Generation Enterprises Research Articles

A study of electricity sales offer strategies applicable to the participation of multi-energy generators in short- and medium-term markets

Due to the increasing proportion of renewable energy, a multi-layered and multi-timescale energy market has emerged in many countries such as China. In the meanwhile, power generation companies must develop more intelligent and dynamic offer strategies to adapt to today's intricate energy trading. Because of the difficulty in describing the dynamic trading environment caused by the uncertainty of renewable energy, previous studies have not fully explored the offer strategy especially in both short-term and medium-term electricity markets. In response to this challenge, this research introduces a novel biding strategy framework leveraging a Asynchronous Advantage Actor-Critic (A3C) algorithm, which can effectively address the decision making in dynamic and uncertain energy markets. The framework focuses on intra-monthly transaction clearing mechanisms with the aim of optimally enhancing earnings. The research formulates an offer model both for thermal and renewable power generation enterprises, which is applicable to medium-term monthly and intra-monthly trading. The study then validates this framework through three distinct analyses: the returns of various bid methods under standard scenarios, the offer strategies return of power generation companies with diverse cost profiles, and the impact of varying renewable energy proportions. The multi-angle simulations confirm that the model presented in this paper offers a scientific basis for the development of offer strategies for power generation companies and enable power generating firms to effectively adopt to the current power market.

Innovation and Practice of Diversified Business Model of New Energy Power Generation Enterprises

With the growing global demand for clean energy, new energy power generation enterprises are facing new opportunities and challenges. This paper explores the diversified business model of new energy power generation enterprises. Firstly, the theoretical basis of the business model of new energy power generation enterprises is analyzed, including the characteristics and income sources of single and multiple industrial chain models. The advantages and applications of different business models are analyzed through the study of cases at home and abroad, such as the European aggregated generation side resource model, the United States focused on controllable load demand response model, and the domestic cases of Shanghai and Shenzhen virtual power plant projects and distributed photovoltaic power generation. Then it puts forward the diversified business model innovation strategy of new energy power generation enterprises, covering the innovation of comprehensive energy services, the development of power side energy storage business, and the expansion of key businesses and profit models. Finally, the research conclusions are summarized and the future research directions are prospected. In short, the exploration of diversified business models of new energy power generation enterprises is of great significance to promote the development of clean energy and improve the competitiveness of enterprises.

Coordination of Renewable Energy Integration and Peak Shaving through Evolutionary Game Theory

This paper presents a novel approach to optimizing the coordination between renewable energy generation enterprises and power grid companies using evolutionary game theory. The research focuses on resolving conflicts and distributing benefits between these key stakeholders in the context of large-scale renewable energy integration. A theoretical model based on replicator dynamics is developed to simulate and analyze the evolutionary stable strategies of power generation enterprises and grid companies with particular emphasis on peak shaving services and electricity bidding. These simulations are based on theoretical models and do not incorporate real-world data directly, but they aim to replicate scenarios that reflect realistic behaviors within the electricity market. The model is validated through dynamic simulation under various scenarios, demonstrating that the final strategic choices of both thermal power and renewable energy enterprises tend to evolve towards either high-price or low-price bidding strategies, significantly influenced by initial system parameters. Additionally, this study explores how the introduction of peak shaving compensation affects the coordination process and stability of renewable energy integration, providing insights into improving grid efficiency and enhancing renewable energy adoption. Although the results are simulation-based, they are designed to offer practical recommendations for grid management and policy development, particularly for the integration of renewable energies such as wind power in competitive electricity markets. The findings suggest that effective government regulation, alongside well-designed compensation mechanisms, can help establish a balanced interest distribution between stakeholders. By offering a clear framework for analyzing the dynamics of renewable energy integration, this work provides valuable policy recommendations to promote cooperation and stability in electricity markets. This study contributes to the understanding of the complex interactions in the electricity market and offers practical solutions for enhancing the integration of renewable energy into the grid.

The Effect of Carbon Quota Policy on Environmental Sustainability of Power Supply Chain

As is well known, limiting carbon emissions is an important link in mitigating global climate change. Carbon quotas are a widely used policy tool by governments around the world without increasing the financial burden on the government. To study the impact of carbon quota policies on the clean transformation of the key carbon emitting industry—the power industry, we established a duopoly model and conducted an analysis and numerical simulation. The research has found that the effectiveness of carbon quota policies is closely related to the level of competition within the power supply chain and is also influenced by the market share of clean energy power generation enterprises. Moreover, in some cases, it may have the opposite effect.

