Hasil untuk "Distribution or transmission of electric power"

R. Albert, I. Albert, Gary Nakarado

The magnitude of the August 2003 blackout affecting the United States has put the challenges of energy transmission and distribution into limelight. Despite all the interest and concerted effort, the complexity and interconnectivity of the electric infrastructure precluded us for a long time from understanding why certain events happened. In this paper we study the power grid from a network perspective and determine its ability to transfer power between generators and consumers when certain nodes are disrupted. We find that the power grid is robust to most perturbations, yet disturbances affecting key transmission substations greatly reduce its ability to function. We emphasize that the global properties of the underlying network must be understood as they greatly affect local behavior.

Gan Li, Xiao-Ping Zhang

Nirban Chakraborty, Nalin B Dev Choudhury, Prashant Kumar Tiwari

Reynaldo S. Gonzalez, Ahmed Almoola, Krishna S. Ayyagari et al.

Optimal protection coordination (OPC) is a well-established problem with numerous solution methods, including mathematical optimization and genetic algorithms. Traditional OPC formulations for overcurrent relays typically optimize two parameters: the time dial setting (TDS) and the pickup current. However, modern relays offer additional curve characteristics, yet standard formulations do not fully utilize these additional settings. This paper introduces a novel OPC formulation for dual-setting relays that integrates inverse-time and definite-time curve characteristics. The optimization variables include TDS, pickup current, short-time delay (STD), and short-time pickup (STP) To ensure proper coordination, new constraints are developed for the interplay of these four settings per relay. The problem is formulated as a Mixed-Integer Nonlinear Programming (MINLP) task, solved using both a general-purpose MINLP solver and a Genetic Algorithm (GA). The approach is validated on the IEEE 123-bus network integrating inverter-based resources with limited fault current contributions under two switch configurations, which are selected to alter current flows and reassign backup roles among relays. Results demonstrate that incorporating dual-curve settings significantly reduces total relay operation time and improves discrimination times between primary and backup relays, compared to the standard OPC formulation.

Tathagata Sarkar, Ankur Bhattacharjee, Hiranmay Samanta et al.

Abstract Uninterrupted access to electric power has become the basic need of today’s world. Rural parts of many countries still do not have access to electricity or have electric power access to weak distribution grids with inadequate transmission and distribution system infrastructure. However, the countries where there is an abundance of solar radiation, a good potential of bio-degradable waste and average availability of wind source, access to electricity for those remote areas can be managed by distributed power generation. Considering the fact that the renewable energy sources (Solar, Wind etc.) are intermittent in nature, battery energy storage systems (BESS) and other reservoirs like biogas energy sources are the potential candidates to be integrated with the renewable sources to ensure continuous access to electricity and energy security. In this paper, a unique combination of Solar PV, Wind, Biomass and Vanadium Redox Flow Battery (VRFB) storage integrated hybrid Microgrid has been modeled and implemented practically for the first time. The capacity selection of different renewable sources for satisfying daily energy demand and their techno-commercial optimization has been performed through HOMER simulation. Further, the peak load shaving that is a limitation of HOMER model, has been established through PSCAD simulation by providing the real life data of different renewable sources, VRFB storage and the load profile as input to the model. The simulation model performances have been validated by a practical 10 kWP solar PV, 1 kW wind and 15 kVA Biogas generator integrated with 1 kW 6 h VRFB storage based Microgrid installed at India Institute of Engineering Science and Technology campus, India. In addition to these, zero loss of power supply probability (LPSP) has been ensured by implementing smart scheduling and controller considering the intermittency of the renewable sources. As a part of the financial analysis, project Investment on Return (IRR) and pay back has been calculated considering initial investment, operation and maintenance cost and revenue of generation.

Marzieh Poshtyafteh, Hassan Barati, Ali Darvish Falehi

Abstract Considering the importance of the renewable energy sector in the distribution systems, energy operation, and management which are connected to the distribution network (DN) in the form of multiple microgrids (MMGs) is crucial in reducing cost and pollution. Hence, this paper aims to propose optimal energy management for MMGs in the DN. Different objective functions have been taken into account in this optimization, including network cost, pollution reduction, and distribution network power losses. To design the multi‐objective optimization problem, a fuzzy method has been adopted for simultaneous multi‐objective calculations. Furthermore, the effect of the placement of distributed generations (DGs) and microgrids (MGs) is considered to reduce the distribution network power losses. Information gap decision theory (IGDT) has formulated uncertainties about renewable sources and consumers. To solve this optimization problem, a new method of the modified Harris Hawk optimization (MHHO) algorithm has been implemented, compared with the original HHO and genetic algorithm (GA). Finally, the proposed method has been analysed under the IEEE 33‐bus distribution network for a 24‐hour time horizon, including three MGs considering different renewable energy sources (RESs). The simulation results have demonstrated the high performance of the allocated network with the MHHO algorithm compared to the other scenarios.

