Grid Load Balancing Research Articles

Real Time Prediction and Modelling of Power Grid System Stability and Load Balancing

Abstract The extensive geographical distribution of the power system determines its highly complex structure. The system includes key components such as generators, substations, transmission and distribution grids, and users. This system has characteristics such as nonlinearity, time-varying, and parameter uncertainty. Power system stability is inherently linked to its regular operational performance. This article uses Simulink models to simulate the working state of generators and circuit breakers during faults. Under the conditions of a synchronous generator with a rated power of 350MW, a voltage of 10.5KV, a three-phase parallel RLC load rated voltage of 115KV, and a power consumption of 300MW, when a short occurs on line, the speed and relative angle of the generator tend to a fixed value, and the system remains stable. When a fault occurs in the circuit within 0.1 seconds, the circuit breaker will cut off the faulty line within 0.6 seconds. Avoiding the occurrence of oscillations and unstable states in typical dynamic systems.

Coordinating electric vehicle charging with multiagent deep Q-networks for smart grid load balancing

Integrating EVs (Electric Vehicles) with the electrical system presents essential load distribution difficulties because EV recharging structures are unpredictable and variable. The article presents an innovative technique employing multiple-agent deeper Q-Networking (MADQN) to coordinate electric automobiles and improve the electricity system balance of load. The suggested MADQN simulation rapidly optimizes battery charge plans by utilizing the capabilities of multiple agent networks as well as deeper reinforced learning. The framework adjusts to current network situations utilizing cooperative decision-making between substances, considering variables like a need for power, accessibility to green energy sources, and protection of the arrangement. Beneficial load distribution is made possible when reducing expenses and ecological damage because of the system's capacity to gather data from and modify intricate, changing circumstances. The findings from the modelling indicate how well the suggested MADQN method works to enhance network efficiency, lower peak usage, and use more sustainable power resources. These factors help build a more robust, adaptable, intelligent grid environment.

BQL-DRS: A Novel Balanced Q-Learning Based Demand Response System for IoT based Smart Grids

The modernization of electricity networks and the integration of renewable energy resources in Internet of Things (IoT) based smart grids have led to increased variability in market prices, necessitating effective demand response (DR) strategies. To address this challenge, this paper proposes a novel Balanced Q-Learning based Demand Response System (BQL-DRS) that combines both optimistic and pessimistic targets in the Q-learning algorithm to achieve a balanced decision-making process in IoT based smart grids. It optimizes DR actions by efficiently managing consumer demand in real-time, considering IoT data from grid conditions, energy prices, and consumer preferences. The significance of the BQL-DRS lies in its ability to handle dynamic and uncertain IoT based grid environments, enabling it to make informed and cautious decisions while pursuing energy efficiency and cost-effectiveness. By effectively addressing both pessimistic and optimistic scenarios, the BQL-DRS ensures grid stability, load balancing, and substantial cost savings compared to representative models.

Progress towards sustainable energy storage: A concise review

AbstractIn this study, the benefits and challenges of existing energy storage systems are presented. The environmental threats and the apparent unreliability of fossil fuel energy sources necessitate the need for alternative sources of electrical power. Energy storage has been sourced from mechanical, electrical, thermal, chemical, and electrochemical systems. Perhaps, an electrochemical energy storage system, is a better option toward achieving a net zero carbon‐dioxide emission by 2050. Energy storage sources, such as: batteries and supercapacitors, can be reliably fabricated from the hybrid of polymers and two‐dimensional materials for electric vehicles, aviation, and grid load balancing applications. The performances of lithium‐ion batteries are yet to meet the requirements for high‐duty machines and devices. Graphite, the anode of lithium‐ion batteries, suffers from low capacity and high volume‐expansion, resulting in cracking and fracture of the electrodes. Herein, based on evidence from literature, this study suggests substituting graphite with polymer nanocomposite or metal‐oxide nanocomposites such as: conducting polymers, copper oxide, and graphene. This approach, giving attention to these materials' excellent electrochemical, thermal, electrical, mechanical, and redox properties, offers possible ways to overcome the limited limitations confronting lithium‐ion battery technology.

