DC microgrids (MGs) are cyber-physical systems (CPSs) prone to cyber attacks which could disrupt the normal operation of DC MGs. Accurate estimation of the attack vector is crucial to recover correct signals from compromised measurements for safe DC MG operation, while it has not been effectively achieved by existing methods and the accuracy is challenged by unmodeled uncertainties in practical power electronic converters. This paper proposes a digital twin (DT)-based cyber attack detection and mitigation scheme for DC MGs. First, the lightweight radial basis function neural network (RBFNN) is adopted to compensate for the mismatch between the ideal model and the real system for accurate converter modeling. Second, a composite descriptor observer-based local DT is designed to achieve accurate estimations of attack signals and correct observations of converter states. In addition, a global DT is developed at the system level to accurately estimate and eliminate cyber attacks in the secondary control. As a result, the proposed method can mitigate attacks by replacing the corrupted signals with estimated true values provided by DT, leading to accurate and stable operation of the system. Finally, simulation and experimental results are given to validate the effectiveness of the proposed method.

Deep reinforcement learning (DRL) is a promising solution for the coordination of power systems with high penetration of inverter-based renewable energy sources (RESs). Yet, when adopting the DRL-based control method, the safe and optimal operation of the system cannot be guaranteed at the same time, as the conventional DRL agent is not designed to solve the hard constraint problem. To address this challenge, a deep neural network (DNN) assisted projection-based DRL method for safe control of distribution grids is proposed in this section. First, a finite iteration projection algorithm is proposed to guarantee hard constraints by converting a non-convex optimization problem into a finite iteration problem. Next, a DNN-assisted projection method is proposed to accelerate the calculation of projection and achieve the practical implementation of hard constraints in the DRL problem. Finally, a DNN Projection embedded twin-delayed deep deterministic policy gradient (DPe-TD3) method is proposed to achieve optimal operation of distribution grids with guaranteed 100% safety of the distribution grid. The safety of the DRL training is guaranteed via the embedded Projection DNN in TD3 with participation in the gradient return process, which could smoothly and effectively project the DRL agent actions into the feasible area, thus guaranteeing the safety of data-driven control and the optimal operation at the same time. The case studies and comparisons are conducted in the IEEE 33 bus system to show the effectiveness of the proposed method.

The wide integration of inverter based renewable energy sources (RESs) in modern grids may cause severe voltage violation issues due to high stochastic fluctuations of RESs. Existing centralized approaches can achieve optimal results for voltage regulation, but they have high communication burdens; existing decentralized methods only require local information, but they cannot achieve optimal results. Deep reinforcement learning (DRL) based methods are effective to deal with uncertainties, but it is difficult to guarantee secure constraints in existing DRL training. To address the above challenges, this paper proposes a projection embedded multi-agent DRL algorithm to achieve decentralized optimal control of distribution grids with guaranteed 100% safety. The safety of the DRL training is guaranteed via an embedded safe policy projection, which could smoothly and effectively restrict the DRL agent action space, and avoid any violation of physical constraints in distribution grid operations. The multi-agent implementation of the proposed algorithm enables the optimal solution achieved in a decentralized manner that does not require real-time communication for practical deployment. The proposed method is tested in modified IEEE 33-bus distribution and compared with existing methods; the results validate the effectiveness of the proposed method in achieving decentralized optimal control with guaranteed 100% safety and without the requirement of real-time communications.

Nowadays, hybrid energy storage system (HESS) is a popular option to compensate for renewable energy fluctuations in the microgrid. The main advantages of HESS are that it can eliminate bus voltage fluctuations and maximize the strength of multifarious energy storage systems with different characteristics. Therefore, power allocation between different ESSs is a major issue for HESS. Moreover, the high integration of constant power loads brings instability issues to the dc microgrid. To address the above concerns, this article proposes a decentralized power sharing and stabilization method based on active disturbance rejection control (ADRC). First, an ADRC controller is proposed for a single ESS, where the system disturbance is estimated and compensated through an extended state observer (ESO). Thus, the dc-bus voltage is maintained. Second, an ADRC-based decentralized method for HESS is developed. ESO can estimate the output current of converters. Thus, the current sensors of the local controller can be reduced. Furthermore, the proposed method is extended into HESS with multiple batteries and SCs, which verifies its scalability. Stability analysis is performed to guarantee the large-signal stability of the whole system. At last, simulation and experimental have been conducted to verify the feasibility of the proposed method.

Hydrogen electrolyzers are promising tools for frequency regulation of future power systems with high penetration of renewable energies and low inertia. This is due to both the increasing demand for hydrogen and their flexibility as controllable load. The two main electrolyzer technologies are Alkaline Electrolyzers (AELs) and Proton Exchange Membrane Electrolyzers (PEMELs). However, they have trade-offs: dynamic response speed for AELs, and cost for PEMELs. This paper proposes the combination of both technologies into a Hybrid Hydrogen Electrolyzer System (HHES) to obtain a fast response for frequency regulation with reduced costs. A decentralized dynamic power sharing control strategy is proposed where PEMELs respond to the fast component of the frequency deviation, and AELs respond to the slow component, without the requirement of communication. The proposed decentralized approach facilitates a high reliability and scalability of the system, what is essential for expansion of hydrogen production. The effectiveness of the proposed strategy is validated in simulations and experimental results.

