Big data has potential to unlock novel groundbreaking opportunities in power grid that enhances a multitude of technical, social, and economic gains. As power grid technologies evolve in conjunction with measurement and communication technologies, this results in unprecedented amount of heterogeneous big data. In particular, computational complexity, data security, and operational integration of big data into power system planning and operational frameworks are the key challenges to transform the heterogeneous large dataset into actionable outcomes. In this context, suitable big data analytics combined with visualization can lead to better situational awareness and predictive decisions. This paper presents a comprehensive state-of-the-art review of big data analytics and its applications in power grids, and also identifies challenges and opportunities from utility, industry, and research perspectives. The paper analyzes research gaps and presents insights on future research directions to integrate big data analytics into power system planning and operational frameworks. Detailed information for utilities looking to apply big data analytics and insights on how utilities can enhance revenue streams and bring disruptive innovation are discussed. General guidelines for utilities to make the right investment in the adoption of big data analytics by unveiling interdependencies among critical infrastructures and operations are also provided.

Power flow is one of the basic tools for system operation and control. Due to its nature, which determines the complex nodal voltages, line flows, currents and losses, it enforces a large computation load on a power system. A distributed/decentralised algorithm unburdens the central controller and shares the total computation load with all agents. Therefore, such algorithms are an effective method for dealing with power flow complexity. In this study, a distributed method based on a linearised AC power system is proposed. First, the linearisation procedure of a comprehensive non-linear AC power flow (ACPF) is detailed. Second, a distributed method is presented based upon the linear ACPF equations. Three case studies are presented to evaluate the overall performance of the proposed method. In the first case study, the accuracy level of both linearised ACPF and distributed ACPF is assessed. In the second case study, the dynamic performance of distributed ACPF is investigated based on the load sudden changes. In the third case study, the scalability of the proposed distributed ACPF is evaluated by applying it to a larger power system.

Here, a data mining–driven scheme based on discrete wavelet transform (DWT) is proposed for high impedance fault (HIF) detection in active distribution networks. Correlation between the phase current signal and the related details of the current wavelet transform is presented as a new index for HIF detection. The proposed HIF detection method is implemented in two subsequent stages. In the first stage, the most important features for HIF detection are extracted using support vector machine (SVM) and decision tree (DT). The parameters of SVM are optimised using the genetic algorithm (GA) over the input scenarios. In second stage, SVM is utilised to classify the input data. The efficiency of the utilised SVM-based classifier is compared with a probabilistic neural network (PNN). A comprehensive list of scenarios including load switching, inrush current, solid short-circuit faults, HIF faults in the presence of harmonic loads is generated. The performance of the proposed algorithm is investigated for two active distribution networks including IEEE 13-Bus and IEEE 34-Bus systems.

The recent increase in infiltration of distributed resources has challenged the traditional operation of power systems. Simultaneously, devastating effects of recent natural disasters have questioned the resilience of power infrastructure for an electricity dependent community. In this study, a solution has been presented in the form of a resilient smart grid network which utilises distributed energy resources (DERs) and machine learning (ML) algorithms to improve the power availability during disastrous events. In addition to power electronics with load categorisation features, the presented system utilises ML tools to use the information from neighbouring units and external sources to make complicated logical decisions directed towards providing power to critical loads at all times. Furthermore, the provided model encourages consideration of ML tools as a part of smart grid design process together with power electronics and controls, rather than as an additional feature.

This study proposes a new demand-side management (DSM) technique, which is characterised by low computational requirements. The proposed technique relies on developing an operational matrix by the device local controller based on the device characteristics and the customer preferences. This matrix is sent to the energy management system (EMS) without the need to send any further information about the device or the customer preferences; then, the EMS chooses the optimal schedule for the device. To demonstrate the effectiveness of the proposed DSM technique, it is incorporated in an EMS that consists of three units controlled by a centralised microgrid controller (MGC). The three units managed by the MGC are the data collection and storage engine, the forecasting engine, and the optimisation engine. The EMS utilises the rolling horizon concept to manage real-time information and to provide the plug-and-play option for all controllable devices. Simulation results on a typical microgrid system show that the proposed DSM technique outperforms conventional DSM approaches in terms of the computational time.

