Abstract:

The utilization of a new energy hydrogen production system is an effective approach to enhance the absorption capacity of renewable energies such as wind and solar power. The current research on energy management of electrolyzer, both domestically and internationally, primarily focuses on single-electrolyzer. The energy management of single-electrolyzer fails to account for the nonlinearity in its operational characteristics, thereby posing challenges in considering the hydrogen production efficiency of multi-electrolyzers and its impact on system economics. The present study focuses on the energy management of a novel hydrogen production system incorporating multi-electrolyzers. The energy management optimization model incorporates wind power, photovoltaic systems, batteries, and multiple electrolyzers to achieve targets for new energy consumption rate, economic income, and hydrogen production rate. Taking into account the operational characteristics of a single electrolyzer and production constraints, the multi-objective optimization problem is solved by strength Pareto evolutionary algorithm 2 (SPEA2). The simulation research demonstrates that the proposed energy management strategy can achieve a 100% absorption rate of newly generated power from renewable sources, while simultaneously increasing the hydrogen production efficiency per unit by 5.15%. The effective management of energy in a multi-electrolyzers hydrogen production system is crucial for enhancing the efficiency of hydrogen production and effectively addressing the limitations associated with single-electrolyzer operation and energy management.

Abstract:

Hydrogen energy storage has many characteristics such as large energy storage capacity, long storage time, clean and pollution-free, and it realizes the interconnection and complementation of multiple energy networks and collaborative optimization. It is expected to become an important supporting technology to promote the development of distributed energy and improve the efficiency of terminal energy utilization. In order to improve the reliability and renewable energy utilization rate of islanded microgrid, the operation characteristics of typical electric, hydrogen and thermal devices are analyzed, and a multi-objective optimization configuration model of the islanded microgrid is proposed. Then, the target problem is solved based on simulated annealing particle swarm optimization (SAPSO) algorithm to obtain technical and economic indicators under different configuration schemes. Finally, based on the annual natural resource and electric heating load characteristic curve of a certain place in the north, the model built on MATLAB can effectively promote that the load loss rate of the proposed multi-energy complementary configuration scheme decreases by 3.18%, and the utilization rate of renewable energy increases by 8.37% compared with the traditional electric energy storage configuration scheme. Thus, the proposed configuration scheme can effectively promote the consumption of renewable energy and ensure the economy and power supply reliability of the independent micro-grid.

Abstract:

In order to tap the response potential of demand-side resources, a multi-time scale low carbon scheduling strategy for the integrated energy system (IES) with multiple demand responses is proposed. First of all, considering the difference in response of demand-side resources at different time scales, a multiple integrated demand response (IDR) model considering price and incentive is established. Secondly, in order to reduce the impact of source and load forecasting errors on the operation of IES, the day-ahead low carbon economic scheduling model and the intra-day double-time-scale rolling optimization flattening model are constructed respectively. Finally, different scenarios are set up for comparative analysis in the simulation of a numerical example. The results show that compared with traditional IDR, the multiple IDR can effectively tap user response potential and improve system economy. In addition, the multi-time scale scheduling strategy taking into account IDR can effectively alleviate the power fluctuation caused by the source and load error and reduce the carbon emissions of the system, so as to realize the low carbon, economic and stable operation of IES.

Abstract:

As the flexibility needs of new power systems with a high share of new energy sources increase, it is important to develop a flexibility resource market operation mechanism to balance the volatility and uncertainty of large-scale new energy output in real time. To this end, a wind-photovoltaic-thermal-hydrogen coordinated planning method based on a demand proportional allocation mechanism (DPAM) is proposed in the paper. Firstly, a three-phase coordinated operation strategy for wind and photovoltaic systems with hydrogen storage is formulated. Inter-station power trading between wind and photovoltaic based on DPAM coordinates the revenues and expenditures of wind and photovoltaic stations. Thermal power and hydrogen storage systems are used to provide operational flexibility, and a fixed-profit-proportional model is used to ensure the stability of hydrogen storage system revenues. Then, integrating the investment decision and operation simulation, power trading costs and flexibility resource regulation costs are incorporated in the optimization objective. A multi-timescale coordinated planning model with wind serving as the primary power source and thermal power and hydrogen storage as the auxiliary power sources is established. Finally, a provincial grid in northeast China is used as an example for analysis. The results show that the proposed methodology can be used for power planning in an economical and environmentally friendly way, reducing flexibility resource requirements and investment costs while incrersing the power utilization of wind and photovoltaic stations.