Carbon emissions forecasting based on temporal graph transformer-based attentional neural network

In the field of electric carbon, the mapping relationship between carbon emission flow calculation and power flow calculation was studied by combining techniques such as current trajectory tracking, carbon flow trajectory analysis, power system flow calculation methods, and electric network analysis theory. By delving into the mechanism between these two factors, a better understanding of the correlation between them can be achieved. In addition, by using time series data, graph attention neural networks (GNN), distributed computing technology, and spatiotemporal computing engines, carbon emission fluctuations can be decomposed and a high-frequency “energy-electricity-carbon” integrated dynamic emission factor can be achieved. Through the spatiotemporal distribution patterns of this dynamic factor in multiple dimensions, the carbon emissions from key industries in cities can be accurately calculated. In this paper, the LSTM-GAT model is used as the core to construct a key carbon emission prediction model for cities. The study focuses on the power plant, chemical industry, steel, transportation industry, and construction industry, which are high energy-consuming industries with an annual electricity consumption of more than 100 million kWh in a major city of China. By analyzing the entire life cycle from power generation to electricity consumption and conducting current flow analysis, monthly, weekly, and daily carbon emission calculations were performed. Additionally, other factors such as the industrial development index, GDP, coverage area of power generation enterprises, regional population, size, and type of power-consuming units were included in the comprehensive calculation to build a measurement system. By conducting experiments and analyzing historical data, we have found that the LSTM-GAT model outperforms the single models of GCN, GAT, LSTM, GRU, and RNN in terms of lower error values and higher accuracy. The LSTM-GAT model is better suited for predicting carbon emissions and related indicators with an accuracy rate of 89.5%. Our predictions show that the carbon emissions will exhibit a slow growth trend in the future, while the carbon emission intensity will decrease. This information can provide a scientific basis for government decision-making.

Optimal production decision of hybrid power generation enterprises in multi-quota policy coupled markets

At present, China is adopting both Carbon Emission Trading (CET) and Tradable Green Certificate (TGC) markets to optimize the resource allocation of its power industry. To mitigate the impacts of the above dual markets, more and more power generation enterprises are adopting hybrid production mode, generating electricity from thermal and new energy units simultaneously. In these complex production and market conditions, enterprises are difficult to arrange their generation activities. This research describes the production decision process of hybrid power generation enterprises with different market positions, obtains their optimal yield, and then offers market equilibrium results. In addition, the consequences of enterprise collusion behaviors and government interventions are also measured. Numerical experiment results confirm the correctness of the production decision model and reveal the following facts: 1) CET and TGC markets effectively control the carbon emission level and promote the consumption of new energy, but magnify the failure of electricity market to a certain extent. 2) CET and TGC markets promote each other in function. The existence of one market can help the other to achieve the anticipated goal. 3) Enterprises first offset the impact of market condition changes by adjusting the share of new energy generation, and then digest the residual influence by adjusting the total power generation. 4) Enterprise collusion behaviors aggravate the welfare loss of electricity consumer and then lower the expected effects of CET and TGC markets.

Evolutionary game and simulation analysis of three parties of electricity market regulation under government participation mechanism

Promoting the widespread access and integration of new energy generation into existing power markets is a key strategy for China to achieve peak carbon and carbon neutrality. A joint regulatory mechanism composed of government regulators, power generation enterprises, and third-party testing organizations has become an effective way to ensure the stable operation of the power market and safeguard the economic interest of all parties. In order to analyze the key factors affecting the mechanism and improve the regulatory efficiency, this paper constructs a three-party evolutionary game model, explores the motivations of the participants under different strategy choices, and establishes a set of indicator systems for evaluating producers. The evolutionary game model is numerically simulated using the unified dynamics method, and the evolutionary stable strategy analysis is used to reveal how the key parameters affect the outcome of the game and its dynamic process, so as to find out the potential driving factors affecting the strategy choices. The results of the study show that in the early stage of market development, it is crucial to improve the profitability of firms, and the government needs to impose strict penalties and high incentives. As the market gradually matures and becomes more transparent, the government can gradually reduce the level of penalties. At the same time, ensuring accountability mechanisms from higher authorities to the government is key to ensure the effectiveness of regulation. This study provides an important theoretical basis and policy recommendations for constructing a new regulatory structure for the electricity market.

