Intelligent Load Management Research Articles

Ensuring Long Term Sustainability in Networked Microgrids Through Intelligent Load Management and Priority-Based Power Transfer Scheme

Ensuring Long Term Sustainability in Networked Microgrids Through Intelligent Load Management and Priority-Based Power Transfer Scheme

Voltage unbalance and inertia based frequency control framework considering intelligent load management for stand-alone operation of microgrid

The less inertial microgrid with high penetration of Renewable Energy Sources (RES) faces several issues including voltage and frequency unbalance. The intermittent nature of RES-based Distributed Generations (DGs) makes the system unstable in the absence of a control mechanism in a stand-alone microgrid. The integration of single-phase loads and sudden load switching on the distribution side make the system prone to several malicious events. The unbalance in voltage among the phases and the high Rate of Change of Frequency (RoCoF) are the major concern for degrading the system’s performance during peak and off-peak hours. The paper addresses a hierarchical dual loop voltage and frequency control mechanism where the primary control is equipped with droop strategy and Positive-Negative Sequence Control (PNSC) to maintain Voltage Unbalance Factor (VUF) and adhere to IEC-61000-3-13. Further, the inertial loop is also implemented to mitigate frequency deviation and reduce transient overshoot. The secondary control mechanism enables intelligent load management by scheduling Thermostatic Control Loads (TCLs) during peak hours. The utmost care is taken for the Customer’s Thermal Comfort (CTC) while scheduling the loads. The rigorous simulation is done in MATLAB Simulink and Digsilent Power Factory with a modified IEEE 13 bus system considering system dynamics. Further, the real-time simulation is also performed with OPAL-RT to validate the efficacy of the above methodology.

Categorization of Loads in Educational Institutions to Effectively Manage Peak Demand and Minimize Energy Cost Using an Intelligent Load Management Technique

The inclusion of photovoltaics (PV) in electric power supply systems continues to be a significant factor in global interest. However, solar power exhibits intermittent uncertainty and is further unpredictable. Accurate solar generation prediction and efficient utilization are mandatory for power distribution management and demand-side management. Peak demand management and reducing energy costs can be effectively tackled through the implementation of a reliable solar power forecasting system and its efficient utilization. In this regard, the proposed work is related to efficiently managing solar PV power and optimizing power distribution using an enhanced reinforced binary particle swarm optimization (RBPSO) technique. This DSM (demand-side management) strategy involves utilizing a forecast of solar PV generation for the upcoming day and adjusting the consumption schedule of the load to decrease the highest energy demand. The proposed approach improves user comfort by adjusting the non-interruptible and flexible institutional load through clipping and shifting techniques. To evaluate the effectiveness of this approach, its performance is assessed by analyzing the peak demand range and PAR (peak-to-average ratio). It is then compared to the conventional genetic algorithm to determine its effectiveness. Simulation results obtained using MATLAB show that the PAR peak demand before DSM was found to be 1.8602 kW and 378.06 kW, and after DSM, it was reduced to 0.7211 kW and 266.54 kW. This indicates a 29% reduction in Peak demand and performance compared to the conventional genetic algorithm (GA).

Hierarchical dual loop voltage and frequency control in stand alone microgrid with priority based intelligent load management

Hierarchical dual loop voltage and frequency control in stand alone microgrid with priority based intelligent load management

Artificial Intelligence and Machine Learning for Real-time Energy Demand Response and Load Management

Within this compendium, an exhaustive examination is undertaken to scrutinize the intricate amalgamation of artificial intelligence (AI) and machine learning (ML) techniques within the paradigm of real-time energy demand response and load management. Placing paramount importance on the pervasive significance of AI and ML, this research expounds upon their profound capabilities to adroitly harmonize the delicate interplay between supply and demand, meticulously calibrate the multifarious dimensions of grid stability, and optimize the boundless potential inherent in renewable energy resources. An in-depth analysis ensues, encompassing the deployment of AI algorithms, poised at the vanguard of demand response optimization, and the judicious utilization of ML techniques, flawlessly calibrated to deliver unerring accuracy across varying temporal scales in the realm of load forecasting. Furthermore, the seamless integration of AI into the very fabric of intelligent appliances and Internet of Things (IoT)-enabled systems unfolds, illuminating the path towards energy consumption optimization, ascertaining an intricate tapestry of interconnected devices, and engendering an ecosystem of intelligent load management. Notably, this comprehensive exposition delves into the far-reaching implications for optimal load management and resource allocation, magnifying the transformative potential that AI-driven algorithms hold in precisely balancing energy utilization and deftly managing the intricate interdependencies that permeate load distribution. Through meticulous elucidation, this illuminating manuscript emboldens the reader with insights into the progressive advancements and myriad benefits that the tandem of AI and ML confers upon the dynamic energy sector, charting an unyielding course towards unprecedented resilience and sustainable utilization of our cherished renewable energy resources.