Abstract:

The integration of energy storage and wind power is an effective solution to address the challenge of system stability. Hydrogen energy storage system offers large storage capacities to supply electricity, making it highly promising for large-scale consumption of wind power. However, due to the uncertainty of wind power, the thermal demand of hydrogen energy storage system is uncertain when the system switches operation modes frequently. Therefore, the thermal energy demand of the hydrogen storage system in intermittent mode is considered comprehensively. Initially, the basic structure of the electricity and heat integrated energy system connected to the hydrogen energy storage system is introduced, and a wind-hydrogen hybrid system composed of a wind farm and hydrogen energy storage is described. Furthermore, a robust interval optimization dispatch model for the electricity-heat integrated energy system is proposed, taking into account the uncertainty of the thermal balance demand of the wind-hydrogen hybrid system. Finally, based on the duality theory, the proposed model is transformed into a mixed-integer linear programming problem. By comparing and analyzing the optimization results of various scenarios through numerical examples, the effectiveness of hydrogen energy storage in promoting wind power consumption and increasing the comprehensive energy utilization rate of the system is verified. It is also demonstrated that the system's efficiency can be improved by considering the working temperature of electrolytic and fuel cell, leading to an increase in the grid-connected power of wind farms.

Abstract:

The technology of power to hydrogen (P2H) and natural gas mixed hydrogen is highly regarded in theoretical research and engineering application in promoting the consumption of renewable energy and reducing carbon emissions. Targeting the park integrated energy system with high proportion renewable energy, an optimal configuration method of P2H considering natural gas mixed with hydrogen and cross seasonal storage is proposed. Firstly, the operational framework and energy flow relationship of the park integrated energy system are sorted out, and the mathematical models for the internal energy production, conversion, and storage equipment in the park are established. Secondly, a configuration model of P2H is established with the goal of optimizing the annual investment cost of equipment, the annual operating cost, and the carbon trading cost. Finally, the effectiveness of the proposed model is verified by an example analysis. The impact of changes in investment cost of electrolytic cells, the upper limit of mixed hydrogen volume fraction and cost weight coefficients of economy and low-carbon on the planned operation results are analyzed. The simulation results show that the proposed model can effectively improve the absorption capacity of renewable energy and reduce the overall economic cost and carbon emissions.

Abstract:

High-frequency electromagnetic fields can couple to overhead transmission lines. When the wavelength of the high-frequency electromagnetic fields is smaller than the height of the transmission lines, the classical transmission line theory is no longer applicable. Although the traditional full-wave numerical algorithm can solve this problem, the solution is very inefficient, and it needs to consume a lot of time and computational resources. In addition, power system transmission lines often have multiple vertical ends, and the line layout is complex, while previous studies on efficient solutions for power system transmission lines are simple models with only two vertical ends on the left and right, which can not be conformed to the structure of the actual power system transmission lines. In order to solve these problems, a theoretically effective method is proposed in this paper to address the high-frequency electromagnetic fields coupling to multilevel transmission lines of power systems with scatterers. The computationally efficient asymptotic method is used, and the concept of scatterers is introduced into the asymptotic method. Finally, the accuracy and effectiveness of the method are verified by numerical examples.

Abstract:

Overhead transmission line inspection is an important task in power grid maintenance, and the utilization of unmanned aerial vehicles (UAVs) for line inspection has become a significant approach in power inspection operations. Firstly, the overview of the architecture of the human-machine collaborative operation system and the UAV intelligent autonomous operation system in UAV inspection tasks are provided. Next, the current status of datasets for defect inspection in overhead transmission lines is analyzed and the data augmentation techniques are discussed. Subsequently, this paper reviews typical deep learning-based methods for UAV image defect detection in detail, along with evaluation metrics. The advantages and limitations of various approaches are compared and summarized. Furthermore, the impact of image acquisition specifications, dataset formats, and specialized defect detection algorithms are discussed on the detection performance for overhead line defects in UAV image visual inspection methods. The shortcomings of image detection metrics and category definitions in the specialized field of power inspection are pointed out. Finally, future directions for deep learning-based UAV image defect detection tasks are explored.

