New-type power system are the foundations and supports for building new energy systems and achieving dual-carbon reduction goals. The coordinated operation of electricity, carbon, and green certificate markets is an inevitable path for the marketization of new-type power system. To this end, this article proposes a blockchain system for the coordinated operation of electricity, carbon, and green certificate markets. First, we refine the system architecture governing the coordinated operation of the electricity-carbon-green certificate markets and analyze the collaborative operation among the markets from the perspective of each trading entity.Second, the market operation system based on blockchain technology is constructed: a unified point certification mechanism is established to connect the transaction objects between the electric-carbon-green certificate market; Smart contracts combined with blockchain technology ensure transparency and reliability of transactions; The consensus mechanism combined with the points certification is applied to the collaborative market to ensure the consistency and synergy between participants; By tracking the source of electricity, the electric-carbon-green certificate traceability model is constructed to ensure the accuracy of power data. Finally, the paper puts forward the suggestion of constructing the electric-carbon-green certificate system, focusing on the policy, technology and pilot three aspects to provide auxiliary decision for the market-oriented operation of the new power system.
In the future, as distribution networks witness a high penetration of electric vehicles (EVs), aggregating EV charging data within each subarea of a distribution network will play a key role in formulating EV demand response strategies and encouraging the orderly charging of EVs. However, traditional data aggregation methods can increase the burden on utilities, introduce erroneous data, and compromise the privacy of EV owners. To this end, this paper proposes a trustworthy method for the aggregation of EV charging private data based on a double-layer blockchain. First, a blockchain-based multilevel aggregation architecture data is established for EV charging, to autonomously aggregate charging loads and reduce the EV charging data collection burden on the utility. Then, a privacy-preserving and trustworthy aggregation algorithm for EV charging data is proposed, to protect the privacy of EV owners, while ensuring the accuracy of the aggregation results. Finally, the tamper-proof and privacy-preserving performance of the proposed method is demonstrated by both theoretical proofs and simulations.
Addressing the lack of efficient online detection schemes for ledger tampering attacks in the current power industry consortium blockchain, we propose a ledger tampering attack detection method based on endorsement features. First, an attack on the state data of specific nodes in a power industry consortium blockchain was proposed and implemented in a green power-trading simulation environment. Accordingly, endorsement features related to the attack were collected and extracted from the chain-operation data to construct the required dataset for detection. Finally, the boosting random forest algorithm was used to train the detection model, and the model was noninvasively deployed on the blockchain for online detection of ledger tampering attacks. The test results indicate that the proposed method has a smaller operating burden on the power consortium blockchain than rule-based detection methods and excels in terms of identification time and blockchain performance loss, incurring only a 4.03% performance burden. Compared with other machine learning-based detection methods, this method can be adapted to multiple consensus algorithms and has a high accuracy of 95.75%.
To achieve coordinated control of active power and reactive power resources within the active distribution network (ADN) and enhance the reliability and cost-effectiveness of the distribution system's power supply, we propose an optimization strategy for ADN active power and reactive power coordination based on deep deterministic policy gradient (DDPG) scheduling. First and foremost, our approach focuses on minimizing the daily operating costs of the ADN while avoiding voltage and power flow exceeding their limits. It takes into account various factors, including switchable capacitor banks, on-load voltage regulating transformers, micro gas turbines, and energy storage systems. We establish a model for the coordinated dispatch of active and reactive power within the ADN. Next, we transform the real-time scheduling problem in the ADN into a Markov decision process, defining the state space, action space, and reward function for the system. To enhance the offline training speed and reward returns of the DDPG algorithm, we introduce a priority experience replay (PER) mechanism. This leads to the development of an online scheduling framework known as PER-DDPG for the ADN. Finally, we conduct simulations on the modified IEEE-34 node power distribution system. The results of these simulations demonstrate that the PER-DDPG method effectively provides a secure and cost-efficient dispatch strategy for the ADN, achieved through efficient empirical learning.
