Probabilistic power flow (PF) analysis is critical for secure operation of distribution networks under uncertainty from distributed energy resources. However, low observability in secondary networks and frequent topology reconfigurations complicate PF estimation and demand large sample sizes. This paper proposes a meta-graph generative autoencoder (Meta-GGAE) to learn probabilistic PF distributions under sparse sensing. Meta-GGAE employs node-level matrix completion within a bilevel meta-learning framework to infer unmeasured states, where physics-guided regularizers are adaptively learned using a graph attention network. A variational generative encoder embeds system physics to model local dependencies, while a conditional variational decoder captures probabilistic PF outputs by integrating latent variables with attention-based features. The proposed framework reconstructs missing measurements, generalizes to unseen topologies, and enables robust PF estimation under low observability. Simulations on IEEE test systems demonstrate improved accuracy and physical consistency under N-1 contingencies and uncertain injections compared with baseline methods.

This article introduces an efficient multistep nonparametric residual network for improved solar power forecasting. The proposed architecture first incorporates a synchrosqueezing transform to extract high-resolution time-frequency coefficients of solar power inputs in their respective time-frequency scales. An improved residual network, a Multicondense Residual Network (M-cDRN), integrating multiresidual network and condense network techniques to predict solar power coefficients, is proposed. M-cDRN addresses challenges of overfitting and vanishing gradients in residual networks. A quantile regression network is employed to generate quantiles with different proportions. The model's efficacy is validated using real-time datasets from two geographical locations, showing significant improvements in mean square error: 75.36% for sunny, 21.74% for partially sunny, and 35.42% for cloudy days.

Conventional protection methods are constrained for effectively identifying and classifying faults within dc microgrid setups because of the integration of diverse power electronic-based generators and dc loads. For dc microgrids configured in zonal topology with bidirectional power flow, designing effective protection becomes even more complex. Ensuring reliable power supply to consumers while preventing the unnecessary disconnection of renewable resources underscores the importance of selectivity in protection schemes. Addressing this challenge requires advanced, intelligent fault detection schemes. This article proposes a high-speed fault identification algorithm built upon the current derivative signals at both line terminals. Upon fault detection, the type of fault is determined by the current derivative characteristics on the positive and negative poles of either side of a line. MATLAB/Simulink simulations of a zonal dc microgrid with various generating units and loads validate the developed algorithm, considering a range of fault conditions, including internal, external, high-resistance, and noisy conditions. Simulation results demonstrate the ability of the technique to segregate internal and external faults and accurately classify them. Furthermore, the suggested scheme is evaluated on a hardware testbed, confirming its efficacy in real-world dc microgrid applications.

This article investigates the effects of cyber attacks on the global economical dispatch of vicinity interconnected DC MG clusters. First, it articulates an analytical detection and mitigation strategy integrated with logical operation aided event generators for retaining the feasible operation region of the economic dispatch problem in multiple MG clusters. Second, separate defense actions are designed for leader and follower nodes in a MG, unlike common mitigation action for all nodes in previous works. Third, a variable averaging gain for processing the neighbor’s information at every node is proposed. It aids in distinguishing an actual instance of power generation unit saturation from a cyber attack. The entire system is modelled in a real time digital simulator and the results obtained demonstrate the efficacy of the proposed algorithm for attaining a secure global economic dispatch. It also exhibits an important feature of prioritising resiliency over economics by transitioning from economic dispatch mode to supplying to critical loads under adverse events. Finally, for testing the application of the proposed algorithm to a large-scale system, it is extended to five interconnected MG clusters containing 20 DC-DC power electronic converters.Note to Practitioners—Cybersecurity becomes a critical concern for vicinity connected microgrids as an increasing amount of IoT devices are adopted for communication, monitoring, and operation support purposes. This cyber-physical structure provides an attack surface because adversaries can compromise the physical structure by attacking its dependent cyberspace. For mitigation of the attacks, twining the efforts of securing from both the cyber and the physical testbed is required. As such, this paper provides the owners and operators with in-depth knowledge about the requirement of different control strategies for the leader and follower nodes in the interconnected network of microgrids. It also emphasizes the need for prioritising resilience under adverse events. The technique is simple and user-friendly for implementation in the already existing control strategies. The real-time simulation results prove its effectiveness so that it can be put into practice.