CFD analysis of flank clearance sizes on micro-scale transcritical CO[formula omitted] scroll expander

The scroll-type expander can be the promising candidate for micro-scale (<10 kW) transcritical CO2 waste heat recovery power system. The performance of the expander significantly depends on the flank clearance design. The current paper provides a transient CFD analysis for transcritical CO2 scroll expander, which includes 19 flank clearance sizes from 20 to 200μm. The results showed that increasing the flank clearance led to a significant exergy efficiency drop from 89.13% to 46.72%. Although large flank clearance is normally perceived as negative, it indeed eliminates the possibility of over-expansion. The “turning point” for the selected expander geometry is roughly around 40 μm. Excessive flank clearance enlargement resulted in a severe under-expansion issue, and the average expansion ratio was reduced from 3.19 to 1.8. The velocity of leakages also increased from 90 to 370 m/s due to the increased pressure differences. The pressure imbalance issue between two symmetrical working processes got continuously optimised by increasing the flank clearance. The optimal flank clearance range of 100 to 150μm has been established with appropriate pressure balance and efficiency. This paper provides useful insights and design guidelines in the sizing of operational flank clearances for transcritical CO2 scroll expander. The micro-scale (grid) power plant has the potential to change the energy structure considering grid stability and load balancing, and the performance of micro-scale expanders is the key to achieving the objective.

Electric vehicles based electric power grid support: a review

&lt;span lang="EN-US"&gt;Grid connected electric vehicles (EVs) can provide energy quality services to ease intermittent renewable energy sources dependent, enhance grid stability performance, reliability and load balancing in the power system. Moreover, incorporating sufficient EVs into the power grid will assist reduce greenhouse gases. However, the unrestrained EVs charging impact the raise of peak request, frequency deflection, voltage instability, power quality from the acceptable limits. It can also cause overloading of the power system equipment and an increase in power losses. In this paper, the influences EV technologies on various energy systems, control scheme strategy, benefits, and motivational challenges are investigated. An overview of the impacts of uncontrolled EV loading on the electric power system and how controlled charging becomes benefit impacts is presented. Moreover, a review of the controlled charge and discharge benefits of EVs and the electrical serving involved in reduction of frequency deviation, voltage stability, power quality improvement is introduced. This review shows that integration of optimal control of EVs with a suitable optimization technique can improve and provide on time multi ancillary services of EV integrated with weak grid. In addition, this review provides research tracks which can be followed by researchers.&lt;/span&gt;

A novel integrated system of hydrogen liquefaction process and liquid air energy storage (LAES): Energy, exergy, and economic analysis

With the global positive response to environmental issues, cleaner energy will attract widespread attention. To improve the flexible consumption capacity of renewable energy and consider the urgent need to optimize the energy consumption and cost of the hydrogen liquefaction process, a novel system integrating the hydrogen liquefaction process and liquid air energy storage (HLP–LAES) is proposed. The operating strategy of the system ensures that it can produce liquid hydrogen and has the function of energy storage and power generation. A 3E study (energy, exergy, and economics) is carried out to evaluate the techno-economic performance of the proposed energy-intensive process. After optimization of key parameters, when the daily output of liquid hydrogen is 50 tons, the maximum daily power generation can reach 131.3 MWh, accounting for 1.21% of the daily electricity consumption of urban and rural residents in Qinghai Province, China. The round-trip efficiency of the proposed process is 58.9%, the specific energy consumption of liquid hydrogen is 7.25 kWh/kgLH2, and the total exergy efficiency is 53.2%. The payback period will be reduced to 1.7 years when the liquid hydrogen is sold at 7.0 $/kgLH2. If the proposed process is retrofitted from a stand-alone hydrogen liquefaction plant, the shortest retrofitting payback period is 9.2 years. The charging and discharging processes of the LAES are innovatively redesigned to better match the hydrogen liquefaction process, while achieving deep coupling and good synergy between the systems. This work aims to provide a reference for the efficient consumption of renewable energy, grid load balancing, and commercialization of the combination of liquid air energy storage and hydrogen liquefaction plants.

