With the continuous development of the power system, accurately predicting the power grid energy storage capacity demand is crucial for enhancing the stability and economy of the power system. This paper proposes a prediction method for power grid energy storage capacity demand based on the Long Short-Term Memory (LSTM) network. The LSTM network can effectively handle the long-term dependency problem in time series data and is suitable for the time series prediction of power grid energy storage capacity demand, which is affected by various complex factors. This paper utilizes its unique gating mechanism to process the time series affected by complex factors such as the intermittency of new energy generation and dynamic load changes. By collecting multi-dimensional data of a certain regional power grid over many years, including historical load, new energy generation, meteorology, and holidays, the model is optimized using the Adaptive Moment Estimation (Adam) optimizer and Dropout technology. Experiments show that the LSTM model can effectively improve the prediction accuracy and provide strong support for the planning and construction of power grid energy storage.

The Variable Frequency Transformer (VFT) is a controllable and bidirectional transmission device which integrates the principles of both rotatory transformer and phase shifting transformer, in order to regulate the power flow between two power grids. As most of the research work is focused on operating VFT at constant voltages; the analysis of VFT operating under varying voltage conditions remains unexamined. Keeping this gap in view, the paper presents detailed working of a VFT system used to control the power flow in-between synchronous grids operating under variable voltage conditions. For this, the two independent synchronous networks, under consideration, are connected to the stator and rotor winding of VFT, respectively. The power flow in-between the connected networks is first analysed under constant voltage conditions. The analysis shows that the power flow in-between the two synchronous connected networks, varies linearly with the applied torque and reverses as the direction of torque is reversed. The power flow is then analysed under variable voltage conditions, the obtained results show that reducing the voltage of either of the synchronous networks, increases the power flow towards that network while decreasing the power flow towards the other network. Further, the simulation results obtained using MATLAB/Simulink are validated using the real-time Typhoon HIL emulator.

With the rapid development of renewable energy and electric vehicles, the application of integrated photovoltaic storage and charging power stations in smart grids is becoming increasingly widespread. Improving the supply and demand balance capacity and response speed of the power system has become a key issue that urgently needs to be solved at present. Based on the theory of power supply and demand elasticity, where the elasticity coefficient ranges from 0.2 to 1.8 and the time scale is 15 minutes, this paper proposes a demand response strategy for integrated photovoltaic storage and charging power stations. Firstly, by analyzing the characteristics of supply and demand elasticity changes, a dynamic model describing the interaction between photovoltaic power generation, energy storage systems and electric vehicle charging loads was constructed; Then, based on different assumptions of supply and demand elasticity, an optimized demand response strategy aimed at improving the economic benefits of the system and the stability of power supply and demand balance is proposed. The effectiveness of the demand response strategy under different supply and demand elasticity scenarios was verified through simulation analysis. The results show that reasonable adjustment of supply and demand elasticity can significantly improve the dispatching flexibility and response capacity of the power system, and promote the efficient utilization of green energy at the same time.

Under the driving force of energy green transition and the “dual carbon” goals, the installed capacity of renewable energy in the new power system has grown rapidly. However, its intermittent and volatile characteristics have significantly increased the peak-shaving pressure on the power system and posed new challenges to the market-oriented accommodation of renewable energy. In response, the National Development and Reform Commission and the National Energy Administration issued the “Guiding Opinions on Promoting the Integrated Development of Power Source-Grid-Load-Storage and Multi-energy Complementarity”, advocating the optimization of peak-shaving capacity through multi-energy complementarity to improve the overall flexibility and adaptability of the system. Under the current framework of peak-shaving service rules, this study constructs a medium- and long-term joint peak-shaving market clearing model involving multi-energy complementary systems, comprehensively considering the economic operation costs of both thermal power and renewable energy. Using actual operation data from a certain region in Northeast China, simulation analysis is conducted to evaluate the role of the multi-energy complementary system in enhancing the market-oriented accommodation and market competitiveness of renewable energy. The results show that the wind-PV-thermal multi-energy complementary system achieved a total profit of 44.975369 million yuan in the medium- and long-term and peak-shaving two-stage market, significantly improving market competitiveness and economic benefits. At the same time, compared with external renewable energy, it reduced the market-oriented accommodation cost of renewable energy by 0.9994 million yuan. It significantly enhanced the energy supply security and operation reliability of the new power system, and has important theoretical and practical value for the large-scale accommodation of renewable energy.