Virtual synchronous generator (VSG) is an encouraging concept to ensure provision of inertia, following large integration of renewable sources in system. However, apart from VSG contribution in improving system's frequency stability, it is also important to ensure long term robust and stable operation of VSG itself. In this regard, active power loop (APL) of VSG constituting variable control parameters is analyzed subsequent to contingency scenarios. It is observed that dynamics of APL outputs (virtual frequency and active power) is critically sensitive and prone to intrinsic oscillations. Furthermore, during disturbance in system, utilization of constant control parameters of APL illustrates contradictory effects in dynamics of its outputs. As a solution to above issues, novel adaptive inertia and adaptive damping coefficient is proposed. The work provides distinct approach in the following manner: 1) judicious selection of different operation range of natural undamped frequency(${\omega }_n$) and damping ratio($\zeta $) of APL as compared to existing strategies, along with 2) reduced computational complexity and improved accuracy of controller, 3) thereby reduction in over(-under)shoots and intrinsic oscillations in virtual frequency and output active power of APL during transient period. This strengthens classical VSG design and contributes in its robust long term industry/real-time application. The efficacy of the proposed strategy is showcased by real-time simulations along with validation at PHIL level.

Under the smart grid environment, implementation of load management programs, integration of active elements, and associated uncertainties have increased the complexity in operation and management of the existing distribution networks many folds as before. Therefore, for optimal operation of such a complicated distribution network, this article proposes a synergistic day-ahead scheduling scheme emphasizing the combined impact of network reconfiguration, active elements, and demand response. Minimizing the overall cost associated with the operation and management of distribution system, the formulation is proposed as a mixed-integer second-order cone programming problem. This formulation captures the exact characteristics of power flow in network and ensures its fast convergence to global optima. Further, the Benders decomposition method is employed to deal with computational complexities in achieving a solution. Test results on a modified IEEE 33-bus network show notable enhancement in the techno-economic performance of the network.

The modern active distribution systems necessitate integrating storage systems, thereby facilitating the large-scale proliferation of photovoltaic (PV) energy resources. This further calls for the optimal planning of energy storage systems, satisfying all the operational and economic constraints. This article describes an exhaustive storage integration method, deeming the life cycle of the battery energy storage, the uncertainty of load and PV output, and the islanded mode of operation of the system. A two-stage mixed-integer linear programming problem is formulated that determines the capacity and the number of discharge cycles of the batteries in the first stage. The lifetime of the battery based on the partial depth of discharge is analyzed in the second stage. Furthermore, the uncertainty and variability of PV and demand are taken into account through probabilistic analysis and time-period clustering. The method is validated on a standard 33-bus radial distribution network for the allocation of distributed lithium-ion batteries. Also, the method’s scalability is validated on a practical Indian distribution network and a 141-bus distribution network of metropolitan area of Caracas with distributed PV installations on various nodes.

Appliance identification and classification are primary requirement for successful deployment of demand side management in smart grid. Traditionally, load identification is carried out after extracting handcrafted features from electrical signals followed by classification algorithms, which is laborious and time consuming. To nullify the abovementioned shortcomings, this article proposes a 1-D convolutional neural network (CNN) with sinusoidal kernel initializers for classification of domestic appliances operating individually or in combination with other devices. For efficient operation, 15 primary and 225 multiscale sinusoidal kernels (MSK) are formed and included in the CNN architecture for extracting important discriminative features from the current signals. Classification accuracy of the proposed CNN module, named as MSK-CNN, is further improved by adding a new nonlinear activation function (AF), SL-ReLU, having an adjustable logarithmic function along with softsign and ReLU functions. The efficacy of MSK-CNN is tested on a practical dataset, created by the authors, consists current signals of different real life residential appliances. It is showed that MSK-CNN can successfully distinguish 18 single and 12 appliance combinations, and can attain an accuracy of 98.61%. The experimental outcomes reveal superiority of the MSK-CNN over classical CNN modules with other kernel initializers and AFs in terms of classification accuracy and training convergence.

