The increasing reliance on electrical energy necessitates resilient power distribution systems capable of withstanding disruptions caused by natural disasters or cyber-attacks. This paper presents a novel dynamic network reconfiguration framework to enhance the resilience of power distribution systems by forming islanded microgrids during main grid failures. The framework utilises a reinforcement learning approach based on deep Q-networks (DQN) to dynamically optimise the operation of remote-controlled switches for network reconfiguration. A convolutional neural network is integrated into the DQN architecture for Q-value estimation, enabling efficient decision-making under varying operational scenarios. The proposed method is validated using a modified CIGRE medium-voltage distribution network, considering diverse scenarios of load demand and generation capacity. Results demonstrate the framework's capability to prioritise critical loads, maintain system stability, and ensure compliance with operational constraints, such as voltage profiles and line loading limits. The reconfiguration process dynamically forms islanded microgrids, effectively balancing supply and demand while minimising power losses and reducing switching operations. This study underscores the potential of deep reinforcement learning in enhancing grid resilience. Future work aims to incorporate risk-aware learning to address uncertainties, further safeguarding power distribution networks as critical infrastructure.

Integration of renewable energy resources (RERs) and complexities associated with varying load patterns urge the demand for energy storage systems (ESS) to ensure real power balancing of distribution system. Among various ESS, battery energy storage system (BESS) guarantees the reliable operation of microgrids in terms of economic and technical functionalities. The scheduling of microgrids becomes essential for the economic and optimal performance of both generating sources and loads. In this work, the load-shifting capability of BESS is utilized for the optimal scheduling of grid-connected microgrids. BESS has a significant role in the effective utilization of power from RERs during grid balancing. This paper discusses the scheduling of grid-connected microgrid with two scheduling strategies, which differ in the charging behaviour of BESS. Scheduling decisions are based on the value of locational marginal price (LMP) of distribution system. In scheduling strategy-I, BESS charges at off-peak hours, either from the PV system or from the grid. Meanwhile, in scheduling strategy- II, BESS charges from the PV system at peak or off-peak hours. These two scheduling methods are also compared with the scheduling of the distribution system without integrating the microgrid. The cost of power generation, power from the main grid, and active power losses of the distribution system are calculated by performing optimal power flow (OPF). The effectiveness of the proposed scheduling strategies are validated on the IEEE-69 bus system for 24 h time duration by using MATPOWER simulation tool. The results obtained after OPF establishes the requirement of effective utilization of BESS in PV integrated distribution system.

Microgrids help in the better utilization of distributed energy resources. However, due to the intermittent nature of such resources, the net energy availability within microgrids varies with time. This adversely affects the power balance within microgrids, especially if energy storage systems or grid support are unavailable. In such cases, the microgrids in a locality can form a network to trade power among themselves. The most transparent and fair method is power trading using a double auction with sealed bidding. A novel method for sealed bid power trading using consortium blockchains that is entirely decentralized is introduced, ensuring better operational and data security. Here, all bidding data are shared twice. During bidding time, bids are shared by participating microgrids using one-way encryption and are added to the blockchain by all participants. After the bid closure, the respective microgrids share the same data unencrypted. By encrypting and comparing the bid data with the encrypted data already added to the blockchains, other participants can verify whether the microgrids have kept their bid data unmodified post-bid-closure. If the data has been kept unmodified, all participants add the bid data as verified data to their blockchain. Successful bids are then identified using a smart contract, and power transfer is effected accordingly. All power and monetary transactions are recorded distributively in blockchains. A decentralised framework for the implementation of the method is also envisaged. Further, a novel approach is proposed for distributively authenticating the blocks shared by participating microgrids using public and shared keys. The method and the framework are then successfully tested for decentralization, transparency, resilience, public verifiability, and transaction enforcement using simulations in a benchmark test system for networked microgrids.