The execution of the energy transition and “dual carbon” objectives is progressively enhancing the penetration rate of renewable energy in the new power system. This paper examines the bidding strategy for virtual power plants (VPPs) incorporating renewable energy within the rolling trading framework of the mid-to-long-term centralised electricity market, in response to the challenges posed by power generation uncertainty and market-driven consumption due to large-scale renewable energy integration. An outer clearing model is developed to enable VPP participation, with the objective of maximising societal welfare, hence determining transaction volumes and clearing prices. Subsequently, taking into account the predicting inaccuracies of wind and solar energy, an internal optimisation model is formulated with the aim of maximising the income of the Virtual Power Plant (VPP), therefore measuring the incremental revenue. A dual-layer optimisation model appropriate for Virtual Power Plant (VPP) participation is developed and subsequently utilised to analyse the optimised bidding strategies for VPPs in the medium to long-term monthly centralised market.

With the rapid growth of my country’s new energy installed capacity, the phenomenon of water abandonment, wind abandonment, and light abandonment in the power system has gradually intensified. Especially in areas rich in hydropower resources, the problem of water abandonment is particularly prominent. In order to effectively solve the problem of abandoned water and improve the utilization rate of hydropower, this paper studies the annual power balance and short-term optimal scheduling problem considering abandoned water. Firstly, this paper analyzes the current situation of water abandonment in power system at home and abroad and its impact on power grid operation, combined with the actual operation data of a hydropower station in China, constructs an annual power balance model, and adopts multi-objective optimization method to coordinate and optimize the power balance and water abandonment. The research shows that considering the abandoned hydropower, the waste of hydropower resources can be effectively reduced by reasonable power balance, and the regulation ability and power supply reliability of the power grid can be improved. This paper also combines the optimization algorithm of short-term dispatching to simulate and analyze the dispatching strategy under different load demands and water conditions. The results show that the optimized dispatching strategy can reduce the amount.

The physical and chemical characteristics of Li-ion batteries’ (LIBs’) performance are greatly influenced by the crystal structure system. Accordingly, identifying and classifying the crystal structure of LIBs is critical for improving their performance and safety. This study classifies LIBs into three main classes of crystal systems, monoclinic, orthorhombic, and triclinic, utilizing machine learning techniques. The performance of different models, including ensemble and non-ensemble models, was checked on this challenging classification task via key evaluation indicators. Among the different models, the standard Random Forest (RF) model provided a very strong performance; after optimization, this model was further improved to outperform all the other models with the best accuracy, precision, and generalization for both the training and test datasets. Also, the important axis of this work was the role of features in driving the classification performance. Enriched by the intrinsic and derived features, representative of the structural and physical properties of battery materials, models gained an enhanced capability to understand and distinguish crystal systems. All these features became critical for the improvement of model accuracy and interpretability. Sensitivity analysis and SHAP evaluation revealed the fact that Band Gap, Formation Energy, Volume, Density, and Volume to site features have high importance to subtle differences among the three crystal classes. These findings provide leverage in advancing the research into batteries and set a basis for future applications within the classification tasks of material science.