High-precision wind power output forecasting stands as a pivotal technology in achieving carbon neutrality. As the smart grid infrastructure steadily advances, the wind power output forecasting system is transitioning from a decentralized mode to a centralized one, characterized by the integration of data and models from various wind farm stations. Consequently, this paper introduces a wind power output forecasting method based on multi-source data graph representation learning. The graph data is established using a stacked integrated learning framework and graph theory. In this framework, the nodes represent historical wind power output data, predicted values from the base method, meteorological and location data, among others. The edges denote correlations among multiple wind farm stations, facilitating the fused representation of data from these diverse stations. Subsequently, residual graph convolutional neural networks are constructed to learn and train the generated graph data. The effectiveness of the proposed algorithm is substantiated through validation using publicly available datasets, indicating superior results.
The substantial integration of wind power into the grid eases the load on the grid significantly, though its randomness and volatility also diminish grid inertia, thereby increasing the challenge of grid frequency regulation. Consequently, various frequency regulation techniques rooted in wind farms have received extensive attention in recent decades. Among these methods, adaptive inertial droop control of wind turbines is widely recognized as one of the most efficient approaches to fortify grid inertia. It effectively addresses the decoupling issue between wind farm power and power system frequency. Nevertheless, existing studies predominantly focuses on the first frequency drop (FFD) or overlooks the influence of control parameters. To address this gap, this paper introduces a novel step start-up adaptive inertial droop control strategy based on the dandelion optimizer (DO) algorithm. This strategy aims to curtail system frequency fluctuations and mitigate the impacts of the FFD, second frequency drop (SFD), and third frequency drop (TFD). Additionally, the DO algorithm optimizes the control parameters of the proposed controller for load increments of 50 MW, 100 MW, and 150 MW. These optimized parameters are then applied to various load variation scenarios, and their performance is rigorously evaluated through simulations in MATLAB/Simulink. The simulation results clearly demonstrate that the DO optimization-based controller responds promptly to multiple load increment changes and substantially reduces system frequency degradation. Compared to the conventional trial-and-error method, the DO-based approach yields a remarkable reduction of 11.34% and 13.74% in SFD and TFD, respectively, when the load is increased.
The risk of active distribution network voltage violations increases with an increase in the percentage of renewable energy integration. The long control timescale of traditional voltage regulation equipment makes it difficult to respond to the voltage regulation demand in time. To alleviate the local voltage overrun problem in distribution networks, a voltage regulation strategy based on deep reinforcement learning is proposed using a battery energy storage systems and a static var compensator (SVC) for combined active and reactive power regulation. First, the capacity decay mechanism of distributed energy storage is studied. Voltage regulation is then derived as a Markov decision process to mitigate voltage fluctuations by determining the optimal output point of the SVC and energy storage. Finally, a deep deterministic policy gradient algorithm is used to implement offline training and online decision making. The validation of the algorithm shows that the proposed strategy can effectively suppress voltage fluctuations from a nonglobal information perspective. In addition, the voltage deviation was significantly reduced compared to that without regulation. By comparing it with other deep reinforcement learning and heuristic algorithms, we verified that the proposed method can effectively cope with uncertain environments and suppress voltage fluctuations.
Leveraging electric vision imaging technology to identify instrument readings in substations offers substantial benefits in real-time equipment monitoring and elevating operational and maintenance intelligence. However, many existing instrument image recognition solutions for substations rely on pointer deflection angles, despite the need for enhanced precision and robustness. These methods overlook critical data, such as device status, numerical intervals reflected by dial color, and dial character information. This paper introduces a novel method for automatic recognition of instrument readings and dial information in substations. First, it proposes a dial type position detection algorithm based on YOLO-E, achieving image calibration through perspective transformation. Second, building upon OCRNet's object region context extraction structure, it incorporates a parallel branch with polarized self-attention to rationally utilize channel feature maps with varying weights. This results in a dial segmentation algorithm based on an improved OCRNet. By segmenting scales, pointers, and color bands, this method achieves precise segmentation and identification of meter readings and crucial additional information. Finally, using PGNet, the method recognizes dial information, enabling automatic acquisition of data like meter range parameters and readings for multi-range dials. A case study demonstrates that, compared to other advanced electric vision algorithms, the proposed method not only enhances reading recognition accuracy but also effectively detects and extracts additional dial information. This advancement supports the digital transformation of operations and maintenance.
