This paper introduces a multi-stage coordination model incorporating industrial hubs, distribution system operators (DSOs), and the transmission system operator (TSO) into separate stages within a hierarchical optimization framework. In this concept, the hubs incorporate electrical, thermal, and cooling storage systems, enabling them to participate in integrated demand response (IDR) programs. This model effectively harnesses the flexible capacities of industrial hubs in congestion management (CM) markets, integrating dispatchable units and battery storage systems under their management, thereby facilitating the transfer of flexibility from distribution-level to transmission-level markets. The proposed model is implemented using GAMS on the IEEE 14-bus transmission network, connected to two IEEE 21-bus distribution networks at buses 5 and 9. Simulation results demonstrate that the proposed decentralized scheme significantly reduces CM costs at both the TSO and DSO levels, while also increasing the daily profits of the hubs from CM markets.

This study presents the design of a robust control strategy utilizing an internal model control (IMC)-based proportional integral–one plus derivative with filter (PI-(1+DF)) controller to mitigate the impact of time-delay attacks (TDAs) on renewable-integrated power system (PS). Before implementing mitigation, an adaptive recursive least square filter with a forgetting factor (ARLS-FF) is employed as an online detection mechanism for TDAs. The design methodology incorporates Kharitonov’s stability theorem to identify the worst-case plant, for which the proposed controller parameters are obtained using the IMC framework. A critical feature of IMC structure is the filter coefficient (μ), which is determined based on the maximum inverse sensitivity (MT) and the characteristics of considered TDA, underscoring the proposed control scheme’s enhanced efficacy. Stability under time-varying attacks is rigorously analyzed through the Lyapunov-Krasovskii function (LKF) and linear matrix inequalities (LMIs) based approach with vivid robustness assessment ensuring resilience to parametric uncertainties. Finally, benchmarking on the IEEE-39 and IEEE-118 bus systems validates the proposed controller’s superiority on large-scale and realistic interconnected PSs.

Blockchain technology (BT) is considered a proven secure platform for financial affairs. However, its applicability as an irrefutable attack-resistive platform for securing peer-to-peer (P2P) networks is a matter of debate among researchers. In this regard, this article concentrates on using the BT for securing fully P2P networks. A new fair and energy-efficient method is presented for consensus achievement in the blockchain layer for fully P2P transactive energy systems. Also, a new product differentiator index is represented in this article for bilateral trading among the agents that is more compatible with the security layer. Finally, a self-adaptive-self-tuning fast alternating direction method of multipliers distributed algorithm is presented to clear the P2P market. Results demonstrate the superiority of the proposed BT's consensus method in terms of fairness and energy efficiency compared to the proof-of-stake and proof-of-work consensus methods and its attack resistivity against Sybil attack and 51% attack.

The frequency and rate of change of frequency (ROCOF) signals acquired from phasor measurement units (PMUs) are instrumental in the security monitoring and feedback control of power grids, necessitating minimal response time and high precision. Existing algorithms rarely achieve a simultaneous balance between superior noise filtering performance and rapid response time when measuring frequency and ROCOF. To address this problem, this article introduces an innovative algorithm that integrates$\alpha \beta \gamma$series filters. The proposed algorithm leverages an$\alpha \alpha \beta$(aab) filter, which combines an$\alpha$filter and an$\alpha \beta$filter, and the reference P-class PMU algorithm from the IEC/IEEE 60255.118.1-2018 standard file. By providing an original mathematical modeling approach to aab parameter acquisition, this filter effectively amalgamates the advantages of the two foundational filters. Extensive experimental simulations demonstrate that our proposed algorithm has exceptional performance across various metrics of frequency and ROCOF measurement, achieving results that compare favorably with other advanced algorithms.

This work develops a distributed proportional-integral (PI) controller combined with the signal-to-interference-plus-noise ratio (SINR)-dependent dynamic event-triggered (DET) communication strategy to cope with the frequency regulation and power sharing problems subject to jamming attacks in an inverter-based islanded microgrid. In order to simplify the controller structure, power sharing and frequency restoration can be implemented simultaneously in the designed secondary control layer instead of the traditional hierarchical control implementation. Besides, the dynamic event-triggered communication strategy configured with each secondary controller can adaptively adjust the triggered threshold based on the SINR signal, which can reduce the congestion of communication networks caused by jamming attacks and ensure system control performance. A cyber-physical microgrid testbed is built based on the real-time simulator, OPAL-RT, and network simulation software, EXata, to verify the effectiveness of the proposed SINR-dependent event-triggered based distributed secondary controller.