In the realm of power systems, the assessment of dominant oscillatory modes plays a critical role. If operators can swiftly identify and detect low-frequency electromechanical oscillatory modes and their characteristics, they can respond more effectively to specific events. With the progress in wide area monitoring systems (WAMS) and the abundance of measurements, there is a growing need for mode estimation algorithms capable of processing massive amounts of data in a fast and efficient manner. Deep learning algorithms, particularly the Long Short-Term Memory (LSTM), have outperformed other prediction techniques when it comes to forecasting oscillatory modes. This paper adopts a systematic approach to investigate the impact of deep-stacked unidirectional (uni-LSTM) and bidirectional LSTM (bi-LSTM) networks on predicting oscillatory mode parameters in power systems. The simulations are conducted using MATLAB software and the Python Tensorflow library, allowing for a comprehensive evaluation of the performance of these AI-based algorithms in power system analysis. The results are evaluated using analytical techniques based on the simulation results of the Kundur two-area system and the IEEE 39 bus system. The results confirm the superior viability and adaptability of bidirectional LSTM for the application of power oscillation analysis.

Renewable resource proliferation results in clusters with reactive power interdependence within emerging deregulated power grids. The clusters generally form as balancing areas (BA) and the reactive power interdependence is endured through voltage-controlled areas. In such grids, renewable generation uncertainty could manifest as propagating voltage disturbances towards the relatively weaker clusters. Operational and topological constraints of locally optimized conventional reactive support systems limit their capability in alleviating the propagation. This work demonstrates voltage disturbance propagation between BAs and proposes a framework to defend and mitigate them. The framework comprises a proactive resource procurement followed by a proactive first line of defense and a two-stage mitigation strategy. The strategy intelligently utilizes distributed resources through wide area supplementary control. The strategy avails the cyber-physical features of the grid to formulate the multi-criterion decision logic and associated defense as well as mitigation plan. This complex process is realized by an analytical hierarchical process (AHP). Extensive case studies conducted on modified IEEE 68 bus system illustrates the vitality of this decision logic and validates the effectiveness of defense plan and real-time mitigation strategy. The proposed approach also leads to better utilization of available reactive resources and facilitates improved participation in the deregulated market.

Estimation of electromechanical mode properties is crucial in modern power systems for providing the operators with an adequate indication of the stress in the system. Measurement-based approaches use signal processing algorithms for mode identification and parameter estimation. This paper presents a novel framework for the assessment of low-frequency oscillation modes using real-world synchrophasor data with minimum computational effort. A nonstationary approach known as Time-Varying Filter based Empirical Mode Decomposition (TVF-EMD) technique is used to identify the dominant low-frequency modes present in the ambient PMU data. The combination of TVF-EMD with Teager Kaiser Energy Operator (TKEO) precisely estimates the instantaneous mode parameters, such as frequency, amplitude, and damping ratio. The efficacy of the proposed approach is demonstrated by applying it in a synthetic signal, simulated data of a standard IEEE test system, and in real-world PMU data of the Indian power grid. The proposed method is compared with the existing methodologies and the observations reveal that the proposed method has robust performance in estimating the instantaneous mode features in the power system with less computational complexities.

Hybrid AC/DC urban microgrids (HUMG) have emerged as a candidate solution to reliably, efficiently, and economically meet the increasing consumer electric demand and to ensure the quality of delivered power. Smart buildings (SB) are prevalent prosumers in HUMGs. To effectively utilise renewable resources in SBs, it is imperative to have assistance from battery energy storage systems (BESSs). Also, uncertainty in generation and demand necessitates real-time ancillary power support for SBs. Smart building interconnection forming SB clusters is an emerging solution for providing ancillary power support. Existing SB interconnection topologies require multiple power conversion stages, additional infrastructure, and underutilise the existing AC grid infrastructure. Most of these SB interconnections are not sustainable due to higher losses from more power conversion stages, increased cost, and excessive dependence on BESSs. A composite transmission system-based ancillary power dispatching architecture (CTS-APDA) for the sustainable operation of SB clusters is proposed in this paper. A multi-agent system based hierarchical control and decision logic in the CTS-APDA ensures the optimal ustilisation of battery resources. The operation of the proposed CTS-APDA and its efficacy as a fast-acting ancillary support is validated through extensive case studies. The proposed CTS-APDA will foster participation of electric vehicles in the transactive energy market of emerging SBs.

