The Association of Southeast Asian Nations (ASEAN) has experienced rapid social and economic development in the past decades, while energy shortage, environmental pollution, and climate change are the factors that prevent a sustainable development process. Deployment of solar photovoltaic (PV) power is one of the effective alternatives to overcome the above barriers and assist ASEAN to achieve the aspirational target of 23% renewable energy (RE) in the total primary energy supply (TPES). In this study, SWOT analysis is adopted to analyze the internal strengths and weaknesses and the external threats and opportunities tightly related to the development of solar PV power in ASEAN countries. Through the SWOT analysis, great potential for the development of solar PV power in ASEAN is found. As long as appropriate policies are implemented and proper actions are taken, huge space for deployment of solar PV power can be expected. Based on the SWOT analysis, countermeasures that emphasize further deployment of solar PV power in ASEAN countries are put forward. The tactics include arousing people’s awareness of a sustainable development process, government issue coherence and stable incentive policies, fostering a solar PV industry chain and master key technology, and seek opportunities via an international cooperation.
Accelerating the development of new energy is an inevitable trend of the energy low-carbon transition. Information technology means and new energy and new energy business management are combined by most energy enterprises, so as to further improve the coordination of source, network and storage load in the development of new energy, solve the problem of new energy consumption, and improve the management efficiency. This study takes the “new energy cloud” platform of State Grid Corporation of China as the research object. Firstly, the basic architecture and operation mode of the cloud platform is analyzed; secondly, according to the characteristics of the operation mode, the evaluation index system of the “new energy cloud” operation mode is constructed from four dimensions of business, capital, learning and growth, and data. At the same time, the actual data of nine provinces that have deployed the “new energy cloud” platform are selected, and the weight of the evaluation index is determined by using the CRITIC—AHP method. This study can provide a model reference for related energy enterprises to build new energy business management platforms, and provide a practical reference for the evaluation of new energy business operation mode.
In order to promote the transformation of energy consumption structure to low carbon, in recent years, China has vigorously promoted the development of new energy and increased the proportion of new energy installed capacity in the power sector. However, due to the lack of benefits evaluation of the whole process of new energy grid connection at present, the existing project experience cannot provide reference and guidance for subsequent project construction to improve the benefits of new energy grid connection. Based on the existing operation data of the “New Energy Cloud” platform of State Grid Corporation of China, this study constructed the evaluation index system for the whole process of new energy grid-connection, and evaluated the benefits and quality of the project from the three stages before, during and after the grid-connection. The research results will be applied to the development of the evaluation module of the “new energy cloud” platform of State Grid Corporation of China, and can also provide a reference for the evaluation of the construction of new energy projects in the energy industry.
SummaryTraditional power grid investment forecasting models often ignore the cyclical characteristics of historical investment data, leading to one‐sided investment allocation results and insufficient model generalization ability. This article proposed a power network investment forecasting method based on particle swarm optimization‐gate recurrent unit (PSO‐GRU) neural network. First, the temporal attention mechanism is introduced into the traditional GRU network, which improves the ability of the network to extract temporal features. Then, in order to avoid the adverse effects of unreasonable parameter configuration on model training, an optimized particle swarm optimization algorithm was proposed to optimize the parameter set of GRU and improve the training accuracy of the model. The data provided by an electric power company in China is used for experimental analysis, and the predicted results are compared with other electric power investment models. The RMSE of the PSO‐GRU model proposed in this article is 0.1223, which is superior to other algorithms and has certain effectiveness.
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