An increase in the utilization of renewable energy has called for a cost-effective and reliable solutions to overcome their intermittency. With this regard, this paper offers a unique way of interfacing dc microgrid (DCMG) to the doubly fed induction generator (DFIG) wind system during grid-connected, fault, and isolated working conditions making it economical. The control flow strategy proposed here uses the grid-side converter (GSC) of the DFIG system to serve multiple purposes; first, the GSC is used to regulate the DCMG voltage during the grid-connected mode and avoids the need for an additional converter for the DCMG to connect it to the grid. Second, the GSC is allowed to function as a simple diode bridge rectifier thereby allowing the DFIG machine to feed DCMG during isolated conditions hence avoiding the need for replacing the converter. This paper also improves the fault ride through performance of the DFIG wind system without any additional investment. This is possible due to the usage of already available ultracapacitor in the DCMG. Hence, the DFIG wind system need not have to finance separately to protect its dc link during the fault. Furthermore, a small-signal-based stability analysis performed also indicates the robustness of the proposed control strategy. A 2.2-kW hardware prototype has been developed and the results obtained from the elaborate experimentation justify that this paper is economical, efficient, reliable, and offers better power quality compared to the existing work.

This article elucidates a real-time energy management strategy for a smart residential apartment building having nonidentical occupants at the dwelling units (DUs). The aim of the present article is to design a distributed energy management algorithm, which can optimize the real-time demand of the entire building against abruptly updated rooftop solar generation and real-time price (RTP) of energy. The proposed energy management strategy differentiates among the DUs by considering a new parameter named load criticality level, which is defined as the value imposed by the DU residents to their power consumption. The optimization portfolio is developed as a novel bilevel, stochastic, multiobjective optimization problem where the maximization of utility of the consumed power is considered simultaneously with the cost minimization. To this end, a virtual energy trading platform is designed in this article between central building management system and the DUs, where they interact with each other by following the directives of single-leader multifollower Stackelberg game. The solution strategy is proposed as a Lyapunov optimization, which needs only the current values of the uncertain parameters, such as load variation, renewable generation, and energy price, and do not require any knowledge about their probabilistic variation, to eliminate the complexities regarding time average stochastic equations. Strenuous simulation on real-time data of four DUs, it is proved that the proposed framework can track the abrupt change in RTP and solar generation efficiently. Comparing with two benchmark methods viz. centralized process and greedy algorithm, the superiority of the designed energy management portfolio is established.

The ac-dc distribution systems have recently gained huge popularity due to advancements in power converters, high penetration of renewable energy resources, and wide usages of dc loads. However, load flow in such systems is a challenging task due to nonlinear characteristics of power converters. This paper presents a novel load-flow algorithm for ac-dc distribution systems, utilizing the concept of graph theory and matrix algebra. Four developed matrices, loads beyond branch matrix, the path impedance matrix, the path drop matrix, and the slack bus to other buses drop matrix, and simple matrix operations are utilized to obtain load-flow solutions. These matrices reveal the network topology and relevant information about the behavior of ac-dc distribution network during load-flow studies. In contrast with traditional load-flow methods for HVDC systems, the proposed technique does not require any lower-upper decomposition, matrix inversion, and forward-backward substitution of the Jacobian matrix. Because of the aforementioned reasons, the developed technique is computationally efficient. The proposed method has been tested using several case studies of ac-dc distribution network, which includes different operating modes of various power converters. Results show the feasibility and authenticity of the proposed method.

