Load Control Algorithm Research Articles

Control-Centric Living Laboratory for Management of Distributed Energy Resources

Variability and uncertainty of renewable distributed generation increase power grid complexity, necessitating the development of advanced control strategies. demonstrates a real-world testbed and the implementation of control strategies on it to mitigate the challenges associated with variability and uncertainty of renewable distributed generation. This control-centric testbed includes 4.6 MW of controllable building loads, a 1 MW solar array, and a 125 kW / 130 kWh battery energy storage system (BESS). The capabilities of the testbed are illustrated by highlighting the implementation of three specific scenarios relevant to future smart grid infrastructures. In the first scenario, photovoltaic output variability is mitigated with the BESS using adaptive moving average and adaptive state of charge control methods. The second and third scenarios demonstrate peak load management and load following control to manage uncertainty using the Intelligent Load Control (ILC) algorithm. The ILC modifies controllable loads using a prioritization matrix and an analytical hierarchy process. The three scenarios all operate at a different time-constant, and are each effectively addressed, demonstrating the versatility and flexibility of the presented testbed. This demonstrated ability to rapidly test the efficacy of alternate control algorithms on a real system is crucial to the maturation of future smart-grid.

Energy Management in a Standalone PV System with Priority Controller

In many developing countries, meeting the energy demand has become a major challenge. Such problem is more prominent in rural and remote areas of the country. The load requirements in these areas are less and the same can be addressed with renewable energy sources. The proposed work deals with a MPPT based standalone PV system using a priority controller. The system can be used to meet out the critical load demands in rural areas. Due to change in weather conditions, an unregulated output in PV array is observed. Hence, maximum power is tracked using a DC-DC converter, where the tracked data is with respect to temperature and irradiance levels. To acquire the maximum power point (MPP), an incremental conductance (IC) algorithm is employed and it is executed by controlling the duty cycle of DC-DC boost convertor. Thus, the attainment of energy management in loads and battery storage is supported by priority load control algorithm. The proposed system assures better energy management and supplies energy for critical loads. The entire system has been simulated and validated using MATLAB/SIMULINK.

Maximum Power Control Algorithm for Power Take-Off System Based on Hydraulic System for Floating Wave Energy Converters

In this study, a hydraulic system generator power converter was modeled to verify the performance of a hydraulic-based power take-off (PTO) system. Moreover, the characteristics and output performance of the PTO system were analyzed with various load control algorithms applied for maximum power control. The simulation performance was verified through a comparison with actual sea test results. Unlike previous studies on hydraulic-based PTO system control for input power performance, the performance of a hydraulic-based PTO system was analyzed through electrical load control in this study. The electrical load control was analyzed by applying a speed control algorithm based on the perturb and observe algorithm and an optimal torque control algorithm. A load control algorithm suitable for maximum power control of the PTO system was proposed by analyzing the characteristics and power generation performance of the system according to the control variables of each algorithm. The proposed optimal torque control algorithm proved to be suitable for maximum power control of the considered PTO system.

Multiobjective Metaheuristic Load Control Algorithm for Interaction between Smart Home and Humans

Technology has boosted electric power consumption both locally and worldwide, resulting in a substantial increase in demand for electric power. A multiobjective smart house human-computer interaction load control method is suggested to meet the goal of lowering power usage and pricing in smart home load control. It presents a model that includes marginal costs and establishes an electricity price model that takes the load rate into account. It identifies switch appliances and temperature control appliances and gathers human activities, indoor and outdoor temperature, and light intensity using intelligent equipment to create a multiparameter comfort model. It creates a multiobjective model of comfort and electricity price with the purpose of reducing electricity price and multiparameter comfort, and it improves particle swarm performance by using a distance ratio based on fitness value. The optimization algorithm solves the model, determines the best smart home human-computer interaction load control scheme, creates the smart home remote control system’s functional modules, and optimizes the multiobjective smart home human-computer interaction load control algorithm using a frequency-duration parameter tracking learning model. The testing findings suggest that the proposed algorithm can cut power prices in a reasonable manner, as well as regulating 40% reduced electricity usage and load in smart homes.

