Load Centers Research Articles

Anti-Swaying Control Strategy of Ship-Mounted 3-RCU Parallel Platform Based on Dynamic Gravity Compensation

It is essential to ensure stability during marine transportation or the installation of high center of gravity loads. The heavy loads increase gravity disturbance, affecting the steady-state-error control of the multiple degrees of freedom (DOFs) motion compensation platform. In this paper, we propose a proportional derivative (PD) controller with dynamic gravity compensation (PDGC) for a 3-RCU (revolute–cylindrical–universal) parallel platform to improve the control effect of marine motion compensation for high center of gravity loads. We introduce an evaluation parameter of load stability and a weighting coefficient of anti-swaying control to tune the controller performance. The controller can set its control target between the two, keeping the load contact surface level and allowing the load center of gravity with the least movement. By deriving the Jacobian matrix, the gravity disturbance in the joint space is calculated and is compensated in the controller. First, we verify the control superiority of this controller over the PD controller under sinusoidal excitation in simulation and validate the effectiveness of the proposed anti-swing strategy. Then, the experiments are conducted with random excitation. The root mean square (RMS) value of the load’s residual angle with the proposed controller is reduced to 32.2% and 17.6% in two directions, respectively, compared with the PD controller under class 4 sea state excitation. The proposed method is effective for the anti-swaying control of ship-mounted 3-RCU parallel platforms.

Mechanical Analysis of Semi-Rigid Base Asphalt Pavement under the Influence of Groundwater with the Spectral Element Method

Over prolonged exposure to groundwater conditions, semi-rigid base asphalt pavements can undergo significant changes in their internal moisture field, resulting in substantial variations in the pavement’s stiffness and, consequently, affecting the overall load-bearing capacity and stability of the road structure. This paper employs FWD non-destructive testing equipment to assess its mechanical performance and conduct data analysis and conducts a mechanical response study of asphalt road surfaces, considering the influence of roadbed moisture levels. Using the dynamic analytical theory, the fundamental equations and stiffness matrices for a linear elastic half-space model were established, leading to the development of a computational model for the mechanical response of semi-rigid base asphalt pavements under FWD dynamic loading, with an examination of the surface deflection in relation to changes in groundwater levels. Numerical examples and engineering applications were employed to validate the proposed model. The research findings indicate: With the passage of time, surface deflection values initially increase and then decrease, exhibiting a sinusoidal variation pattern similar to that of the applied load. As the distance from the loading center increases, the moment of peak deflection continually lags behind. The average absolute relative error between the results obtained using the method proposed in this study and the traditional ABAQUS finite element method was only 0.70%. The correlation coefficient between the theoretically computed deflection curve and the measured deflection curve using the spectral element method was greater than 0.9, with an average absolute relative error of 4.92% between the theoretical peak deflection and the measured peak deflection. As the groundwater level rises, surface deflection noticeably increases, with an approximately 40% increase in deflection values at the loading center. These research findings can be utilized to analyze the dynamic deflection of semi-rigid base asphalt pavements under various groundwater conditions, providing significant practical value for assessing road structural performance and serviceability.

Optimisation of Multi-Type Logistics UAV Scheduling under High Demand

At present, interest in the application of unmanned aerial vehicles (UAV) for delivery is growing. A new “multi-type of UAV collaborative delivery” mode has been proposed. Through a combination of large, medium and small UAVs, the delivery capabilities of the UAV logistics system are significantly improved. Sometimes there is high demand, resulting in planned delivery routes that are no longer feasible, and even cause a shortage of distribution centre capacity and drones. This study explores logistics delivery strategies to solve problems caused by high demand. In this study, a multitype and multidistribution UAV model was established with the objective of minimising the total cost of distribution by considering factors such as the UAV energy consumption, load and distribution centre conditions. An improved ant colony algorithm was designed and its effectiveness was verified through the variability of the calculation time and multiple calculation results of different-scale examples. Finally, the classic vehicle routing problem (VRP) case is used in three scenarios to analyse the UAV scheduling optimisation problem. The results indicate that assisted delivery can reduce costs by 3% while ensuring delivery timeliness. The results of this study can provide guidance and benchmarks for the application of UAVs in urban logistics delivery systems.