Abstract:

Line commutated converter based high voltage direct current (LCC-HVDC) is the key to large-scale grid connection and long-distance transmission of renewable energy. However, faults such as DC blocking and commutation failure may lead to short-term excess reactive power and transient overvoltage at the sending end, endangering operational safety. In this paper, a method based on grid-forming based reactive power compensation device (GFM-RPC) to suppress transient overvoltage at the renewable energy transmission terminal is proposed, which is different from the traditional reactive power compensation with the characteristic of current source based on voltage-current cascade control. A voltage dynamic analysis model based on differential-algebraic relationship is constructed to clarify the mechanism of GFM-RPC suppressing transient overvoltage, and the advantages of the proposed method compared to existing methods based on static synchronous compensator (STATCOM) for suppressing transient overvoltage is compared and analyzed. The simulation is used to verify the effect of GFM-RPC on the suppression of transient overvoltage at the renewable energy transmission terminal, and the influence of the main parameters on the overvoltage suppression effect is analyzed. It is shown that reactive power compensation devices such as STATCOM with external characteristics of current sources exhibit a reverse regulation characteristic of deteriorating voltage dynamics at the moment of DC transmission system fault, while the reverse regulation characteristic can be eliminated by GFM-RPC, as well as suppression of voltage magnitude overshoot can be achieved through reasonable parameter configuration for GFM-RPC.

Abstract:

The existing methods for evaluating voltage sag severity do not sufficiently consider the effect of the multiple line characteristic factors on the line failure probability, which leads to a large error in the evaluation results. Therefore, an evaluation method for voltage sag severity based on multiple line characteristic factors fusion is proposed. Firstly, based on line historical fault data, the influence degree of multiple line characteristic factors on line fault which employ association rules to quantify is researched. Secondly, by improving the D-S evidence theory to fuse multiple line characteristic factors, an accurate line annual failure probability model is established, and the voltage sag severity of nodes by introducing maximum entropy into the method of fault positions are obtained. Finally, a comprehensive voltage sag severity index considering both voltage sag severity of power grid side and tolerance characteristics of sensitive equipment on the user side is proposed to evaluate node voltage sag severity. Based on the actual power quality monitoring data for validation and comparison with the evaluation cases that do not fully consider the line characteristic factors, the results show that the proposed method can effectively improve the accuracy of voltage sag severity evaluation.

Abstract:

Aiming at the voltage fluctuation problem caused by indirectness and uncertainty in new energy power generation system, the backstepping control strategy is firstly applied in electric spring (ES) system, and an ES backstepping control strategy with command filtering is proposed. According to the mathematical model of ES, the controller is designed by backstepping method and command filter is added to solve the computational inflation problem in ES backstepping control. Meanwhile, the error compensation signal is designed, and the stability of the system is verified by the Lyapunov stability theory. The effect of ES on critical load (CL) voltage stabilization is simulated when the grid-side voltage of the new energy power generation system fluctuates, and the sensitivity analysis of different system parameters to the control strategy is discussed in this paper. Compared with traditional proportional integral (PI) control, the dynamic response speed of CL voltage is improved by 0.07 s, while the CL voltage distortion rate is reduced by 31%. In parameter sensitivity analysis, the maximum deviation rate of CL voltage is 0.318%. Meanwhile, the maximum voltage amplitude fluctuation range that it can withstand is increased by 52.9 V. It is verified that the control strategy proposed in this paper has the advantages of fast response, low harmonic content and strong anti-interference ability.