An Incentivized and Optimized Dynamic Mechanism for Demand Response for Managing Voltage in Distribution Networks

The voltage regulation in distribution networks is one of the major obstacles when increasing the penetration of distributed generators (DGs) such as solar photovoltaics (PV), specially during cloud transients, causing potential stress on network voltage regulations. Residential demand response (DR) is one of the cost-effective solutions for voltage management in distribution networks. However, the main barriers of DR implementation are the complexities of controlling a large number and different types of residential loads, satisfying customers’ preferences and providing them fair incentives while identifying the optimum DR implementation locations and sizing as well as cooperating with the existing network equipment for the effective voltage management in the networks. A holistic and practical approach of DR implementation is missing in the literature. This study proposes a dynamic fair incentive mechanism using a multi-scheme load control algorithm for a large number of DR participants coordinating with the existing network equipment for managing voltage at medium voltage (MV) networks. The multi-scheme load control is comprised of short-interval (10-minute) and long-interval (2-hour) DR schemes. The dynamic incentive rates are optimised based on the energy contribution of DR participating consumers, their influence on the network voltage and total power loss improvement. The proposed method minimises the DR implementation cost and size, fairly incentivises the consumers participating in the DR and priorities their consumption preferences while reduces the network power losses and DGs’ reactive power contributions to effectively manage the voltage in the MV networks. An improved hybrid particle swarm optimisation algorithm (IHPSO) is proposed for the load controller to provide fast convergence and robust optimisation results. The proposed approach is comprehensively tested using the IEEE 33-bus and IEEE 69-bus networks with several scenarios considering a large number of DR participants coordinated with the DGs and on-load tap changer (OLTC) in the networks.

Real-time replication of three-dimensional and time-varying physiological loading cycles for bone and implant testing: A novel protocol demonstrated for the proximal human femur while walking

In vitro real-time replication of three-dimensional, time-varying load profiles acting on human bones during physical activity can advance bone and implant testing protocols. This study aimed to develop a novel protocol for applying the three-dimensional, time-varying hip contact force while walking to a human femur specimen. The target force profile was obtained from the literature. A proximal femur from an elderly female donor was instrumented using ten rosette strain gages and tested using a custom-made hexapod robot. A load-control algorithm determined the robot position generating the target force at low frequency (0.0004 Hz). Five cycles of the robot position were played back at five intermediate frequencies up to real-time (0.04, 0.08, 0.16, 0.4, and 0.8 Hz). The hip reaction force, the length of the actuators (position), and cortical strains were compared. The error in the load-control force was 0.3 ± 4.2 N (mean ± SD). The last three force, position, and strain cycles varied by less than 1.1% for every frequency analyzed. Across frequencies, the force increased by 28% at 0.8 Hz as a logarithmic function of frequency (R2 = 0.98). The position and strain error linearly increased with frequency up to 0.4 Hz. The median position error and the interquartile range of the strain error reached 15% and 13% at 0.8 Hz. Changes of force and cortical strain at increasing frequencies were linearly related (R2 = 0.99). Therefore, the protocol developed can provide repeatable three-dimensional time-varying load profiles, although the comparison of the specimen deformation obtained across frequencies should be considered with care, particularly in the higher frequency range. This information supports the design of dynamic tests of bone and implants.

Distributed Automatic Load Frequency Control With Optimality in Power Systems

With the increasing penetration of renewable energy resources, power systems confront new challenges in maintaining power balance and the nominal frequency. This article studies load-side frequency control to handle these challenges. In particular, a fully distributed automatic load control (ALC) algorithm, which only needs local measurement and local communication, is proposed. We prove that the load control algorithm globally converges to an optimal operating point that minimizes the total disutility of users, restores the nominal frequency and the scheduled tie-line power flows, and respects the load capacity limits and the thermal constraints of transmission lines. Furthermore, the global exponential convergence of the reduced ALC algorithm without considering the capacity limits is proved and leveraged to study the dynamical tracking performance and robustness of the algorithm. Finally, the effectiveness, optimality, and robustness of the proposed ALC algorithm are demonstrated via numerical simulations.

