With increasing wind power penetration, the inherent uncertainty of wind power poses significant challenges to dispatch decisions in power systems. To address this issue, this paper proposes a two-stage distributionally robust chance-constrained (TDRC) model for the unit commitment problem with wind power uncertainty. In this model, an ambiguity confidence set is developed to characterise wind power uncertainty with unknown probability distributions, and wind power curtailment and load shedding levels are modelled as chance constraints to balance wind power uncertainty and system security of dispatch decisions. A hybrid parallel solution (HPS) is proposed for efficient computation by integrating Benders decomposition (BD) and column-and-constraint generation (C&CG) methods. Case studies on the IEEE 24- and 118-bus systems demonstrate the rationality of the proposed approach, while experiments on a practical 126-bus system using the cyber-physical power system (CPPS) dispatch platform further validate the effectiveness and practical applicability of the proposed TDRC model.

Transitioning to net-zero emission energy systems is currently on the agenda in various countries to tackle climate change, a global challenge that threatens the lives of future generations. To fully decarbonize energy systems, a radical paradigm shift through deep integration of renewable resources supported by storage technologies is envisaged in multisector energy systems, especially in the electric power sector. As a result, inverter-based resources (IBRs), mainly wind, photovoltaics (PVs), and batteries, will dominate the electric power grids. This transition involves phasing out conventional fossil fuel-based plants and decommissioning associated synchronous machines, the grid’s primary reactive power sources. The ongoing removal of these primary reactive power sources introduces critical operational challenges that could compromise the reliability and stability of the grid. The inverters used for integrating IBRs can deliver diverse crucial ancillary services, particularly reactive power support. However, the potential of IBRs to address reactive power requirements in future decarbonized grids still needs to be fully addressed. The existing literature lacks a comprehensive approach to coordinating and harmonizing the efforts of various stakeholders and drivers to leverage the reactive power capability of IBRs.To bridge this gap, this article thoroughly reviews the reactive power implications for future grids with a considerable share of primary IBRs, comprising distributed and large-scale wind, PV and battery storage plants. This article starts with a summary of the concept, measurement methods, and importance of reactive power for voltage control and how it is managed today utilizing conventional sources. The reactive power transition from current to future grids within the context of the greater energy transition is then discussed by shedding light on its diverse aspects. Afterward, the reactive capability curve of each IBR is derived from the equivalent circuits and equations. Various grid codes and integration requirements of IBRs are then analyzed from a reactive power support viewpoint. Also, the concepts related to reactive power and voltage control comprising control extents, modes, and techniques are elaborated. Finally, recommendations are provided to set the stage for leveraging the capabilities of IBRs to address the reactive power requirements of future grids. The presented material sheds light on the pivotal role of reactive power in future grids and provides a roadmap for policymakers, utilities, and grid operators to manage a seamless transition to a decarbonized grid.

Due to recent technical improvements, the smart grid has become a feasible platform for electricity market participants to successfully regulate their bidding process based on demand-side management (DSM) perspectives. At this level, practical design, implementation, and assessment of numerous demand response mechanisms and robust short-term price forecasting development in day-ahead transactions are all critical. The accuracy and effectiveness of the day-ahead price forecasting process are crucial concerns in a deregulated market. In this market, the reason for low accuracy is the limitation of electricity generation compared to the electricity demand variations. Hence, this study proposes a suitable technique for forecasting electricity prices using a multi-head self-attention and Convolutional Neural networks (CNN) based approach. Further, this study develops a feature selection technique using mutual information (MI) and neural networks (NN) to choose suitable input variable subsets significantly affecting electricity price predictions simultaneously. The combination of MI and NN reduces the number of input features used in the model, thereby decreasing the computational complexity of the NN. The actual data sets from the Ontario electricity market in 2020 are acquired to verify the simulation results. Finally, the simulation results proved the efficiency of the proposed method by demonstrating increased accuracy by attaining the lowest average value for MAPE and RMSE with a value of 1.75% and 0.0085, respectively, and compared to results obtained by recent computational intelligence approaches. By attaining accurate electricity price results, the significance of this study can be summed up as aiding the electricity industry's operators in administering effective energy management, efficient resource allocation, and informed decision-making.

Evolving dynamics of modern power systems caused by high penetration of renewable energy sources increased the risk of failures and outages due to declining power system inertia. Large-scale wind farms must participate in frequency control that responds optimally in due time and adaptively in case of detecting power imbalance in the grid. Existing research studies have shown interest on stepwise inertial control (SIC) on wind turbines (WTs). However, the adequate power increment and time duration of WTs using SIC are the key questions that have not yet been fully addressed. This paper proposes an intelligent learning-based control system for WTs participation in frequency control, as well as for mitigating negative effects of the SIC. First, an appropriate optimization model for grid frequency control is defined. Then, the model is solved using lightning flash algorithm (LFA), imperialist competitive algorithm, and particle swarm optimization to control the WTs in a wind farm. The obtained dataset by LFA are applied to an artificial neural network that is trained with the Levenberg-Marquardt algorithm and LFA. The proposed control system optimally adjusts the power increment and duration time of participation for each WT in the farm. Analyses on a 100 MW wind farm has been integrated into the IEEE 9-bus system and experimental tests have proved the efficacy of the proposed approach.