Abstract:

With the continuously challenges of energy and environmental, the development of distributed power generation technologies focused on renewable energy is rapidly advancing. Microgrid based on inverter power source is the important form of distributed generation, but it is difficult to isolate faults selectively and accurately when short circuit fault occurs in grid, and will result in overall shutdown. Aimed at the characteristics of inverter power sources and microgrid structures, a protection method based on equivalent distance for lines of source-grid-storage microgrid is proposed. Realtime information of the fault line is modulated into the terminal frequency by modulation techniques. Subsequently, virtual short-circuit currents are obtained via demodulation techniques, and fault distances are measured through equivalent internal potential. It is verified by theoretical analysis and simulation experiments in four tipical cases, such as single power source circuits, and this method can effectively calculate virtual short-circuit currents. These currents are equevlant to the virtual currents output when traditional rotating motors act as power sources. The proposed method can use the aforementioned virtual short-circuit current to determine the distance between the inverter power source and the fault point.

Abstract:

To address the flexible and economical peak shaving issue of power grid in the presence of large-scale renewable energy penetration, a multi-source complementary peak shaving scheduling method with detailed consideration of nuclear power risk quantification is proposed. Firstly, the mechanism of low load peak shaving for nuclear power is analyzed, and its peak shaving risk quantitative indicators are considered to balance its economical efficiency and safety. Secondly, a wind-solar-thermal-nuclear-storage based multi source complementary peak shaving scheduling model is established, which takes the minimum total operating cost as the optimization goal, and considers the loss of wind and solar power waste and the increased power generation cost of different power sources participating in peak shaving, and the mode is solved. Finally, case study demonstrates the effectiveness of the proposed optimization scheduling model and its superiority. The results show the proposed method can achieve a 94.17% reduction in wind and solar waste compared to thermal power peak shaving and nuclear power peak shaving without energy storage participation, and a 1.26% reduction in carbon emissions. This indicates a significant improvement of the peak shaving ability of a high proportion of wind and solar power grid within multi source complementary mode. The proposed method provides a practical approach to realize economical and low carbon economy operation of multi source complementary system.

Abstract:

False data injection attack (FDIA) is a serious threat to the security and stable operation of smart grids. In this paper, a FDIA detection algorithm that combines unsupervised and supervised learning is proposed, solving the problems of scarce labeled data and extremely imbalanced normal and attack samples. Firstly, contrastive learning is introduced to capture the features of a small amount of attack data, and it generates new attack samples to achieve data augmentation. Then, various unsupervised detection algorithms are used to perform feature self-learning on a large number of unlabeled samples, addressing the problem of scarce labeled samples. Finally, the features extracted by the unsupervised algorithm are fused with the historical feature set, and a supervised XGBoost classifier is constructed to identify and output the detection results. The results on the IEEE 30-node system show that the proposed method can enhance the stability of the FDIA detection model under scarce labeled samples and imbalanced data, compared with other FDIA detection algorithms. The proposed method can improve recognition accuracy and reduce false alarm rate.

Abstract:

With the increasing popularity of electric vehicles, the number of electric vehicle users in industrial parks is increasing, and their charging and discharging behaviors pose great challenges to the planning and operation of park integrated energy system (PIES). A two-layer optimal scheduling of PIES considering the charging and discharging willingness of electric vehicles is proposed. Firstly, a charging and discharging willingness model is established based on factors such as dynamic real-time electricity price, battery charge capacity, battery loss compensation, and additional participation incentives. An improved electric vehicle charging and discharging model is obtained on this basis. A two-layer optimal scheduling model is established with the goal of minimizing the charging cost of the car, and the inner model is transformed into the constraints of the outer model through the Karush-Kuhn-Tucker (KKT) condition, so as to quickly and stably solve the single-layer model. Finally, the simulation solution is performed, and three different scenarios are set up. The proposed model is compared with the general charging and discharging willingness model. The effectiveness and feasibility of the two-layer optimal scheduling of PIES proposed in this paper are verified.

