Abbreviation (ISO4): South Power Sys Technol
Editor in chief:
State of charge(SoC) balancing is the key to achieve dynamic energy balancing among multiple modules in cascaded H-bridge battery energy storage systems(BESS), but traditional SoC balancing techniques could exacerbate DC-side transient voltage surges. This paper proposes a compound control strategy integrating SoC balancing and voltage surge suppression. Firstly, a mathematical model of battery energy SoC and output power is established, and a power balancing coefficient is constructed to integrate SoC balancing objective into charge and discharge control. An adaptive power balancing coefficient control method is proposed, which achieves SoC balancing while satisfying the rated power output of the system. Furthermore, addressing the issue of excessive DC-side voltage fluctuations caused by sudden changes in grid-side power, a closed-loop transfer function model of the DC/DC converter is established, revealing the mechanism by which key parameters affect transient respons. And voltage surge suppression is achieved through a method based on capacitor voltage feedforward. Finally, simulations and hardware-in-loop experiments verify the superior performance of the proposed control strategy.
With the implementation of China′s “dual carbon” strategy, ultra/high-voltage direct current transmission system has been widely applied as an important technical means for new energy consumption. The power capacitors, as key devices for filtering and reactive power compensation, are widely used in ultra/high-voltage direct current transmission systems. However, the low-frequency vibrations and noises of power capacitors pose a serious challenge to the environmental standards of converter stations. To reduce the emission of low-frequency vibrations and noises of power capacitors, the electric field force of electric core under AC voltage is investigated for a start, and then the time/frequency domain distribution characteristics of the electric field force on electric core under both fundamental frequency voltage and higher harmonic voltage are theoretically derived. Based on this, the excitation of the electric core is used to simulate the vibration characteristics of the power capacitor under electric field force. Based on the vibration characteristics of power capacitors under electric field force, a low stiffness vibration isolation system design for noise reduction of power capacitor body is proposed, and the low-frequency vibration isolation performance of the low stiffness vibration isolator is simulated and studied. Furthermore, combined with acoustic finite element analysis, the comparative study is conducted on the low-frequency noise radiation characteristics of power capacitors are compared with and without the low stiffness vibration isolation system embedded. The results show that the proposed low stiffness vibration isolation system can effectively reduce the low- frequency noise emissions at frequencies 100 Hz, 200 Hz, 600 Hz, 700 Hz and 800 Hz in spite of 500 Hz, which indicate that the noise reduction of power capacitor body can be achieved efficiently.
Inverter interfaced distributed generation (IIDG), influenced by grid connection requirements and control strategies, can be equivalently represented as a controlled current source externally, leading to unstable internal impedance after faults and resulting in poor adaptability of power-frequency variation distance protection in IIDG grid-connected environments. To address this issue, the reasons for the poor adaptability of power-frequency variation distance protection on the IIDG grid side are thoroughly analyzed. Based on the concept of coordinated control and protection, a positive-sequence fault component current suppression strategy is proposed. This strategy temporarily restores the fault component network under IIDG grid-connected conditions to a passive network similar to that under the influence of synchronous generators. PSCAD/EMTDC simulation results demonstrate that the proposed Strategy enables power-frequency variation distance protection to reliably distinguish between internal and external faults while improving its tolerance to fault resistance. This significantly enhances the operational reliability of power-frequency variation distance protection in IIDG grid-connected scenarios. Moreover, the control objective can be achieved within a very short time window. Once the protection meets the tripping conditions, the conventional IIDG fault ride-through control can be restored, ensuring no impact on its grid-connected capability during faults.
Under the influence of typhoon weather, islands, constrained by geographical conditions, are more vulnerable to disasters. Moreover, islands possess richer renewable energy resources, making it particularly crucial to ensure the reliable and economical operation of island microgrids. Considering the impact of typhoons on island power distribution networks, and based on the load distribution, renewable energy reserves, and mobile emergency power supply vehicle deployment in island areas, a multi-objective optimal scheduling model is established for island microgrids under typhoon conditions. The model aims to minimize load loss and enhance voltage stability in the distribution network while ensuring stable operation. By coordinating maintenance teams, flexible emergency resource regulation is implemented. An improved particle swarm optimization algorithm is employed to obtain the global optimal solution. Through optimization of inertia and learning factors in the algorithm, dynamic dense distance sorting is used to update the non-dominated solution set, improving the algorithm's accuracy and global optimization capability. Applied to practical case studies, the results demonstrate the feasibility and effectiveness of the optimized scheduling model.
To address the misclassification issues in existing density peak clustering algorithms caused by significant local fluctuations in data or uneven distribution of load categories, a power load curve clustering method based on singular spectrum analysis and improved density peak clustering algorithm is proposed. Firstly, singular spectrum analysis is employed to decompose the original power load data into a low-frequency component containing the main contour information and a high-frequency component representing noise. Secondly, the local density of the density peak clustering algorithm is redefined by integrating the ideas of K-nearest neighbors and natural nearest neighbors, followed by clustering the low-frequency component. Finally, applied to real-world power load datasets, the proposed method is compared with other clustering algorithms, and case study results validate its effectiveness in real data.
