Search

Severe disturbances in a power network can cause the system frequency to exceed the safe operating range. As the last defensive line for system emergency control, under frequency load shedding (UFLS) is an important method for preventing a wide range of frequency excursions. This paper proposes a hierarchical UFLS scheme of "centralized real-time decision-making and decentralized real-time control" for inter-connected systems. The centralized decision-layer of the scheme takes into account the importance of the load based on the equivalent transformation of kinetic energy (KE) and potential energy (PE) in the transient energy function (TEF), while the load PE is used to determine the load shedding amount (LSA) allocation in different loads after faults in real-time. At the same time, the influence of inertia loss is considered in the calculation of unbalanced power, and the decentralized control center is used to implement the one-stage UFLS process to compensate for the unbalanced power. Simulations are carried out on the modified New England 10-generator 39-bus system and 197-bus system in China to verify the performance of the proposed scheme. The results show that, compared with other LSA allocation indicators, the proposed allocation indicators can achieve better fnadir and td. At the same time, compared with other multi-stage UFLS schemes, the proposed scheme can obtain the maximum fnadir with a smaller LSA in scenarios with high renewable energy sources (RES) penetration. [ABSTRACT FROM AUTHOR]

• A novel hybrid model and data-driven voltage stability online assessment approach is proposed. • The parameters in the model-driven method are modified based on the response data. • To improve accuracy, impedance difference is included in the data-driven input data. • The proposed method combines the clear mechanism of the model-driven and high accuracy of the data-driven. With the continuously increasing integration of renewable energy sources into power grids, the dynamic response of a power system is becoming more complex. For example, the interaction between the dynamic loads and low-voltage ride-through of renewable energy generators makes the voltage response more rapid and unpredictable. Ensuring the accuracy and speed of traditional voltage stability assessment methods is difficult. This study developed a novel hybrid model and data-driven voltage stability assessment approach. First, the equivalent parameters of a power system were calculated based on the measured data, and the parameters were constantly modified based on the response data. To further improve the accuracy of the approach, a data-driven method was introduced to correct the assessment results using a Thevenin equivalent-based assessment. The difference between the Thevenin and system impedances, which better reflects the system stability, was included in the data-driven input data. Finally, by combining the clear physical mechanism of the model-driven method and high accuracy of the data-driven method, the final the assessment process was a serial combination of the model- and data-driven methods. The effectiveness of the method was verified using an IEEE New England 10-generator 39-bus test system and a 100-bus actual system in China. The results showed that the method developed was more accurate and had higher robustness under data loss and noise conditions than other methods. [ABSTRACT FROM AUTHOR]

• Analyzed the distribution principle of disturbance power in different regions, and was able to obtain the unbalanced power of the system based solely on disturbance region data. • Particle swarm optimization (PSO) and LSTM is introduced to obtain the error between the calculation result of the multi region frequency response model (RFR) model in disturbance region and the real value, Then, in order to improve the integrity and interpretability of the model, the model-based method and the data-driven method are integrated through a parallel scheme, and the final result is the superposition of the two methods. • An active load shedding control strategy based on TFSI prediction results in the disturbance region is proposed. This strategy can perform load shedding operations in the disturbance region in advance before the frequency reaches the load shedding threshold, and can quickly prevent frequency drops to achieve smaller frequency deviations. With the expansion of power grid scale, the online prediction and control of transient frequency is very important for the system security. Some known and unknown factors are ignored in traditional prediction analysis methods based on physical model, which leads to the inevitable error in calculation results. This paper proposes a model-data integration driven method for transient frequency prediction. In the model part, a multi region frequency response model was established, and the disturbance region was considered as the weak link of system frequency variation from the spatial dimension. In the data-driven part, the long short-term memory network based on particle swarm optimization is used to obtain the error between the calculated results of the model-based method and the truth value, and the model-based method and the data-driven method are integrated through the parallel mode. The final transient frequency security indicators result is the superposition of the calculated results of the two methods. Based on the TFSI prediction results, an active load shedding control strategy was constructed, which can act before the traditional load shedding frequency threshold. The performance of TFSI prediction and active load shedding control was verified on the modified New England 10-generator 39-bus system and 197-bus system in China. The TFSI prediction results show that the proposed method is more accurate and faster than other integration methods, and it is robust in both data loss and unknown scenarios. And, the constructed active load shedding control strategy has better frequency dynamics compared to traditional load shedding strategies based on fixed frequency thresholds. [ABSTRACT FROM AUTHOR]