The high proportion of new energy access to regional systems is weak and has gradually become a critical factor restricting the consumption of new energy. Therefore, there is an urgent need to propose a new energy consumption capacity evaluation method suitable for new weak energy cluster grid systems. First, based on the calculation method of the capacity short-circuit ratio (SCR-S) index and the constraint of the critical short-circuit ratio (CSCR) index, the relationship between the new energy consumption capacity and the strength of the grid-connected system is established. The mechanism of the weak grid system that restricts new energy consumption is expounded, and the improvement mechanism of reactive power compensation for the new energy consumption of the weak grid system is analyzed. Second, based on the traditional time-series production simulation algorithm, a short-circuit ratio (SCR) constraint is added, and a time-series production simulation model considering the SCR constraint is proposed to accurately evaluate the new energy consumption capacity of the weak grid system. Finally, combined with anew energy collection and transmission system project in the three northern regions of China, the application research of new energy consumption evaluation of weak grid systems based on the SCR constraint is conducted, and the effect of reactive power compensation methods on new energy consumption is further analyzed quantitatively.
Large-scale access to new digital infrastructures such as 5G base stations and data centers has impacted the stable operation of distribution networks. Thus, it has become increasingly pertinent to advance collaborative optimization scheduling between new digital infrastructure and such distribution networks. Therefore, to reduce distribution load fluctuation and operational cost, this study proposes a multi-objective optimization method for a transformer substation that accommodates access to 5G base stations and data centers. First, a multi-agent centralized collaborative optimization framework in the multi-station integration scene that considers new digital infrastructure such as 5G base stations and data centers is proposed. Second, 5G base station and data center energy consumption characteristics are analyzed, and a multi-objective optimization model that considers grid operation economics and stability is constructed. Subsequently, a multi-objective model solution method based on the ε- constraint is proposed. Finally, the effectiveness of the proposed multi-objective optimization method is verified using simulation. The results demonstrate that the proposed method reduces new digital infrastructure operational cost and power load fluctuation whilst improving power system stability. This solution effectively achieves a win-win situation for multiple stakeholders involved.
Construction of power transmission and transformation projects is accelerating in the context of new power systems. New digital technologies such as big data and the Internet of Things are being increasingly integrated with engineering construction. Continuous improvements in the integration of new power equipment have significantly increased the complexity, bringing new challenges to the traditional review mode of power transmission and transformation projects. To address these challenges, this study analyzes the review business processes of power transmission and transformation projects and summarizes key review elements. Subsequently, a knowledge-map-based auxiliary review system architecture for power transmission and transformation projects is proposed, and the key technologies required to realize the auxiliary review system are studied and analyzed from the aspects of knowledge map construction, reasoning, and analysis. Finally, a typical application scenario of key indicator error correction, modification suggestion generation, and knowledge map quality analysis is proposed for engineering review business needs, providing theoretical support for improving the quality and efficiency of power transmission and the transformation project review business and pointing out the development direction and construction ideas.
