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Microgrid (MG) is one of the practical and best concepts to provide energy access to rural communities, where electric grid extension is not techno-economically feasible. Since the trend of load consumption is not uniform with a low load factor in a rural area, the required rating of the system becomes very high. Similarly, the generation is fixed for these MGs, whereas the load increases continuously over time. Such a system faces supply deficit issues triggering a high number of interruptions that may cause frequent blackouts. Hence, rolling blackout and load clipping techniques are preferred during the peak load period in most of the rural MGs. These issues lead to an unreliable power supply and low satisfaction level of the user. This paper presents the load prioritization technique to guarantee the continuous supply for the essential loads within the rural community. A day-ahead energy allocation technique is mathematically formulated and optimized to maximize the total hours of energy served. This technique maximized the hours of energy served to the load with higher priority followed by the load with lower priorities. From this study, it is found that the proposed strategy helps to improve the hours of energy served in the overall system, by improving the state of charge (SoC) level of the battery system. The result shows that the user satisfaction level has been improved by 5% through 100% of continuity for the essential loads.

Static security assessment (SSA) is fundamental in electrical network analysis. However, the growing complexity and variability of grid’s operating conditions can make it tedious, slow, computationally intensive, and limited or impractical for on-line applications when traditional approaches are considered. Since this may hinder the emerging analytical duties of system operators, data-driven alternatives are required for faster and sophisticated decision-making. Although different machine learning algorithms (MLAs) could be applied, Convolutional Neural Networks (CNNs) are one of the most powerful models used in many advanced technological developments due to their remarkable capability to identify meaningful patterns in challenging and complex data sets. According to this, a CNN based approach for fast SSA of power systems with N-1 contingency is presented in this paper. To contribute to the automation of model building and tuning, a settings-free strategy to optimize a set of hyperparameters is adopted. Besides, permutation feature importance is considered to identify only a subset of key features and reduce the initial input space. To illustrate the application of the proposed approach, the simulation model of a practical grid in Mexico is used. The superior performance of the CNN alternative is demonstrated by comparing it with two popular MLAs.

This paper proposes a novel hierarchical optimal control framework to support frequency and voltage in multi-area transmission systems, integrating battery energy storage systems (BESSs). The design is based on the coordinated active and reactive power injection from the BESSs over conventional synchronous generator-based control for fast and timely mitigation of voltage and frequency deviations. The principle of this new idea is to use two hierarchical schemes, one physical and one logical. The objective of the first scheme prioritises the power injection from the BESSs installed in the area where a contingency occurs, consequently reducing the disturbance of the dynamics in the neighbouring areas. In the second scheme, operational rules for aggregated BESSs in each are incorporated, increasing the safety of the asset. The proposed approach exploits the advantages of time-synchronised measurements, the eigensystem realisation algorithm (ERA) identification technique, the optimal linear quadratic Gaussian (LQG) controllers and a new aggregating agent that coordinates the power injection of BESSs in a hierarchical and scalable scheme to precisely regulate frequency and voltage of modern transmission grids, increasing their reliability and stability. The feasibility and robustness of the proposal is demonstrated using simulated scenarios with significant load changes and three-phase, three-cycle faults on a modified Kundur-system with four interconnected areas, mitigating frequency and voltage contingencies in less than 450 ms.

In this work, the impact of implementing a large amount of decentralized renewable energysources (RES) of different scales on an extra-large power grid is investigated. Three scenariosare created, substituting 10%, 20%, and 30% of the conventional energy production by RES.For this purpose, the initial dynamic model of Continental Europe in combination with theindustrial power system application DIgSILENT PowerFactory was used. In order to comparethe behavior of different applied scenarios, a performance index was developed to evaluate andrank the effects of network disturbances by means of time-domain simulations. The performanceindex was designed based on three different criteria that analyze the oscillatory content andthus, the severity of a given event. The initial power flow of the dynamic model was identifiedas a limiting factor for the integration of RES, therefore two additional power flows weredeveloped following an innovative procedure. Through the methodologies mentioned above, itwas found that Turkey is the most sensitive to such changes, which are amplified by increasingimplementation of RES and often lead to inter-area oscillation.

Under-frequency Load Shedding Schemes (UFLS) are a well-established part of any under-frequency defence plan. Their future effectiveness in systems with increased penetration of renewable energy sources without rotating mass should however be questioned: lower inertia implies higher frequency gradients; more power electronics implies higher harmonics. Both factors will concur to make frequency measurements and short relay operation times more challenging: In this contribution, the impact of the penetration of renewable sources on the rate of change of frequency (RoCoF) in continental Europe has been simulated with the dynamic ENTSO-E model for different penetration levels, confirming the higher RoCoF and lower frequency nadir to be expected in case of large disturbances. The operation of the current UFLS scheme is included in the model and has been compared with a scheme incorporating a RoCoF criterion. The authors have also performed laboratory tests in order to assess the use of RoCoF-based load shedding as an improvement to frequency-only based load shedding schemes. Central European transmission and distribution system operators have established joint bodies for agreeing on the best evolution of underfrequency defence plans: challenges include frequency measurement and correct coordination of several control schemes and approaches within the CE transmission system community.

Steady-state response of the grid under a predefined set of credible contingencies is an important component of power system security assessment. With the growing complexity of electrical networks, fast and reliable methods and tools are required to effectively assist transmission grid operators in making decisions concerning system security procurement. In this regard, a Convolutional Neural Network (CNN) based approach to develop prediction models for static security assessment under N-1 contingency is investigated in this paper. The CNN model is trained and applied to classify the security status of a sample system according to given node voltage magnitudes, and active and reactive power injections at network buses. Considering a set of performance metrics, the superior performance of the CNN alternative is demonstrated by comparing the obtained results with a support vector machine classifier algorithm.

