In order to improve the automatic generation control (AGC) performance of thermal generators, this paper presents a stochastic model predictive control (SMPC) approach for a battery/flywheel hybrid energy storage system (HESS) to distribute power. The approach combines an adaptive Markov chain for power demand prediction of HESS, a scenario tree generation and model predictive control strategy. To develop an effective prediction model to deal with the randomness of a thermal generator in response to AGC command, a Markov chain is used to describe the randomness of the HESS power demand, and a posteriori information is used to adapt to the fluctuation of the AGC command. A scenario tree generation approach is proposed to make better use of the Markov probability matrix. Based on these efforts, an SMPC approach is proposed for HESS energy management. Simulation results show that the regulation performance of the proposed approach outperforms conventional approaches, and that its performance is close to the model predictive control strategy with prescient information of the future power demand. In addition, compared with other approaches such as rule-based strategies, it does less damage to the battery and yields higher annual average net benefits in the whole life cycle.

Due to limitations of topology, the series architecture electric energy router (SA-EER) for low-voltage distribution network (LVDN) has the disadvantages that the energy transmission range is limited to 100% and reactive powers are unable to operate flexibly. For this reason, a new type of EER with series-parallel architecture (SPA-EER) is constructed in this paper, and it can break through the above disadvantages in SA-EER. In addition to series-parallel architecture, a two-degree-of-freedom power flow flexible control strategy (TDF-PFFCS) is proposed to further support the realization of these two breakthroughs. According to TDF, the operation states of SPA-EER can be discriminated into 6 modes, and then their operation mechanisms are analyzed. Considering the rated capacity limitations of LVDN ports and power electronic converters in SPA-EER, the operation ranges of TDF are discussed, and constraint relationships between TDF and these rated capacities are further established, so as to avoid the issue of over-limit rated capacities. Finally, the simulation results prove that the constructed SPA-EER and proposed TDF-PFFCS can achieve the transmission targets that the maximum allowable amounts of active and reactive powers are 200% and 120% respectively, which provides new design ideas and solutions for EERs to realize the high-power energy flexible transmission.

Distributed generation is a vital component of the national economic sustainable development strategy and environmental protection, and also the inevitable way to optimize energy structure and promote energy diversification. The power generated by renewable energy is unstable, which easily causes voltage and frequency fluctuations and power quality problems. An adaptive online adjustment particle swarm optimization (AOA-PSO) algorithm for system optimization is proposed to solve the technical issues of large-scale wind and light abandonment. Firstly, a linear adjustment factor is introduced into the particle swarm optimization (PSO) algorithm to adaptively adjust the search range of the maximum power point voltage when the environment changes. In addition, the maximum power point tracking method of the photovoltaic generator set with direct duty cycle control is put forward based on the basic PSO algorithm. Secondly, the concept of recognition is introduced. The particles with strong recognition ability directly enter the next iteration, ensuring the search accuracy and speed of the PSO algorithm in the later stage. Finally, the effectiveness of the AOA-PSO algorithm is verified by simulation and compared with the traditional control algorithm. The results demonstrate that the method is effective. The system successfully tracks the maximum power point within 0.89 s, 1.2 s faster than the traditional perturbation and observation method (TPOM), and 0.8 s faster than the incremental admittance method (IAM). The average maximum power point is 274.73 W, which is 98.87 W higher than the TPOM and 109.98 W more elevated than the IAM. Besides, the power oscillation range near the maximum power point is small, and the power loss is slight. The method reported here provides some guidance for the practical development of the system.

The widely used flywheel energy storage (FES) system has such advantages as high power density, no environment pollution, a long service life, a wide operating temperature range, and unlimited charging–discharging times. The flywheel array energy storage system (FAESS), which includes the multiple standardized flywheel energy storage unit (FESU), is an effective solution for obtaining large capacity and high-power energy storage. In this paper, the strategy for coordinating and controlling the charging–discharging of the FAESS is studied in depth. Firstly, a deep analysis is conducted on the loss generated during the charging–discharging process of the FESU. The results indicate that the loss is related to the charging–discharging of power. To solve the problems of over-charging, over-discharging, and overcurrent caused by traditional charging–discharging control strategies, this paper proposes a charging–discharging coordination control strategy based on the equal incremental principle (EIP). This strategy aims to minimize the total loss and establish a mathematical model of optimal coordination control with the constraints of total charging–discharging power, rated power limit, over-charging, over-discharging, and overcurrent. Based on the EIP, the optimal distribution scheme of power charging–discharging is determined. Secondly, this paper gives the specific implementation scheme of the optimal coordinated control strategy. Lastly, the charging–discharging coordinated control strategy is verified by examples. The results show that the coordinated control strategy can effectively reduce the loss during the charging–discharging process and can prevent over-charging, over-discharging, and overcurrent of the system. Overall, it has a better control effect than the existing charging–discharging control strategies.

Tracking inertia in real-time has emerged as a crucial topic for the modern low-inertia power system. However, in practice, large disturbances are infrequent, posing a challenge for routine monitoring of inertia through fault-based methods. To tackle this issue, we propose a Bayesian method for tracking inertia in real-time for both synchronous and asynchronous generators, utilizing only ambient measurements. To circumvent the curse of dimensionality inherent in Bayesian inference, an ambient measurement-based decentralized Bayesian framework is devised. When relying on ambient measurements, the inertia estimation generally requires data from a much longer time window than methods that depend on large disturbances. This extended time window, however, introduces severe error accumulations that compromise the accuracy of the decentralized model. To address this issue, we further incorporate the variational mode decomposition (VMD) to recover the precision of the decentralized scheme. Finally, we propose a cost-effective multiple-importance-sampling (MIS) algorithm to obtain the non-Gaussian posterior distribution of inertia. Simulation results demonstrate the excellent performance of the proposed method. It can not only precisely estimate inertia under continuous load changes but also maintain high accuracy even in the presence of discrete load fluctuations, including load jumps.

The circulating current is one of the important issues for parallel converters. It affects the system stable operation and degrades the power quality. In order to reduce the circulating current of the parallel converter and reduce the harmonic pollution to the power grid, a new circulating current suppression strategy is proposed for the parallel current source converter without any communication line. This strategy is able to realize the current sharing between parallel modules by changing the external characteristics of the parallel modules to thus suppress the circulating current among the parallel current source converters. The proposed control strategy adopts DC-side droop control and AC-side virtual impedance control. The DC-side droop control is used to generate the reference voltage of each parallel module, while the AC-side virtual impedance is used to the circulating current suppression. We performed a time domain test of the parallel converter, and the results show that the proposed control strategy reduced the RMS circulating current of the parallel converter by 50% and effectively reduced the grid-side current THD while ensuring the stable operation of the converter. The effectiveness of the proposed control strategy was, therefore, verified.