Explore the vast range of titles available.

In a smart grid, consumer demand information is collected, centrally evaluated against current supply conditions and the resulting proposed price information is sent back to customers for them to decide about usage. As the whole process is time dependent, dynamically estimating grid stability becomes not only a concern but a major requirement. Decentral Smart Grid Control (DSGC) systems monitor one particular property of the grid—its frequency. So, it ties the electricity price to the grid frequency so that it is available to all participants, i.e., all energy consumers and producers. DSGC has some assumptions to infer the behavior of participants. DSGC system is described with differential equations. In this paper, we study on optimized deep learning (DL) models to solve fixed inputs (variables of the equations) and equality issues in DSGC system. Therefore, measuring the grid frequency at the premise of each customer would suffice to provide the network administrator with all required information about the current network power balance, so that it can price its energy offering—and inform consumers—accordingly. To predict smart grid stability, we use different optimized DL models to analyze the DSGC system for many diverse input values, removing those restrictive assumptions on input values. In our tests, DL model accuracy has reached up to 99.62%. We demonstrate that DL models indeed give way to new insights into the simulated system. We have learned that fast adaptation generally improves system stability.

Grid integration of solar photovoltaic (PV) systems and electric vehicles (EVs) has been increasing in recent years, mainly with two motivations: reducing energy cost, and reducing emission. Several research studies focuses on the individual impact of grid integration of PVs and EVs. However, it is worth noting that with the increasing penetration of PVs and EVs, the power grid will be experiencing the combined impact of PV–EV integration. To present a thorough understanding, this study first presents a detailed study on the impact of grid integration of PVs and EVs individually, followed by combined impact of PV and EV, on the aspects of grid stability, power quality and energy economics. It has been identified from the literature review that individually PVs and EVs can negatively affect the grid stability and power quality due to the intermittent nature of PV energy and uncertainty of EV load. However, several research works have reported that coordinated operation of the PVs and EVs can negate the issues arising due to individual integration of PVs and EVs. Furthermore, large‐scale penetration of PVs and EVs are expected in future energy market, and coordinated operation of them can potentially help lowering energy costs and carbon footprint.

With the evolution of renewable energies, many doubly fed induction generators (DFIGs) are being connected to the power grid, whose operation and grid‐connection stability have a major impact on the power grid. Currently, most studies focus on either modeling the mechanical–electrical section or the electrical‐grid section, and discussions have been limited to shaft oscillation or frequency coupling problems. In this study, a mechanical–electrical‐grid model of a DFIG was established to examine the impacts of wind speed and system control parameters on electrical damping and grid‐connection stability. The accuracy of the proposed model and validity of the analyses were verified using simulations. The following were observed: (1) In the case of changing wind speeds, the wind speed and the applied control model determine the shaft oscillation of DFIG, whereas the grid‐connected impedance on the rotor side is dependent on the wind speed. (2) At a constant wind speed, changes in control parameters under different control modes affect the dynamic characteristics of the drive train differently, whereas the grid‐connected impedance on the rotor side is primarily determined by the proportional gain of the inner/outer loop of the control system. The conclusions drawn from this study can further improve the safe and stable operation of DFIG wind power generation systems as well as their connection to the power grid.

A number of analyses, meta-analyses, and assessments, including those performed by the Intergovernmental Panel on Climate Change, the National Oceanic and Atmospheric Administration, the National Renewable Energy Laboratory, and the International Energy Agency, have concluded that deployment of a diverse portfolio of clean energy technologies makes a transition to a low-carbon-emission energy system both more feasible and less costly than other pathways. In contrast, Jacobson et al. [Jacobson MZ, Delucchi MA, Cameron MA, Frew BA (2015) Proc Natl Acad Sci USA 112(49):15060–15065] argue that it is feasible to provide “low-cost solutions to the grid reliability problem with 100% penetration of WWS [wind, water and solar power] across all energy sectors in the continental United States between 2050 and 2055”, with only electricity and hydrogen as energy carriers. In this paper, we evaluate that study and find significant shortcomings in the analysis. In particular, we point out that this work used invalid modeling tools, contained modeling errors, and made implausible and inadequately supported assumptions. Policy makers should treat with caution any visions of a rapid, reliable, and low-cost transition to entire energy systems that relies almost exclusively on wind, solar, and hydroelectric power.

