Search Results (37)

As China’s power market reforms deepen, the scale of market operations and the number of participants have reached new highs, introducing increasingly complex threats and heightened risk scenarios. Traditional risk early warning systems for electricity sales companies are heavily influenced by subjective factors, incomplete data, and poor real-time performance, which cannot meet the requirements of sustainable development. To achieve efficient, full-chain, and sustainable risk control, this paper proposes a data-driven risk warning method for electricity sales companies, encompassing the entire sales process. Firstly, based on data correlations across the electricity sales process, appropriate data sources for risk warnings are identified. Key elements are then extracted using Principal Component Analysis (PCA), while historical business data is adaptively clustered, with risk warning levels classified using the Adaptive Sparrow Optimization Density Peak Clustering Algorithm (DPC-SSA). Lastly, dynamic risk warnings are generated through the stacking identification model. The effectiveness and practicality of the proposed method are validated through an analysis using real data from a provincial power trading management platform. Full article

Jiangxi Province relies heavily on thermal power and energy imports but is rich in natural resources, particularly lithium. This study explores strategies for advancing wind–solar–storage systems to help Jiangxi transition to a low-carbon energy structure. Using LEAP and NEMO models, four scenarios are examined: the reference (REF) scenario, new energy storage policy scenario (NPS), high wind–solar power capacity scenario (HWSS), and comprehensive optimization scenario (COS). Key findings show that the COS and HWSS offer significant advantages over the REF scenario and NPS in terms of energy storage efficiency, carbon emission reduction, and cost savings. By 2035, under the COS, wind and solar power share rises to 48%, reducing coal use by 5.9 million tons and electricity imports by 40.0 TWh compared to the REF scenario. Battery storage utilization increases by 1499.8 GWh, nearly four times that of the REF scenario. This scenario also cuts CO₂ emissions by 16.8% and lowers cumulative social costs by 5.19 billion USD, delivering optimal economic efficiency. The study also identifies challenges such as high investment costs, underdeveloped business models, and low resource utilization, and recommends setting higher targets, implementing flexible solutions, promoting market reforms, and increasing R&D efforts, among other measures. Full article

Building the regional electricity spot market (RESM) in a representative area is an important move to promote the electricity market reform and new power system construction in China. In this paper, the RESM operation model and optimization method are established, which take into account the special power grid operation mechanism and market construction achievements in the provincial electricity spot market. Firstly, the influencing factors, core elements, market structure, and operation model of RESM construction in China are analyzed. Secondly, a bi-level optimization model of the RESM is established. The lower layer is the pre-clearing model of the provincial electricity spot market, which is used to optimize the unit combination strategy, considering unit operation constraints and power grid security constraints in the province. The upper layer is the optimization clearing model of the RESM, which is used to optimize the clearing price and adjust the unit operation strategy and inter-provincial electricity trading strategy, considering the security constraints of regional power grid tie lines. Finally, the RESM composed of power grids in the Yangtze River Delta region of China is simulated as an example. The analysis focuses on the operational state of the power grid after the operation of the RESM, considering its safety benefits, economic benefits, and environmental benefits. The optimization of the RESM can effectively solve the serious regional power grid congestion problem, which is achieved through the superposition and printing of pre-clearing results in various provinces, and the average daily cost of electricity purchasing in the region has been reduced by about CNY 11 million, while the annual cost has been reduced by about CNY 4 billion. In addition, the total carbon emissions have been reduced by 11,000 tons per day and 0.18 kg per kilowatt hour on average, and scenes without power abandonment account for more than 95% of the total scenes. Full article