Flexible Loads Scheduling Algorithms for Renewable Energy Communities

Renewable Energy Communities (RECs) are emerging as an effective concept and model to empower the active participation of citizens in the energy transition, not only as energy consumers but also as promoters of environmentally friendly energy generation solutions, particularly through the use of photovoltaic panels. This paper aims to contribute to the management and optimization of individual and community Distributed Energy Resources (DER). The solution follows a price and source-based REC management program, in which consumers’ day-ahead flexible loads (Flex Offers) are shifted according to electricity generation availability, prices, and personal preferences, to balance the grid and incentivize user participation. The heuristic approach used in the proposed algorithms allows for the optimization of energy resources in a distributed edge-and-fog approach with a low computational overhead. The simulations performed using real-world energy consumption and flexibility data of a REC with 50 dwellings show an average cost reduction, taking into consideration all the seasons of the year, of 6.5%, with a peak of 12.2% reduction in the summer, and an average increase of 32.6% in individual self-consumption. In addition, the case study demonstrates promising results regarding grid load balancing and the introduction of intra-community energy trading.

Dynamic Pricing Strategy of Charging Station Based on Traffic Assignment Simulation

The number of electric vehicles is increasing rapidly worldwide, leading to increasing demand for charging. This will negatively impact the grid. Therefore, it is essential to relieve the power grid operation pressure by changing the charging behaviour of users. In this paper, the charging behaviour of electric vehicles was guided by price instruments to maintain grid balance This paper uses travel simulation to establish the relationship between travel demand and electricity prices. The results were evaluated through the amount of grid voltage drop and network loss. Furthermore, we used the differential evolutionary algorithm to calculate the optimal operation status of the grid, which contains the minimal network loss and maximal voltage drop at different charging stations and the charging price. Finally, the effectiveness of the mechanism proposed in this paper was compared with other simulation examples. The results showed that the pricing strategy could guide users’ charging choices and maintain the grid load balancing. The simulation results show that the average bus voltage increases by 1.26% and 6.59%, respectively, under different requirements.

A multi-shift phase angle control strategy for bidirectional wireless power transmission system

Using the charging and discharging capability of battery-based loads and a two-way wireless energy transmission system, grid-load balancing can be achieved in both directions. The system can be applied to many locations by deploying power flow in real time. In the scenario of vehicle network interconnection, it realizes peak-shaving and valley-filling to optimize grid dispatching capability; in the scenario of modular satellite interconnection, it realizes load balancing and effectively improves energy utilization. Therefore, a suitable control strategy is needed to precisely change the output power flow and the output power magnitude. A method to coordinate multiple phase shifting angles in the system hierarchy to achieve power flow regulation is proposed for a bidirectional wireless power transmission system with a dual full-bridge topology. In this paper, we first model the bidirectional transmission system and analyze the relationship between each phase shift angle and active and reactive power. The traditional control strategy is optimized by reducing the number of control loop variables and using the system power factor angle instead of the difficult-to-control external phase shift angle, while ensuring the voltage gain for optimal system efficiency. Finally, only two control loops are used to control the direction and magnitude of the output power respectively, and the effectiveness and correctness of the control strategy are verified by using the corresponding simulation experiments to achieve smooth switching of the bidirectional electric power transmission power flow.

Hydroelectricity Modeling for Low‐Carbon and No‐Carbon Grids: Empirical Operational Parameters for Optimization and Dispatch Models

AbstractHydroelectric power has unusual technical characteristics that could become more valuable as the penetration of variable generation renewables grows, but its use for electricity generation is constrained by complex physical, safety, and socioenvironmental considerations. Hydroelectricity can therefore be difficult to represent in national‐scale energy models, and is frequently presumed to be either overly flexible or inflexible. While a few grid models address this complexity via detailed hydraulic process models, more simplified optimization and dispatch models could benefit from the use of empirical parameter values. In this study, we combine a new data set comprising 7.8 million flow‐hours of data from 2011 to 2016 at 158 dams across the United States with monthly and hourly generation data from the U.S. Energy Information Administration (EIA) to elucidate such empirical constraints for the continental United States. We introduce an approach for estimating power generation from hourly water discharge, then present regionally resolved interannual seasonal and diurnal generation patterns; frequency analyses of ramp rates; minimum and maximum generation rates; daily reversals; and load duration curves, all available interactively with a new data visualization tool. We suggest that due largely to hydropower's role as power generation that also serves non‐energy purposes, it acts as a predictable variable generator with constrained dispatchability, more like a supply side analog to demand response resources than like a battery. This observation is particularly relevant for high penetration renewable energy scenarios, given hydroelectric generators' expected value for grid stabilization and load balancing.