Although several multi-agent deep reinforcement learning (MADRL) algorithms have been employed in power distribution networks configured with high penetration level of Photovoltaic (PV) generators for active voltage control (AVC), the impact of the voltage fluctuation of a single PV node on voltage violations of other PV nodes in the network is ignored. Consequently, it leads to the conservativeness of the existing MADRL based AVC algorithms. In this paper, a robust MADRL control algorithm is designed to minimize the nodal voltage violation and line loss with the exploration of coupling voltage fluctuations across all the controlled nodes by coordinating PV inverters, and a physics factor is utilized to guide (physics-guided) the training policy with the expectation of a better performance compared to existing purely data-driven methods. In the proposed physics-guided multi-agent adversarial twin delayed deep deterministic (PG-MA2TD3) policy gradient algorithm, a physics factor, global sensitivity of voltage (GSV), is properly embedded in the algorithm to measure the influence of the nodal voltage fluctuation on voltage violations on the other controlled nodes with PV inverters and this GSV is shared in the learning center to guide the centralized learning and decentralized execution process. The multi-agent adversarial learning (MAAL) embedded with the GSV to seek an adaptive descend gradient for reducing the Q-value function appropriately rather than always assuming the worst case. Therefore, this physics-guided method can reduce the conservation and provide significantly better reward. Finally, the proposed algorithm is compared with several other methods on IEEE 33-bus, 141-bus and 322-bus with three-year data in Portuguese and the results indicate the proposed method can obtain the minimal voltage fluctuation and the best reward in the comparisons.

The advancing machine learning techniques have been widely applied to data-driven dynamic stability assessment (DSA) in modern smart grids. However, how to extract critical spatial-temporal features from wide-area system stability dynamics still remains an open issue. Emphasizing on short-term voltage stability (SVS) assessment, this paper develops a novel sequential feature learning approach to address this problem in two steps. First, based on visualized voltage contours, it tactfully constructs a comprehensive spatial-temporal sequence model to dynamically characterize multiplex spatial-temporal SVS evolution trends. Second, the time series shapelet classification method is leveraged to subtly extract critical consecutive SVS features in sequential forms, i.e., the multidimensional shapelets (discriminative subshapes). Test results on the real-world Hong Kong power grid demonstrate the efficacy, adaptability, and scalability of the proposed approach for SVS assessment. In addition to the outstanding performances on online DSA, with its favorable interpretability, it is capable of providing intuitive insights into regional SVS patterns from spatial-temporal perspectives.

Real-time multiple event analysis is important for reliable situational awareness and secure operation of the power system. Multiple sequential events can induce complex superimposed pattern in the data and are challenging to analyze in real time. This paper proposes a method for accurate detection, temporal localization, and classification of multiple events in real time using synchrophasor data. For detection and temporal localization, a Teager-Kaiser energy operator (TKEO) based method is proposed. For event classification, a time series classification based method using energy similarity measure (ESM) is proposed. The proposed method is tested for simulated multiple event cases in the IEEE-118 bus system using DigSilent/PowerFactory and real PMU data for the Indian grid.

Given the increasing integration of renewable energy sources into existing grids, power injection using Grid Supporting Inverters (GSIs) is gaining in popularity. This paper addresses the issue of dynamically identifying key parameters of a GSI used for power injection into a microgrid. It is shown that the dynamics of the system is based on two essential parts that can be assimilated to simple first-order filters: The DC-bus and AC-line filtering. A simple implementation of an Extended Kalman Filter (EKF) used for estimating in real-time both filter's output and key parameters in this noisy environment is proposed. The design was tested using a DSP-accurate implementation using the Matlab/Simulink environment and presented results show that predefined AC-line filter's parameters were successfully retrieved as the state of the system. The proposed design is suitable for progressive failure indication.

This paper proposes a control algorithm that controls a DC/DC converter within a capacitorless battery charger for Electric Vehicles (EVs). The charger is composed of an AC/DC converter connected to a three-phase DAB (Dual Active Bridge) DC/DC converter with no capacitor between those two. The proposed control adds an additional degree of liberty in order to deliver the reference charging or discharging power while mitigating the DC/DC converter's drawbacks, dealing with a varying voltage since there is no capacitor, operating in a wide range power spectrum, and controlling the transitional instants. The control algorithm's performance is verified using Matlab/Simulink, and the results are discussed.

This paper addresses the time-delay effects of the wide-area monitoring and control systems (WAMCS) in smart power grids which may critically impact system stability. The main purpose is to conduct a detailed delay analysis of the WAMCS in case of grid contingences. This analysis is performed via an advanced WAMCS testbed where a flexible ac transmission system (FACTS) device is utilized and controlled via a wide-area controller (WAC). Phasor measurements units (PMUs) are adopted to collect the real-time measurements for the WAC. The testbed results from an interface of four main segments known as the WAC; the actual FACTS device, the local area controller, and the power grid system along with the PMUs are simulated via a real-time digital simulator. To mimic the real case scenario, both hardware-in-the-loop and software-in-the-loop schemes are adopted in the experimental testbed, considering time-delay effects. The results obtained clarify the effect of delay in WAMCS in case of smart grid contingences.