The potential availability of renewable energy sources is unquestionable and the government is setting steep targets for renewable energy usage. Renewable-based DGs, reduce dependence on fossil fuels, mitigate global climate change, ensure energy security, and reduce emissions of CO₂ and other greenhouse gases. This study addresses microgrid system analysis with hybrid energy sources and reconfiguration simultaneously for efficient operation of the system. Microgrid zones are formulated categorically with the existing distribution system. In this study, wind, solar and small hydro-based DGs are considered. Uncertainties of renewable power generation and load are also taken care in the optimization problem. A multi-objective optimisation method proposed in this paper for optimal integration of renewable-based DGs and reconfiguration of the network to minimise power loss and maximise annual cost savings. Optimal location and sizes of DG units are determined using gravitational search algorithm and general algebraic modelling system respectively. Optimal reconfiguration of the microgrid system is obtained using genetic algorithm. Simulation results are obtained for the IEEE 33-bus system and compared with existing methods as available in the literature. Furthermore, this study has been carried out with a 24-hr time-varying distribution system. The simulation results show the efficiency and accuracy of the proposed technique.

Generation maintenance scheduling (GMS) is one of the most important scheduling problems in the restructured power systems. The maintenance time interval of generation units is the crucial factor of GMS for an operation lifespan of generation units, particularly within the smart grid which needs high reliability. Accordingly, this study proposes a multi-objective-GMS (MO-GMS) optimisation model for maintenance scheduling of generation units based on the global criterion approach, adopting a suitable compromise function. The proposed MO-GMS model determines the maintenance intervals, aims to maximise both the generation company's (GenCo's) financial returns from selling electricity and the system reserve at every time interval from the independent system operator (ISO) standpoint. This method searches the optimal maintenance weeks for each generation unit, considering the objectives of both GenCo and ISO, simultaneously. The proposed MO-GMS model is formulated as a mixed-integer non-linear programming problem and examined on the IEEE 24-bus and IEEE 118-bus test systems. The success of the proposed multi-objective model is validated by comparing the obtained results with intelligent optimisation algorithms.

Local area packetised-power network (LAPPN) provides flexible local power dispatching in the future energy internet. With interconnections among multiple LAPPNs, power dispatching can be further extended to intra- and inter-LAPPN power interchanges. It becomes a significant issue to schedule the two kinds of power interchanges as, from a system perspective high utilisation of available scheduling time slots and low overall transmission loss should be guaranteed, and from a subscriber perspective a high scheduled ratio of transmission requests with a fair transmission sequence in terms of transmission urgency is expected. To this end, the authors propose a cooperative power dispatching framework for connected LAPPNs, including subscriber matching and coordinated power transmission scheduling. The former matches subscribers from different LAPPNs, considering both subscriber preferences and power transmission loss. The latter coordinates the intra- and inter-LAPPN power packet transmission to maximise the amount of energy delivered with guaranteed fairness on user urgency. Simulation results of a two-LAPPN system are provided, which demonstrate that the proposed framework can achieve effective and efficient power dispatching in terms of the mentioned concerns, and reveal facts on ideal system capacity and how to manipulate the proportions of the two kinds of transmissions according to the network status.

Electrified transportation technology has matured in different parts of the globe. However, this technology is in an advent stage in the Indian market. Due to this fact, a lot more challenges are being encountered in the development of electrified transportation in India; with the scarcity of viable charging stations posing as a significant bottleneck. In this study, the techno-economic analysis of different solar-based charging schemes that are available in the existing environment and present a modest, economical and reliable method of charging an electric vehicle (EV) )(e.g. e-rickshaw) through a solar panel that ultimately enhances the driving range and overall reliability of the system has been done. To validate the performance, the prototype of vehicle-integrated photovoltaic (PV) charging system has been developed and test results are demonstrated. Economic analysis is done based on the yearly average solar irradiance profile in Aligarh, India. Further, this work presents a comparative analysis of CO₂ emission for 100 km driving range from the EVs charge by different charging schemes and internal combustion engine vehicles.

