the growth of model complexity. Therefore, the need for developing ESMs, which are realistic but also solvable with acceptable computational resources without losing output accuracy, arises. The purpose of this study is to propose a statistical approach to investigate asynchronous extreme events for different regions and then assess their ability to keep the output accuracy at the level of the full-resolution case. Design/methodology/approach To extract the extreme events from the residual demands, the paper focuses on analyzing the tail of the residual demand distributions by using statistical approaches. The extreme events then are implemented in an ESM to assess the effect of them in protecting the accuracy of the output compared with the full-resolution output. Findings The results show that extreme-high and fluctuation events are the most important events to be included in data input to maintain the flexibility output of the model when reducing the resolution. By including these events into the reduced data input, the output's accuracy reaches the level of 99.1% compared to full resolution case, while reducing the execution time by 20 times. Originality/value Moreover, including extreme-fluctuation along with extreme-high in the reduced data input helps the ESM to avoid misleading investment in conventional and low-efficient generators.

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