The increasing penetration of renewable source-based generators in the power grid has paved the way for several hybrid power grid topologies. These topologies ensure efficient power exchange between AC/DC sources and their corresponding loads. The hybrid power grid topologies have also increased the grid’s power transfer capacity, which is necessary to accommodate the increasing penetration of renewable source-based generators. Composite AC/DC power distribution architecture is an emerging hybrid power grid topology where DC power is carried through the AC distribution lines using zig-zag transformers. This paper presents a detailed design and development of the composite AC/DC power distribution architecture. The thermal and magnetic design considerations for the zig-zag transformer and the thermal design considerations for the transmission line are studied in detail. The thermal design of the transformer is performed based on the standard IEEE Std C57.91-1995. Detailed case studies are performed on the composite AC/DC distribution architecture to corroborate the adequacy of the developed design.

The world is drifting towards a renewable dominated power scenario. Among the renewable energy sources, wind power projects are gaining the prominence. In the new energy mix, many conventional paradigms are being redefined. For years, steam turbine time constants which were assumed as constant proved to be no longer constants as the generation schedule varies. With the increased penetration of renewable, conventional power plants are facing threat of shut down. Meanwhile, majority of synchronous machines in these conventional power plants such as thermal and nuclear stations are operated at partial generation schedule between 70 % and 90 %, due to various economic and environmental factors. The shut down of conventional power plants results in abatement of numerous synchronous machines. This work proposes the use of abandoned thermal generating units as synchronous condenser to aid dynamic frequency regulation in a high renewable penetrated power system with varying generation schedule. The synchronous condenser provides momentary active power support following a load change. Simulation studies were carried out under various scenarios of generation schedule and incorporation of synchronous condenser. Obtained results bridges the concepts of varying generation schedule and synchronous condenser and their effect on dynamic frequency regulation of power system has been analysed.

Gas Insulated Transmission Lines (GIL) and Gas Insulated Substations (GIS) are the technologies that utilize less space than conventional methods for the transmission of high power. This high power transmission necessitates electrical insulators (spacers) with high mechanical stability, dielectric properties and thermal conductivity. In this context, a number of novel materials and preparation methods are under research for developing a suitable material for the spacer. The present work aims to characterize epoxy alumina nanocomposites filled with surface-functionalized alumina nanoparticles. Alumina nanoparticles were functionalized using silane coupling agents; (3-Aminopropyl) triethoxy silane (APTES) and (3-Glycidyloxypropyl) trimethoxy silane (GPTMS); to analyze their effects on the performance of epoxy nanocomposites. The primary investigation shows that the nanocomposites filled with APTES treated alumina nanoparticles increase the volume resistivity, tensile strength, and thermal conductivity by 11%, 17% and 11%, respectively, as compared to unfilled epoxy. These results indicate that the epoxy nanocomposites filled with APTES treated alumina nanoparticles can be a promising substitute for epoxy spacer used in GIL and GIS.

The modernized power grid is being pushed to become more interconnected as the demand for electric power rises. It causes a loss of inertia in the power system, resulting in more severe disruptions. Quickly identifying the low frequency oscillatory modes and associated characteristics will allow the power system operator to respond to a specific occurrence without wasting time. This paper provides a comparative review of two unsupervised learning techniques approaches for an electromechanical mode shape estimation. This paper aims to generate an alarm in the control room, which prompts the controls to act when damping is weaker in the system. The proposed method is studied using PMU data extracted from a Kundur two-area system at various disturbance conditions. As a Performance comparison, accuracy and computational time are verified for both techniques. DBSCAN clustering method shows superior accuracy and viability than other clustering methods.

Integration of Renewable Energy Sources (RESs) to distribution grid lead to formation of microgrids, which is a strategic approach to balance the generation and load in distribution system. This paper presents a scheduling algorithm for distribution system with minimizing the cost. Main objective of this paper is to minimize electricity cost by integration of RESs, in addition to that power imported from main grid, local marginal price of renewable resource connected bus and total distribution losses are calculated. The proposed algorithm is demonstrated by hourly varying load on an IEEE-69 bus system and results are compared with and without addition of RESs.