The hybrid networking of power line and wireless dual-mode communication can achieve complementary advantages, demonstrating significant application prospects in fields such as the Internet of Things for power systems and smart homes. To enhance the access flexibility and network resource utilization of dual-mode communication MAC algorithms, a superframe time slot resource allocation algorithm for dual-mode gateways based on deep reinforcement learning is proposed. Firstly, an interaction model between the dual-mode communication gateway and terminals is established, detailing the superframe structure and the specific execution steps of each phase. Secondly, key reward functions, state spaces, and action spaces for machine learning applications are defined, with the gateway monitoring and collecting parameters such as superframe access throughput. Through iterative learning and training, optimized parameters including the number and proportion of superframe time slots are obtained. Finally, simulations are conducted to verify the effectiveness and reliability of the proposed algorithm. Comparisons are made with fixed superframe structure and non-superframe structure algorithms, analyzing network throughput, average delay, packet loss rate under different algorithms, as well as the influence patterns of key parameters on system performance. Simulation results indicate that the proposed algorithm can effectively improve system performance in terms of throughput and delay, enabling flexible and efficient resource allocation.
The actual travel behavior of electric vehicles has a certain degree of randomness and uncertainty, the spatial and temporal distribution characteristics of their charging loads pose double challenges to the low-carbon economic operation of the integrated energy system in parks. To solve the imbalance between the supply and demand of renewable energy existing in the park and the overall economic problems of integrated energy systems in parks, a low-carbon operation model of integrated energy systems in parks that considers the travel chain of electric vehicles is presented. Firstly, the composition and operation mechanism of integrated energy systems in parks containing EVs is introduced, and the microgrid operator model containing the green certificate-carbon trading mechanism and the electrical and thermal loads of user aggregator are established, respectively. Secondly, the nonhomogeneous semi-Markov chain theory is used to simulate the travel behavior of EVs, construct a stochastic travel chain for EVs, and establish a charging decision mechanism by combining the transfer law and residence time distribution of vehicles between states. Finally, to maximize the interests of each subject and reduce the carbon emissions in the park, a Stackelberg game model between users containing electric vehicles and the microgrid operator is constructed. The results show that the established model can meet the charging demand of EVs under the complex travel chain, effectively increase the economic and environmental benefits of integrated energy systems in parks.
With the development of society, air conditioning load becomes an indispensable resource in demand response, and reasonable regulation and control of air conditioning can alleviate the shortage of power resources. Therefore, an air conditioning control strategy is proposed considering multiple factors. Firstly, based on the electric heating parameter model of inverter air conditioner, combined with multiple factors such as user satisfaction, thermal comfort and load controllability, the response potential evaluation model of air conditioning load cluster is established. Secondly, the micro-element method is introduced to decompose the demand response time of grid dispatching, and the micro-element virtual energy storage prioritization strategy is proposed based on the idea of virtual energy storage. Finally, a two-stage air conditioning load prioritization control model considering multiple factors is constructed. The simulation results show that the control strategy proposed in this paper can meet the needs of tracking power grid dispatching, improve the user′s willingness and reduce the user′s controlled frequency. In this way, under the condition of meeting the power demand of the power system, the satisfaction of users in participating in demand response can be effectively improved.
Aiming at the problem of high-dimensional solving and coexisting discrete and continuous variables in the site selection and capacity planning of distributed power generation, a method is proposed to first determine candidate locations and then a heuristic algorithm is used to optimize the capacity and location. Firstly, the nodes are screened according to the three indicators: active loss improvement rate, betweenness centrality and closeness centrality, and adjacent nodes are merged according to the node power load conditions to form a set of candidate nodes. Subsequently, an optimization model with the goal of minimizing total active loss, maximizing voltage stability, and minimizing total capacity of distributed power generation is constructed. In order to effectively solve the model, dung beetle algorithm is improved based on chaotic mapping, adaptive weights and Levy flight strategy to optimize the locations and capacities of distributed generation in the candidate nodes. Finally, simulation verifications on IEEE-33 and IEEE-69 node systems show that the proposed method performs well in reducing total active power loss and node voltage deviation.
With the integration of large-scale wind power clusters into the power system, wind farms are increasingly involved in reactive power regulation of the power grid. Based on this, an upper level optimization model is first established to minimize the active power loss and voltage deviation in the power system. Subsequently, the model is solved to reduce system network loss and achieve safe and energy-saving operation of the power grid. Additionally, a detailed analysis of the wind power cluster is conducted at a lower level, estimating its reactive power potential. Taking the reactive power potential of the wind farm as the objective function and the reactive power output of each wind turbine unit as the optimization variable, this study coordinates the reactive power output within the wind farm cluster and employs an improved whale optimization algorithm to solve this bi-level optimization model. Finally, the proposed optimization strategy and algorithm are verified through case studies to effectively reduce network losses and voltage deviation in the power system while maximizing utilization of wind power′s reactive power potential.