Improving Power Quality Using D-STATCOM with Capacitor as Energy Storage System

Power electronics are used more frequently to raise awareness of power quality which must be considered when designing modern power systems for effective operation and continuous power supply to various load centers. Otherwise, it will be hazardous for residential and commercial customers, reducing the production of their work due to faults and equipment damage. Electronic technology known as Flexible AC Transmission System (FACTS) may improve power stability to make the electric power system efficient. Oscillation damping in the Power system is still a big concern for operating a safe and dependable power system. The static synchronous compressor (STATCOM) is connected to assist power networks with poor power quality and voltage regulation. Although there are several uses, voltage stability is the most frequently used. An average model of the D-STATCOM control approach is presented in this work to improve the transmission of power quality flow and voltage stability in electric power systems. An increase in system voltage stability, reactive power flow control for different loads, and dynamic response of STATCOM are the primary goals of this research work.

Spatially resolved land and grid model of carbon neutrality in China

China has committed to achieve net carbon neutrality by 2060 to combat global climate change, which will require unprecedented deployment of negative emissions technologies, renewable energies (RE), and complementary infrastructure. At terawatt-scale deployment, land use limitations interact with operational and economic features of power systems. To address this, we developed a spatially resolved resource assessment and power systems planning optimization that models a full year of power system operations, sub-provincial RE siting criteria, and transmission connections. Our modeling results show that wind and solar must be expanded to 2,000 to 3,900 GW each, with one plausible pathway leading to 300 GW/yr combined annual additions in 2046 to 2060, a three-fold increase from today. Over 80% of solar and 55% of wind is constructed within 100 km of major load centers when accounting for current policies regarding land use. Large-scale low-carbon systems must balance key trade-offs in land use, RE resource quality, grid integration, and costs. Under more restrictive RE siting policies, at least 740 GW of distributed solar would become economically feasible in regions with high demand, where utility-scale deployment is limited by competition with agricultural land. Effective planning and policy formulation are necessary to achieve China's climate goals.

IoT Based Smart Energy Meter

In Modern day smart grid technology relies heavily on communication networks for two way communication between load, generation, transmission, and control centre. As a part of the smart grid, smart meters use advanced metering infrastructures (AMI) that are widely distributed and interconnected to the communication network. In modern Technology the smart grid plays very important role for two way communication between Supplier and consumer. Smart meter is main component of smart grid. Currently we are paying electricity bill like one person came to home to read the electricity board and handover the bills to the owner of that house. The drawback of this it requires more man power, time, and many times it has error in bill amount. To eliminate this error smart energy meter is introduced. This system also helps in detecting and control over power theft. this smart energy meter helps us in home automation using IoT and enabling wireless communication which is a great step towards Digital India .Efficient energy utilization plays a very vital role for the development of smart grid in power system By providing real-time monitoring and control of energy consumption, it can help users to reduce their energy usage, save money, and promote sustainable energy consumption. As a first step towards such cyber-attack related researches and searching for possible mitigation techniques, it is necessary that a smart meter be developed that has the feature of network integration. The present contribution reports the design, fabrication, and operation of an Internet of Things (IoT) based smart meter using Arduino to serve as an integral part of a smart grid system.