Large penetration of wind generating units in power systems necessitates a flexible unit commitment tool to handle the intermittent nature of these units as well as demand. Moreover, power system operators face not only the risks of wind power curtailments, but also probable unit outages. Therefore, assessing a tradeoff between operational costs and such risks is very important. In the proposed approach, the probability of the residual demand falling within the up-and-down spinning reserve imposed by n - 1 security criterion is converted into a risk index. A new biobjective probabilistic risk/cost-based unit commitment model is proposed to simultaneously minimize both the operational costs and risk. The novel formulation presented provides a new power redispatch process to satisfy up-and-down ramp rate constraints. A new operational-cycles-based unit commitment algorithm is developed. The approach profits from a new nondominated sorting backtracking search optimization algorithm for extracting the Pareto-optimal set. The proposed approach is shown to provide superior results when applied to two test systems: 10-unit and IEEE 118-bus, 54-unit system.

In this study, an efficient and smart fuzzy logic controller (FLC) is designed for charging/discharging (C/D) nodes of plug-in electric vehicles (PEVs). The designed controller controls the amount of power to be compensated by these nodes in order to meet the required peak shaving and voltage flattening. The main focus of this work is to practically coordinate the mobile PEVs in a multiobjective security constrained dynamic optimal power flow (OPF) problem that aims at simultaneously minimizing the operation cost and emission over 24-h time horizons. In the formulation of the problem, nonsmooth, nonconvex, and nonlinear natures of valve-point effects, multifuel options, prohibited zones, and ac power flow equations are also considered. This complex problem needs a robust, fast, and powerful optimization algorithm, which is able to extract the Pareto-optimal surface (POS). Hence, a new improved black hole (IBH) algorithm is proposed with a new formulation for updating particles to allow a greater exploration and appropriate exploitation of the search space. The proposed framework is applied on IEEE 118-bus test system incorporating several aggregated PEVs to show its efficiency and ability.

The concept of smart microgrid (SMG) is widely recognized as one of the most promising and enabling technologies for smart grids. In this context, the development of integrated and decentralized frameworks for executing complex control and monitoring applications is still in its infancy and needs to be researched. To address this issue, this paper conceptualizes a self-organizing computing framework, based on self-organizing agents, for solving the fundamental control and monitoring problems of an SMG without the need for centralized data acquisition and processing. Simulation results obtained for an 18-bus test network are presented and discussed in order to demonstrate the significance and validity of the proposed new framework.

Load modeling is a very important aspect of voltage stability analysis as load characteristics governs voltage behavior. In this paper a compact solution to extract parameters of a load model is presented, which includes estimation of power components based on Improved Recursive Newton Type Algorithm and estimation of parameters of a dynamic Load Model using Genetic Algorithms. The paper demonstrates the influence of the preprocessing of the instantaneous values on the final estimation of load parameters. All tests were carried out using 9-buses P.M.Anderson system built in DIgSILENT.

Wide-area monitoring, protection, and control (WAMPAC) involves the use of system-wide information and the communication of selected local information to a remote location to counteract the propagation of large disturbances. Synchronized measurement technology (SMT) is an important element and enabler of WAMPAC. It is expected that WAMPAC systems will in the future reduce the number of catastrophic blackouts and generally improve the reliability and security of energy production, transmission, and distribution, particularly in power networks with a high level of operational uncertainties. In this paper, the technological and application issues are addressed. Several key monitoring, protection, and control applications are described and discussed. A strategy for developing a WAMPAC system in the United Kingdom is given as well.

The microgrid (MG) paradigm is a new concept which is considered as a solution for addressing technical, economical, and environmental issues of modern power systems. The application of MG is the subject of extensive studies and experimental tests. It is recognized that there are a number of technical challenges concerning the operation, monitoring, control, and protection of MGs systems. In this respect, the rapid development of the information and communication technologies (ICTs) has opened the door for feasible and cost-effective solutions allowing more extensive intra- and interutility information exchange, diffusion, and open access to a wide range of real-time information. Consequently, the ICTs could represent a strategic tool in supporting effective MG operation. According to this statement, the paper proposes an advanced framework based on the service-oriented architectures for integrated MG modeling, monitoring, and control. The proposed framework is platform, language, and vendor independent, and thus it is an ideal candidate for an effective integration in existing energy management systems and distribution management systems (EMSs/DMSs).