Exploiting inherent load flexibility within data centers is key to achieving synergy between computing power and electricity. This study focuses on the idle flexibility of integrated backup and energy storage batteries on the side of high-voltage direct current (HVDC) communication power supplies in data centers. An application scenario for this idle flexibility, which enhances the supported workload of cabinets to achieve premium applications, is proposed. Meanwhile, it simultaneously provides load flexibility to the grid to obtain compensation from electricity ancillary service markets and reduce electricity costs. Specifically, this study presents the principle for this application scenario and discusses the value assessment under different scenarios, providing a theoretical foundation for determining the idle flexibility of HVDC-side integrated energy storage and backup batteries. Finally, prospects for exploring the synergy between computing power and electricity are discussed.
The era of intelligence has driven computing power resources to become highly flexible and adjustable. They have also made the bidirectional collaborative optimization of computing and electricity into a new method of economic optimization in comprehensive energy systems. The explosive growth in computational demand has led to a shortage of computational resources. It also brings the challenges of high energy consumption and carbon emissions for data centers, where the annual electricity consumption can reach billions of kilowatt-hours. When the cost of computing resources is high and the stability of power grid operations is affected, there is an urgent need to explore bidirectional collaborative technologies between computing and power nodes with adjustable resources to reduce the energy cost and enhance the stability and economic efficiency of power grid operation. This study constructs a bidirectional collaborative scheduling architecture for adjustable resources in computing and power nodes, and quantitatively models the diverse adjustable resources within them. Considering the matching and real-time adjustment characteristics between computing tasks and resources, a dual-layer two-stage collaborative optimization scheduling model is proposed by scheduling computing tasks under the computing node and adjustable loads under the power node. Through numerical examples, it was verified that the bidirectional collaborative optimization of adjustable resources for computing and power nodes is feasible and effective. Under the setting of an overall adjustable resource of approximately 2300 MW for power nodes, the cost reduction provided by computing nodes of 50 MW can account for 4.71% of the power node operation, while reducing its daily operating costs by approximately 0.70%.
The computing power integrated energy system (CPIES), as a new type of energy system framework, realizes the intelligent management of energy production, transmission, storage, consumption, and information processing by integrating computing and energy resources with the acceleration of digital transformation and the popularity of renewable energy. First, the system architecture and key components of the CPIES, development status and trend, and the interaction mechanism between computing power and energy are outlined. Second, an in-depth analysis of the basis for the integration of information and communication technology (ICT), artificial intelligence (AI), big data analysis, and the internet of things (IoT) with the CPIES is presented to promote the intelligence and efficiency of energy systems. Furthermore, the application of information and energy technologies in CPIES is described in detail, demonstrating their potential to enhance energy utilization efficiency and other aspects. Finally, advanced international CPIES projects are listed, the challenges faced by current projects are identified, and countermeasures are suggested. The research results not only help to promote the theoretical development and practical application of CPIES but also provide new ideas and solutions for the digital transformation and sustainable development of the energy industry.
With the increasing complexity of grid configuration and operation modes as well as the modernization of the international security system and security governance capacity, grid resilience construction has become an important means of improving power security. This study first expounds on the basic concept and characteristics of grid resilience and then reviews the main achievements and lessons drawn from domestic and foreign resilience research, as well as resilience enhancement measures and key technologies. Considering a typical receiving urban distribution network in Beijing as an example, the measures for building a resilient distribution network are outlined based on five perspectives: improving the perception, coordination, adaptability, defense, and resilience. The current resilience level of urban power grids is evaluated based on four aspects: grid, power supply, load, and emergency type. The constraints and challenges facing the future development of urban power grids are clarified based on the following three aspects: supply security, system security, and non-conventional security issues. Finally, the basic technical paths and key technologies for grid resilience construction are discussed, and the key technology layout for improving the resilience level is clarified based on grid characteristics.
