Power grid resilience, reliability, and sustainability can be improved significantly, by decomposing the large grids into networked microgrids (NMGs). However, optimal energy management problem and preserving the security in NMGs are more complicated and challenging. This paper aims to propose an effective secured stochastic energy management framework for NMGs based on the modified blockchain approach utilizing Directed Acyclic Graph (DAG). Using the decentralized and transparent blockchain technology will help to have higher security, lower risks within the network, eliminating the financial fraud, and cutting down the total operational cost. In order to address the issues arising in the traditional blockchain models mainly due to the storage and high complexities of hash address calculations, in this paper, a new modified blockchain technology is developed based on the DAG method. Also, a novel data restoration technique is developed to provide a way to restore the data with appropriate accuracy. Moreover, the unscented transform (UT) approach is employed to model the uncertainties of forecast errors in hourly load demand, solar power, and wind turbines output power. The proposed model is tested on an NMG system with four MGs including two residential MGs (as the non-crucial loads), a commercial MG (as the intermediate level loads), and a hospital MG (as the crucial loads).

A novel neural network (NN)-based nonlinear decentralized adaptive controller is proposed for a class of large-scale, uncertain, interconnected nonlinear systems in strict-feedback form by using the dynamic surface control (DSC) principle, thus, the “explosion of complexity” problem which is observed in the conventional backstepping approach is relaxed in both state and output feedback control designs. The matching condition is not assumed when considering the interconnection terms. Then, NNs are utilized to approximate the uncertainties in both subsystem and interconnected terms. By using novel NN weight update laws with quadratic error terms as well as proposed control inputs, it is demonstrated using Lyapunov stability that the system states errors converge to zero asymptotically with both state and output feedback controllers, even in the presence of NN approximation errors in contrast with the uniform ultimate boundedness result, which is common in the literature with NN-based DSC and backstepping schemes. Simulation results show the effectiveness of the approach.

Traditionally voltage control in the distribution power system (DPS) is performed through voltage regulating devices (VRDs), including on-load tap changers (OLTCs), step voltage regulators (SVRs), and switched capacitor banks (SCBs). The recent IEEE 1547:2018 from March 2018 requires inverter fed distributed energy resources (DERs) to contribute reactive power to support the grid voltage. To accommodate VAR from DERs, well-organized control algorithm is required to use in this mode to avoid grid oscillations and unintended switching operations of VRDs. This paper presents two voltage control strategies (i) static voltage control considering voltage-reactive power mode (IEEE 1547:2018), (ii) dynamic and extensive voltage control with maximum utilization of DER capacity and system stability. Further, effective time-graded control is implemented between VRDs and DER units to reduce the simultaneous and negative operation. The proposed voltage control strategies are tested in a realistic 140-bus southern California distribution power system through extensive time-domain simulation studies. The results show that voltage quality in a distribution system is effectively achieved through the proposed voltage control strategies with a significant reduction in the number of switching operations of VRDs. In addition, proposed voltage control strategies increase the reliability and security of the DPS during unexpected failures.

In this paper, the direct neural dynamic programming technique is utilized to solve the Hamilton-Jacobi-Bellman equation forward-in-time for the decentralized near optimal regulation of a class of nonlinear interconnected discrete-time systems with unknown internal subsystem and interconnection dynamics, while the input gain matrix is considered known. Even though the unknown interconnection terms are considered weak and functions of the entire state vector, the decentralized control is attempted under the assumption that only the local state vector is measurable. The decentralized nearly optimal controller design for each subsystem consists of two neural networks (NNs), an action NN that is aimed to provide a nearly optimal control signal, and a critic NN which evaluates the performance of the overall system. All NN parameters are tuned online for both the NNs. By using Lyapunov techniques it is shown that all subsystems signals are uniformly ultimately bounded and that the synthesized subsystems inputs approach their corresponding nearly optimal control inputs with bounded error. Simulation results are included to show the effectiveness of the approach.

An improved model predictive direct power control (MPDPC) for three-phase grid-connected converters is proposed. In the proposed method, two voltage vectors are applied during a control period and their duty cycles are determined by a fuzzy logic-based modulator. The inputs to the modulator are the active and reactive power errors and the output is the duty cycle of the first (main) voltage vector. The fuzzy rules are developed based on expert knowledge and the fact that small/large power errors can be compensated by applying the main voltage vector for a small/large portion of the switching period. The candidate voltage vector pairs are examined on a control Lyapunov function and the pair that satisfy the closed-loop stability criteria are selected. The voltage vector pairs are then applied following a proposed switching pattern through which reduced average switching frequency is achieved. Comparative simulation and hardware-in-the-loop studies between the proposed method and a most-recently introduced duty cycle based MPDPC confirm that in addition to lower average switching frequency, better quality currents and active and reactive powers can be achieved under the proposed MPDPC.

Penetration of distributed energy resources (DERs) such as wind, solar units are rapidly increasing in distribution power system (DPS). In view of power grid reliability and efficiency, cyber and physical secure of these units are highly important since it can directly impact power system operations. This paper investigates the dynamic behavior of power distribution during cyber, physical strikes on reactive power control in DER units. Various fault scenarios including modifications in voltage-reactive power curve pattern such as slope of the curve, dead band; and changes in capacitive or/and inductive reactive power delivery to the power grid are presented. Further, fault scenarios are investigated with the presence of dynamic VAR (volt-ampere reactive) compensation (DVC) units in the system. A southern California 140-bus distribution power system is selected as a test system and faults are analyzed in both Matlab/Simulink and ETAP simulation platforms. The results evidence that the modifications in reactive power control values at DER units significantly affects the voltage quality/security of the system and change the number of switching actions of voltage regulators (VRs). In contrast, DPS connected with DVCs improves voltage quality and reduces the number of switching actions of VRs, also voltage quality/security is maintained during faults.