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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.

The burning of fossil fuels and the emission of greenhouse gases motivates policymakers to think about the transition in their approach towards electric vehicles (EVs) from conventional ones. Transportation vehicles’ electrification drives the attention of various researchers and scientists towards the emergence of charging stations (CSs). CS placement is a matter of great concern for large scale penetration of EVs. Old infrastructure causes several challenges in planning the ideal placement of the CS since EVs have not prevailed in recent years. Recently, a lot of studies have been performed on CS placement, which attracts the attention of researchers. Various approaches, objective functions, constraints and range of optimisation techniques are addressed by researchers for optimal placement of CS. This study provides the research outcomes in respect of the placement of CS over the past few years based on objective functions, solution methods, geographic conditions and demand-side management.

Distribution system resilience is an emerging topic of interest given an increasing number of extreme events and adverse impacts on the power grid (e.g. Hurricane Maria and Ukraine cyber-attack). The concept of resilience poses serious challenges to the power system research community given varied definitions and multivariate factors affecting resilience. The ability of nature or malicious actors to disrupt critical services is a real threat to the life of our citizens, national assets and the security of a nation. Many examples of such events have been documented over the years. Promising research in this area has been in progress focused on the quantification and in enabling resilience of the distribution system. The objective of this study is to provide a detailed overview of distribution system resilience, the classification, assessment, metrics for measuring resilience, possible methods for enabling resilience, and the associated challenges. A new multi-dimensional and multi-temporal resilience assessment framework is introduced along with a research roadmap outlining the future of resilience to help the reader conceptualise the theories and research gaps in the area of distribution system cyber-physical resilience.

Deregulation exposes the inherent volatility of the electricity price. Accurate electricity price forecasting (EPF) could help the market participants to hedge against the price movements and maximise their profits. The existing methods have limited capability of integrating other external factors into the forecasting model, such as weather, electricity consumption and natural gas price. This study proposes a deep recurrent neural network (DRNN) method to forecast day-ahead electricity price in a deregulated electricity market to explore the complex dependence structure of the multivariate EPF model. The proposed method can learn the indirect relationship between electricity price and external factors through its efficient diverse function and multi-layer structure. The effectiveness of the method is validated using data from the New England electricity market. Compared with the up-to-date techniques, the proposed DRNN outperforms the single support vector machine (SVM) by 29.71%, and the improved hybrid SVM network by 21.04% in terms of mean absolute percentage error.

The smart grid paradigm and the development of smart meters have led to the availability of large volumes of data. This data is expected to assist in power system planning/operation and the transition from passive to active electricity users. With recent advances in machine learning, this data can be used to learn system dynamics. This study explores two model-free reinforcement learning (RL) techniques – policy iteration (PI) and fitted Q-iteration (FQI) for scheduling the operation of flexibility providers – battery and heat pump in a residential microgrid. The proposed algorithms are data-driven and can be easily generalised to fit the control of any flexibility provider without requiring expert knowledge to build a detailed model of the flexibility provider and/or microgrid. The algorithms are tested in multi-agent collaborative and single-agent stochastic microgrid settings – with the uncertainty due to lack of knowledge on future electricity consumption patterns and photovoltaic production. Simulation results show that PI outperforms FQI with a 7.2% increase in photovoltaic self-consumption in the multi-agent setting and a 3.7% increase in the single-agent setting. Both RL algorithms perform better than a rule-based controller, and compete with a model-based optimal controller, and are thus, a valuable alternative to model- and rule-based controllers.

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.

This study introduces a proposed control method for microgrids (MGs) in islanded (off-grid) mode. The proposed control method is developed by modifying the droop control method using H-infinity controller. In this control method, the droop control loop, current and voltage control loops are adjusted to respond to system load variation. The proposed method is an adaptive control one as it regulates the system voltage and frequency to their nominal values after system load variations. Also, it is a repetitive control method as it depends on the internal model principle that provides good performance for voltage and current error tracking. To prove the applicability and effectiveness of the proposed method, it is applied to a test system using MATLAB/Simulink under three different loading conditions. The results are compared with those of droop control and they prove the effectiveness of the proposed method in adjusting MGs under the off-grid mode of operation. Also, a system stability analysis is performed based on root locus and system step response. Robustness analysis is performed to prove the ability of the proposed controller to restore the system performance after the fault clearance.

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.

Advances in distributed generation and increased contribution of renewable energy source (RES) require development of smart grid technologies. Smart metering systems, as a part of smart grid technologies, in cooperation with modern buildings equipped with building management system allows for improvement of energy efficiency. It is possible to partially cover the power demand of a building from the local RESs. However, in order to ensure maximum added value, energy management system (EMS) is essential. This article presents the project and practical implementation of an EMS implemented in smart-meter. The designed system is based on an original algorithm using fuzzy logic. The rule base was created in FCL language and the implementation was carried out in C++ with the object-oriented programming (OOP). For the efficiency rating indicator, peak-to-average ratio (PAR) was selected. This ratio depending on the daily load profile decreased within a range from 15 to 54%, and the average value was 30%. The proposed energy management algorithm helps to reduce energy consumption at peak demand by 34%, with the total reduction of energy consumption during the day of 7%. The described solution demonstrates a potential for real implementation and was tested in hardware.

The invent of advanced metering infrastructure (AMI) opens the door for a comprehensive analysis of consumers consumption patterns including energy theft studies, which were not possible beforehand. This study proposes a fraud detection methodology using data mining techniques such as hierarchical clustering and decision tree classification to identify abnormalities in consumer consumption patterns and further classify the abnormality type into the anomaly, fraud, high or low power consumption based on rule-based learning. The proposed algorithm uses real-time dataset of Nana Kajaliyala village, Gujarat, India. The focus has been on generalizing the algorithm for varied practical cases to make it adaptive towards non-malicious changes in consumer profile. Simultaneously, this study proposes a novel validation technique used for validation, which utilizes predicted profiles to ensure accurate bifurcation between anomaly and theft targets. The result exhibits high detection ratio and low false-positive ratio due to the application of appropriate validation block. The proposed methodology is also investigated from point of view of privacy preservation and is found to be relatively secure owing to low-sampling rates, minimal usage of metadata and communication layer. The proposed algorithm has an edge over state-of-the-art theft detection algorithms in detection accuracy and robustness towards outliers.