With transformation of global energy structure and the demand for low-carbon development, the importance of energy storage systems in power system has become increasingly prominent. This study aims to analyze the contribution of energy storage systems to the integrated low-carbon operation of power systems. In the background, traditional power systems face challenges such as volatility in renewable energy access and uncertainty in load demand. As a flexible adjustment resource, energy storage systems can stabilize fluctuations and improve system efficiency. The research constructs a power system simulation model with energy storage, sets various operating scenarios, and compares and analyzes the system’s operating status before and after the energy storage system is connected. Experimental results show that the energy storage system has significantly improved the consumption capacity of renewable energy, with the utilization rates of wind power and photovoltaic increasing by 15% and 20%, respectively. At the same time, through peak shaving and valley filling effect of energy storage system, the peak-valley difference rate of the system load is reduced by 10%, effectively alleviating the pressure on power grid. In terms of low-carbon operation, the optimal dispatch of energy storage system has reduced the system’s carbon emissions by 12%, providing strong support for low-carbon transformation of power system. In addition, economic analysis shows that although the introduction of an energy storage system increases initial investment, the long-term operating cost is reduced by 8% by improving the system’s operating efficiency and reducing the loss of wind and solar abandonment.

Nepal’s educational system has evolved from the ancient education of the Vedic and Buddhist system to the modern education system of global standard. In its structured educational system, basic (1–8th grade), lower secondary (9–10th grade), higher secondary (11–12th grade), and higher education (colleges and universities), Nepal has introduced a wide range of modern educational measures and pedagogical approaches such as a letter grading system, semester system in higher education, student-centered learning, experiential learning, project-based learning, e-learning, and hands-on education, among others. Nepal has also focused on building educational infrastructures including school buildings, laboratories, e-learning facilities, and training teachers for practical education. However, Nepal is facing the challenges of delivering practical education, developing skilled manpower, employment generation, checking massive outmigration of students for higher education abroad, poverty alleviation, climate resiliency, and ecological and environmental sustainability. There is a serious mismatch between the Nepalese education system and the country’s geography, history, culture, tradition, ecology, environment, economic development, and other facets of Nepalese lives. Therefore, there is an urgent need to restructure the Nepalese education system to align with the country’s natural, cultural, and human resources. For this, a theoretically grounded educational transformation framework is necessary. The Asta-Ja Framework, meaning eight “Ja” in the Nepali language, Jal (water), Jamin (land), Jungle (forest), Jadibuti (medicinal and aromatic plants), Janashakti (human resource), Janawar (animals), Jarajuri (crop plants), and Jalabayu (climate) serves as a robust framework for aligning natural and human resources to educational system for practical education in Nepal. The Asta-Ja Framework represents the four sub-systems of the Planet Earth, hydrosphere (Jal), lithosphere (Jamin), biosphere (Jungle, Jadibuti, Janashakti, Janawar, and Jarajuri), and atmosphere (Jalabayu). Five themes of the Asta-Ja Framework, the theory and principles, Asta-Ja assessment, security challenges, industries and businesses, and governance, serve as critical elements in developing a practical, problem-solving, and holistic educational system for sustainable economic development in Nepal. This article demonstrates how the five themes of the Asta-Ja Framework can be well integrated into higher education and high-school curricula and formal and informal training programs in Nepal. By integrating Asta-Ja in education and training programs, Nepal can transform its educational system into a practical, problem-solving, innovative, and community-oriented educational system enhancing ecological and environmental sustainability, accelerated economic growth, employment generation, and fast-paced socio-economic transformation in Nepal.

In the past ten years, electricity consumption in the Markazi province of Iran has risen discernibly, escalating from 40,568.7 MWh to 45,018 MWh, an annual growth rate of nearly 11%. In response to this surge and with a primary focus on mitigating air pollution and safeguarding the environment, there has been a notable emphasis on adopting clean energy sources. Now the question is, which one of our options is appropriate? Multi-Criteria Decision Making (MCDM) techniques are widely utilized in evaluating energy technologies. These methods are valuable as they consider both positive and negative criteria, mirroring real-world scenarios. Additionally, each method in this approach has its distinct advantages and limitations. In this study, the ELECTRE method was employed alongside four overarching aspects, encompassing ten criteria, to select the optimal option. The Shannon entropy method was also used to evaluate the importance of each criterion for the ranking of Renewable Energy Sources (RES). The obtained weights revealed that the environmental aspect exerts the most significant influence on determining the optimal option. The conclusive findings of the research indicate that solar photovoltaics emerged as the optimal choice, with wind energy closely following in the ranking.