Nora Agah, Eve Tsybina, Viswadeep Lebakula et al.

Increasing fuel prices and capacity investment deferral place an increasing demand for peak reduction from distribution level systems. Residential and commercial devices, such as HVAC systems and water heaters, are increasingly involved in load control programs, and their use may generate synchronization and rebound effects, such as artificial peaks caused by device optimization. While there have been concerns over device synchronization, few studies quantify the extent of this effect with numerical values. In this study, we attempt to investigate whether control efforts result in device synchronization or rebound effects. We focus on three clustering methods – Ward’s clustering, Euclidean K-means, and Density-based spatial clustering of applications with noise – to evaluate the extent of synchronization of a fleet of water heaters and HVAC systems in Atlanta, Georgia. Our findings show that synchronization and rebound effects are present in the neighborhood’s water heaters, but none were found in the HVAC systems. Further, high usage water heaters are more susceptible to synchronization and rebound effects.

Mousumi Basu, Saborni Das, Chitralekha Jena et al.

Abstract This manuscript suggests quasi‐oppositional fast convergence evolutionary programming (QOFCEP) technique to determine short‐term generation scheduling of fixed head hydrothermal system comprising pumped storage hydro plants (PSHPs), solar PV plants (SPVPs) and wind turbine generators (WTGs) with demand response program (DRP). PSHPs are used with fixed head hydro power plants (FHPPs), thermal generating units (TGUs), SPVPs and WTGs to operate system conveniently. Uncertainties related to renewable generations and load demands have been considered. Self‐adaptive tent mapped scenarios of uncertain parameters are generated within higher level of confidence space to deal with the system uncertainties. DRP is applied to level the load demand curve and to enhance system flexibility and reduce operational cost. Numerical results of typical test system are matched with those attained by fast convergence evolutionary programming (FCEP) and evolutionary programming (EP).

Sachin Kumar, R. K. Saket, D. Dheer et al.

This study presents a comprehensive survey on the reliability evaluation of the electrical network system. The impacts of integration of new and renewable energy sources (electric vehicle, energy storage system, solar, and wind) on the reliability of electrical power system (EPS) are discussed. The impacts of these renewable sources have merits/demerits when these sources are integrated with the conventional electric power system. However, the merits are predominant as it includes unlimited, free, and cost-effective resources. The recent researches depict that the uncertainties of renewable energy resources leads to the probabilistic and reliability analyses of EPS. EPS includes offshore and onshore wind farms, micro-grid, energy storage system, and other high voltage grids. It also contains the failure-prone components related to the power systems. For the accomplishment of these aspects, the handling methods of uncertainty parameters in generation, transmission, and distribution systems are discussed. The incorporation of electric vehicles, wind energy system, and energy storage system for reliability assessment is also discussed briefly. This study also presents the scope of a new research area for the researchers on the reliability assessment of renewable energy integrated power system.

Yashen Lin, J. Eto, Brian B. Johnson et al.

Managing the stability of today’s electric power systems is based on decades of experience with the physical properties and control responses of large synchronous generators. Today’s electric power systems are rapidly transitioning toward having an increasing proportion of generation from nontraditional sources, such as wind and solar (among others), as well as energy storage devices, such as batteries. In addition to the variable nature of many renewable generation sources (because of the weather-driven nature of their fuel supply), these newer sources vary in size—from residential-scale rooftop systems to utility-scale power plants—and they are interconnected throughout the electric grid, both from within the distribution system and directly to the high-voltage transmission system. Most important for our purposes, many of these new resources are connected to the power system through power electronic inverters. Collectively, we refer to these sources as inverter-based resources.

Xiaofei Wang, F. Li, Linquan Bai et al.

