Environmental Impact and Levelised Cost of Energy Analysis of Solar Photovoltaic Systems in Selected Asia Pacific Region: A Cradle-to-Grave Approach

The role of trade liberalization in promoting regional integration and sustainability: The case of regional comprehensive economic partnership

In globalization’s era, the sustainability of a region is inseparable from the in-depth and close economic and trade cooperation of intra-regional countries to achieve complementary advantages, intra-regional and extra-regional positive economic cycles, and stable and balanced benefits distribution. For Asia-Pacific countries, the lack of deep cooperation in the past has affected their sustainability, but this can be made up for by the RCEP agreement aimed at achieving intra-regional trade liberalization. We adopt the Global Trade Analysis Project (GTAP) simulation analysis method to quantitatively analyze the impact of changes in macroeconomic and international trade indicators of several intra-regional countries after implementing the RCEP tariff reduction and exemption on the RCEP. Simulation results and comparative analysis based on international relations prove that despite the interference of trade benefits conflicts and international political factors, the RCEP can still exist in long term, and effectively promote regional economic integration and sustainability in the Asia-Pacific region. It is also a development opportunity for intra-regional countries and can also be used in the context of globalization providing references for integration and sustainability in other regions.

Evaluation of Transfer Learning and Fine-Tuning to Nowcast Energy Generation of Photovoltaic Systems in Different Climates

New trends of Machine learning models are able to nowcast power generation overtaking the formulation-based standards. In this work, the capabilities of deep learning to predict energy generation over three different areas and deployments in the world are discussed. To this end, transfer learning from deep learning models to nowcast output power generation in photovoltaic systems is analyzed. First, data from three photovoltaic systems in different regions of Spain, Italy and India are unified under a common segmentation stage. Next, pretrained and non-pretrained models are evaluated in the same and different regions to analyze the transfer of knowledge between different deployments and areas. The use of pretrained models provides encouraging results which can be optimized with rearward learning of local data, providing more accurate models.

Selection of Renewable Energy in Rural Area Via Life Cycle Assessment-Analytical Hierarchy Process (LCA-AHP): A Case Study of Tatau, Sarawak

With a growing global population and energy demand, there is increasing concern about the world’s reliance on fossil fuels, which have a negative impact on the climate, necessitating the immediate transition to a cleaner energy resource. This effort can be initiated in the rural areas of developing countries for a sustainable, efficient and affordable energy source. This study evaluated four types of renewable energy (solar, wind, biomass, and mini-hydro energy) using the integrated Life Cycle Assessment (LCA) and Analytical Hierarchy Process (AHP) approaches to select the best renewable energy source in Tatau, Sarawak. The criteria under consideration in this study included the environment, engineering and economics. The LCA was used to assess the environmental impact of renewable energies from gate-to-grave boundaries based on 50 MJ/day of electricity generation. The AHP results showed that solar energy received the highest score of 0.299 in terms of the evaluated criteria, followed by mini-hydro, biomass and wind energy, which received scores of 0.271, 0.230 and 0.200, respectively. These findings can be used to develop a systematic procedure for determining the best form of renewable energy for rural areas. This approach could be vital for the authorities that are responsible for breaking down multi-perspective criteria for future decision making in the transition into renewable energy.

Research on the Optimal Operation of a Novel Renewable Multi-Energy Complementary System in Rural Areas

Sustainable development is an inevitable choice for the development of human society, and energy is closely related to sustainable development. Improving energy structure, increasing energy efficiency, and vigorously developing renewable energy are of great significance to the sustainable development of rural areas. Moreover, the establishment of a distributed multi-energy complementary system (MECS) using abundant renewable energy such as wind, solar, and biomass energy is an effective way to solve the rapid growth of rural power demand, weak rural power grids, and rural environmental pollution. This paper proposes a new type of Wind–Solar–Biomass–Storage MECS composed of wind power generation (WPG), photovoltaic power generation (PVG), biogas power generation (BPG) and energy storage system (ESS) and establishes a MECS optimization operation model with the goal of maximizing daily operating economic benefits, considering the characteristics of each power generation system and power demand characteristics. By using the multi-population genetic algorithm (MPGA), the simulation experiments of the MECS operation under four typical weather scenarios are carried out. The results show that the MECS can operate stably in different scenarios and achieve the goal of maximizing economic benefits, which verifies the feasibility of the MECS model. In addition, the simulation results are compared with the standard genetic algorithm (SGA), which shows the effectiveness of the optimization method. This paper takes Chinese rural areas as an example for research. The proposed MECS and optimal operation model are also applicable to developing countries with a high proportion of the rural population.