Yiming Ma

Micro coal-fired units (MCFU) and combined heat and power plants (CHP) in integrated energy system (IES) will emit a large amount of carbon dioxide when providing loads to customers, which will lead to higher operating costs of IES. To solve this challenge, an optimal dispatch model of power-to-gas (P2G) and methane reactor (MR) considering the reward and punishment costs based on carbon emission trading mechanism is proposed, to reduce carbon emissions of IES and enhance the accommodation of renewable energy (RE). Subsequently, considering the uncertainty of RE, a combination optimization method for MCFU and CHP units is developed based on the Lagrange multiplier method. Finally, considering the mechanism of electricity spot market (ESM), a transaction strategy for IES participating in ESM is proposed to further enhance the accommodation of RE. The effectiveness of the proposed framework is demonstrated through simulations.

With the increasing capacity of wind power generators (WTGs), the volatility of wind power could significantly challenge the stability and economy of electric and heating networks. To tackle this challenge, this paper proposes an optimal dispatch framework based on controllable load (including controllable electric load and controllable thermostatically load) to reduce wind power curtailment. A forecasting model is developed for the controllable load, which comprehensively considers autocorrelation, weather factor, and consumers’ behavior characteristics. With adjusting controllable load, an optimal dispatch model of power system is then established and resolved by Sequential Least Squares Programming (SLSQP) method. Our method is verified through numerous simulations. The results show that, compared with the state-of-the-art techniques of support vector machine and recurrent neural networks, the root mean square error with the proposed long short-term memory can be reduced by 0.069 and 0.044, respectively. Compared with conventional method, the peak wind power curtailment with dispatching controllable load is reduced by nearly 10% and 5% in two cases, respectively.

Cloud computing can improve the calculation and data storage ability for the control center in the power system. A new framework of the cloud-based control center is proposed in this paper. This cloud computing system can collect the load data from smart meters of the grid and classify demand-side management (DSM) loads that meet the specific requirements. The selected loads belong to the off-peak period (from 21:00 to 07:00 next day) and can contribute to shifting the night peak load. A feature extraction combined with Variational Mode Decomposition (FE-VMD) of the loads which can be trained in recurrent neural network (RNN) is proposed in this paper. Using the feature value to replace the actual load data, input data can be significantly reduced which is suitable for a vast amount of load in the power system. A case study of real load data from 200,000 customers has been classified with this method, and the accuracy is compared with the other methods. From simulation with MATLAB, it can be seen that the FE-VMD combined with the RNN method provides the best result of 89.8% recognition accuracy among these methods.

The electromagnetic signal environment of the modern battlefield is complex. The radar signal recognition technology based on the traditional five parameters cannot meet the actual needs. For this reason, it is urgent to explore new identification methods to improve the technical level of existing radar countermeasure equipment in electronic warfare. Aiming at the research challenge of low-accuracy intra-pulse waveform recognition in a dense modern electronic warfare environment, In this paper, we propose a new method to extract the characteristics of radar emission signals. This method first reconstructs the radar transmitter signal into a complex network, and then extracts the characteristics of the complex network. In our algorithm, the features are extracted from network topology statistics with each vector point of the reconstructed phase space represented by a single node and edge determined by the phase space distance. Through analyzing the global properties of network nodes, we found that the network constructed by the method described in this article inherited the dynamic characteristics of all aspects of the intentional intra-pulse modulation type of radar signals in time domain. Furthermore, we investigate the stability and sensibility of feature parameters with the variation range from 5dB to 20dB. Computer simulation demonstrates how the topological indices of the network can be used to distinguish different modulation types of radar emitter signals. At the same time, the experimental results show that the extracted feature vectors have good ability of noise-resistance and good clustering quality in low SNR environment when the radar signals are corrupted by measurement noise.

In renewable energy generation applications, phase locked loop (PLL) is one of the most popular grid synchronization technique. The main objective of PLL is to rapidly and precisely extract phase and frequency especially when the grid voltage is under non-ideal conditions. This motivates the recent development of moving average filters (MAFs) based PLL in a quasi-type-1 system (i.e., QT1-PLL). Despite its success in certain applications, the transient response is still unsatisfactory, mainly due to the fact that the time delay caused by MAFs is still large. This has significantly limited the utilization of QT1-PLL, according to common grid codes such as German and Spanish grid codes. This challenge has been tackled in this paper. The basic idea is to develop a new hybrid filtering stage, consisting of adaptive notch filters (ANFs) and MAFs, arranged at the inner loop of QT1-PLL. Such an idea can greatly improve the transient response of QT1-PLL, owing to the fact that ANFs are utilized to remove the fundamental frequency negative voltage sequence (FFNS) component while other dominant harmonics can be removed by MAFs with a small time delay. By applying the proposed technique, the settling time is reduced to less than one cycle of grid frequency without any degradation in filtering capability. Moreover, the proposed PLL can be easily expanded to handle dc offset rejection. The effectiveness is validated by comprehensive experiments.

Frequency stability in an isolated grid can be easily impacted by sudden load or wind speed changes. Many frequency regulation techniques are utilized to solve this problem. However, there are only few studies designing torque compensation controllers based on power performances in different Speed Parts. It is a major challenge for a wind turbine generator (WTG) to achieve the satisfactory compensation performance in different Speed Parts. To tackle this challenge, this paper proposes a gain scheduled torque compensation strategy for permanent magnet synchronous generator (PMSG) based wind turbines. Our main idea is to improve the anti-disturbance ability for frequency regulation by compensating torque based on WTG speed Parts. To achieve higher power reserve in each Speed Part, an enhanced deloading method of WTG is proposed. We develop a new small-signal dynamic model through analyzing the steady-state performances of deloaded WTG in the whole range of wind speed. Subsequently, H_∞ theory is leveraged in designing the gain scheduled torque compensation controller to effectively suppress frequency fluctuation. Moreover, since torque compensation brings about untimely power adjustment in over-rated wind speed condition, the conventional speed reference of pitch control system is improved. Our simulation and experimental results demonstrate that the proposed strategy can significantly improve frequency stability and smoothen power fluctuation resulting from wind speed variations. The minimum of frequency deviation with the proposed strategy is improved by up to 0.16 Hz at over-rated wind speed. Our technique can also improve anti-disturbance ability in frequency domain and achieve power balance.

In most grid-connected power converter applications, the phase-locked loop (PLL) is probably the most widespread grid synchronization technique, owing to its simple implementation. However, its phase-tracking performance tends to worsen when the grid voltage is under unbalanced and distorted conditions. Many filtering techniques are utilized to solve this problem, however, at the cost of slowing down the transient response. It is a major challenge for PLL to achieve a satisfactory dynamic performance without degrading its filtering capability. To tackle this challenge, a hybrid filtering technique is proposed in this paper. Our idea is to eliminate the fundamental frequency negative sequence (FFNS) and other harmonic sequences at the prefiltering stage and inner loop of PLL, respectively. Second-order generalized integrators (SOGIs) are used to remove FFNS before the Park transformation. This makes moving average filters (MAFs) eliminate other harmonics with a narrowed window length, which means the time delay that is caused by MAFs is reduced. The entire hybrid filtering technique is included in a quasi-type-1 PLL structure (QT1-PLL), which can provide a rapid dynamic behavior. The small-signal model of the proposed PLL is established. Based on this model, the parameter design guidelines targeting the fast transient response are given. Comprehensive experiments are carried out to confirm the effectiveness of our method. The results show that the settling time of the proposed PLL is less than one grid cycle, which is shorter than most of the widespread PLLs. The harmonic rejection capability is also better than other methods, under both nominal and adverse grid conditions.