Traditionally, the electric distribution system operates with uniform energy prices across all system nodes. However, as the adoption of distributed energy resources (DERs) propels a shift from passive to active distribution network (ADN) operation, a distribution-level electricity market has been proposed to manage new complexities efficiently. In addition, distribution locational marginal price (DLMP) has been established in the literature as the primary pricing mechanism. The DLMP inherits the LMP concept in the transmission-level wholesale market but incorporates characteristics of the distribution system, such as high $R/X$ ratios and power losses, system imbalance, and voltage regulation needs. The DLMP provides a solution that can be essential for competitive market operation in future distribution systems. This article first provides an overview of the current distribution-level market architectures and their early implementations. Next, the general clearing model, model relaxations, and DLMP formulation are comprehensively reviewed. The state-of-the-art solution methods for distribution market clearing are summarized and categorized into centralized, distributed, and decentralized methods. Then, DLMP applications for the operation and planning of DERs and distribution system operators (DSOs) are discussed in detail. Finally, visions of future research directions and possible barriers and challenges are presented.

A. Lunardi, Luís F. Normandia Lourenço, Enkhtsetseg Munkhchuluun et al.

The move towards a greener energy mix to fight climate change propels investments in converter-interfaced resources such as wind and photovoltaics, energy storage systems and electric vehicles. The ongoing evolution of the power system is occurring at a very fast pace, challenging transmission and distribution system operators to seek solutions that are not only adequate for this moment but also for future scenarios. Ongoing research in the fields of power electronics, power systems and control aims at developing control strategies that will help the energy transition to occur, while keeping a stable, secure and reliable power system. The objective of this paper is to present a critical review of the control strategies developed for grid-connected power converters found in renewable energy systems, energy storage systems and electric vehicles. The impact of grid-connected converters on the stability of power grids is also reviewed, highlighting the promising control strategies for enhancing system stability.

Samuel Admasie, Jin‐Sol Song, Chul‐Hwan Kim

Abstract Economic operation and reliable supply‐demand balance are problems of paramount importance in power grids with a massive share of intermittent renewable energy sources (RESs) of great interest. This paper sought an optimal coordinated generation scheduling for day‐ahead power system operation considering RESs and energy storage units. Renewable power generation, particularly, wind and photovoltaic are uncontrollable, whereas can be predicted using forecasting models. Within the proposed framework, a hyperparameter‐optimized long short‐term memory (LSTM) regression model is employed to forecast the day‐ahead weather from the historical time‐series weather data. Eventually, an empirical formula is used to estimate the power conversion from the day‐ahead weather forecasts for a selected PV module and wind turbine. The objective of the scheduling framework is to keep a delicate supply‐demand balance at the lowest possible cost of generation while maintaining the prevailing generation and system constraints. A variance measure uncertainty handling‐based grey wolf optimizer (GWO) technique is used to find the optimal day‐ahead generation schedules and dispatches under RESs forecast uncertainty. The proposed generation scheduling framework is examined on the IEEE 6 and 30‐bus systems. In the studied scenarios, the coordinated operation of generators can decrease the total day‐ahead operating cost for the modified IEEE 6‐bus system by 2.57% compared to supplying electricity generation with conventional generators alone. Likewise, the total operating cost from the coordinated operation of all generation portfolios was reduced by 6.93% from the operating cost of generation during base case simulation (supply only from dispatchable thermal units) on the modified IEEE 30‐bus system. Moreover, the case studies show that coordinated generation scheduling can mitigate the RESs power variability problem, provide secure supply‐demand operation, and minimize the operating cost of electricity generation.

Ausnain Naveed, Şahin Sönmez, Saffet Ayasun et al.

Abstract This study focuses on analyzing the robust stability regions and robustness margin of a time‐delayed load frequency control (LFC) system with Electric Vehicles (EVs) using the Kharitonov Theorem. Communication time delays in LFC systems can jeopardize stability and reliability, leading to suboptimal controller performance. Integrating EVs into LFC systems enhances dynamical stability but introduces additional complexity. Therefore, it is crucial to employ robust analysis and controller design techniques to ensure system stability. In this study, we utilize the Kharitonov Theorem to determine the robust stability regions and stability boundaries in the parameter plane of the Proportional‐Integral (PI) controller. By considering communication time delays and parametric uncertainties in the LFC system with EVs (LFC‐EVs), the robust PI controller gains using these methods are efficiently computed. To evaluate the performance of the theoretically computed robust controller parameters, time‐domain simulations are conducted.