Accelerated developments of renewable energy production technologies and their integration to existing AC microgrids have led to the emergence of several new inverters control schemes. The recently introduced proportional resonant (PR) controllers have the ability to precisely track AC quantities and are increasingly used in grid supporting inverters, especially for current control loops which are at the heart of power transfers. In this paper, the PR-controller action is analyzed and an enhancement is proposed. It is shown that the classical implementation is particularly sensitive to high frequency noises coming from measurements and relocating the proportional action (RPA) on the feedback path can overcome this issue. The proposed improvement was evaluated using bit-true simulations in the Matlab/Simulink®environment. Obtained results show good noise and overshoot suppression using the proposed PR-controller with RPA.

In present work, a direct adaptive fuzzy control (DAFC) system, with Takagi-Sugeno structure, is proposed to control a switched reluctance motor by direct torque control technic. The objective of the DAFC is to change the rules definition in the direct fuzzy logic controller (FLC) and rule base table according to minimize the error between the reference flux output and the machine flux. This method is used to adjust the parameters of inference functions of fuzzy direct torque control. The boundedness of all signals is guarantee by the developed control law and adaptive law in the closed-loop system and the controller design is made according to the synthesis of Lyapunov.

Dynamic states of a synchronous machine, i.e. rotor angle and rotor speed, are important variables for power system control, studies and analysis. Availability or estimating of these variables would help us to monitor effectively the stability condition of the power systems in order to develop local or wide-area control loop to improve power system stability and reliability. To estimate the rotor angle or rotor speed of a synchronous machine, availability, measuring or estimating the input signals, i.e. field voltage (Efd) and input mechanical torque (Tm) is always an issue. To solve this problem, in this paper the Extended Kaiman Filter with Unknown Inputs, known as EKF-UI method, is applied for dynamic state estimation of a synchronous generator using available Phasor Measurement Unit (PMU) signals while we are assuming there are two unknown inputs: Efdand Tm. Using the employed EKF-UI technique, the unknown input signals of the synchronous machine (Efdand Tm) are estimated simultaneously along with the states and outputs of the machine.

A strategy for direct torque control by fuzzy logic (FDTC) of switched reluctance motors is presented. Two fuzzy controllers are used in a cascade configuration to control the motor speed in the outer loop and to implement the direct torque and flux control scheme using space vector in the inner loop. Simulation results are presented and the controller performance is compared with those obtained with conventional DTC.

This paper is a step towards the dynamic modelling and control of complete wind/diesel generation system for islanded areas. It mainly focuses on the control strategy of the grid-side inverter usually used in variable speed wind turbines connected in cogeneration schemes. The considered conversion system is equipped with a geared squirrel cage induction generator (SCIG). The developed control strategy is based on the voltage oriented control (VOC) of a 3-phase inverter connected to a weak grid. The controller's PI regulators are designed using the classical direct synthesis approach and cascade control loops technique in the synchronous dq- reference frame. A grid voltage regulation loop, widely used in D-STATCOM systems is designed to add the automatic reactive power management feature to the inverter. Various simulation results using Matlab/Simulink®software for different system's contingencies such as sudden step changes of the wind power are presented. The reactions of the system to step changes in grid voltage, due to the small size of the considered grid, are also presented and extensively discussed.

This paper presents a small signal analysis and modeling in an isolated Wind-Diesel Power plant. Such systems are increasingly implemented for power generation in isolated areas where large continental power grids are not reachable. The considered system is mainly constituted by a diesel prime mover driving a Synchronous Generator (Diesel genset), a Self-Excited Induction Generator based Fixed-Speed Wind Turbine, a static synchronous reactive power compensator (STATCOM) and all the active power sinks lumped as a single Load. The reactive power expression of each aforementioned subcomponent is carried out and a small signal analysis is used to derive relationships between reactive powers transients and voltage drifts on the main distribution AC bus. The produced dynamic models have been implemented and simulated using Matlab/Simulink software tools and the main results are carried out. The main results show the influence of the steady state prior to the transients, the effect of the compensation and the influence of the wind power variations. The validity of the model is proven by good accuracy with other models developed in the literature. Presented models can be used for system stability analysis, voltage regulation and reactive power compensation purposes.

This paper deals with an improved approach for estimation of generator rotor speed in power systems. We propose a hybrid speed estimator, which combines classical approach, based on a mathematical model of the electrical machine, with Al-based approach, implemented by an artificial neural network (ANN). The classical estimation model processes the machine stator voltages and currents and provides intermediary information on the generator speed. The ANN component acts as a function approximator, mapping the intermediary speed components to a more reliable estimation of the generator speed. Multi-layer feedforward ANNs are used for this purpose. Data for training the ANN are obtained through off-line simulations of a generator operating in a two-machine model. After ANN design and training, the performance of the hybrid estimator is tested with simulated on-line measurements in a wide range of operating conditions. Results obtained with the hybrid estimator are compared against those provided by a classical mathematical model-based estimator and an Al-based estimator.