This study presents a distributed prioritised coordinated control topology that has the capability to reduce the total generation cost and maintain the average DC microgrid voltage. The main advantage of this topology over the addressed ones is developing a prioritised normalised running cost methodology. In this methodology, the best economical combination of DGs is selected to fulfil the load demand and maintain the minimum required spinning reserve power. The developed coordinated controllers contain tertiary and secondary levels cooperating to guarantee economical load sharing between the dispatchable DGs through precise adjustment of the primary voltage levels. Simulation and experimental results verify the efficacy and feasibility of the proposed dispatch scheme compared to the conventional power-sharing techniques. In addition, system reliability enhancement during communication link failure and DG plug-n-play are investigated as well.

Energy sharing through a microgrid (MG) is essential for islanded communities to maximise the use of distributed energy resources (DERs) and battery energy storage systems (BESSs). Proper energy management and control strategies of such MGs can offer revenue to prosumers (active consumers with DERs) by routing excess energy to their neighbours and maintaining grid constraints at the same time. This paper proposes an advanced power-routing framework for a solar-photovoltaic (PV)-based islanded MG with a central storage system (CSS). An optimisation-based economic operation for the MG is developed that determines the power routing and energy sharing in the MG in the day-ahead stage. A modified droop controller-based real-time control strategy has been established that maintains the voltage constraints of the MG. The proposed power-routing framework is verified via a case study for a typical islanded MG. The outcome of the optimal economic operation and a controller verification of the proposed framework are presented to demonstrate the effectiveness of the proposed power-routing framework. Results reveal that the proposed framework performs a stable control operation and provides a profit of 57 AU$/day at optimal conditions.

Dynamically charging electric vehicles (EVs) have the potential to significantly reduce range anxiety and decrease the size of battery required for acceptable range. However, with the main driver for progressing EV technology being the reduction of carbon emissions, consideration of how a dynamic charging system would impact these emissions is required. This study presents a demand-side management method for allocating resources to charge EVs dynamically considering the integration of local renewable generation. A multi-objective optimisation problem is formulated to consider individual users, an energy retailer and a regulator as players with conflicting interests. A 19% reduction in the energy drawn from the power grid is observed over the course of a 24 h period when compared with a first-come-first-served allocation method. This results in a greater reduction in CO₂ emissions of 22% by considering the power grid's make-up at each time interval. Furthermore, a 42% reduction in CO₂ emissions is achieved compared to a system without local renewable energy integration. By varying the weights assigned to the players’ goals, the method can reduce overall demand at peak times and produce a smoother demand profile. System fairness is shown to improve with an average Gini coefficient reduction of 4.32%.

Microgrid (MG) technology evolves as a promising solution to deal with the intermittent renewable generations and frequently changing load demand. This paper proposes a fully distributed and bounded secondary control algorithm with flexible convergence time for voltage and frequency restoration. It also enables accurate active power sharing for an islanded MG, compared with the well-known consensus-based distributed control approach. The proposed control scheme achieves accelerated fixed-time convergence. The upper bound on the convergence is established by using the Lyapunov stability theory. The bounded, distributed control approach restores the voltage and frequency in fixed-time while sharing the active power precisely. Further, the proposed controller is adaptive to the communication topology change and supports the plug and play feature of MG. Extensive simulations have been pursued using MATLAB/SimPowerSyetem toolbox considering frequent load perturbation and communication topology change. The obtained results are analysed to verify the performance of the proposed control algorithm. It is observed that the proposed bounded input controller converges faster than the conventional method.