This study investigates the benefits gained by Chinese listed enterprises participating in China's Belt and Road Initiative (BRI). We dispel the concern that the BRI provides a way for Chinese firms to shift excess capacity, instead finding that the capacity utilization of BRI participant firms is worsening. State-owned enterprises (SOEs) are the primary contributors to this effect. Moreover, we present evidence that overinvestment and decreased innovation capabilities are the underlying channels of the overcapacity problem. Finally, further analyses reveal distinct benefits accruing to the different ownership structures of BRI-participating enterprises, implying diverse motivations. We demonstrate that domestic political and overseas market resources for non-SOEs in the post-BRI period are significantly improved, which is not the case for SOEs. • We provide evidence for the motivations of Chinese firms participating in the Belt and Road Initiative (BRI). • We find that the BRI has a negative effect on the capacity utilization of participating firms. • We reveal distinct benefits between firms with different owner structures, implying varied BRI participating motivations. • We provide rich robustness checks, reasonable mechanism analysis and much accurate measurement. [ABSTRACT FROM AUTHOR]

The large-scale access of a substantial proportion of the distributed photovoltaic (PV) power sources has introduced considerable source-side randomness and volatility to the distribution network in the development of PV power generation in the whole county of China. This paper proposes a cooling-heat-electric multi-energy coupled power distribution network expansion bi-level planning model to reduce the influence of uncertainty and improve the PV consumption rate (PVCR). It is based on the distributed hydrogen-thermal storage system (DHTSS) in a high-proportion PV scenario. First, an irradiation-load-temperature multi-grid joint scenario generation model is constructed based on each meteorological cluster to effectively describe the uncertainty of PV power generation and multi-energy consumption. Subsequently, a refined distributed PV system physical model chain is constructed to address the low accuracy of the traditional PV output model in the high PV output (HPVO) scenarios. Lastly, considering the power supply reliability objective of the low-probability with high-load (LPHL) scenarios, a multi-objective bi-level expansion planning model of the distribution network is constructed to accurately determine the capacity of each equipment in DHTSS, the distribution lines, and low-voltage transformers to be expanded in the distribution network. • To propose an irradiation-load-temperature multi-grids joint scenarios generation model. • To propose a method of refined distributed PV system physical model chain. • To propose an operating model of the distributed hydrogen-thermal storage system. • To propose a multi-objective bi-level distribution network expansion planning model. [ABSTRACT FROM AUTHOR]

To fulfill the commitment to carbon emission reduction, the grid penetration rate of renewable energy in China has increased rapidly. High penetration of renewable energy brings a significant challenge to the peaking ancillary services providers. In northern China, coal-fired units still play a significant role in peak-shaving, especially in areas where pumped hydropower, gas-fired power, and controllable load are limited. The current peaking ancillary service market in China cannot effectively mobilize the enthusiasm of peaking power plants. Fair calculation of the peak-shaving costs of coal-fired power plants is significant for formulating an ancillary service market mechanism. Current research on peak-shaving costs calculation of coal-fired power fleets is limited to the operating coal consumption costs, wear-and-tear costs, and the additional environmental costs (do not contain pollutant tax and ashes treatment). However, this costs calculation lacks consideration of the additional peaking coal consumption costs, the additional peaking environmental costs with pollution tax and ashes treatment, and the power output loss caused by the peak-shaving process. Therefore, this study proposes a novel calculating framework that includes all the potential peak-shaving costs of the coal-fired generating units mentioned above. To evaluate the proposed framework, a case study was conducted in a coal-fired power plant in Dalian City, Liaoning Province, China, in 2020. The results show that the total costs of peak-shaving in 2020 was only 4.96 million Yuan according to the existing method. However, according to the proposed costs calculation framework, the peak-shaving costs in 2020 should be 16.97 million Yuan. The power grid compensated 12.83 million Yuan to the power plant, and the real peaking net profits was -4.14 million Yuan, which means coal-fired power units are unprofitable under the current peaking compensation mechanism. Compared with the existing traditional costs calculation method, the proposed method could provide a more comprehensive and accurate costs accounting for the deep peak-shaving process of coal-fired units. This study may help formulate the benchmark and reasonable compensation in the peak-shaving ancillary service market. • A novel peak shaving cost calculation model is proposed for coal-fired power plants. • Minutes-level operational data are used to analyze peak shaving costs and profits. • Coal-fired power plants may not benefit under the current compensation mechanism. • The economic comparison between different coal prices for peak shaving. [ABSTRACT FROM AUTHOR]