Energy is often described as the lifeblood of a nation’s economy, and the world energy trilemma calls for collaboration and innovative solutions at the national level. This is where Education for Sustainable Development (ESD) plays a crucial role, helping integrate the achievement of the United Nations Sustainable Development Goals (SDGs) while addressing the challenges posed by the energy trilemma. Europe’s strong commitment to transitioning to sustainable energy is evident in its response to geopolitical changes and climate targets. Notably, the Baltic States have taken decisive action in response to the war in Ukraine, choosing to completely halt electricity imports from Russia and Belarus. This shift was supported by increased energy imports via interconnectors from Finland, Sweden, and Poland, with electricity imports rising to 13,053 GWh—an increase of 2.6% in 2023 compared to the previous year. Latvia, which holds the highest green energy potential in the Baltic Sea region, has nevertheless lagged behind its Baltic counterparts in terms of implementation. In 2021, Latvia ranked third among European Union (EU) countries for renewable energy share in final energy consumption, with 42.1%, significantly higher than the EU average of 21.8%. However, further progress is needed to meet Latvia’s 2030 target of 14% renewable energy use in transport. The Baltic States aim to produce 98–100% of their electricity from renewable sources by 2050. The Baltic States should be regarded as a unified energy system, with a coordinated strategy for achieving sustainable energy development through collaboration and joint planning. This analysis highlights the complexities of managing energy markets amidst global and regional challenges, emphasizing the importance of well-designed public interventions to secure long-term benefits. The study concludes with a call for enhanced interagency cooperation to reform ESD and create a new interdisciplinary sector dedicated to “Sustainable Development”. Full article

A new type of power system with a high proportion of renewable energy sources (RES) penetration has become a global development trend. Meanwhile, the marketization reforms of the electricity market pose challenges to traditional energy. A multi-energy model including a wind turbine (WT), photovoltaic (PV) energy, energy storage (ES), and a thermal power system is proposed in this paper, participating in a joint market mechanism for energy and frequency regulation ancillary services. Unlike existing joint markets, this paper considers the market coupling clearing of various energy sources and the uncertainty of RES generation. Specially, a mechanism for the participation of storage and thermal power units in the frequency regulation ancillary service market is designed. Finally, a practical 118-node case study is provided to validate the impact of renewable generation uncertainty on the participation of multi-energy coupled systems in joint and single energy markets. Compared to the single electricity energy market, the simulation results show that the model can reduce the impact of RES uncertainty on ES generation and increase the cleared electricity quantity of thermal power units by 16%. Moreover, the model also increases the market revenue of thermal power units and storage by 30% and 44%, respectively. Full article

As China progresses with its electricity market reforms in pursuit of “carbon peak and carbon neutrality” objectives, the increasing integration of renewable energy sources introduces new risks and uncertainties, necessitating the development of an efficient electricity financial market. This paper outlines the fundamental principles of electricity financial derivatives, assesses their applicability to the Chinese market through an analysis of international experiences from the United States, Nordic countries, and Australia, and highlights critical issues for the construction of a robust market framework. It offers strategic recommendations regarding the structural and developmental aspects of China’s electricity financial market and proposes derivative instruments tailored to China’s market to improve liquidity and risk management mechanisms, thereby facilitating the renewable energy transition. The study demonstrates that these derivatives are instrumental in mitigating price volatility, managing transmission congestion, and supporting the shift to renewable energy. This provides a pragmatic approach for the reform and advancement of China’s electricity financial market, aligning with global strategies and addressing the unique challenges of China’s energy transition. Full article

This study investigates the mechanisms by which service-oriented transformation affects employee satisfaction with performance evaluation systems within the context of China’s electricity market reform. Using CGN New Energy’s Guangxi Branch as a case study and applying the grounded theory method, the research systematically analyzes employees’ perceptions of fairness, transparency, and career development during the transition from a product-oriented to a service-oriented model, based on in-depth interviews and surveys. The findings reveal that while servitization enhances employee skill development and career satisfaction, it also introduces challenges, such as increased work pressure and the demand for more transparent performance evaluation systems. This study provides valuable insights into optimizing management practices and performance evaluation systems in renewable energy companies, contributing both theoretically and practically to the literature on organizational transformation in the energy sector. These conclusions are not only significant for Chinese enterprises but also offer important reference points for global energy companies undergoing similar transitions. Full article