Determination of Optimum Resources for the Blang Kejeren Electrical System Using PSS Sincal Application

Currently, the Customer Service Unit (ULP) of the Blang Kejeren State Power Company (PLN) uses the system of the Diesel Power Power Centre (PLTD). Sistem ini terdiri dari PLTD yang dimiliki PLN dan PLtD Lewa, serta beberapa PLTMH (Pusat Listrik Tenaga Mikro Hidro). Aim of the study is to assess the technical viability of the 20 kV ULP Blangkejeren system if supplied with the Kutacane GI and parallel with Distributed Generation (DG). This is because there is no distribution network that can connect the Blangkejeren ULP load centre to the Kutacane GI. Using the PSS Sincal Power System Analysis Application, load scenarios for power plants and distribution networks are modeled and simulated. The findings indicate that the 20kV Ulp Blangkjeren system provided by GI Kutacana, PLTMH, PLTM Lawe, and PLTD Rema is the most appropriate scenario. This situation is regarded as optimal, with a network voltage of 21.07 kV on the ULP Blangkejeren Connection Guard Bus.

Large scale integration of wind power – influence of geographical allocation

This paper investigates the influence of geographical allocation of wind power generation in Northern Europe, assuming large scale integration of wind power. The work applies a linear cost optimization model of the heat and power sector with a 1-hour time resolution. The model minimizes the sum of running costs to meet the heat and power demand and the wind power and transmission investment costs. Wind data are taken from modelled wind speed data from the Swedish Meteorological and Hydrological Institute. The Nordic countries and Germany were divided into regions and the 200 sites with the highest yearly output were chosen to represent the region. The model gives the most favourable distribution of wind power between the regions. In addition, the paper provides an assessment of the effect of geographical distribution of wind power with respect to influence on the aggregated wind power production (only considering the wind power generation itself). The modelling results show that the largest investments in wind power are made in the windy region of Southern Norway. However, depending on the cost of transmission allocating wind power near large load centers in Germany may also be favourable. As for the assessment of distribution of wind power, the wind data gives that if the 400 best sites in Europe were used, this would result in a capacity factor of 38.5% and a lowest output of 2.5 % of rated power (applying 2009 wind data).

Bioinspired airwings: Design and additive manufacturing of a geometrically graded microscale maple seed

Additive manufacturing is a key enabler for the fabrication and industrial application of open-celled cellular lattice structures bearing significant potential for lightweight design with advantageous stiffness–weight ratio and reduced build time. For components with strut thicknesses in the micrometer region, however, the influence of the process dominates the quality and properties of the lattice component. This includes geometrical accuracy, surface roughness, internal porosity, nodal constrictions, and elongated grains in the microstructure. The utilization of process-property correlation in the additive design process is rarely shown in the available literature.In this contribution, we develop an ultralight thin-walled maple seed that exhibits a geometrically graded strut design to maintain center of gravity and load per area. This is done by a preliminary closed-form analytical consideration to determine the design space. Second, topology optimization is carried out to obtain a re-designable structure. Using non-dimensional scaling laws enables an accurate prediction of the desired strut thickness. The maple seed has been manufactured with each strut being assigned different process parameters according to the scaling law, coated with clear varnish, and subjected to a flight test as a proof of concept.

Artificial intelligence-based optimal EVCS integration with stochastically sized and distributed PVs in an RDNS segmented in zones

The growing interest in electric vehicles (EVs) for transportation has led to increased production and government support through legislation since they offer environmental benefits such as reduced air pollution and carbon emissions compared to conventional combustion engine vehicles. This shift toward EV technology aligns with the goal of preserving the natural environment. To fully utilize EVs, effective management of the power grid is crucial, particularly in radial distribution network systems (RDNS) as they pose stress and deviation of power system parameters from their normal. This study proposes a novel strategy for maximizing EV utilization through EV charging stations (EVCSs) in an RDNS by considering factors such as load voltage deviation, line losses, and the presence of distributed solar photovoltaic systems at load centers. The research begins by segmenting the RDNS into zones, followed by the application of an artificial intelligence-based hybrid genetic algorithm (GA) and particle swarm optimization (PSO) approach known as hybrid GA–PSO. This approach identifies optimal locations for EVCSs integrated with photovoltaics within the network. Subsequently, the employment of individual GA and PSO algorithms to optimize EVCS placement focuses on minimizing power loss and enhancing voltage. The effectiveness of the hybrid GA–PSO algorithm is compared to that of separate GA and PSO methods. Extensive simulations using the IEEE 33-node test feeders validate the proposed techniques, demonstrating the usefulness of the hybrid GA–PSO algorithm in identifying optimal EVCS placement within each zone. The results also highlight the advantages and novelty of hybrid GA–PSO in achieving optimal EVCS placement with stochastically sized and distributed photovoltaic in an RDNS.