Development of a control algorithm and conditioning monitoring for peak load balancing in smart grids with battery energy storage system

As the traditional electricity grid transitions to the smart grid (SG), some emerging issues such as increased renewable energy penetration in the power system that cause load unbalances require new control methods. Storage of energy seems to be the best option to struggle with such issues. In this manner, energy storage technologies ensure the operating flexibility of the distribution system operator in the power system in terms of both sustainability of energy and peak load balancing. In this study, a grid condition monitoring user-interface and control algorithm is developed for the peak load reduction and supply-demand balancing in a SG system by using an energy storage unit. For this purpose, a battery energy storage system (BESS) is designed, scaled and integrated into the SG didactic test system, designed by the De Lorenzo Company. Online grid condition monitoring and control software is developed for grid connected photovoltaic (PV) system and the BESS in the LabVIEW? program. Moreover, an algorithm is developed that provides the conditions for the integration of the BESS into the system. The proposed algorithm is tested with real daily load data of the Manisa province in Turkey. Also, various case studies are performed to validate the effectiveness of the algorithm. Consequently, the proposed algorithm provides an average load factor improvement of 8.46% and the algorithm-controlled BESS increases the revenue of the system by 3.51% compared to the grid-connected PV system alone.

The Fuzzy AHP Based Intelligent Middleware for Load Balancing in Grid Computing Environment

Background: In grid computing, several computing nodes work together to accomplish a common goal. During computation some nodes get overloaded and some nodes sit idle without any job, which degrades the overall grid performance. For better resource utilization, the load balancing strategy of a grid must be improved. Objective: A good load balancing strategy intelligently perceives grid information and finds the best node to transfer jobs from an overloaded node. In our study, we found that the good load balancing strategies have two prominent needs while decision making i.e. consider multiple parameters and handle uncertainty presents in the grid environment. Methods: This paper proposed a model, an intelligent fuzzy middleware for load balancing in a grid computing environment (IFMLBG) which fulfilled both the needs. The processing of IFMLBG is based on Chang’s extent analysis for the fuzzy analytical hierarchy process (FAHP). FAHP hierarchically structured the load-balancing problem and used the non-crisp input to handle the uncertainty of the grid environment. Chang’s analysis is performed to generate weights to prioritize nodes and find the best one. Results: The results show that the IFMLBG Model assigned more weight to the best-selected node as compared to the AHP model and performs well with prudent nodes and criteria. Conclusion: This paper comprehensively described the design of an Intelligent Fuzzy middleware for Load Balancing in Grid computing (IFMLBG) which used Chang’s extent analysis for FAHP and implemented using four parameters and four computing nodes. The Chang’s extent analysis for FAHP takes triangular fuzzy numbers as input and generates weights for nodes. We compared IFMLBG with the classical AHP model on thirteen datasets and concluded that IFMLBG gives more weight to select the node as compare to the AHP model. The results also show that IFMLBG would work better with the number of parameters and computing nodes.

Hybrid algorithm for cloud-fog system based load balancing in smart grids

Energy management is among the key components of smart metering. Its role is to balance energy consumption and distribution. Smart devices integration results in a huge data exchange between different parts of the smart grid causing a delay in the response and processing time. To overcome this latency issue, the cloud computing has been proposed. However, cloud computing does not perform well when there are large distances from the cloud to the consumers. Fog computing solves this issue. In this paper, a cloud-fog computing system is presented to achieve an accurate load balancing. The hybridization of whale optimization algorithm with bat algorithm (WOA-BAT) is proposed for load balancing. The model performance is compared to state of art load balancing techniques as throttled, round robin, whale and particle swarm optimization algorithms in terms of processing and the response time. The results reveal that the proposed WOA-BAT has better results in terms of response time than the three algorithms with 4.3% improvement compared to RR and TH. It also outperforms all the algorithms in terms of processing time by at least 22.3%.