Renewable energy, such as wind power and photovoltaic power, has uncertain and intermittent characteristics and zero marginal cost characteristics. The traditional power market mechanism is difficult to adapt to the new power system with a high proportion of renewable energy, and the original market system needs to be reformed. This paper discusses the application of a VCG auction mechanism in the electricity market, proposes a two-stage VCG market-clearing model based on the VCG mechanism, including the day-ahead market and the real-time market, and discusses the nature of the VCG mechanism. In order to address the discrepancy between the actual output of stochastic generator sets in the real-time market and their pre-scheduled output in the day-ahead market due to prediction deviations, a method for calculating punitive costs is proposed. A reallocation method based on market entities’ contributing factors to budget imbalance is proposed to address the issue of budget imbalance under the VCG mechanism, in order to achieve revenue and expenditure balance. Through an example, the incentive compatibility characteristics of the VCG mechanism are verified, the problems of the locational marginal pricing (LMP) mechanism in the stochastic electricity market with a high proportion of renewable energy are analyzed, the electricity prices of the LMP mechanism and the VCG mechanism under different renewable energy proportions are compared, and the redistribution of the budget imbalance of the VCG mechanism is analyzed. Full article

Since the 1990s, market liberalization of the electricity industry has advanced all around the world. To survive in the drastically changing business environment, incumbent electric utility companies have conducted operational reforms, including Mergers and Acquisitions (M&As), to enhance and/or complement existing business capabilities. The purpose of this study was to measure the operational efficiencies of 31 of the world’s largest electric utility companies using data from 2010 to 2020 and examine regional differences in and the impacts of M&As on the efficiencies. For this purpose, we applied a new type of Data Envelopment Analysis (DEA) and Tobit model regression. We provide findings from the empirical analyses and discuss the business implications of M&As for electric utility companies. The operational efficiency measures were different among regions, but did not show statistically significant changes over the study period from 2010 to 2020. Furthermore, the results of regression analyses indicate that the increasing number of M&A buying transactions and M&A total transactions has a negative marginal impact on the operational efficiency or leads to a lower operational efficiency for utility companies. Since electricity utility companies have not received gains in operational efficiency from increasing the number of M&A transactions, they need to be more cautious about whether M&A transactions can provide value to the operation and technology management. Full article

Electric vehicles have gained significant popularity in the market, with sales increasing yearly. The introduction of new policies and reforms aimed at promoting environmental sustainability, coupled with the release of more advanced electric vehicles with higher driving ranges and technical specifications, has encouraged more people to consider switching to electric vehicles. However, there is still a lack of understanding of public perception and the factors influencing the decision to switch to electric vehicles, especially among people from different demographic groups. In this study, we leverage machine learning techniques to analyze public opinion about electric vehicles across different demographic groups on two online social networks (OSNs), namely Reddit and Twitter. Our analyses provide valuable insights into how users on these platforms perceive electric vehicles and the factors that influence their perception. This information can be used to inform market strategies and future policies aimed at promoting the adoption of electric vehicles. Full article

The European electricity market model continues to evolve in the face of new challenges. This systematic literature review aims to assess the status of research and discussion on the current model and its market mechanisms. In addition, it aims to ascertain the kinds of modelling tools that have been used to model market mechanisms or formulate proposals for changes to current practice. The results show that the challenges of individual market mechanisms have been identified quite extensively in the research sample. However, the number of papers identified for inclusion in the systematic literature review was quite small, from which it can be concluded that there are surprisingly few publications focusing on this particular topic. Nevertheless, the trend indicates a probable increase in research in the subject area in the future. The general standpoint among researchers seems to be that the goals set by the EU are, as it were, a law of nature that cannot be deviated from. Consequently, new radical change proposals or comprehensive reforms were not encountered in the study. In addition, it was found that optimization was the most commonly used modelling method in the papers included in the literature review. Full article

With the deepening reform of the power system, power sales companies need to adopt new power sales strategies to provide customers with better economic marketing solutions. Customer-side configuration of an energy storage system (ESS) can participate in power-related policies to reduce the comprehensive cost of electricity for commercial and industrial customers and improve customer revenue. For power sales companies, this can also attract new customers, expand sales and quickly capture the market. However, most of the ESS evaluation models studied so far are based on historical data configuration of typical daily storage capacity and charging and discharging scheduling instructions. In addition, most models do not adequately consider the performance characteristics of the ESS and cannot accurately assess the economics of the energy storage model. This study proposes an intelligent power sales strategy based on load forecasting with the participation of optimal allocation of ESS. Based on long short-term memory (LSTM) artificial neural network for predictive analysis of customer load, we evaluate the economics of adding energy storage to customers. Based on the premise of the two-part tariff, the ESS evaluation model is constructed with the objective of minimizing the annual comprehensive cost to the user by considering the energy tariff and the savings benefits of the basic tariff, assessing the annualized cost of ESS over its entire life cycle, and the impact of battery capacity decay on economics. The particle swarm optimization (PSO) algorithm is introduced to solve the model. By simulating the arithmetic example for real customers, their integrated electricity costs are significantly reduced. Moreover, this smart power sales strategy can provide different sales strategies according to the expected payback period of customers. This smart sales strategy can output more accurate declared maximum demand values than other traditional sales strategies, providing a more economical solution for customers. Full article