The large-scale integration of renewables in the modern power system will lead to a large number of power electronics in the power system and pose interaction stability challenges. Impedance-based stability analysis methods have been widely adopted for the stability evaluation of interconnected power converter systems. However, they are small signal stability analysis tools that can only effectively estimate stability near a certain operating point; they are not effective for grid-converter interaction systems due to the wide variation of operating points caused by the fast and large fluctuations of renewable energy and load. To address this challenge, this article proposes a double deep neural network (DNN)-based black-box modeling and stability region estimation approach for grid-converter interaction systems. First, a DNN-based multioperating point (MOP) impedance model is proposed to build the impedance model covering multiple operating points. Next, a DNN-based stability evaluation model is developed based on the MOP impedance model and the physical nature of the whole system for the estimation of the stability region. The proposed double DNN-based method can achieve fast and accurate online estimation of the stability region for grid-converter system under large variations of renewable energy. Numerous experiments are conducted to demonstrate the effectiveness of the proposed method to achieve accurate identification of the MOP impedance model and to generate an accurate stability region of the system.

Deep reinforcement learning (DRL) is a promising solution for voltage control of distribution grids with high penetration of inverter-based renewable energy sources (RESs). Yet, when adopting the DRL-based control method, the safe and optimal operation of the system cannot be guaranteed at the same time, as the conventional DRL agent is not designed to solve the hard constraint problem. To address this challenge, this paper proposes a deep neural network (DNN) assisted projection based DRL method for safe control of distribution grids. First, a finite iteration projection algorithm is proposed to guarantee hard constraints by converting a non-convex optimization problem into a finite iteration problem. Next, a DNN assisted projection method is proposed to accelerate the calculation of projection and achieve the practical implementation of hard constraints in DRL problem. Finally, a DNN Projection embedded twin-delayed deep deterministic policy gradient (DPe-TD3) method is proposed to achieve optimal operation of distribution grids with guaranteed 100% safety of the distribution grid. The safety of the DRL training is guaranteed via the embedded Projection DNN in TD3 with participation in gradient return process, which could smoothly and effectively project the DRL agent actions into the feasible area, thus guaranteeing the safety of data driven control and the optimal operation at the same time. The case studies and comparisons are conducted in the IEEE 33 bus system to show the effectiveness of the proposed method.

Hybrid energy storage system (HESS) is effective to compensate for fluctuation power in renewables and fast fluctuation loads in DC microgrids. To regulate DC bus voltage, a power management strategy is an essential issue. In the meantime, the increasing integration of constant power loads (CPLs) in DC microgrids brings great challenges to stable operation due to their negative incremental impedance. In this paper, a fast composite backstepping control (FBC) method is proposed for the HESS to achieve faster dynamics, smaller voltage variations, and large-signal stabilization. In the FBC method, a higher order sliding mode observer (HOSMO) is adopted to estimate the coupled disturbances. Furthermore, the FBC method is integrated with the droop control; so that the FBC-based decentralized power allocation (FBC-DPA) strategy for HESS in DC microgrids is developed. The proposed FBC method is designed based on the Lyapunov function to ensure its stability. Moreover, the design guidelines are provided to facilitate the application of the proposed method. Both simulation and experimental studies under different operating scenarios show that the proposed method achieves faster voltage recovery and smaller voltage variations than the conventional backstepping control method.

The renewable energy hydrogen based dc microgrid is an attractive solution for renewables integration, as the hydrogen is a clean fuel, that extra renewable energy source generation can be stored as hydrogen through electrolysis technology, and be used later through fuel cell technology. However, the efficiency of the electrolyzer and fuel cell change significantly under the wide operation ranges, and they have different degradation mechanisms that are greatly impacted by current ripples. Moreover, to achieve consistent power supply with 100% RESs, the electrolyzer and fuel cell need to be optimally coordinated. To address the issues, this paper proposes an MPC based power management method to achieve smooth power sharing and reduce the current ripple, also can guarantee the system stability under uncertainties of the renewable energy source and load. It consists of a baseline MPC for optimized transient performance and a sliding mode observer to estimate system uncertainties. Both the simulation and experiment results can validate the effectiveness of the proposed method.

This paper proposes a neural network-based digital twin for online health monitoring of vulnerable components in converters. The proposed digital twin consists of a physics-informed model with uncertain parameters, and a neural network (NN) for real-time model updating and health monitoring of components. This method is noninvasive, without extra circuits, and can identify parameters in real-time with high efficiency. Simulation and experiment are conducted to validate the effectiveness of the proposed method in accurate parameter identification and degradation monitoring of capacitor and MOSFET.