Abstract:

Aiming at the problem of insufficient primary frequency regulation resources of power grid in the future, an adaptive control strategy based on charging and discharging margin is proposed for virtual energy storage of electric vehicle aggregation to participate in primary frequency regulation of power grid. Firstly, regulatory and operating range of the electric vehicle is analyzed. Secondly, the primary frequency regulation method of electric vehicle aggregation participating in the power grid is studied. Considering the margin of charging and discharging time and state of charge (SOC), charging and discharging margin index of electric vehicles is designed. Then, an adaptive primary frequency regulation control strategy based on charging and discharging margin is proposed to optimize the droop power of electric vehicles participating in primary frequency regulation, which considers both requirements of primary frequency regulation of power grid and charging, discharging of electric vehicles. Then, the primary frequency regulation capability of virtual energy storage in electric vehicle aggregation is evaluated by regular update, and the evaluation index of primary frequency regulation effect is proposed. Finally, through the simulation analysis of regional power grid cases, the effectiveness of the proposed strategy in reducing the system frequency deviation and optimizing the primary frequency regulation output of electric vehicles is verified.

Abstract:

Gas gap switch has good application prospects in power systems, due to its quick response and simple structure. However, there is still little research on insulation recovery characteristic. Therefore, the double pulse method is used to study the influence of switch gap distance, trigger medium pressure and trigger medium type on the insulation recovery characteristics of gas switch. The experimental results show that the insulation recovery characteristics of induced trigger gas gap switch experience three stages: transition period, fast recovery period and saturation period. The duration of the saturation period is much longer than the sum of the previous two stages, and there was no ‘platform phenomenon’ in the rapid recovery period. With the decrease of gap distance, the insulation recovery rate of gas switch increases gradually, and the basic recovery time of gap insulation (insulation recovery coefficient R_U>90%) can be reduced by 50%. The influence of trigger medium pressure on the insulation recovery of gas switch is significant, and the influence characteristics on the insulation recovery process are different. Increasing the trigger medium pressure will slow down the insulation recovery process of gas switch. In 0.1~0.3 MPa compressed dry air, the basic recovery time of gas gap switch insulation corresponds to 11~40 ms. The strong electronegative gas SF₆ has a significant effect on the insulation recovery rate of gas switches, and its insulation recovery rate is close to 4 times that in air. The research results provide theoretical guidance for the rapid insulation recovery of gas gap switch.

Abstract:

The insulation performance of transformer bushings is a crucial aspect that directly affects the safe operation of equipment. To diagnose the insulation status of transformer bushings and mitigate the impact of small-sample imbalanced data on diagnostic results, a particle swarm optimization combined with back propagation neural network (PSO-BPNN) and adaptive synthetic sampling (ADASYN) method are employed to fault diagnosis of transformer bushing. Initially, historical fault data of transformer bushings are gathered, and a sample set of dissolved gases in transformer oil with distinct fault categories is established. The ADASYN algorithm is used to synthesize the minority class samples in the original data, which allowed for obtaining balanced fault data. The balanced dissolved gases in oil served as the model input, and the fault status is used as the label output to diagnose the transformer bushings using the PSO-BPNN model. To diagnose the bushings under the original sample set, the back propagation neural network (BPNN), genetic combined with back propagation neural network (G-BPNN), cuckoo search combined with back propagation neural network (CS-BPNN), and PSO-BPNN models are used. The results reveal that the PSO-BPNN model based on ADASYN balanced data exhibited the highest accuracy among the various models for fault diagnosing the insulation status of transformer bushings. This approach effectively mitigate the impact of small sample imbalanced data on diagnostic results, and provide an effective method for assessing the insulation performance of transformer bushings.

Abstract:

Currently, the C₄F₇N/CO₂ hybrid gas is regarded as one of the most promising SF₆ substitutes. In order to explore the arc quenching capabilities of the C₄F₇N/CO₂ gas mixture, a set of arc energy balance equations is developed in accordance with the arc energy balance theory to determine the change law of overpressure within the arc-quenching pipe with time. In the paper, COMSOL Multiphysics software is used to establish a two-dimensional magneto-hydrodynamic model of the arc quenching structure of the pressure-explosive gas flow lightning gap, and the arc quenching characteristics of the different ratios of C₄F₇N/CO₂ gas mixtures in the pipeline are simulated and analyzed in the condition that the amplitude of the lightning current is 4 kA under the action of the impulse arc. Based on the above method, the change laws of conductivity, velocity and pressure are analyzed in the model by combining the arc energy balance theory. When compared to the theoretical results, it can be shown that 20%C₄F₇N/80%CO₂ extinguishes arcs more effectively than 10%C₄F₇N/90%CO₂ or 5%C₄F₇N/95%CO₂ does, while the arc quenching time is in line with the specifications for air blowing to extinguish arcs.