With the continuous promotion of China’s electricity market reform, the introduction of competition in the power generation market provides a new research direction for the generation expansion planning (GEP) problem, which is of great significance in the promotion of the optimization of the power energy structure. In the context of marketization, the electricity price expectation during the planning period is a key factor of GEP for independent power generation groups. There is some literature showing that the electricity price expectation in the planning period can be estimated according to certain laws of market supply and demand, while it seems to us that a future Pay as Bid (PAB) mechanism is better to determine the electricity price expectation. In this paper, to explore the impact of these two different electricity price formation mechanisms on the evolution of the generation market, a multi-agent framework is first established to describe the interaction process among the generation market agents; then, a GEP model for independent power generation groups is developed in the market competition environment, and four representative scenarios are finally designed for detailed comparative studies. Based on these case studies, the conclusion can be summarized as: (1) the PAB bidding mechanism has a lower electricity price and higher market installed capacity almost all the time during the whole planning period for all four scenarios; (2) it is more important that PAB can reduce the impact of parameter uncertainty in the laws of market supply and demand, which can obtain more reliable and reasonable results regarding the long-term evolution of the generation market. Full article

In the face of the new reform of China’s “unified electricity market”, the frequency regulation ancillary service market in each region of China is still divided between using the MCP and PAB pricing mechanisms. The purpose of this paper is to explore the different impact the laws of the above two pricing mechanisms have on the frequency regulation market and to provide relevant suggestions for this electricity market reform. This paper simulates the competitive activities of the frequency regulation market based on the multi-agent simulation model, conducts a comparative experiment by changing the pricing mechanism to a single variable based on the rules of the Sichuan frequency regulation market in China, and concludes that MCP can make the market fast and stable, and that its market settlement price is low, which is suitable for the “unified electricity market”. Although PAB makes the market settlement price high, it can ensure the retention of high-performance units in the market, and the stable settlement price makes this model able to accurately reflect the “price signal”, making it suitable for late adoption in the “unified electricity market”. Full article

The introduction of the new round of electricity reform policies has made the electricity sales companies’ trading environment increasingly complex. In the medium- and long-term market and spot market, following the new policy-oriented optimization of trading decisions is the focus of electricity sales companies. The main objective of this study is to consider the impact of the latest policies of China’s current electricity reform on each subject of electricity trading and to propose a method for electricity sales companies to make optimal decisions on renewable energy source (RES) power and conventional energy source (CES) power mixed with power trading in the medium- and long-term and spot markets to improve the efficiency of electricity market trading, promoting the consumption of renewable energy and helping the synergistic development of the electricity market and the tradable green certificate (TGC) market. This paper first discusses the impact of the new electricity reform policies on the transactions of various subjects in the electricity market and constructs the model of the consumer utility function, the profit model of an electricity sales company, and the profit model of power generators with energy storage. Considering the complex power supply and demand relationship among the various subjects of the electricity market, a game model is established for the decision on mixed trading between the medium- and long-term market, the spot market, and the tradable green certificate market to minimize the comprehensive power purchase cost of an electricity sales company. To reduce the decision-making risk caused by the uncertainty of spot price, the prophet model is used to predict the spot price; finally, through the analysis of the decision-making model of the electricity sales companies, the optimal transaction decisions of the electricity sales companies in different trading periods and different scenarios are solved. The test results show that the proposed model can significantly improve the profitability of the electricity sales companies and provide a decision-making reference for electricity sales companies to participate in the medium- and long-term market and spot market. Full article