Power Performance Analysis According to the Configuration and Load Control Algorithm of Power Take-Off System for Oscillating Water Column Type Wave Energy Converters

A power take-off (PTO) system for an oscillating water column (OWC) wave energy converter comprises a turbine-generator-power converter. In this study, only the topologies of the power converter that affect the load control algorithm are compared. A power converter for renewable energy is composed of a diode-dc/dc converter and a pulse-width modulation (PWM) converter operating at small and large capacities, respectively. However, selecting a power converter according to the capacity based on the characteristics of the wave energy converter, in which the input energy is highly fluctuating, can significantly reduce the power performance. Thus, to verify load control characteristics according to the topology of the power converter, the turbine-generator-power converter was incorporated in the modeling, and the power performance based on the power converter topology under various wave conditions was analyzed. Further, torque control to obtain the maximum power among load control algorithms was applied under irregular wave conditions, and the power performance and PTO system characteristics according to the torque coefficient were analyzed. The results of this study suggested an increase in the torque coefficient of the maximum power control for the operational stability of the OWC-WEC, and it was confirmed that the RPM characteristics of the PTO system were reduced.

Low-Complexity Decentralized Algorithm for Aggregate Load Control of Thermostatic Loads

Thermostatically controlled loads such as refrigerators are exceptionally suitable as a flexible demand resource. This paper derives a decentralised load control algorithm for refrigerators. It is adapted from an existing continuous time control approach, with the aim to achieve low computational complexity and an ability to handle discrete time steps of variable length -- desirable features for embedding in appliances and high-throughput simulations. Simulation results of large populations of heterogeneous appliances illustrate the accurate aggregate control of power consumption and high computational efficiency. Tracking accuracy is quantified as a function of population size and time step size, and correlations in the tracking error are investigated. The controller is shown to be robust to errors in model specification and to sudden perturbations in the form of random refrigerator door openings.

Power demand control algorithm for single controllable load with parametric and variable load factors

Power demand control algorithm for single controllable load with parametric and variable load factors

Demand Smoothing in Military Microgrids Through Coordinated Direct Load Control

In small microgrids and individual branches of a bulk electrical grid, the aggregate electrical load can contain significant and frequent peaks caused by large individual loads. These peaks can reduce overall system efficiencies if generation resources, e.g., diesel generators, are dispatched based on peak demand. This problem is particularly severe in military forward operating base (FOB) microgrids, in which the load profile is dominated by environmental control units (ECUs) that operate under thermostatic control. Leveraging the intrinsic energy storage capabilities associated with large loads such as these ECUs and coordinating their operations across neighboring facilities provides an opportunity to reduce peak demand while maintaining system performance. Using a military FOB microgrid as a use case, this paper presents two direct load control (DLC) algorithms for coordinating ECU operations and reducing peak demand. This coordinated control is demonstrated through simulations and field tests at the U.S. Army’s Base Camp Integration Laboratory using novel controller hardware. Both simulation and field tests indicate that the DLC algorithms can reduce peak loads by 25% or more without sacrificing thermal comfort.