Large capacity, medium and long time scale gravity energy storage technology is an important means of new energy consumption and power system peak shaving. According to the operation characteristics of MW-level solid gravity energy storage system and the problem of intermittent power fluctuation caused by multi-weight switching, a solid gravity energy storage system is designed and a multi-machine coordinated system operation strategy is proposed. Firstly, the structure, operation characteristics and energy storage characteristics of the shaft-type gravity energy storage system are analyzed, the main electrical equipment and load types are determined, and its mathematical model is established. On this basis, the grid-connected simulation system of gravity energy storage motor is constructed, and two soft start modes of variable frequency electric auxiliary and variable load mechanical auxiliary are proposed. The single-machine control of power generation electric and the multi-machine coordinated operation strategy of linear variable frequency speed regulation are proposed. Finally, based on MATLAB / Simulink platform simulation, the feasibility and effectiveness of the designed MW-level gravity energy storage system and operation control strategy are verified.
In the context of building a new power system, energy sharing is an effective means to improve the operational efficiency of the park. The connection between parks through connecting lines enables the flow and sharing of energy, effectively promoting the optimal allocation of resources. However, the uncertainty of the source and load sides may lead to unstable power supply, increasing the complexity of energy sharing between parks. To cope with the risks brought by the uncertainty of distributed photovoltaics and flexible loads, various flexible resources are considered，including distributed photovoltaics, biomass power generation, micro gas turbines, energy storage equipment, electric vehicles, and flexible loads. The trapezoidal fuzzy membership parameter is used to describe the uncertainty of the response of distributed photovoltaics and flexible loads, and a fuzzy chance constraint model for power balance is established and transformed into a clear equivalence class. A multi-park energy sharing model with multiple flexible resources is constructed with the objective function of minimizing energy consumption costs in the park. The alternating direction method of multipliers(ADMM) is used to solve the objectives of each park alternately, protecting the internal operation information of each park. The calculation results show that energy sharing effectively coordinates multiple flexible resources to reduce energy costs and peak valley differences with load electricity consumption. Among them, the abandoned photovoltaic rate is reduced by 16.4 %, improving the stability, economy, and environmental protection of the park operation.
As the issue of contract performance risk in the electricity market gradually becomes more prominent, how to implement appropriate measures to handle the contract breaches of electricity retailers has become a key focus of research for regulatory agencies to manage and promote the healthy development of the electricity market. In response, this paper designs a flexible disposal technology tailored for regulatory agencies, considering the varying degrees of penalty amount by electricity retailers, and establishes a leader-follower game framework between the regulatory agency and the electricity retailers. The study analyzes how the regulatory agency selects appropriate disposal strategies based on different breach amounts from electricity retailers and disposal costs, as well as how electricity retailers respond under different disposal strategies. By simulating price fluctuations in the spot market, the relationship between the leader's varying intensity of disposal strategies and the follower's performance behavior is explored, and optimal disposal strategies are proposed for the leader. Case studies show that the flexible disposal technology can effectively control both the performance behavior of electricity retailers and the disposal costs.
The safety of overhead transmission lines is threatened by icing. Due to the different degrees of damage caused by ice types including glaze, mixed rime, rime, and wet snow to overhead transmission lines, the de-icing measures taken are also different. When the icing of overhead transmission lines reaches a certain level, appropriate operation and maintenance decisions will be made according to different icing type, including melting the ice, using mechanical removal and adjusting the operation mode. Icing type prediction can provide insights into future icing risks. On the basis of study on identifying insulator icing types through images, a data-driven ice type prediction model is proposed, which fuses monitoring images with micrometeorological data from the past three days. Based on the icing monitoring data of China Southern Power Grid from 2014 to 2021, the nearest micrometeorological monitoring time from the same terminal is searched according to the image capture time. Micrometeorological time series from this time and the past three days is combined with the image to form a sample, constructing a fused dataset of images and micrometeorological data for the data-driven ice type prediction model. The Grid Search Method-eXtreme Gradient Boosting （GSM-XGBoost） is used as the model algorithm, and the micrometeorological time series at 6h intervals from the past three days are used as inputs. The icing types identified from images are used as outputs. With 4 503 fused samples of the training set and 1 931 fused samples of the test set, the macro precision （P m）, macro recall （R m）, and macro F1 score of the data-driven icing type prediction model are 95.0 %, 96.3 %, and 95.6 %, respectively. The icing type prediction for overhead transmission lines is achieved accurately.
ISSN 1674-0629 (Print)
Started from China Southern Power Grid Corporation
Published by: China Southern Power Grid Corporation