The contradiction between the configuration cost of edge computing terminals and the demand for computing resources has become increasingly prominent with the increasing complexity of the control structure of distribution networks. In this context, this study presents the service architecture and control configuration technology of distribution-network edge computing based on the hybrid control theory. First, the hybrid characteristics of the distribution network and its distributed devices are discussed. The control structure of the distribution network based on hybrid control is established, and an edge computing service architecture, including the information and power subsystems, is constructed. Second, an event-driven control technology based on activity on edge network (AOE), which can realize the rapid deployment of edge computing services, is proposed. Finally, considering the economic dispatching service of the distribution network as an example, the application effect of the proposed edge-computing distributed control method is analyzed, and the practical application of edge computing in the control of the distribution network is introduced. The example shows that the proposed edge computing service architecture has the advantages of low delay, simple policy configuration, and less consumption of computing and communication resources, which are suitable for popularization and application in the new distribution network.
As major national infrastructure projects, UHV projects are key to ensuring power grid security, promoting energy consumption, and optimizing resource allocation. To better meet the requirements of "large-scale centralized construction, high-intensity innovation and research, and high-quality transformation and upgrading" of UHV projects, conducting digital construction of UHV projects based on building information modeling (BIM) technology is imperative. First, this study focuses on the construction of a BIM standard system for UHV projects, designs the framework of the standard system, and discusses the expansion and improvement of the standard system. Subsequently, the construction status of BIM management and control platforms for UHV projects are introduced, and the functions and applications of the subsystems are summarized. Finally, this study analyzes typical application scenarios of BIM technology in the design and construction stages of UHV projects. Based on the current application status of BIM technology in the design, construction, and operation stages of UHV projects, this study summarizes its applicability and practical results and provides experience and suggestions for the digital construction of UHV projects.
Pollution source enterprises are numerous and widespread. The production and pollution treatment processes of each enterprise vary, a lack of effective and uniform regulatory indicators and early warning systems are concerning. This creates problems, such as difficult supervision, poor real-time performance, and a large workload. This study proposes a method for identifying the environmental anomalies of enterprises based on electricity data mining. First, K-means clustering is used to identify the operating status of the equipment, and a model of the enterprise production line is constructed based on dynamic time-warping distance. Next, continuous and intermittent production lines are classified based on historical data statistics. Furthermore, the Fourier transform is used to identify the production cycle of the production line to establish a model of the environmental conditions suitable for the enterprise. Subsequently, the environmental condition identification method is proposed to identify the environmental conditions for continuous and intermittent production lines. Finally, the proposed method is validated using the monitoring data of actual pollution source enterprises. The electric power intelligent environmental protection platform developed based on the proposed method has been implemented in certain provinces, achieving suitable results. This platform enables the environmental protection department to grasp the situation of enterprise environmental protection, providing both technical means and data support.
To accurately estimate the carbon emission obligation of the user side of a new power system and promote the continuous development of low-carbon electricity, this study uses the node carbon intensity calculation method to characterize the carbon flow distribution of the system and preliminarily calculates the carbon emission obligation on the load side. Moreover, under the premise that the total carbon emission obligation allocation amount remains unchanged before and after correction, the initial carbon emission obligation on the load side is modified and analyzed based on an innovative combination of the comprehensive load method, where the peak load characteristics of the user at the current stage are fully considered. Next, considering the long-term changes in electricity demand and emission reduction capabilities on the load side, a carbon emission obligation allocation interval reflecting the long-term electricity consumption characteristics of the users was constructed. Based on this, the satisfaction of each load node with the carbon emission obligation accounting results was accurately evaluated, providing a direction for the formulation of low-carbon transformation strategies and the construction of demand-side response capabilities in the power system. The application results in the 14-node power system show that compared to the initial accounting results of the node carbon intensity allocation method, the load-side carbon emission obligation accounting results based on the user electricity consumption characteristics of the power system are both reasonable and accurate, providing data support for the clean and low-carbon development of the power industry, and have a certain reference value.
