We present a winning method of the IEEE DataPort Competition on Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm. The day-ahead load forecasting approach is based on a novel online forecast combination of multiple point prediction models. It contains four steps: i) data cleaning and preprocessing, ii) a new holiday adjustment procedure, iii) training of individual forecasting models, iv) forecast combination by smoothed Bernstein Online Aggregation (BOA). The approach is flexible and can quickly adjust to new energy system situations as they occurred during and after COVID-19 shutdowns. The ensemble of individual prediction models ranges from simple time series models to sophisticated models like generalized additive models (GAMs) and high-dimensional linear models estimated by lasso. They incorporate autoregressive, calendar, and weather effects efficiently. All steps contain novel concepts that contribute to the excellent forecasting performance of the proposed method. It is especially true for the holiday adjustment procedure and the fully adaptive smoothed BOA approach.

The publication of the 2020 Special Section of Invited Papers on Emerging Topics in the Power and Energy Society (PES) received broad interest from diverse readers of the PES community. We continued to publish the special section for another year. After a rigorous peer-review process, a total of 13 papers [A1]–[A13] were accepted for publication in this invited section of the IEEE Open Access Journal of Power and Energy (OAJPE) in 2021.

After major disturbances, power system behavior is governed by the dynamic characteristics of its assets and protection schemes. Therefore, modeling protection devices is essential for performing accurate stability studies. Modeling all the protection devices in a bulk power system is an intractable task due to the limitations of current stability software, and the difficulty in updating the setting data for thousands of protection devices. One of the critical protection schemes that is not adequately modeled in stability studies is distance relaying. Therefore, this paper proposes an iterative algorithm that uses two methods to identify the critical distance relays to be modeled in stability studies: (i) apparent impedance monitoring, and (ii) the minimum voltage evaluation (MVE). The algorithm is implemented in Python 3.6 and uses the GE positive sequence load flow analysis (PSLF) software for performing stability studies. The performance of the algorithm is evaluated on the Western Electricity Coordinating Council (WECC) system data representing the 2018 summer-peak load. The results of the case studies representing various types of contingencies show that to have an accurate assessment of system behavior, modeling the critical distance relays identified by the algorithm suffices, and there is no need for modeling all the distance relays.

There is a growing acceptance of using synchrophasor data collected over large power systems in control centers to enhance the reliability of power system operations. The spatial and temporal nature of power system ambient and disturbance response allows the analysis of large amount of synchrophasor data by low-rank methods. This paper provides an overview of several applications of synchrophasor data utilizing the low-rank property. The tools to capitalize on the low-rank property include matrix completion methods, tensor analysis, adaptive filtering, and machine learning. The applications include missing data recovery, bad data correction, and disturbance recognition.

The fault location problem has been tackled mainly through impedance-based techniques, the travelling wave principle and more recently machine learning algorithms. These techniques require both current and voltage measurement. In the case of impedance-based methods they can provide multiples solutions. In the case of the travelling wave approach it usually requires high sampling frequency measurements together with sophisticated identification algorithms. Machine learning techniques require training data and re-tuning for different grid topologies. This paper proposes a new fault location method based on the fault’s transient intermediate frequency response of the system immediately after a fault occurs. The transient response is characterized by the travelling wave phenomenon together with intermediate frequencies of oscillation, which are dependent on the faulted section and the fault location. In the proposed fault location solution, an offline methodology identifies these intermediate frequencies and their dependency on the fault location is fitted using a polynomial regression. The online fault location is performed using those polynomial regressions together with voltage measurements from the system and simple signal processing techniques. The full method is tested with an EMT simulation in PSCAD, using the exact frequency dependent model for underground cables.

The task of determining the phase connection of customers, known as phase identification, is crucial to obtain accurate distribution system models. This paper starts with a thorough literature review of the existing phase identification methods, which are broadly divided into three categories: hardware-based, real power-based, and voltage-based methods. This is followed by multiple case studies assessing the accuracy of six real power- and voltage-based phase identification algorithms on four realistic distribution test systems. Synthetic load profiles along with network models are used to quantify accuracy of each method for different scenarios: varying advanced metering infrastructure (AMI) coverage, number of initially mislabeled customer phases, number of available samples, and measurement noise. A case study using a real AMI data set, including field verification, is also provided. Finally, several aspects key to accurate phase identification are discussed in detail.

This paper presents MATLAB-based programs developed for power system dynamic analysis. The programs can be used for educational purposes and research studies. With the program, time-domain simulation, system linearization, modal analysis, participation factor analysis and visualization, optimal placement of controller, feedback signal selection, frequency response analysis, and control design can be obtained. In addition to solving a power system problem, the package provides a symbolic and vectorized representation of the model in time domain and state space. The package uses the full advantages of MATLAB's powerful solvers for solving non-stiff and stiff problems. Both explicit and implicit techniques are used for solving the differential algebraic equations (DAEs). The synchronous machines are assumed to be equipped with exciter, turbine, and stabilizer. The loads can be modeled as voltage-dependent and independent loads. The test systems used in this paper are the IEEE 9-bus and 68-bus systems, and Texas's 2007-bus synthetic power system. Different types of disturbances are applied to the systems including generator-side and network-side disturbances. The results demonstrate the efficiency and educational values of the package for researchers and students.

Advanced Distribution Management Systems (ADMS) are being widely adopted by electric utilities for managing and optimizing the operations of their distribution systems. Distributed photovoltaic (DPV) systems with smart inverters can be controlled to adjust active power and reactive power outputs, and they are envisioned to become a part of (centrally or distributed) controllable assets managed by the ADMS for optimizing grid operations. This paper proposes an optimal energy dispatch strategy controlling DPV systems for regulating distribution voltages and achieving conservation voltage reduction. A convex optimization model is proposed with the use of linearized power flow, and the gradient projection algorithm is used to solve the optimal active power and reactive power outputs of smart inverters. The proposed optimal energy dispatch is implemented using an open-source ADMS platform, and simulation results have demonstrated the effectiveness of the proposed approach on improving distribution grid operations.

Microgrids (MGs), as novel paradigms of active Distribution Networks, have been gaining increasing interest by the research community in the last 20 years. Currently, they are considered as key components in power system decentralization, providing viable solutions for rural electrification, enhancing resilience and supporting local energy communities. Their main characteristic is the coordinated control of the interconnected distributed energy resources (DER), which can be realized by various methods, ranging from decentralized communication-free approaches to centralized ones, where decisions are taken at a central point. This paper provides an overview of this development focusing on the technical control solutions proposed by reseachers for the various levels of MG organization hierarchy. A critical assessment of selected, popular technologies is provided and open research questions regarding the trend to more decentralized power systems are discussed.

8.2.1.C & 8.2.2.