Distributed photovoltaic siting optimisation considering load centre similarity

The load fluctuation law remains challenging for traditional distribution line users. Furthermore, as the number of distributed photovoltaic installations connected to the distribution network rises, the issue of voltage overload on the distribution lines becomes more prevalent. This, in turn, adds complexity to power system operation. To configure the PV access location, whilst ensuring the line voltage is qualified, this paper applies the DBSCAN density clustering and K-means optimization algorithm to classify the distribution network line load. Different characteristics of the load collection are then constructed, and the concept of the load centre distance is proposed. PV access points are selected based on the load center characteristic curve and PV curve similarity. Model simulation is used to verify the conclusions, which demonstrate the rationality and effectiveness of the proposed method. The model simulation is conducted to confirm the soundness and credibility of the conclusions drawn.

Intelligent ANFIS-Based Distributed Generators Energy Control and Power Dispatch of Grid- Connected Microgrids Integrated into Distribution Network

Power supply management is a critical problem in the operation of a distribution network, considering the causes of large power losses and interruptions in the main grid caused by the unknown connection of loads and DGs that affect power delivery to customers downstream of a distribution network. The above-mentioned problems can be reduced by the integration of microgrids close to load centers and developing a controller using adaptive control techniques to enhance reliable power supply. For this reason, this paper presents an intelligent method for distributed generators' energy control and power dispatch of microgrids integrated into a distribution network employing an Adaptive Neuro- Fuzzy Inference System (ANFIS). The aim is to control distributed generators energy sources, loads, and power dispatch of grid-connected microgrids among multiconnected power sources to maintain a stable power supply without using any optimization techniques. The proposed intelligent ANFIS system is trained for power-sharing purposes and applied to the microgrid controllers. The mathematical modeling of distributed generators, system design, simulation, and testing of the proposed method were done using MATLAB/Simulink software. The results show that the proposed controller is capable of power dispatch and controls the energy harvest of distributed generators. Additionally, it can assign microgrid power source(s) to additional load(s) connected to the active distribution network without interruptions of power flow. The obtained results outperform similar works that used hybrid ANFISPID (Adaptive Neural Fuzzy Inference System-Proportional- Integral-Derivative), PSO-ANFIS (Particle Swarm Optimization-Adaptive Neural Fuzzy Inference System), and GA-ANFIS (Genetic Algorithm-Adaptive Neuro-Fuzzy Inference System) by automatically connecting and controlling distributed generators energy sources with effective power dispatch in mitigating downtime of grid power operations.

Composite reliability evaluation of Integrated Nepal Power System

Abstract the worth of reliability has been increasing with the advancement of technology. Based on the involvement of the various functional zones, the assessment of the reliability of the power system is divided into 3 Hierarchy levels (HL). The composite reliability (HL II) involves the availability of generation systems and transmission systems in evaluating the reliability of power systems. In this paper, the composite reliability of the Integrated Nepal Power System (INPS) is assessed using sequential Monte Carlo Simulation (MCS). Based on the trip data of the various Hydro Electric Power Plant (HEPP) units of Nepal and of transmission line, the artificial UP and DOWN sequence of the composite system is simulated. The power output of the Hydro-Electric Power Plant (HEPP) is varied based on the variation of seasonal flow. The system state is compared with the hourly load data of various load centers of INPS to determine the reliability indices like LOLP, LOLE, and EENS. As the eastern part of INPS doesn’t have a generating unit, it is found to have lower reliability. The reliability of INPS can be improved by improving the reliability of load centers that have a lower value of reliability indices.