The integration of various types of renewable energy sources and storage systems into power system increases the complexity of the conventional power system, necessitating a comprehensive optimal power flow analysis with multiple and/or competing objectives. From this viewpoint, the paper proposes optimal utilization of different power system entities, by solving the Optimal Power Flow (OPF) problem using the heuristic Particle Swarm Optimization (PSO) technique. The proposed methodology optimizes the power flow of a complex, nonlinear hybrid power system to fulfill several objectives, viz., line loss minimization, cost minimization, and profit maximization under varying environmental conditions and system attributes. A modified IEEE system replacing conventional sources with renewables and storage system is considered for study. Results corroborate the effectiveness of the strategy.

Increasing penetration of renewable energy significantly alters the network power flow in terms of direction as well as magnitude. The intermittent and uncertain nature of renewable inputs is a major concern since the power flow in the system is affected considerably. Hence to support bulk integration of renewable energy in the power system, bulk or distributed energy storage systems can be implemented. The energy storage system is utilized to absorb the excess power and meet the demand to avoid violation of system constraints. In this work, a multi-period ac optimal power flow (OPF) problem with energy storage systems (ESSs) is formulated and a set of candidate buses for ESS installation are identified based on economic criterion. A novel hybrid approach of conventional cum heuristic method, newton raphson based particle swarm optimization (NRPSO) method is proposed to optimize the allocation problem. Tests are carried out on IEEE 14-bus system and modified IEEE 14–bus system integrated with wind generation for variable load condition using MATLAB, to assess the optimal location of energy storages on system operation.

Emerging deregulation policies together with the distributed renewable resource had led to the restructuring of power grid operation as several interconnected self-reliant clusters. Forecasting models play a vital role to handle its inherent variability so that the safe and reliable operation of the grid can be ensured. However, the irradiance variation caused by a fast-moving cumulus cloud in the nowcasting horizon results in a fairly higher forecast error. This in turn leads to fluctuating power injections with considerable ramp rates. In a deregulated grid with weaker reactive power support, these fluctuating injections can cause severe voltage quality issues. Since the availability of the resources is uncertain in the deregulated grid, conventional decentralized control may not be sufficient to handle these voltage quality issues. Though much research is happening on these issues, the characteristics of fast-moving cumulus cloud-induced power quality issues are not well investigated. In this regard, this work investigates the voltage quality issues caused by the grid tied solar farms. Real-world data of irradiance variation under fast-moving cumulus clouds are considered for the investigation. The investigation carried out and the results demonstrated can benefit the power quality remedial measures in emerging deregulated grids.

This paper presents a robust dynamic approach for the monitoring and estimation of electromechanical oscillatory modes in the power system in real-time with less computational burden. Extensive implementation of phasor measurement units (PMU) and the utilization of advanced signal processing techniques help in identifying the dynamic behaviors of oscillatory modes. Conventional nonstationary analysis techniques are computationally weak to handle a larger quantity of data. This research utilizes the time-varying filter based empirical mode decomposition method for signal decomposition, which is highly tolerant to noise and computationally more robust. Low frequency modes are estimated by analyzing the power spectral density of the most suitable decomposed mode, the selection of which is done using correlation analysis. Instantaneous mode shapes of the signals are determined using cross-power spectral density functions, which will give the operator much information about the nature of oscillations and provide proactive steps to improve the operation of the power system. The proposed approach has been tested using signals obtained from two areas Kundur system and actual PMU data recorded from Power System Operation Corporation (POSOCO) Limited of the Indian power grid. The results confirm the superior viability and adaptability of the proposed approach in estimating the electromechanical modes. The instantaneous mode shapes are analyzed accurately with less computational complexity compared to the existing nonstationary strategies which are used for power system mode estimation.