Evolutionary game analysis of violation regulation in the electricity market based on blockchain technology

Electricity market violations affect the overall operations of the electricity market. This paper explores the evolutionary stability strategies of electricity generation enterprises and electricity consumers under two modes: traditional regulation and blockchain regulation to analyze blockchain technology’s mechanism and conditions in solving electricity market violations. The experimental results indicate that the likelihood of consumers accepting electricity and the regulatory capacity of regulatory agencies play a crucial role in determining the violation approach adopted by electricity generation enterprises. Under traditional regulatory models, due to information asymmetry, regulatory agencies may not be able to detect violations promptly. Meanwhile, electricity consumers may choose to accept violations by power generation companies due to high appeal costs. Blockchain technology enables regulatory agencies to improve their regulatory capabilities by eliminating information asymmetry, reducing the cost of complaints from electricity consumers, thereby elevating the risk for enterprises engaging in market violations and optimizing the evolutionary game towards an optimum state.

Institutional barriers to the development of small-scale power generation in Russia

The energy strategy of Russia involves a transition to a diversified energy generation characterised by a structural diversity of plant types, as well as the use of small-scale energy technologies. The purpose of the study is to identify the barriers that hinder the implementation of small-scale power generation in the Russian Federation and justify the avenues for its development. Methodologically, the paper relies on the systems approach. Methods of content and logical structural analysis, in-depth interviews are applied. The empirical evidence comes from analytical reports, normative and legal documents, as well as results of surveys of 96 specialists in the field and heads of power plants and power engineering enterprises conducted during 2021– 2023. The findings indicate there are four groups of problems in the Russian energy system: technological backwardness; difficulties with integration of small-scale power generation facilities into the energy market; absence of state support mechanisms; surplus of power generation capacity. Accordingly, the paper provides recommendations for overcoming them, in particular, it is necessary to introduce mechanisms for the support of small-scale power engineering and integration with engineering and consultancy companies, as well as make use of energy service agreements; second, to introduce standards for connecting small-scale power generation facilities, develop guidelines for designing and commissioning them; third, to amend tax regulations and learn from successful foreign experience; fourth, to replace retired power generation equipment with new small-scale power generation facilities located closer to load centres. The theoretical significance of the research is linked with detailing the concept “small-scale power generation” as a segment of energy sector as well as its structure in relation to the Russian Federation, specifying classification characteristics of low-capacity power units. The findings are practically valuable for government authorities, power generation enterprises and equipment manufacturers due to the developed organisational and methodological guidelines.

Research on Decision Optimization and the Risk Measurement of the Power Generation Side Based on Quantile Data-Driven IGDT

In an environment marked by dual carbon goals and substantial fluctuations in coal market prices, coal power generation enterprises face an urgent imperative to make scientifically informed decisions regarding production management amidst significant market uncertainties. To tackle this challenge, this paper proposes a methodology for optimizing electricity generation side market decisions and assessing risks using quantile data-driven information-gap decision theory (QDD-IGDT). Initially, a dual-layer decision optimization model for electricity production is formulated, taking into account coal procurement and blending processes. This model optimizes the selection of spot coal and long-term contract coal prices and simplifies the dual-layer structure into an equivalent single-layer model using the McCormick envelope and Karush–Kuhn–Tucker (KKT) conditions. Subsequently, a quantile dataset is generated utilizing a short-term coal price interval prediction model based on the quantile regression neural network (QRNN). Interval constraints on expected costs are introduced to develop an uncertainty decision risk measurement model grounded in QDD-IGDT, quantifying decision risks arising from coal market uncertainties to bolster decision robustness. Lastly, case simulations are executed by using real production data from a power generation enterprise, and the dual-layer decision optimization model is solved by employing the McCormick–KKT–Gurobi approach. Additionally, decision risks associated with coal market uncertainties are assessed through a one-dimensional search under interval constraints on expected cost volatility. The findings demonstrate the effectiveness of the proposed research methodology in cost optimization within the context of coal market uncertainties, underscoring its validity and economic efficiency.