Contingency Screening and Coherent Identification are two fundamental parts of power system planning and operation. A common characteristic among these two methods is the need to analyze multiples contingencies. However, most of the current work existing in the literature is based on the paradigm of analyzing one contingency at a time, using 2D arrays (matrices) for the event representation. The drawback with this type of representations is the impossibility to consider multiple contingencies simultaneously. In this paper a reformulation of the problem using 3D arrays (tensors) is presented. Then, the extraction of the information is carried out using PARAFAC2. With this information, a severity index for contingency screening is proposed and identification of the coherent areas is accomplished. The approach is validated in the IEEE NETS-NYPS test system. The results confirm that the proposed approach allows to extract more information than in the traditional form.

Identification of coherent generators and the determination of the stability system condition in large interconnected power system is one of the key steps to carry out different control system strategies to avoid a partial or complete blackout of a power system. However, the oscillatory trends, the larger amount data available and the non-linear dynamic behaviour of the frequency measurements often mislead the appropriate knowledge of the actual coherent groups, making wide-area coherency monitoring a challenging task. This paper presents a novel online unsupervised data mining technique to identify coherent groups, to detect the power system disturbance event and determine status stability condition of the system. The innovative part of the proposed approach resides on combining traditional plain algorithms such as singular value decomposition (SVD) and K -means for clustering together with new concept based on clustering slopes. The proposed combination provides an added value to other applications relying on similar algorithms available in the literature. To validate the effectiveness of the proposed method, two case studies are presented, where data is extracted from the large and comprehensive initial dynamic model of ENTSO-E and the results compared to other alternative methods available in the literature.

Distributed Energy Resources (DERs) are being integrated into the power market by customers rather than large scale energy suppliers, thereby slowly transforming the centralized, unidirectional market to a decentralized, bidirectional market and transitioning customers into prosumers. Various system architectures are used in the real field to coordinate the energy distribution in the micro/ mini-grids integrated with DERs, all of which have their strengths, weaknesses and challenges. Peer-to-peer (P2P) is an emerging architecture in the field of electrical energy trading and Distributed Generation (DG) management that can be applied in local energy markets. This paper focuses on P2P energy trading, with an in-depth discussion on its various operating algorithms, their principles, characteristics, features and scope through state of art review on P2P. Furthermore, the energy system of Nepal is used as a case study in this paper, and the micro/mini-grids of Nepal and their associated challenges, constraints and opportunities for improvement are discussed. Finally, an energy trading model is proposed to address the problems occurring in the specific case of Nepalese energy market.

The influence on frequency dynamics of centralized and decentralized approaches to achieve desired penetration levels of converter-interfaced generation (CIG) in the ENTSO-E initial dynamic model of Continental Europe is investigated in this paper. Simulation studies are also carried out to determine the effect that the particular set of synchronous generators to be displaced may have on system frequency response. Furthermore, the contribution of CIG equipped with additional control functions to mitigate frequency stability issues and enhance system dynamics is also analysed. All the simulation studies are carried out with the test power system implemented in DIgSILENT PowerFactory and a task automation script in Python

In this paper, a deep learning approach for power quality monitoring in systems with distributed generation sources is presented. The proposed method focuses in the multi-scale analysis of multi-component signals for power quality disturbances classification. The proposed methodology combines a signal processing stage using variational mode decomposition (VMD) to obtain the times scales of multi-component signals, and a deep learning stage using a simple feedforward neural network (FFNN) to classify the disturbances. The simple proposed architecture allows minimum training time of the classification model. In addition, the proposed method is able to classify different disturbance combinations based on a reduced training-set. The proposed VMD-FFNN method is tested using synthetic and simulated signals, and it is compared with other well-known methods based on convolutional and recurrent deep neuronal networks. Finally, the proposed method is assessed using lab measurements in order to shown its performance in a real-world environment.

This paper provides an open-source Python-based module overview focused on simulating the integration of electric vehicles in a low voltage grid. This module aims to investigate the possible effects that the integration of electric vehicles could have on the operability of the power network. The electric grid conditions are estimated by analysing line loading, voltage values at the final customer, and transformers’ loading. The following tool enables modelling electric grids composed of basic grid elements such as lines, two-winding transformers, predefined load profiles and generated electric vehicle load profiles based on a statistical approach. The module performs time-series simulations through a secondary software, OpenDSS, with result exporting functionality for further analysis, and a graphical user interface.

Short-term electricity demand forecasting is one of the best ways to understand the changing characteristics of demand that helps to make important decisions regarding load flow analysis, preventing imbalance in generation planning, demand management, and load scheduling, all of which are actions for the reliability and quality of that power system. The variation in electricity demand depends upon various parameters, such as the effect of the temperature, social activities, holidays, the working environment, and so on. The selection of improper forecasting methods and data can lead to huge variations and mislead the power system operators. This paper presents a study of electricity demand and its relation to the previous day’s lags and temperature by examining the case of a consumer distribution center in urban Nepal. The effect of the temperature on load, load variation on weekends and weekdays, and the effect of load lags on the load demand are thoroughly discussed. Based on the analysis conducted on the data, short-term load forecasting is conducted for weekdays and weekends by using the previous day’s demand and temperature data for the whole year. Using the conventional time series model as a benchmark, an ANN model is developed to track the effect of the temperature and similar day patterns. The results show that the time series models with feedforward neural networks (FF-ANNs), in terms of the mean absolute percentage error (MAPE), performed better by 0.34% on a weekday and by 8.04% on a weekend.