This study addresses the greatest concern facing the large-scale integration of wind, water, and solar (WWS) into a power grid: the high cost of avoiding load loss caused by WWS variability and uncertainty. It uses a new grid integrationmodel and finds low-cost, no-load-loss, nonunique solutions to this problem on electrification of all US energy sectors (electricity, transportation, heating/cooling, and industry) while accounting for wind and solar time series data from a 3D global weather model that simulates extreme events and competition among wind turbines for available kinetic energy. Solutions are obtained by prioritizing storage for heat (in soil and water); cold (in ice and water); and electricity (in phase-change materials, pumped hydro, hydropower, and hydrogen), and using demand response. No natural gas, biofuels, nuclear power, or stationary batteries are needed. The resulting 2050–2055 US electricity social cost for a full system is much less than for fossil fuels. These results hold for many conditions, suggesting that low-cost, reliable 100% WWS systems should work many places worldwide.

Deregulated power systems have reformed the dynamics of modern electricity markets through promoting competition, efficiency, and consumer-oriented benefits while, at the same time, creating new challenges in system stability and sustainability. The article offers an extensive review of renewable energy sources (RES) and electric vehicle (EV) integration in deregulated power systems, emphasizing their synergetic potential for augmenting grid resilience, economic viability, and environmental performance. The research thoroughly analyzes the contribution of erudite smart grid (SG) infrastructures, demand-side management (DSM), and vehicle-to-grid (V2G) technologies in reducing intermittency, voltage and frequency deviation stabilization, and facilitating cost-optimized dispatching of hybrid RES (HRES). The prime contribution of this research is the techno-economic assessment of RES–EV integration approaches, illustrating the viability of grid parity attainment under certain market pricing scenarios, thereby ensuring sustainable competitiveness in reformed energy markets. Additionally, the article outlines how EVs offer ancillary services, including frequency regulation, load balancing, and peak shaving, reducing dependence on expensive infrastructure reinforcements. Integrating technological advancements, regulatory frameworks, and market-oriented operational models, this review provides a framework to bridge the knowledge gap of integrated RES–EV research under deregulated power systems. The findings set out here are highly applicable to policymakers, utilities, and energy market participants because they identify avenues to promote decarbonization at pace, strengthen system flexibility, and progress toward a low-carbon, sustainable electricity future.

The incorporation of renewable energy sources in power grids has necessitated innovative solutions for effective energy management. Smart grids have emerged as transformative systems which integrate consumer, generator and dual-role entities to deliver secure, sustainable and economical electricity supplies. This review explores the important role of artificial intelligence and machine learning approaches in managing the developing stability characteristics of smart grids. This work starts with a discussion of the smart grid’s dynamic structures and subsequently transitions into an overview of machine learning approaches that explore various algorithms and their applications to enhance smart grid operations. A comprehensive analysis of frameworks illustrates how machine learning and artificial intelligence solve issues related to distributed energy supplies, load management and contingency planning. This review includes general pseudocode and schematic architectures of artificial intelligence and machine learning methods which are categorized into supervised, semi-supervised, unsupervised and reinforcement learning. It includes support vector machines, decision trees, artificial neural networks, extreme learning machines and probabilistic graphical models, as well as reinforcement strategies like dynamic programming, Monte Carlo methods, temporal difference learning and Deep Q-networks, etc. Examination extends to stability, voltage and frequency regulation along with fault detection methods that highlight their applications in increasing smart grid operational boundaries. The review underlines the various arrays of machine learning algorithms that emphasize the integration of reinforcement learning as a pivotal enhancement in intelligent decision-making within smart grid environments. As a resource this review offers insights for researchers, practitioners and policymakers by providing a roadmap for leveraging intelligent technologies in smart grid control and stability analysis.