Quality analysis of electrical energy

This study exposes the measurements of electrical parameters carried out with a power quality analyzer in a transformer. The first measurement taking into account a capacitor bank that was already installed and then taking it out of operation. It presents the results graphed in "Power Log" and analyzed with respect to the standards CFE-L00045, IEEE 519- 1992 and the Regulatory Manual of Technical Requirements for the Connection of Load Centers. By disconnecting the capacitor bank, with the data records obtained from the measurement, it is possible to consider adding a harmonic filter that is presented as the proposal for improving the quality of electrical power, since it achieves an increase from 0.73 to 0.98 in the power factor without compromising the permissible values of harmonics in the system. The filter is calculated and simulated in "DigSILENT Power Factory" to demonstrate the feasibility of the proposal.

The Value of Energy Storage in Facilitating Renewables: A Northeast Area Analysis

The cross-regional and large-scale transmission of new energy power is an inevitable requirement to address the counter-distributed characteristics of wind and solar resources and load centers, as well as to achieve carbon neutrality. However, the inherent stochastic, intermittent, and fluctuating nature of wind and solar power poses challenges for the stable bundled dispatch of new energy. Leveraging the regulation flexibility of energy storage offers a potential solution to mitigate new energy fluctuations, enhance the flexibility of the hybrid energy systems, and promote bundled dispatch of new energy for external transmission. This paper takes energy storage as an example and proposes a capacity configuration optimization method for a hybrid energy system. The system is composed of wind power, solar power, and energy storage, denoted by the wind–solar–energy storage hybrid energy systems. The objective is to quantify the support provided by energy storage to bundled dispatch of new energy, namely determining the new energy transmission capacity that can be sustained per unit of energy storage. The results demonstrate that the proposed method effectively improves the bundled dispatch capacity of new energy. Moreover, the obtained configuration results can be tailored based on different wind–solar ratios, allowable fluctuation rates, and transmission channel capacities, rendering the approach highly valuable for engineering practicality.

SHORT-TERM FORECASTING DAILY ELECTRICITY LOADS USING SEASONAL ARIMA PATTERNS OF GENERATION UNITS AT PT. PLN (PERSERO) TARAKAN CITY

Electrical power requirements at load centers tend to change over time, so the State Electricity Company (PLN) as a provider of electrical energy must be able to predict electrical load requirements every day. The city of Tarakan as a reference center in the northern region of Indonesia is developing rapidly. Along with this growth, the need for electric power is of course also increasing, so we must be able to provide an economical and reliable electric power supply system. This research aims to predict the electricity load at PT. PLN (Persero) Tarakan City. The author will carry out short-term forecasting using time series data in the form of daily electrical power usage data using the Autoregressive Integrated Moving Average (ARIMA) method. The ARIMA method or often called the Box-Jenkins technique shows that this method is suitable for predicting a number of variables quickly, simply and cheaply because it only requires variable data to be predicted. Analysis based on the Box-Jenkins time series taking into account the influence of seasonal patterns. The prediction results show that the data contains seasonal elements with the best model being SARIMA with a MAPE of 3 percent.

Study on the Dynamics Characteristics of HTS Maglev Train Considering the Aerodynamic Loads under Crosswinds

High-temperature Superconducting (HTS) maglev trains are vulnerable to the effects of crosswinds when operating at high speeds in open-air conditions, potentially compromising riding comfort and safety. This study established a vehicle dynamic model based on the nonlinear maglev-track relationship and added aerodynamic loads under crosswinds to the train’s simplified load center to address this issue. Using the maximum vibration acceleration limit and the Sperling index, we evaluated the riding comfort of the HTS maglev train under different conditions. Further, the vibration acceleration power spectral density was analyzed to reveal the impact of increasing the train’s operating speed and crosswind speed. The results indicated that the lateral and vertical Sperling index achieved an “excellent” rating, even at crosswind speeds of up to 20.7 m/s when the train was traveling at speeds of up to 600 km/h. However, it was noted that particular attention should be given to the riding comfort in the head car when the speed reaches 600 km/h. Moreover, the influence of the increase in train speed on the vibration frequency domain distribution of the three car bodies and the train’s riding comfort is greater than that of the increase in the crosswind speed. These findings may provide a valuable reference for the future engineering application of the HTS maglev train.