Effects of Load-Based Frequency Regulation on Distribution Network Operation

This paper examines the operation of distribution networks that have large aggregations of thermostatically controlled loads (TCLs) providing frequency regulation to the bulk power system. Specifically, we assess the prevalence of distribution network constraint violations, such as over- or under-voltages and overloading of transformers. Our goal is to determine the set of constraints that are at increased risk of being violated when TCLs provide regulation. We compare network operation in two cases: first, with TCLs operating freely; and second, with TCLs controlled to track a regulation signal. Using GridLAB-D, we run power flow simulations of five real distribution networks. Our results indicate that voltage limits are at increased risk of violation when TCLs provide regulation because of increased voltage variation. Effects on transformer aging are more nuanced and depend on the method used for dispatching TCLs. Overall, we find that, for the networks studied, the set of constraints at risk of violation is a small subset of all network constraints. Therefore, network-protecting load control algorithms may only need to consider a small set of constraints, enhancing their computational efficiency.

A regional power grid operation and planning method considering renewable energy generation and load control

The active development and application of renewable energy, such as wind power and solar power, has become an inevitable tendency, which will promote the increase of the proportion of renewable energy generation accordingly in regional power grids. The intermission and randomness of renewable energy generation lead to enormous challenges in the operation and planning of power systems. One of the current methods to accommodate and reduce renewable energy fluctuation is conventional storage devices, e.g. battery energy storage devices, but this approach has the limitation of a high operating cost, which hinders the technology from popularizing. We present a novel load control algorithm employing the technology of residential thermostatically controlled appliances to maximize renewable energy usage in regional power grids. The thermal dynamics of the thermostatically controlled loads are modeled as a simplified first-order equivalent thermal parameter model. The new load control algorithm, based on the state-queueing model with modified colored power algorithm, is developed to manage activities of residential thermostatically controlled appliances, which allows customers to set different colors as the response levels of their appliances. Such an algorithm can ensure both the comfort level and the fairness of customers. Both a practical case and a real case are applied to demonstrate the effectiveness and practicability of the proposed algorithm.

Delamination initiation and migration modeling in clamped tapered laminated beam specimens under static loading

The Clamped Tapered Beam Specimen (CTBS) designed to study the initiation of matrix cracking and delamination and their propagation including delamination migration from one interface to another was considered. Regularized eXtended Finite Element Method (Rx-FEM), which allows modeling the matrix cracking in a direction independent of finite element boundaries, was used for the analyses. The key parameters which were compared with the data obtained during the simultaneously conducted experimental program included peak loads, matrix crack initiation locations and delamination migration locations. The predicted peak loads were within the experimental range when high transverse strength values obtained from three point bend tests were used in the predictions. The matrix crack initiation location as well as delamination migration locations were correctly predicted by taking into account residual thermal stresses and implementing energy dissipation based load control algorithms to model delamination migration during unstable crack propagation.

A spectral stochastic formulation for nonlinear framed structures

The present paper proposes a stochastic formulation which enables the effective coupling of spectral stochastic finite elements with geometrically nonlinear analysis of framed structures. This is achieved by projecting the stochastic part of the incremental displacements, formulated in the framework of a Newton–Raphson solution scheme, to a polynomial chaos expansion basis. The nonlinear solution is consequently achieved by introducing these expansion coefficients in the iterative solution algorithm as unknowns to be evaluated. With this approach, an augmented system of nonlinear equations is produced and the values of the coefficients are obtained through convergence of the iterative algorithm. In a similar to the deterministic problems manner, the nonlinear load or displacement control algorithms are extended to their stochastic counterparts and a discussion on the appropriate choice of such algorithms depending on the problem at hand is presented. This work focuses on stochastic beam finite element systems in which uncertainty is considered in the system properties. The efficiency of the proposed methodology is demonstrated in benchmark problems with strong geometrically nonlinear behavior. In order to verify the validity of the proposed approach the results obtained are compared to those of Monte Carlo simulations and fair accuracy can be reported.