To promote the commercial application of photovoltaic electrolysis hydrogen production technology, this study proposes a scheduling strategy for photovoltaic electrolysis hydrogen production that takes into account the dynamic hydrogen price mechanism. First, two operation modes, residual electricity grid connection and residual electricity hydrogen production, were considered. A photovoltaic electrolysis water hydrogen production system operation model was constructed, and under relevant constraints, a scheduling strategy for the photovoltaic electrolysis water hydrogen production system was proposed. The system aims to maximize overall revenue and introduces a utility function to optimize the satisfaction and sales revenue models of hydrogenation users. Second, to maximize sales revenue and user satisfaction, an optimal dynamic hydrogen price was obtained using the NSGA2. Finally, the CPLEX solver was used to obtain the optimal scheduling strategy scheme for different scenarios. The optimized operation results and their economic benefits were analyzed, and the operation results and economic benefits under different scenarios were compared. The simulation results show that the dynamic hydrogen pricing mechanism adopted in the photovoltaic electrolysis hydrogen production scheduling strategy can effectively increase hydrogen sales revenue, guide hydrogenation demand in an orderly manner, and improve the economic efficiency of photovoltaic electrolysis hydrogen production.
In the context of flexible DC interconnection and the large-scale integration of new energy, when a system experiences a short-circuit fault, low-voltage ride-through (LVRT) control causes uncertainty in fault characteristics, increasing the risk of incorrect protection action. This study aims to establish an accurate real-time risk assessment model for protection and combine the characteristics of LVRT control to construct a short-circuit analysis calculation model that takes uncertainty into account. Considering the short-term random fluctuations of the new energy output and measurement errors, an uncertainty analysis model for protection actions was constructed based on the stochastic response surface method to evaluate the probability of incorrect protection actions and to propose a risk assessment and warning method for inaccurate protection actions. Finally, a PSCAD simulation model is constructed to verify the accuracy of the short-circuit calculation model considering LVRT control. Based on Monte Carlo sampling, the probability of the adequacy of protection actions based on the random response surface method and its equivalent effect were verified. Through multiple case scenarios, it was demonstrated that the proposed protection risk warning method can calculate the real-time risk of incorrect protection actions, thereby providing a reference for improving protection measures.
To address the problems of control signal loss and increased frequency deviation, an event-triggering load frequency control (LFC) strategy was proposed to address periodic denial of service (DoS) attacks and time delays in new energy interconnected power systems. First, a new energy-interconnected power system LFC model under network attacks and communication time delay was established. Generalized cross-correlation (GCC) estimation was used to detect online system time delays. A sliding mode control algorithm was used to reduce the impact of time delay and enhance the robustness of the system. Second, an improved periodic event-triggering mechanism was proposed based on the traditional sliding mode control, which determined the sampling state based on the divided time interval to reduce the amount of data exchange in the network and solve periodic DoS attacks. Finally, simulation experiments were conducted under different scenarios using MATLAB/Simulink to demonstrate that the proposed strategy can effectively overcome the problem of increased frequency deviations caused by network attacks and communication delays. This can also improve the frequency stability of new energy-interconnected power systems.
In the context of “carbon peaking” and “carbon neutrality,” the generation method for power systems is gradually transitioning from traditional generation to renewable energy source generation, posing challenges to the stability of the power supply in the grid. The significance of flexible generator units has become increasingly evident. Concurrently, ongoing reforms in the electricity market are yet to clearly define the self-profit development trends of flexible units. Traditional development models often lack the accurate elements of the electricity price generation process. This paper proposes a dual-timescale dynamic model that combines system dynamics with spot market clearing to simulate the self-profit growth trends of flexible gas units and energy storage systems under more realistic electricity price scenarios. First, the quantitative and feedback relationships among market elements are analyzed, and a system dynamics model is established to simulate the investment and development of various power generation types. Second, a spot market-clearing model based on unit commitment optimization is presented to validate the system's operational constraints and calculate spot market-clearing results. A dual-timescale dynamic model is introduced that integrates the spot market-clearing model as a core component of the system dynamics model. Finally, different subsidy schemes for flexible units are discussed, and recommendations for power system planners and relevant policymakers are provided based on the simulation results.
