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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.

There is an increasing need for monitoring and controlling uncertainties brought by distributed energy resources in distribution grids. For such goal, accurate multi-phase topology is the basis for correlating measurements in unbalanced distribution networks. Unfortunately, such topology knowledge is often unavailable due to limited investment. Also, the bus phase labeling information is inaccurate due to human errors or outdated records. For this challenge, this paper utilizes smart meter data for an information-theoretic approach to learn the topology of distribution grids. Specifically, multi-phase unbalanced systems are converted into symmetrical components, namely positive, negative, and zero sequences. Then, this paper proves that the Chow-Liu algorithm finds the topology by utilizing power flow equations and the conditional independence relationships implied by the radial multi-phase structure of distribution grids with the presence of incorrect bus phase labels. At last, by utilizing Carson's equation, this paper proves that the bus phase connection can be correctly identified using voltage measurements. For validation, IEEE systems are simulated using three real data sets. The simulation results demonstrate that the algorithm is highly accurate for finding multi-phase topology even with strong load unbalancing condition and DERs. This ensures close monitoring and controlling DERs in distribution grids.

Dynamic rating is an approach which implies to operate an electrical network closer to its thermal limits. This approach may be very beneficial for wave farms, as they are expected to present a highly fluctuating electrical current profile while benefiting from the large thermal inertia of the soil where their export cable will be buried. However, as the implementation of this approach is still in its infancy for offshore wind farms, it may be expected that the first wave farms, under the form of small-scale test sites, will be sized with respect to electrical current limits at a first stage. This sizing may be upgraded at a second stage when design methods will have included dynamic rating. However, this raises the question of the economic feasibility of this two-step approach, which is studied in this paper. Also, performing such a techno-economic analysis requires developing an electrothermal model of the export cable able to represent its transient response in a sufficiently precise manner while requiring also a reasonable computing time. In this perspective, a comparative analysis between several electrothermal modelling methods is also described in this paper.

The awareness capability of output power for renewable resources is essential for enhancing the resilience of power systems. Photovoltaic (PV) forecasting technology is an essential technology for increasing the operation efficiency and controllable resources for power systems after extreme natural events. Conventional Markov chain (MC) methods often ignore the time characteristics and the actual distribution of the PV output power sequence when making PV forecasts. This article proposes improved MC methods of equal quantity and clustering‐based division methods. The methods can consider the interval distributions of the PV output power time series and select an hour as the time interval. As a sequence, the predicted power at the next moment can be closer to the expectation of the output power distributions. Such a method is combined with a similar day algorithm to calculate the forecast result. Case studies were conducted with one‐year operation data from a 25‐MW PV station. The results indicate that the proposed methods can effectively improve the accuracy of prediction results compared with traditional methods.

A dissipating energy-based technique is proposed to locate the source of forced oscillations (FOs) in power systems. The network and load information is incorporated into the developed algorithm and continuously updated using supervisory control and data acquisition (SCADA) measurement. The effect of electromechanical damping on system response in FO scenario is discussed; and therefore, the efficiency of the proposed technique to locate the source has been investigated. The proposed methodology is tested and verified for different simulation test cases and for different scenario viz. for single and multiple sources of disturbances. In the case of multiple sources of disturbances with different time of initiation of the disturbance, the proposed technique successfully locates all sources in their time durations of disturbance. Different load models have been evaluated for their impact on the success of the proposed algorithm in a real-time digital simulation environment. The proposed technique is successfully verified for the test cases reported by the IEEE PES Task Force on Oscillation Source Location.

Recently, demand response programmes (DRPs) have captured great attention in electric power systems. DRPs such as time-of-use (ToU) programme can be efficiently employed in the power system planning to reform the long-term behaviour of the load demands. The composite generation expansion planning (GEP) and transmission expansion planning (TEP) known as composite GEP–TEP is of high significance in power systems to meet the future load demand of the system and also integrate renewable energy sources (RESs). In this regard, this study presents a dynamic optimisation framework for the composite GEP–TEP problem taking into consideration the ToU programme and also, the incentive-based and supportive programmes. Accordingly, the performances of the capacity payment and feed-in tariff mechanisms and the ToU programme in integrating RESs and reducing the total cost have been evaluated in this study. The problem has been formulated and solved as a standard two-stage mixed-integer linear programming model aimed at minimising the total costs. In this model, the ToU programme is applied and the results are fed into the expansion planning problem as the input. The proposed framework is simulated on the IEEE Reliability Test System to verify the effectiveness of the model and discuss the results obtained from implementing the mentioned mechanisms to support the RESs integration.

A computationally improved algorithm is presented to find the best transmission switching (TS) candidate for boosting resilience of electricity grids subject to (N‐2) contingencies. Here, resilience is computed as the reduction in load shed after the above‐mentioned (N‐2) contingencies. TS is a planned line outage, and past research shows that changing the transmission system's topology changes the power flow and removes post contingency violations. Finding the best TS candidate in a computationally suitable time for effectively boosting resilience is a challenge. The best TS candidate is found using a novel heuristic method by decreasing the search space based on proximity to the bus with the maximum load shedding (LSBmax). The LSBmax algorithm is faster than existing algorithms in the literature; and, it is compatible with both the AC and DC optimal power flow formulations. To validate the authors' claims of speedup and accuracy, two metrics are used to analyze the results from the IEEE 39‐bus and 118‐bus systems. Finally, the inherent parallelism of the LSBmax algorithm is leveraged on a high‐performance computing platform and applied to the large‐scale Polish 2383‐bus test system to validate scalability in both size and speedup in computation time.

Power system instability primarily results from the deviation of the frequency from its predefined rated value. This deviation causes voltage collapse, which further leads to sudden blackouts of the power system network. It is often triggered by a lack of reactive capacity. The solution to the reactive capacity problem can be obtained in two stages. In the first stage, the vulnerable buses, also known as ‘weak buses’, where voltage failure might occur are identified, and the Var compensating devices are mounted at those locations. The proposed approach utilizes three simple vulnerable bus detection methods: the fast voltage stability index, line stability index, and voltage collapse proximity index (VCPI). In the second stage, various optimization algorithms are implemented to determine the optimal setting of Var sources, such as particle swarm optimization, differential evolution, the whale optimization algorithm, the grasshopper optimization algorithm, the salp swarm algorithm, grey wolf optimization, and oppositional grey wolf optimization (OGWO). The results indicate that the best approach to poor bus recognition is the VCPI, and the OGWO technique provides a much less expensive system than other optimization strategies used for problems of optimal reactive power planning.

The ability to control tens of thousands of residential electricity customers in a coordinated manner has the potential to enact system-wide electric load changes, such as reduce congestion and peak demand, among other benefits. To quantify the potential benefits of demand-side management and other power system simulation studies (e.g. home energy management, large-scale residential demand response), synthetic load datasets that accurately characterise the system load are required. This study designs a combined top-down and bottom-up approach for modelling individual residential customers and their individual electric assets, each possessing their own characteristics, using time-varying queueing models. The aggregation of all customer loads created by the queueing models represents a known city-sized load curve to be used in simulation studies. The three presented residential queueing load models use only publicly available data. An open-source Python tool to allow researchers to generate residential load data for their studies is also provided. The simulation results presented consider the ComEd region (utility company from Chicago, IL) and demonstrate the characteristics of the three proposed residential queueing load models, the impact of the choice of model parameters, and scalability performance of the Python tool.