A power distribution network is a critical infrastructure in any society and any disruption has an enormous impact on the economy and daily lives. Therefore, the objective of this study is to transform the conventional power distribution systems into resilient autonomous microgrid networks by optimally sizing and siting the distributed generators (DGs). First, N main DGs are placed to transform an existing network into an autonomous microgrid network. Second, all the possible combinations of the initially deployed DGs are made and then the outage of 1 to N − 1 DGs is considered. Considering the outage of DGs in each combination (one at a time), the resiliency of the network is analysed. Amount of load shedding, total power loss in the network, and voltage limits are analysed in this step. Finally, based on the resiliency analysis, additional DGs are placed to enhance the resiliency of the transformed network. Heuristic methods (particle swarm optimisation and genetic algorithm) are used for both sizing and siting of DGs during the first and the second steps. The objective of the formulation is to minimise load shedding, total power loss (active and reactive), and voltage deviations in the network during DG outages.

Increasing use of distributed generation (DG), mainly roof-top photovoltaic (PV) panels and electric vehicle (EV) charging would cause over- and under-voltage problems generally at the remote sections of the low-voltage (LV) distribution feeders. As these voltage problems are sustained for a few hours, power electronic compensators (PECs) with input voltage control, i.e. electric springs cannot be used due to the unavailability of non-critical loads that can be subjected to non-rated voltages for a long duration of time. However, PECs in output voltage control mode could be used to inject a controllable series voltage either somewhere on the feeder (mid-feeder compensation, MFC) or between the feeder and each customer (point-of-load compensation, PoLC) both of which are effective in tackling the voltage problem without disrupting PV power output and EV charging. In this study, a comparison between the MFC and PoLC option is presented in terms of their voltage control capability, required compensator capacity, network losses, PV throughput, and demand response capability. The criteria for selection of the optimal location of these compensators are also discussed. Stochastic demand profile for different types of residential customers in the UK and a typical European LV network is used for the case study.

Low-frequency oscillations in power system degrade power quality and may trigger blackouts. This study identifies the source location of these oscillations using measurements from phasor measurement unit (PMU), offline credibility estimation and classification models. The performance of these classification models is ranked for each reported feature to use highly ranked models during the online stage. This proposed framework named as credibility search ensemble learning was tested and validated with promising results using western interconnection power system in North America (WECC-179). The reliability and robustness of the proposed framework were checked against measurement errors in PMUs as well as for practical topology change scenarios. Experimental results and performance comparison with average weight-based approach proved that the proposed approach is capable enough to predict the source location of oscillations with good accuracy. An interfacing tool, for MATLAB-WEKA, was developed and employed in this work for validation and testing of the proposed approach.

The purpose of this research is to present an innovative load frequency control in the presence of wind turbines in islanded micro-grid (MG). As islanded MG suffers from low inertia and insufficient primary frequency response (PFR), utilising the variable wind turbines in de-loaded area can be considered as an alternative solution to deal with frequency control problems. In this context, the de-load area is referred to a region where wind turbines release their stored kinetic energy in rotational masses following frequency disturbances. For effective utilisation of wind turbines, a self-tuning, adaptive fuzzy droop is proposed, whose membership function parameters are optimised through artificial bee colony algorithm based on a multi-objective decision making process. A comparison is made between the obtained results of the self-tuning, adaptive fuzzy droop with conventional proportional integral derivative droop control in order to assess the proposed method performance in different disturbances.

Challenges facing power system protection in a wide-area system are latency and full coverage of a wide-area disturbance. The complexity of large-scale power system configurations has led to challenges in the design of coordination and operating systems for protection relays. Local measurements used for primary and backup protection cannot consider wide system disturbances. A new wide-area-monitoring-system-based primary protection is presented for a complex power system involving double and single lines. It is based on describing the non-linear dynamic operation of the transmission lines during a fault in the form of a set of differential equations that are solved through paths movements in a phase diagram. The fault on the lines can be precisely recognised. The speed of the traditional communication media limits wide-area monitoring as backup protection. This study presents the primary protection scheme for double and single circuits in a wide area for the first time based on fourth-generation technology with low latency. The justification for applying the proposed scheme as primary protection in a wide-area-monitoring system is discussed. The number of relays on the studied configuration is reduced from 18 local relays to only 5 phasor measurement units for protecting the lines in the area.