Productivity Decomposition of Chinese Energy Generation Enterprises: an Index Calculation Approach Embedded in the Data Envelopment Analysis Framework

To explore the development of China's energy power generation enterprises, undertake reasonable resource allocation and optimization, and promote sustainable development. We apply an index calculation approach within the data envelopment analysis framework, collecting data from 50 enterprises in 2007-2022, resulting in a total of 800 observations (16*50) for the in-depth analysis of productivity decomposition. The results highlight that during the study years, the sample enterprises experienced the following changes on average per year: an increase of 0.244% in efficiency change (effch), a decline of 0.293% in technical progress (techch), an increase of 0.119% in pure efficiency change (pech), an increase of 0.134% in scale efficiency change (sech), and a decline of 0.057% in total factor of productivity (tfpch). These findings suggest an ongoing improvement in overall efficiency, indicating that energy generators are achieving better results in terms of operations, resource utilization, and scale effects, but need to focus on research and adoption of new technologies while seeking opportunities to further enhance productivity.

Optimization of Employee Performance Management System in Power Generation Enterprises

Optimization of Employee Performance Management System in Power Generation Enterprises

Research on Carbon Emission Accounting and Emission Reduction Route of Thermal Power Plants

The power industry is a major contributor to carbon dioxide (CO2) emission, which accounting for 40% of total domestic carbon emission. Based on the carbon emission accounting method, this paper conducts carbon emission accounting for a number of power generation enterprises, analyses and concludes that the carbon quota allocation is being tightened year by year, proposed countermeasures and emission reduction routes in terms of carbon asset management and carbon reduction technology research. According to the actual situation of a coal-fired power plant in Anhui province, different carbon reduction routes are developed, among which photovoltaic power generation project has the most impressive economic benefits, but carbon capture project has the largest carbon reduction benefits. Under the premise of similar initial investment between the two projects, the carbon reduction brought by the latter is 5 times that of the former. It is suggested to choose the “new energy power generation+carbon capture and utilization” coupling technical route, which can improve the flexible operation of thermal power units and the absorption rate of new energy, and achieve efficient decarbonization. It provides a sustainable development technical route with both economic and environmental benefits for the low-carbon process.

Economic model of power generation enterprise

These days the economic system of any scale faces a challenge not only to best meet the needs of consumers, but also to ensure their comfortable living conditions (especially agricultural), that is, preserve the environment and use natural resources wisely. Power generation enterprises make a significant contribution to the violation of the environmental situation. This is partly due to the alienation of territories that could be used for other human activities, as well as significant emissions into the atmosphere. A power generation enterprise, compared with organizations of other types of activity, is characterized by the method of generating electricity which implies not the impact on the material and raw materials and the production of economic benefits, but the conversion of natural energy into electrical energy. These models are of a general abstract nature and are more applicable to organizations producing tangible economic benefits, rather than the generation of electrical energy, which is the key economic benefit, though, conventionally tangible one. Existing microeconomic models do not show specifics of their functioning – the scale of the enterprise and the conditions for profit maximization. The economic models of a power generation enterprise, the specifics of its functioning and profit-making are presented in the article.

Соціальна відповідальність у сфері енергетики в інтересах розвитку розподіленої генерації та суспільної стабільності

The essence and content of the processes of ensuring social responsibility by entities managing distributed generation, energy business corporations and joint-stock companies and energy regulators are defined and detailed. This made it possible to classify the main types of social responsibility and to formulate the target tasks of energy market participants. A complex of technical and technological procedures and means of coordination of the processes of ensuring the stability of the functioning of the national energy system is proposed for use in order to harmonize the volumes and modes of electricity supply to the network from distributed generation and energy business corporations and joint-stock companies. It includes: smart power system management systems; smart energy management systems; demand forecasting methods; network integration devices; technologies of energy storage and accumulation; remote monitoring and control technologies used by operators; standardization of activities and ensuring mutual compatibility of technologies; development and implementation of contingency plans and emergency scenarios to eliminate violations of energy supply regimes. For public administration entities and regulators in the performance of social responsibility functions and effective control over the activities of economic entities, it is recommended: a) to conduct an analysis of the activities of managers, an assessment of their compliance with the assigned tasks; b) clarify the responsibility of managers; c) approve reporting systems; d) determine rewards and amounts of punishment; e) engage independent experts or audit firms to evaluate activities; g) adapt the existing system of discussion and feedback; h) exercise public pressure and public reaction. The author's work made it possible to develop and propose for use a five-stage Algorithm of management actions for the coordination of the processes of ensuring the stability of the functioning of the national energy system with the rationalization and coordination of the compatibility of volumes and modes of supply to the electricity network from distributed generation enterprises and energy business corporations and joint-stock companies. Within its limits, a number of target tasks are formulated and the deadlines for those responsible for their implementation are determined.