Abstract:

Power losses on the upper and bottom insulated gate bipolar transistors (IGBTs) in half bridge sub-modules (HBSMs) are uneven when hybrid modular multilevel converters (MMCs) work as inverters. The loss of the bottom IGBT in HBSM is much higher than any other device, which results in the high thermal stress and failure rate. To address this problem, a device power loss distribution optimization method is proposed in this paper. Firstly, device power losses are calculated, and the distribution characteristic is analyzed. Secondly, the influence of sub-module capacitor voltage on the device power loss is analyzed, and it is found that the loss of the bottom IGBT can be reduced for a low sub-module capacitor voltage. Thirdly, through combining third harmonic voltage injection and the differential distribution of the arm output voltage reference, a loss distribution optimization method based on low sub-module capacitor voltage is proposed. Finally, the simulation and experiment results verify that the proposed method can improve the uneven device loss distribution of hybrid MMC and the operation reliability of the converter.

Abstract:

To ensure the three-level neutral point clamped (NPC) grid-connected inverter in a grid-connected system continuous operation after a single-phase bridge arm short circuit or open circuit failure, an optimal compensation fault-tolerant control strategy with low common-mode voltage is proposed in this paper, which is based on space vector pulse width modulation (SVPWM). Firstly, the reference voltage vector synthesis rule is determined by analyzing the common mode voltage corresponding to the switch state of the eight-switch three-phase inverters (ESTPI), which is the topology of the three-level NPC inverter with one phase failure. Then, the mechanism of neutral point potential fluctuation is analyzed by the neutral point current change in a fundamental wave period. Further, the space vector synthesis is compensated based on this mechanism. Finally, the low-pass filter and hysteresis controller are designed to optimize vector synthesis compensation, so as to ensure the quality of grid-connected current and effectively restrain the neutral point potential offset of the DC bus. The simulation results show that the proposed fault-tolerant control strategy can realize the stable and reliable operation of the grid-connected system after single-phase bridge arm failure, and the common-mode voltage can be reduced for one-third of the period time. The quality of grid-connected current is improved significantly. The proposed controller has good control characteristics when the grid-connected current changes.

Abstract:

The traditional method for analyzing the mechanism of stator winding inter turn short-circuit faults of synchronous condensers usually assumes that the stator current of the motor is close to three-phase symmetry, and based on this, a mathematical representation of the fault current of synchronous condensers is established. However, once a stator winding inter turn short-circuit fault occurs, the symmetry of the three-phase stator current of the synchronous condenser will be disrupted, making the mathematical representation established by traditional fault mechanism analysis methods unable to accurately reflect the changes in the internal electrical quantities of the motor. A mathematical model is established for the instantaneous active and reactive power of a synchronous condenser after a fault by introducing the symmetrical component method. Using the second harmonic in the instantaneous active and reactive power for stator winding inter turn short-circuit fault diagnosis is proposed. The simulation and experimental results indicate that compared to the traditional method of diagnosing faults using the ratio of the third harmonic of stator current to the amplitude of the fundamental wave, the proposed method can improve the diagnostic sensitivity by at least seven times under mild fault conditions, making it easy to complete early fault diagnosis. At the same time, the fault feature quantities in the proposed method are not affected by the synchronous condenser operating conditions and fault locations, and also have strong robustness.