This work deals with doubly fed induction generator (DFIG) modeling and stability when connected to weak AC grids. A detailed state-space model that includes the phase-locked loop (PLL) is developed. This work aims to determine the influence of the network’s strength on DFIG stability through the short-circuit ratio (SCR). The critical values of the proportional control parameters of the grid-side and rotor-side converters (RSC and GSC), as well as PLL, which make the system unstable, are calculated for different SCR values. Finally, PSCAD/EMTDC dynamic simulations are used to validate the critical control parameters obtained by studying the eigenvalues of the DFIG state-space model regarding system stability.

Diagnosing the stability of power grids based on artificial intelligence technology is a research trend. The existing artificial intelligence stability analysis methods rely on a large number of fault case data, while the graph neural network method ignores the correlation characteristics of nodes themselves and the correlation of long-distance nodes. In order to solve the problems, the graph multi-attention neural network (GMANN) was proposed. The self-attention, which characterizes the correlation between different state quantities of a single node, is proposed, firstly. Then, long-distance association attention, which represents the correlation of distant nodes in the power grid, was proposed. The long-distance correlation attention and the adjacent correlation attention in graph attention network are fused to form a global attention that reflects the global importance of the power grid. The channel attention that characterizes the importance of different pooling methods of graph convolutional network is extracted and used to obtain the importance of different convolution operations. Finally, based on the multi-source attention fusion strategy, global attention and channel attention are embedded in the graph attention neural network to form a multi-level evaluation model to achieve power grid stability evaluation efficiently guided by multiple attentions. The proposed GMANN is verified based on simulated fault cases obtained from the 10-machine 39-node system in New England. The results show that the accuracy of the GMANN can reach 97.34%, which is better than other methods. And the missed judgment rate of important indicators is significantly better than other methods.

Over the last few years, the number of grid-connected photovoltaic systems (GCPVS) has expanded substantially. The increase in GCPVS integration may lead to operational issues for the grid. Thus, modern GCPVS control mechanisms should be used to improve grid efficiency, reliability, and stability. In terms of frequency stability, conventional generating units usually have a governor control that regulates the primary load frequency in cases of imbalance situations. This control should be activated immediately to avoid a significant frequency variation. Recently, renewable distribution generators such as PV power plants (PVPPs) are steadily replacing conventional generators. However, these generators do not contribute to system inertia or frequency stability. This paper proposes a control strategy for a GCPVS with active power control (APC) to support the grid and frequency stability. The APC enables the PVPP to withstand grid disturbances and maintain frequency within a normal range. As a result, PVPP is forced to behave similar to traditional power plants to achieve frequency steadiness stability. Frequency stability can be achieved by reducing the active power output fed into the grid as the frequency increases. Additionally, to maintain power balance on both sides of the inverter, the PV system will produce the maximum amount of active power achievable based on the frequency deviation and the grid inverter’s rating by working in two modes: normal and APC (disturbance). In this study, a large-scale PVPP linked to the utility grid at the MV level was modeled in MATLAB/Simulink with a nominal rated peak output of 2000 kW. Analyses of the suggested PVPP’s dynamic response under various frequency disturbances were performed. In this context, the developed control reduced active power by 4%, 24%, and 44% when the frequency climbed to 50.3 Hz, 50.8 Hz, and 51.3 Hz, respectively, and so stabilized the frequency in the normal range, according to grid-code requirements. However, if the frequency exceeds 51.5 Hz or falls below 47.5 Hz, the PVPP disconnects from the grid for safety reasons. Additionally, the APC forced the PVPP to feed the grid with its full capacity generated (2000 kW) at normal frequency. In sum, the large-scale PVPP is connected to the electrical grid provided with APC capability has been built. The system’s capability to safely ride through frequency deviations during grid disturbances and resume initial conditions was achieved and improved. The simulation results show that the given APC is effective, dependable, and suitable for deployment in GCPVS.