The increase in electric power demand pushes the modern power system for more interconnected networks. It leads to a lack of inertia and creates more critical disturbances in the power system. When this oscillation isn't damped out, it results in cascade tripping. Immediate detection of low-frequency oscillatory modes and their parameters will help the power system operator to act on a particular event without consuming much time. This research paper proposes novel strategies for identifying low-frequency modes using deep learning techniques, and the model can predict the LFO modes in different topologies. This work presents the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) approach to predict the instantaneous mode oscillatory parameters in the power system. Once the LSTM-RNN model is trained for different power disturbance situations, it can be used for any events associated with the system. Simulation results are verified using two area Kundur systems at various disturbance conditions. The simulations are performed using MATLAB software and python tensor flow library. The results are validated using statistical methods, and it confirms the superior viability and adaptability of the proposed approach in predicting the instantaneous mode parameters.

The market-oriented policies devised for emerging deregulated grid facilitates wider opportunities and associated challenges as well to the grid operators and stakeholders such as microgrid aggregators, fast and flexible reserve providers, load serving entities, and so on. How to turn these challenges to opportunities is the key to have a better prospect toward the evolving deregulated grid. Uncertainties associated with the renewables and limited reactive power support are prime concerns among these challenges hindering the prospect of widespread renewable dominant microgrid owing to the accompanying power quality issues. Even though researches cover the frequency regulation issues and counter measures in such uncertain deregulated grid, the disturbance propagation characteristics in such grids is emerging as a prime concern to be addressed. In congruent to this, the challenges posed by wide area propagation of disturbance instigated by renewable dominant uncertain microgrids in evolving deregulated market-oriented grid are investigated in this chapter. Besides, a proactive defense system that utilizes the characteristics of these disturbance propagation to mitigate the spread of the disturbance is presented and discussed. This defense strategy caters the ancillary service capabilities of microgrids itself to mitigate the disturbance spread caused by other microgrids. Case study conducted on NY-NE 16 machine 68 bus system substantiates the wide area propagation characteristics of disturbance induced by uncertain renewable dominant microgrid and efficacy of presented proactive defense strategy on alleviating the disturbance spread.

This paper presents a dynamic approach for the monitoring and estimation of electromechanical oscillatory modes in the power system in real time with less computational burden. Extensive implementation of phasor measurement units (PMU) and the utilization of advanced signal processing techniques help in identifying the dynamic behaviors of oscillatory modes. Conventional nonstationary analysis techniques are computationally weak to handle a larger quantity of data in real-time. This research utilizes the variational mode decomposition (VMD) for signal decomposition, which is highly tolerant to noise and computationally more robust. The predefined parameters of the VMD process are assigned using FFT analysis of the signal. The significant decomposed mode resembling the original signal is determined using the correlation coefficient method and used for low-frequency mode estimation. The spectral analysis techniques are used to determine the instantaneous mode shapes, which help to identify the source of oscillation in the power system network. The proposed methodology has been tested using signals obtained from two area Kundur system and actual PMU data recorded from Power System Operation Corporation (POSOCO) Limited of the Indian Power grid. The results confirm the superior viability and adaptability of the proposed approach. The performance comparison with other existing signal processing techniques used to estimate low-frequency modes is also presented to illustrate the effectiveness of the proposed method.

Following the widespread implementation of Phasor Measurement Units (PMU) across the power grid, the measurement-based methods are extensively used to track power system oscillations efficiently in real-time. This paper proposes a novel dynamic approach for rapid monitoring and identification of electromechanical oscillation modes using real-time measurement signals. The measurement-based method has been recently improved by the Variational Mode Decomposition (VMD) technique, a nonlinear, nonstationary analysis tool used to estimate low-frequency modes in the power system. However, the random selection of the initial parameter utilized in the conventional VMD process significantly affects its performance and often leads to computational complexities. Thus, a Modified Variational Mode Decomposition (MVMD) method based on Particle Swarm Optimization (PSO) has been proposed in this work. MVMD eliminates unnecessary decomposition modes involved in the conventional VMD process by optimizing parameters using PSO. The analysis of these modes is accomplished by assessing instantaneous modal parameters using the Hilbert transform and spectral analysis. The raised approach is validated using a test signal, IEEE standard 16 machine 68 bus system and real-world PMU data. Simulation results show that the dominant low-frequency oscillatory mode with high noise tolerance can be effectively determined with less computational complexity.