Abstract:

The loss of excitation fault of large condenser seriously affects the safety and stability of equipment and system. The reliability and selectivity of existing low-voltage and reactive power reverse criteria based on local static threshold are insufficient. In this paper, a loss of excitation protection principle based on intelligent identification of the global dynamic trajectory of the measured impedance is proposed, which can reflect the operating state of the condenser. From the point of view of kinematics, characteristic quantity time series that can accurately restore the measured impedance trajectory under loss of excitation and other conditions is formed, and statistics is further introduced to extract the highly explanatory features. Multiple kernel learning support vector machine (MKL-SVM) is trained by using the combination of global and local kernel functions of adaptive weights to ensure the learning ability of the classification model while enhancing its generalization ability. A two-stage recognition strategy based on the space distance of the classification core is proposed, which can improve the protection reliability while ensuring the system security. The model of condenser connected to power grid is built based on PSCAD simulation platform for verification, and simulation results show that the proposed method does not need to collect the electrical quantities at the rotor side with high identification accuracy, and it still has excellent applicability in the face of new energy access and unknown disturbances.

Abstract:

In the existing locational marginal price mechanism, conventional generators have market manipulation power. When independently participating electricity market, the market share of energy storage plants can be suppressed and squeezed by each generator by taking strategic offers. Energy storage plants are prevented from participating in market clearing by traditional generators, and the total cost of indirect market clearing is caused to increase. To this end, a market competition mechanism is proposed in the paper that includes the participation of traditional units and energy storage power plants. Firstly, the drawbacks of the existing market settlement mechanism and the reasons hindering the participation of energy storage in market clearing are analyzed. Secondly, a market clearing model with energy storage participation is established, and a two-stage stochastic planning model is solved by using the sample mean approximation. Then, a day-ahead market value allocation mechanism adapted to the participation of energy storage is proposed based on the Vickrey-Clarke-Groves (VCG) settlement mechanism. Finally, a strategy to address the system issue imbalance of payments and revenues caused by incentive compatibility is proposed. The IEEE 30-node system is used in the paper. It is demonstrated that the mechanism satisfies the properties of incentive compatibility, imbalance of payments and revenues and weakening of the market manipulation power of conventional generators. The total cost of clearing the system is reduced and the risk is lowered in the face of sharp market price fluctuations.

Abstract:

Resilience describes the system capability of defense and recovery to extreme disasters. The transmission system, as an important part of the power system, may occur large-area power blackouts under extreme mountain fire disasters which are low-probability but highly-risk events. A resilience assessment framework considering the whole process of disaster under extreme mountain fire disasters is developed. Firstly, the influence of extreme mountain fire disaster on transmission line failure rate is quantitatively analyzed, and the mathematical relationship between the location of fire point and the line failure rate is established. Then, failure scenarios are obtained by using the system information entropy to describe the possible failure scale that caused by mountain fire disasters. Secondly, considering the geographical location of failure components, repair crews dispatch and repair time, the power transmission system restoration model, aiming at minimizing load reduction under mountain fire disasters, is established. The resilience assessment method of power transmission system considering the whole process of disasters is proposed. Finally, taking IEEE RTS-79 bus system as an example, the effectiveness of the proposed model and the resilience assessment method is verified. The numerical results show that the proposed method can effectively and comprehensively measure the influence of various factors on the elastic performance of transmission system. In addition, the effects of three typical technical measurements on improving elasticity are quantitatively analyzed, which provides a quantifiable reference for the power sector to formulate prevention and recovery strategies for extreme wildfires.

Abstract:

With the sustained growth of renewable energy, the flexibility of the power system has emerged as a critical indicator, influenced by frequent peak regulation. However, a dilemma exists between maximizing flexibility and optimizing economy, and a multi-objective optimization can serve as means of coordinating conflicts. In this study, condensing cogeneration units equipped with a molten salt heat storage system is research object, an optimization model for day-ahead scheduling is established, with flexibility and economy as the primary optimization objectives. To account for various constraint condition such as unit ramp rate, unit output and thermoelectric coupling properties, an enhanced version of the non-dominated sorting genetic algorithm-Ⅱ (NSGA-Ⅱ) is proposed to efficiently solve the optimization problem of a molten salt heat storage system. Flexibility and economy are used to compare and analyze the non dominated solution set obtained from the Pareto front. At the same time, the heat storage and release characteristics of a molten salt heat storage system are analyzed. The findings reveal that a focus on flexibility necessitates great motten salt heat storage to manage insufficient heating during low-load operations. Conversely, an emphasis on economy leads to frequent adjustments in heat storage to maintain optimal unit performance.