• Energy Research

Summary This paper proposes an approach to detect the possibility of long-term voltage instability, based on online measurement of system bus voltages. An optimization framework is proposed to determine the maximum loading points, with different load increase patterns and different levels of reactive power output. The operating conditions so obtained are used as the training database for an artificial intelligence classifier based on the support vector machines. In an online application, the support vector machine classifier helps in detecting the probability of some generators operating at high reactive power output, which is an important indicator of an impending voltage collapse. The proposed framework is tested with the IEEE 39 bus and the Nordic 32 bus systems. The test results demonstrate that the proposed scheme gives reliable prediction of the power system long-term voltage stability.

This paper proposes a quasi-steady-state modeling approach for an approximation of long-term frequency dynamics in power systems. A specific phenomenon of concern is an onset of frequency swings during load/generation imbalance scenarios. The effects of system voltage characteristics, system inertia, and, more importantly, damping controllers are explained and quantified using the described quasi-steady-state models. Application of the methodology to a 14-generator benchmark system demonstrates that described models are suitable for simulation of different disturbance scenarios that can trigger frequency instability. Moreover, linearization of the proposed models can provide convenient means for impact assessment and coordinated design of damping controllers. To demonstrate this, coordinated tuning of multiband power system stabilizers to improve frequency dynamics has been performed and validated through nonlinear simulations using a commercial transient stability software.

The aim of economic load dispatch (ELD) is to deliver required electrical power for a specified period at the lowest possible generation cost using available generating units (GUs). It is imperative to lower the generation costs in order to reduce the consumer costs and to generate adequate revenue from large capital investments in the power sector. There are several optimization algorithms (OAs) to solve this issue. In this study, a new method that combines machine learning (ML) with an OA is used to come up with a high-precision, best solution for ELD issues in the quickest time possible. The 'Lagrange Multiplier' (LM) method is used as the OA, while the 'Decision Tree' (DT) algorithm is used as the ML algorithm. ML algorithms require data to train themselves. A data generation algorithm (DGA) is used to generate data considering constraints such as the power balance constraint, transmission loss (TL), generating capacity, and prohibited operating zones (POZs). The DGA is based on the LM method with constraint handling techniques. Without considering ramp rate limits (RRLs), the optimal load sharing data is generated over the whole power capacity range of the committed GUs. The power capacity ranges from the sum of the minimum power capacity to the maximum power capacity of the committed GUs. This range is divided into several discrete data points with a step size of 0.01. Optimal load sharing among the GUs has been calculated for each of the data points using DGA. Then the DT model was trained with the generated data that could have been used further to predict the load sharing among the GUs. To impose RRLs, we have developed a search method using the trained DT model. We have validated our proposed method through three case studies: Case 1: 6 GUs with a 1263 MW power demand Case 2: 15 GUs with a 2630 MW power demand and Case 3: 140 GUs with a 49342 MW power demand. Finally, the optimal solution for all the case studies using the proposed method was compared with the existing methods. The proposed method was found to be better than the existing methods in terms of time, precision, and cost. This opens up a new way to help with the ELD issue by combining ML with OA. 2022 Elsevier Ltd The contribution of the corresponding author was supported in part by the Canada National Sciences and Engineering Research Council (NSERC) , under Grant ALLRP 567550-21 . Scopus

This paper summarizes the technical activities of a three-year-long IEEE Task Force (TF) on State Estimation (SE) for Integrated Energy Systems (IES). It presents the formal definition and characteristics of IES, along with the comprehensivediscussion on Electric Power Systems (EPS) model, and static and dynamic models associated with heating and natural gas systems. The paper also identifies the barriers of SE for IES, such as estimation modeling, observability analysis, and measurement requirements, together with addressing multi-scaledynamics. An extensive comparative analysis between Integrated Energy Systems–State Estimation (IES-SE) and more established Electric Power System–State Estimation (EPS-SE) is presented. The paper also provides future research needs and directions related to IES-SE.

This paper addresses a novel approach for rotor angle stability prediction in power systems. In the proposed framework, a fault cluster (FC) concept is introduced to divide an electrical network into several disparate zones. FCs are determined in accordance with the installed PMU locations so that the well-developed wide-area fault detection modules can estimate the origin of any fault in the network among FCs. The proposed framework assigns a stability prediction model to each FC. Parameters of the Thevenin equivalent network (TEN) seen from some generators, selected via a feature selection process, are calculated both in steady-state and during fault. The adopted TEN parameters are then applied as inputs to an ensemble decision tree-based prediction models. The proposed method benefits from parallel computation in the training process and does not require post-fault data. The performance of the proposed framework is validated on several IEEE test systems, followed by a discussion of results.

Direct methods have been a promising development for decades, receiving, however, less attention recently. One of the reasons is the limited capacity to include more realistic models of the power systems equipment. This paper derives a structure-preserving analytical energy function for a sixth-order synchronous generator model with subtransient dynamics included. The procedure used to construct the energy function is demonstrated to be applicable to simple governor models as well. The energy function is used to assess critical clearing times for multiple contingencies via a potential energy boundary surface method in the IEEE 10-generator 39-bus benchmark system. Some comparative analysis with transient fourth-order models is performed. The results are validated against simulations using a commercial transient stability package.

Microgrids (MGs) use renewable sources to meet the growing demand for energy with increasing consumer needs and technological advancement. They operate independently as small-scale energy networks using distributed energy resources. However, the intermittent nature of renewable energy sources and poor power quality are essential operational problems that must be mitigated to improve the MG’s performance. To address these challenges, researchers have introduced heuristic optimization mechanisms for MGs. However, local minima and the inability to find a global minimum in heuristic methods create errors in non-linear and nonconvex optimization, posing challenges in dealing with several operational aspects of MG such as energy management optimization, cost-effective dispatch, dependability, storage sizing, cyber-attack minimization, and grid integration. These challenges affect MG’s performance by adding complexity to the management of storage capacity, cost minimization, reliability assurance, and balance of renewable sources, which accelerates the need for meta-heuristic optimization algorithms (MHOAs). This paper presents a state-of-the-art review of MHOAs and their role in improving the operational performance of MGs. Firstly, the fundamentals of MG optimization are discussed to explore the scopes, requisites, and opportunities of MHOAs in MG networks. Secondly, several MHOAs in the MG domain are described, and their recent trends in MG’s techno-economic analysis, load forecasting, resiliency improvement, control operation, fault diagnosis, and energy management are summarized. The summary reveals that nearly 25% of the research in these areas utilizes the particle swarm optimization method, while the genetic and grey wolf algorithms are utilized by nearly 10% and 5% of the works studied in this paper, respectively, for optimizing the MG’s performance. This result summarizes that MHOA presents a system-agnostic optimization approach, offering a new avenue for enhancing the effectiveness of future MGs. Finally, we highlight some challenges that emerge during the integration of MHOAs into MGs, potentially motivating researchers to conduct further studies in this area.

Abstract Nowadays the contribution of smart load technologies to power system frequency regulation is spurred due to the increasing penetration of renewable energy resources. This paper presents a comprehensive and up-to-date critical review on different decentralized load control strategies. This includes a joint literature- as well as simulation-based investigation in order to scrutinize different decentralized frequency-based load modulation strategies through organizing a taxonomy table and performing different simulation scenarios. Furthermore, the effectiveness of different gain tuning procedures in each control action are scrutinized and compared in terms of frequency nadir and steady state error. The detailed simulation is performed using SimPowerSystem (SPS) toolbox, in phasor mode, on IEEE 39-bus New England test system.