Home energy management systems (HEMSs) encourage participation of residential consumers into the demand response programs. This paper proposes a robust Conditional Value at Risk (CVaR) optimization approach for day ahead HEMS to reduce the effect of risk of real-time exposure to energy price and solar power generation uncertainties. Initially, the CVaR method is integrated with the two-point Estimation (2PE) analysis to approximate the solar power, modeled as Beta probability distribution function, in low computation effort compared to conventional Monte Carlo simulation (MCS) based CVaR approach. Then the optimization constraints are revised to their robust counterparts by accounting a certain amount of uncertainty in the energy prices from their nominal values. Unlike the previous literatures, the optimization problem is developed to minimize the risk value of the energy cost. Again to maximize the life of the plug-in electric vehicle (PEV) a pseudo cost function for the PEV battery degradation is proposed. The entire optimization portfolio is developed as a mixed integer linear programming for its easy execution. Simulation is demonstrated on a smart home, designed as an ac-dc microgrid, having practical appliance data sets, to prove the efficacy of the proposed method.

This paper proposes a novel decentralized coordinated voltage control (CVC) scheme for a distribution system consisting of dc microgrid (DCMG), doubly fed induction generator-based wind system, on-load tap changer (OLTC), and DSTATCOM. The proposed scheme considers the response time of various voltage regulating devices and assigns a master/slave role based on the operating conditions of the grid and availability of the device. This paper offers a distinctive solution to optimally utilize the voltage regulating devices which do not take part in the contingency situation such as OLTC and DCMG converter in order to achieve the following objectives: 1) A better voltage regulation and increased reactive power reserve during normal operating conditions of the grid. 2) To improve the transient performance of the system in terms of reduction in postfault voltage recovery time. Two modified IEEE 33 bus systems are implemented in a real-time digital simulator platform to test the effectiveness of the proposed CVC scheme. Furthermore, power hardware in loop (PHIL) experimentation is conducted with a reduced scale DCMG hardware setup to test the stability, feasibility, and practicability of the proposed scheme. The simulation and PHIL results demonstrate that the proposed CVC scheme provides a better solution compared to existing work by fulfilling the set objectives.

Renewable integrated dc Microgrids (DCMGs) are gaining popularity by feeding remote locations in qualitative and quantitative manner. Reliability of autonomous dc microgrids depend on battery capacity and size due to stochastic behavior of renewables. Over charging and discharging scenarios compel the microgrid into insecure zone. Increasing the storage capacity is not an economical solution because of additional maintenance and capital cost. Thus, interconnecting neighbor microgrids increases virtual storing and discharging capacity when excess power and deficit scenario arises respectively in any of the DCMG. Control strategy plays vital role in regulating the power within and between microgrids. Power control and management technique is developed based on bus signaling method to govern sources, storages, and loads to achieve effective coordination and energy management between microgrids. Proposed scheme is simple and reliable since bus voltages are utilized in shifting the modes without having dedicated communication lines. Proposed scheme is validated through real time simulation of two autonomous dc grids in real time digital simulator (RTDS) and its results are verified by hardware experimentation.

The conventional transmission loss estimation methods used by power system utilities in scheduling problems rely on the exactness of the network model. However, the transmission network model in the system operator database is erroneous and not updated periodically. Therefore, the transmission losses calculated based on the erroneous network model is also erroneous. In this context, this paper proposes an auxiliary hybrid model using a back propagation neural network (BPNN) and a particle swarm optimization (PSO) technique to estimate transmission losses, while solving power system scheduling problems. Here, the historical information of the power system is processed by the BPNN and its control parameters are optimized using PSO. In the proposed PSO-BPNN loss estimator, power system variables such as real power generation levels, reactive power injection values, and ambient temperature are used as the input variables. The proposed loss estimator is validated using IEEE 30 bus system and Ontario power system.