The available transmission capacity (ATC) reflects the power exchange capacity between different regions of a power grid and provides a reference for evaluating the stability of a power grid. With the development of integrated electrical energy systems and increased coupling of natural gas networks and power grids, ATC calculations will become more complex, affecting its calculation efficiency. To solve these problems, this study proposes an ATC calculation method for an electric gas-integrated energy system based on the digital twin concept. First, we integrate data-driven and model-driven data and develop a data mechanism fusion model to satisfy the indicator requirements of the digital twin concept. The data mechanism fusion model can fully mine the information hidden in massive state data, thereby simplifying the iterative calculation process of traditional physical models and shortening the calculation time. The invented model is developed to process the constantly updated state data in the integrated energy system in real time to realize the online calculation of the maximum transmission capacity and extract the characteristics of the system operation state. The extracted features are then used to calculate the ATC of the integrated energy system. Finally, the effectiveness and efficiency of the proposed method are verified using the IEEE30-NGS10 electric gas integrated energy system.
The widespread integration of renewable energy into power systems has led to a gradual decline in power grid reliability, underscoring the increasing importance of ancillary services in ensuring the safe operation of power grids. Based on the characteristics of flexible energy conversion, electricity, gas, and heat coupled integrated energy systems have emerged as promising auxiliary service resources for power systems. Therefore, this study fully explores the potential of integrated energy systems, particularly in leveraging gas-thermal inertia, to contribute to ancillary services across various time scales. First, the resistance capability and time-delay response characteristics of the gas-thermal inertia to power fluctuations were analyzed. Using inertia and frequency regulation ancillary services as examples, a response strategy and scheme are proposed for the participation of integrated energy systems in auxiliary services. Subsequently, a gas-thermal inertia model and an inertia and frequency auxiliary service optimization model involving the integrated energy system are established according to the proposed response strategies. Simulation and analysis of a small-capacity system confirmed that the integrated energy systems considering gas-thermal inertia were capable of participating in ancillary services, resulting in improved system operational economy and ancillary service effectiveness.
Abnormal electricity consumption behaviors often result in irregular metering data from electricity meters. This paper presents an effective means to identify such abnormal electricity consumption behaviors, to facilitate data perception verification for electricity meters and calculate the residual difference between the measured value and perceived amount. An abnormal electricity consumption detection method was proposed based on the power distribution network-associated power flow perception. The characteristics of meter data variations caused by abnormal consumption behaviors were analyzed. Then, an associated power flow sensing model was constructed based on forward power flow mapping and reverse power flow backtracking mechanisms, to realize equivalent power flow calculations independent of topological parameters. The voltage and power residual characteristics constrained by the power flow were extracted. Subsequently, an estimation network based on a Gaussian mixture model was designed to calculate the abnormal probability energy of the residual characteristics and determine the abnormal power users based on a predefined threshold. Finally, practical examples show that the residual features extracted from the model can detect abnormal power users more effectively.
Power-to-hydrogen (P2H) technology integrates renewable power with the methanol synthesis industry, serving as a crucial avenue for the low-carbon transformation of the electrical power, and chemical sectors. However, traditional electrical power-based hydrogen production and methanol synthesis systems operate continuously at a constant load, necessitating the purchase of high-quality electricity at elevated prices to ensure a stable power supply. This results in the high production cost of methanol. To address this challenge, we propose a multi-load interval regulation approach with varying ramp rates for the methanol synthesis section, considering the dynamic and thermodynamic characteristics of the methanol synthesis reaction. We establish a flexible scheduling model for the entire P2H-based methanol synthesis system by coordinating the start-up and shutdown sequences of the hydrogen production cluster, adapting to fluctuating wind power. We also introduce distributionally robust optimization based on Wasserstein distance to maximize system profits and create an optimized scheduling model. An affine policy is employed to facilitate system rescheduling, thereby smoothing the output deviations of renewable power. In conclusion, we conduct case studies on an independent wind power-driven methanol synthesis system and an improved IEEE 14-bus chemical industry park system. The simulation results demonstrate that the flexible operation of the power-to-methanol system can reduce its reliance on power supply stability and leverage the cost-effective electricity from wind power to lower methanol production costs by 14.5%. This approach offers economic advantages, particularly in high-proportion renewable energy grids.