Nuclear Norm-Based Recursive Subspace Identification for Wind Turbine Flutter Detection

Commercial wind turbine blades are progressively becoming longer and more flexible; in order to achieve load reduction, the use of shape modifying devices is currently under research. While such modifications facilitate cost reduction, they also render the blade susceptible to the unstable aeroelastic phenomenon of flutter. To be able to detect the onset of flutter, and to modify the load control algorithm accordingly, it is desirable to perform online identification of system dynamics. In this paper, a recursive subspace identification algorithm is augmented with a nuclear norm-based cost function for the rapid identification of changes in the dominant system behavior. The time-consuming singular value thresholding step involved in the identification is replaced by a fast randomized algorithm. The method developed is used to identify the changes in the dynamics of an experimental wind turbine equipped with shape-modifying actuators, and operated under controlled conditions in a wind tunnel. The proposed identification method shows high sensitivity to changes in system dynamics, and is shown capable of stably and rapidly identifying the onset of aeroelastic flutter.

Метод управління доступом у комп’ютерній системі критичного застосування на базі мережі 4G

The main feature of the 4G network for quality of services management is the adaptation of the radio channel resources to the specified requirements for the quality of the service provided. The article proposes a method of the access control in a mission-critical computer system on the basis of 4G network to ensure the quality of data transmission services in a mission-critical computer system based on the 4G network. It is proposed to perform the adaptation of the radio channel resources to the specified requirements for the quality of the data transmission service using network access control algorithms, network load control algorithms and load network redistribution algorithms. We propose network access control algorithms based on the estimation of full power and bandwidth. The control functions of 4G radio resources are considered and the possible actions on controlling the load of the radio network are formulated in order to prevent its overloads when new subscribers connect to the network. The necessary mathematical basing is given.

Development and validation of an intelligent load control algorithm

The renewable generation technologies form a significant (>20%) fraction of grid capacity, however their generation capabilities remain variable in nature. Therefore, utilities will be forced to maintain a significant standby capacity to mitigate the imbalance between supply and demand. Because more than 75% of electricity consumption occurs in buildings, building loads can be used to mitigate some of the imbalance. This paper describes the development and validation of an intelligent load control (ILC) algorithm that can be used to manage loads in a building or group of buildings using both quantitative and qualitative criteria. ILC uses an analytic hierarchy process to prioritize the loads for curtailment. The ILC process was developed and tested in a simulation environment to control a group of rooftop units (RTUs) to manage a building’s peak demand while still keeping zone temperatures within acceptable deviations. The ILC algorithm can be implemented at a low cost on a supervisory controller without the need for additional sensing. By anticipating future demand, the process can be extended to add advanced control features such as precooling and preheating to alleviate comfort when operation of the RTUs is curtailed to manage the peak demand.

A Simplified Motion Isolated Load Control for Multi-Channel Syncro-Loading System

For the multi-channel syncro-loading (MCSL) test, the specimen may be driven by a particular motion of another position channel, representing an in-service duty cycle. The loading accuracy is largely affected by the specimen motion. To reduce the cross coupling from the motion, the cross coupling characteristics between multi-channels is analyzed, and a simplified motion isolation load control (MILC) algorithm is provided, in which the velocity and acceleration of the specimen, measured by the accelerometer, are added into the forward path of the loading control channel in order to drive the actuator to match the motion of the specimen. The test results indicate that the MILC algorithm can significantly improve the loading accuracy compared with the conventional loading control method.

A Review on Various Load Control Strategies for Battery Energy Storage System in Energy Applications

Battery Energy Storage System (BESS) is one of the most popular and established options used to decouple the timing of generation and consumption of electric energy. In spite of variety types of energy storage exists, BESS has proven to be widely used in these three main energy applications, which are Renewable Energy (RE) integration, load leveling and peak shaving. Concurrently, managing stored energy and generated power is a real challenge where the load control system plays the decisive role to ensure better energy management efficiency with stabilized and guaranteed energy supply. This paper presents an analysis of various load control algorithms and methods used, integrated with BESS to achieve the required output. Besides that, recent research works are also summarized in this paper. Based on the study, battery State of Charge (SOC) is widely and mainly used in the load control algorithm and proven as effective and reliable load control strategy.