Unlocking inter-regional flexibility for the HVDC-connected two-area system with a multistage model

In recent years, the deployment of high-voltage direct current (HVDC) tie-lines in power grids has become a prevalent solution in some countries to transmit renewable energy from remote locations to load centers. However, the variability and uncertainty associated with renewable energy generation pose challenges to effectively utilizing this technology. This work proposes a novel multistage planning-operation model, aiming to unlock the potential flexibility in the HVDC transmission system and increase the renewable penetration. By incorporating flexibility, which is essential for accommodating the uncertainty in renewable generation, our model optimally shares the inter-regional flexibility between the sending- and receiving-end grids. One of the key features of our proposed model is its robustness and non-anticipativity, meaning it can account for different levels of uncertainty and make decisions that are suitable for multiple scenarios. This work develops two solution approaches to solve this challenging multistage model with variable uncertainty sets. We validate the proposed approach through a case study conducted on a real-world inter-regional grid. The numerical results demonstrate that our approach effectively unlocks more inter-regional flexibility and assists in increasing the renewable hosting capacity.

Can inter-provincial transmission reduce regional carbon emissions? Evidence from China

Plagued by worsening pollution in load centers along the eastern coastal regions such as Beijing-Tianjin -Hebei, China is reducing local coal-fired power plants in those regions and relying more on inter-regional power transmission. In recent years, China has accelerated the construction of the West-East Electricity Transmission Project and planned and constructed a number of Ultra-high Voltage (UHV) transmission lines. However, how the impact of inter-regional transmission on carbon emissions needs empirical verification. This paper aims to study the carbon emission reduction effect of inter-regional transmission under nested models of the spatial econometric model for panel data. The empirical results show that although the non-dynamic spatial econometric analysis can find the carbon emission reduction effect of inter-provincial transmission, the long-term effect obtained by the dynamic spatial econometric analysis indicates that the inter-provincial transmission can bring about a slight increase in carbon emissions. The inter-provincial power transmission needs to be supported by sustainable share growth of renewable energy to achieve the targeted mitigation of carbon emissions.

Joint Multi-Objective Allocation of Parking Lots and DERs in Active Distribution Network Considering Demand Response Programs

Renewable energy sources (RESs) and electric vehicles (EVs) have been introduced as efficient technologies to address environmentally friendly and sustainable energy sources. However, the widespread integration of distributed renewable sources into the power grid and the growing adoption of EVs pose new challenges for distribution network operators. These challenges necessitate careful management to mitigate their impacts, particularly in meeting the additional demand arising from EV charging. To achieve these targets, it is necessary to strategically integrate RESs and EVs. This study focuses on the optimal allocation and energy management of distributed energy resources (DERs) and electric vehicle parking lots (EVPLs), taking into account the inherent uncertainty in the output power of these resources. Notably, parking lots (PLs) utilize vehicle-to-grid (V2G) technology of EVs and aggregate and inject their power into the distribution system. Therefore, EVs as a motion type of energy storage system play a significant role, especially in the on-peak hours. The optimization problem is addressed using the salp swarm algorithm (SSA) while adhering to operational constraints related to the power system, as well as both DERs and EVPLs. The main goal is to simultaneously enhance the technical, economic, and environmental performance of the system, by solving a multi-objective optimization problem. The effectiveness of this approach is evaluated using the IEEE 33-bus distribution system, with the study considering five different scenarios. The simulation results reveal that the planned deployment of DERs, given their proximity to the load centers, has effectively mitigated the overload impacts resulting from EVs’ charging. Furthermore, the implementation of a demand response program (DRP), cooperatively with the aforementioned resources, has significantly improved all key operating indicators of the system.