Scenario modelling of biomass usage in the Australian electricity grid

Responding to the global crises - Covid19 and climate change - governments around the world are formulating green recovery plans to stimulate economic growth, boost clean energy technologies and cut emissions. Potential transition pathways for low carbon energy systems, however, remain as open questions. Generally, the simulation of biomass in the grid models is limited in their tempo-spatial resolution, transition pathways description, and/or biomass feedstock supply representation. This study aims to provide spatio-temporal highly resolved grid configurations featuring disaggregated biomass feedstocks, to assess Australia's potential energy transition pathways and 100% renewable electricity supply scenarios under various biomass bidding strategies and cost assumptions. We find that, as carbon prices increase, bioelectricity will prove to be a cost-effective flexible option compared to other low-carbon (such as CSP) and fossil-based flexible options (e.g. coal and gas), with its generation share reaching ∼9%-12% at higher carbon price scenarios. Biomass power plants can be well suited for operating in gap-filling mode to provide flexible power generation and to facilitate grid stability and load balancing. In light of the high biomass resource potential in Australia, keeping bioelectricity in the generation mix is beneficial for reducing system capacity and cost by 32% and 21%, respectively, under a future renewable-dominated Australian grid system.

Empowerment of cluster and grid load balancing algorithms to support distributed exascale computing systems with high compatibility

The nature of the scientific applications that need to be distributed exascale systems is in a way that both traditional and distributed exascale systems programs are required. As a result, the load balancer not only supports traditional mechanisms of the load balancer, but also these mechanisms should be empowered to support the states caused by the occurrence of events with a dynamic and interactive nature. In this paper, a mathematical model examines the traditional load balancer that is used in cluster and grid computing systems that should support which characteristics to manage the dynamic and interactive nature in processes and execution in distributed exascale computing systems. In cluster and grid systems that can support the specified characteristics in this article, in 60% of the cases, the load balancer can manage events with a dynamic and interactive nature and use the mathematical model as the load balancing mechanism.

Empowerment of cluster and grid load balancing algorithms to support distributed exascale computing systems with high compatibility

Empowerment of cluster and grid load balancing algorithms to support distributed exascale computing systems with high compatibility

On the Coordination of Charging Demand of Electric Vehicles in a Network of Dynamic Wireless Charging Systems

The utilization of dynamic wireless charging (DWC) systems to charge on-the-move EVs is currently gaining an increasing popularity, as it addresses range and charging downtime issues of EV users. To ensure optimal utilization of this charging infrastructure, coordination of EV charging demand is essential to achieve grid load balancing and prevent grid overload. In contrast to offline, day-ahead charging scheduling, this work proposes an online, mobility-aware, spatial EV allocation algorithm within a DWC coordination strategy. This strategy allocates EVs requesting charge to the most optimal DWC lanes within an EV charging network (ECN) in an Internet of EVs (IoEVs). A detailed charging request scenario is presented to highlight the required communication for authentication between the EVs and the charging infrastructure, to achieve the desired coordination. Description of the proposed EV allocation algorithm is then presented and the performance of the algorithm is evaluated using a hypothetical case study of predicted EV traffic trips in the cities of Dubai and Sharjah, UAE. Upon parameter optimization, results of the conducted analysis reveal that the proposed EV allocation algorithm achieves an almost flattened load profile across the DWC lanes that reduces the PAER by more than 44% in comparison with a shortest distance allocation algorithm, for a maximum 2&#x00D7; increase in trip length, and sufficient received energy to compensate for the energy consumed during the trip. This acknowledges grid supply limitations, EV traveling velocities and the maximum service capacity per DWC lane.

A Fuzzy Based Grid Maintenances and Load Balancing Technique for Feature Applications

In a Mobile Ad Hoc Network, this article suggests a Fuzzy dependent power scheduling and load balancing strategy for ZRP (MANET). The duty-cycles of border nodes are adaptively modified using this method, which takes into account the queue condition, expected residual capacity, and distance to border nodes. The nodes are active throughout each round and then go into sleep mode depending on the calculated duty-cycle time. When the load reaches a certain threshold, the zone leaders (ZL) are updated adaptively.

Development of a control algorithm and conditioning monitoring for peak load balancing in smart grids with battery energy storage system

Development of a control algorithm and conditioning monitoring for peak load balancing in smart grids with battery energy storage system