An optimized GRT model with blockchain digital smart contracts for power generation enterprises

The traditional power generation rights trading (GRT) market is faced with the problems of weak interconnection of electricity‑carbon market and low security. Using smart contracts in the blockchain, the idea of establishing a weakly centralized GRT structure is proposed in this paper. The carbon emission factor was introduced to improve the GRT model, and carbon emission market is used to further stimulate the emission reduction vitality of generating units. The empirical results show that compared with the benchmark model and improved model 1, the improved GRT model proposed by us has the best emission reduction effect. The contribution of this paper is to make up for the existing research that cannot fully consider the impact of carbon peak and carbon neutralization on the GRT market, as well as the information security issues brought by big data trading on the GRT platform. This paper puts forward some policy implications for the decarbonization and green development of the electricity market advocated by the Chinese government.

Optimal investment portfolio strategy for carbon neutrality of power enterprises

The reduction of carbon emissions in the power sector is a key step towards achieving the carbon neutrality target. This study explores the optimal investment strategy for power generation enterprises of China to achieve carbon neutrality. A portfolio optimization model with carbon emission penalty function is proposed. The real option is integrated into the model to obtain the investment value under uncertainties. Four scenarios are used in an empirical study, including baseline scenario, renewables scenario, cleaning fossil fuels scenario, and non-coal comprehensive energy scenario. The results detail the proportion of generation technology, portfolio revenue, and risk value. The generation of renewables is expected to account for more than 42 % in all scenarios that include both renewable energy and fossil energy, with wind generation accounting for the largest proportion. The cleaning fossil fuels scenario has the maximum conditional value at risk when the carbon neutrality target is included. Although fossil energy power generation is riskier, it occupies at least 40 % in the portfolio to safeguard stability. The electricity price and its volatility are positively correlated with the investment value and the conditional value at risk. The impact of the carbon dioxide price and its volatility on power generation projects is not monotonous.

Research on the Application of Information Management System in the Laboratory of Power Generation Enterprises

Information management systems have achieved many application results in the management of metrological calibration and chemical testing laboratories facing society, but there are few research and application cases that combine the two information systems. The power generation enterprise is equipped with both a metrological calibration laboratory and a chemical testing laboratory. Huadian Laizhou Power Generation Co., Ltd. and Huadian Electric Power Research Institute Co., Ltd. have jointly developed an information system that can manage both laboratory businesses simultaneously. Starting from practical cases, this article discusses the development feasibility of this information system, its differences from conventional laboratory information systems, and the innovation points implemented in the project from the perspectives of information technology and software function settings.

Fuel Procurement strategies of coastal coal-fired power plants under the carbon peaking and carbon neutrality goals

Under the background of the carbon peaking and carbon neutrality goal and the construction of a new power system with new energy as the main body, coal-fired power generation enterprises are facing a complex and severe business situation. Based on the current situation of coal procurement in power plants along the southeast coast of China, this paper constructs a coal procurement model that weighs imported coal and domestic coal, and simulates the impact of demand index, season and weather factors and other factors on the risk resistance capability of power plants. Taking the coal procurement data of H Group as the model parameter data to simulate, the model simulation results show that: flexible adjustment of imported coal proportion according to the seasonal change can effectively ensure the coal demand. It is a long way to go to develop new energy to replace coal. We can gradually establish a low-carbon energy consumption system by actively developing renewable energy. The fluctuation of domestic and foreign coal prices significantly affects the demand of coal, and the price and quality advantages of imported coal cannot be replaced.

Does China's green power trading policy play a role? - Evidence from renewable energy generation enterprises

Green power trading is an institutional innovation proposed by China to promote green and sustainable development. This policy aims to relieve the serious debt pressure of renewable energy generation enterprises, thus laying the foundation for achieving carbon reduction targets. This paper empirically examines the role of green power trading by constructing the difference-in-differences (DID) model. Our findings indicate that green power trading significantly reduces the debt levels of policy-covered generation enterprises. This effect is more pronounced for state-owned enterprises, companies with better ESG performance, companies in regions with higher degree of marketization and companies specializing in renewable energy power generation. Further research discusses the impact mechanism of green power trading in terms of external and internal environmental channels. We find that a strong green development atmosphere, especially the external atmosphere, will significantly reinforce the weakening effect of green power trading on corporate debt. Our research provides empirical evidence for the success of green power trading policy in its pilot phase.