Large-scale doubly-fed wind farms connected through series-compensated transmission lines are prone to subsynchronous oscillations (SSO), which can cause wind turbines to disconnect from the grid, posing a serious threat to the safe and stable operation of the power system. This paper proposes a control method that utilizes a unified power flow controller (UPFC) with additional damping control to mitigate SSO. The approach involves the design of a decoupled linear active disturbance rejection controller (LADRC) that uses a linear state observer to estimate the subsynchronous disturbances in the system in real time. In addition, a delay-compensation mechanism was introduced to eliminate communication delays during signal transmission. Based on the improved LADRC, an additional damping controller for the UPFC was designed to enable the UPFC to exhibit positive damping characteristics in the system, thereby suppressing SSO and enhancing the robustness and resistance of the system to disturbances. Finally, a simulation model of a doubly-fed wind farm connected to the grid through a series-compensated transmission line was developed using the MATLAB/Simulink software. The simulation results demonstrate that the proposed method effectively mitigates SSO under various conditions, including different levels of series compensation, wind speeds, and numbers of connected wind turbines. This can serve as a reference for studying similar problems of long-distance grid connections in double-fed wind farms in China.
In the context of developing a unified national electricity market, the development of a spot market helps promote the sharing and optimal allocation of electricity resources on a larger scale. As important decision-making information for market participants, spot electricity prices are crucial for auxiliary decision-making in the spot market, market operation monitoring, and risk management. The rapid development of machine learning methods provided a feasible approach for electricity price prediction. This study first analyzed the characteristics of spot electricity prices and their influence on the unified national electricity market. The types of prediction models and challenges faced by spot electricity price prediction can be elaborated based on existing research on electricity price prediction mechanisms. In addition, based on the characteristics of data labeling, feature extraction and data flow control, the research status of various machine learning prediction models was summarized, and the characteristics and applicability of different prediction models were analyzed. This study then analyzed the evaluation criteria for spot electricity price prediction models based on machine learning, and summarized the model hyperparameter training requirements and the practical application of relevant prediction methods. Finally, in view of the challenges of machine learning methods in electricity price prediction research, this study outlined future research directions to provide constructive references for the development of the spot market under the construction of a unified national electricity market.
China is in the early stages of power market reform. With the gradual decline in the utilization hours of coal-fired power generation, it is difficult for coal-fired power units to recover all fixed costs by electricity price alone. This can trigger sharp fluctuations in electricity prices, which in turn has socio-economic impacts. To compensate for the loss of coal power in power generation, this study considers utilization hours and operation status. Combined with the capacity price policy issued by the state, based on the unit compensation capacity accounting model, this study developed a capacity compensation price model for coal-fired power units and proposed a capacity compensation cost allocation method for industrial and commercial users. First, a compensation capacity accounting model was constructed based on the available capacity, power consumption, and maintenance of coal-fired power plants. Second, the K-means clustering algorithm was used to determine the probability of power generation of coal-fired units, and a capacity compensation electricity price model considering the fixed cost, capacity subsidy factor, and profit and loss balance point utilization hours was constructed. A capacity cost allocation method for industrial and commercial users was established based on this. Through the above analysis, the capacity price and other factors of power plants A, B, and C in G province are evaluated by simulations. The simulations verify the calculation of the coal power capacity compensation price, which has a far-reaching impact on coal power units and users. Therefore, traditional coal-fired units should seize policy opportunities to promote their transformation into system regulations and basic security power units to help build a new type of power system.