Amir Hossein Babaali, Mohammad Taghi Ameli

Abstract Voltage stability assessment based on machine learning has become an important challenge in power systems. This paper presents real‐time short‐term voltage stability (STVS) assessment based on phasor measurement unit (PMU) data and machine learning (ML). The database is created through time series of measurement data to involve system time‐temporal and dynamics. Then multiple operating states of the power system are classified through the calculation of the Lyapunov exponent and dynamic voltage index according to the database. This paper presents a weighted combination of random forest (RF) and LightGBM (LGBM) classifiers to train a time‐series database. One of the main advantages of this paper is using the gradient concept in data preprocessing, which has enhanced performance metrics and reduced the defect of data noise. Also, hyperparameter optimization is conducted to improve machine performance. Studies on the IEEE 118bus and a real local grid (RLG) demonstrate that the proposed method improves the performance metrics such as accuracy and F1‐score. Also, this approach is robust against PMU data noise and topology changes in the network.

Qingtao Li, Jianxue Wang, Jie Chen et al.

Abstract The continuous growth of renewable energy sources (RESs) has increased the demand for flexibility in managing uncertainties of RES generation. Energy storage systems (ESSs) are recognized as one of the promising methods to address this challenge. For multi‐area power system planning problems, capacity allocations of RESs can vary considerably among areas accounting for the geographic diversities in RES generation and load patterns. This paper presents a hierarchical coordinated planning model considering the interaction of local‐area (LA) planning entities and the system‐wide (SW) planner. A novel multi‐objective LA planning model is proposed to compute optimal capacity configuration ratios of RESs and ESSs based on regional resource characteristics. The SW planner acts as a coordinator and further optimizes specific capacities of generation assets. Optimal configuration ratios produced by LA planning entities are important indices reflecting LA planning preferences and serve as a link between the two planning layers. Numerical results on a modified NREL‐118 test system show that the proposed planning model can reduce carbon emission by an average of 10.5% and improve the RES utilization rate by an average of 1.8% compared with planning models without considering configuration ratios and ESS installations.

Amarachi Umunnakwe, Katherine Davis

The escalating impact of wildfires on critical power systems, including suppression and restoration costs, bankruptcy, loss of lives, necessitates a more sustainable and resilience-oriented response approach. Although power utilities have spear-headed several initiatives, the need for a comprehensive risk management approach that can be easily integrable into current power utility methods and operations cannot be overemphasized. This work proposes a self-sufficient low-cost wildfire mitigation model (SL-PWR), a tool that automates wildfire risk reduction by intelligently functioning from the pre-wildfire phase to prevent wildfires, through the wildfire progression phase for very early detection, to system restoration after damages. Hence, the SL-PWR addresses endogenous and exogenous wildfire mitigation and risk reduction in all system resilience phases, de-compartmentalizing wildfire response. The proposed SL-PWR tool advances on spatio-temporal wildfire detection through data-driven optimization and automation to provide accurate quantitative and visual real-time critical wildfire information to infrastructure operators and emergency management teams. This paper, part of a series, presents the design and development of the SL-PWR’s functional processes, which further enables optimal monitoring for accuracy and rapidity in response, as well as economic decision making of the utility. Results using publicly sourced data from a synthetic utility service area show the performance of the SL-PWR is accurate, enables rapidity, and improves situational awareness during wildfire threats.

Aikaterini V. Gkika, Efstratios A. Zacharis, Dimitrios N. Skikos et al.

Electricity infrastructures are critical lifeline systems that are designed to serve with a high degree of reliability the power supply of consumers under normal operating conditions and in case of common failures or expected disturbances. However, many recent weather-induced disasters have brought unprecedented challenges to the electricity networks, underlining that power systems remain unprepared to absorb disruptive large-scale and severe events. Worse still, it is expected that such climate hazards will take place at rising frequency and intensity rates due to climate change. The intensification of meteorological extremes will lead to higher losses and changes in transmission capacity, increasing the frequency and importance of material damage to the aging electric infrastructure, thus resulting in significant disruptions, cascading failures, and unpredictable power outages. This review paper presents real-life examples of different types of extreme weather incidents and their impacts on the distribution network in Greece, a country that is highly vulnerable because of its location, geomorphology, and the existing overhead network assets, highlighting lessons learned related to adaptation options and disaster management best practices. Literature review and benchmarking with other grid operators are also employed to explore resilience-enhancing technical capabilities, weatherproof solutions, and operational strategies on which policy-making initiatives should focus. HIGHLIGHTS Extreme weather case studies in Greece are analyzed to capture lessons learned.; Passive post-incident adjustment is insufficient and leads to costly repairs.; Power utilities should proactively embrace and embed grid resilience strategies.; System flexibility, network hardening, and quick recovery are key focus areas.; Best practices can be used by policy-makers to implement the bespoke adaptations.;

Ifedayo Oladeji, Peter Makolo, Ramon Zamora et al.

Alexander Fuchs, Turhan Demiray, Mats Larsson