Smart Energy Meter Using IoT

The increasing demand for efficient energy management and the rapid growth of Internet of Things (IoT) technology have led to the development of smart energy metering systems. This abstract presents a conceptual design of a smart energy meter that leverages IoT capabilities for enhanced monitoring, control, and optimization of energy consumption in residential and commercial settings. The proposed smart energy meter integrates advanced metering infrastructure (AMI) with IoT sensors, connectivity, and data analytics to enable real-time energy monitoring, intelligent load management, and energy efficiency improvements. The meter collects data on energy consumption, voltage levels, power quality, and other relevant parameters, which are transmitted wirelessly to a central monitoring system. By utilizing IoT connectivity, the smart energy meter facilitates two-way communication, allowing consumers to access their energy usage data remotely via mobile applications or web portals. This empowers users to monitor their energy consumption patterns, set energy-saving goals, and receive personalized recommendations for optimizing their energy usage. Furthermore, the smart energy meter employs data analytics algorithms to analyze the collected energy data and generate actionable insights. These insights can be used to identify energy wastage, detect anomalies, and suggest energy-saving strategies. Additionally, the meter can automatically adjust energy loads based on peak demand periods, optimizing energy distribution and reducing overall energy costs. The proposed smart energy meter offers several benefits, including real-time monitoring, increased energy efficiency, cost savings, and reduced environmental impact. It enables consumers to make informed decisions about their energy usage, promotes sustainable practices, and contributes to the development of smarter and greener communities.

Intelligent Load Management System Development with Renewable Energy for Demand Side Management

Electric smart grid reliability and stability could be increased by the application of demand response initiatives and renewable energy resources. This study provides a brand new demand side management paradigm for smart grids with renewable energy integration that is based on intelligent optimisation. The suggested system combines real-time demand response programmes from electric utility companies and makes use of fuzzy logic to forecast consumer energy consumption patterns. Using demand response programmes, a smart energy management controller adjusts consumer energy usage forecasts to produce an operation schedule. Using simulations employing real-world data, we assess the efficacy of the suggested intelligent demand side management framework. According to the findings, compared to the load management-free method, total electricity costs and carbon emissions have significantly decreased. A potential strategy for demand side management with the integration of renewable energy, the proposed intelligent hybrid optimisation method of load management achieves superior performance in regulating energy consumption, peak loads, and carbon emissions. By presenting a useful and effective paradigm for demand-side management with renewable energy integration, this research makes a contribution to the field of energy management.

Dynamic centralized control and intelligent load management system of a remote residential building with V2H technology

Energy is the main engine and the active element for all growth and development. It is the basic element for all sectors of the economy and a companion to human life. It is also not forgotten that most of the energy used in the whole world is traditional and unsustainable energy, as well as polluting the environment and causing harmful emissions. Sustainable is based primarily on environmental protection. Massive population growth is accompanied by a significant increase in energy consumption. Currently, most countries are turning to rely mainly on renewable energy sources. To achieve the maximum benefit from the energy, the tendency is to manage energy within the home to avoid the occurrence of energy losses. In this paper, an efficient home energy management system (HEMS) is proposed. This system is based on the use of a field-programmable gate array (FPGA) for home energy management. The proposed HEMS takes into account both generation and consumption as FPGA manages the feeding process through the sources. The prototype is implemented using Altera Cyclone IV EP4CE6 FPGA board using QUARTUS II software. The home is fed through PV, wind and fuel cells (FC) sources. If there is a shortage in energy production and the three sources are unable to cover the loads, the batteries enter into service to cover this shortage. If the sources and batteries are unable to cover the required loads, the EV contributes to covering the load, but with specific constraints set by the owner. The proposed HEMS works to control the feeding of the loads, as it schedules the feeding of the loads according to priority in the event of severe energy shortages and the inability of the sources, batteries and the EV to cover the loads. The experimental and simulation results proved the ability of the proposed system to respond to the demand by managing high energy-consuming household appliances according to the pre-set priority.

Intelligent Multi-Vehicle DC/DC Charging Station Powered by a Trolley Bus Catenary Grid

This article deals with the major challenge of electric vehicle charging infrastructure in urban areas—installing as many fast charging stations as necessary and using them as efficiently as possible, while considering grid level power limitations. A smart fast charging station with four vehicle access points and an intelligent load management algorithm based on the combined charging system interface is presented. The shortcomings of present implementations of the combined charging system communication protocol are identified and discussed. Practical experiments and simulations of different charging scenarios validate the concept and show that the concept can increase the utilization time and the supplied energy by a factor of 2.4 compared to typical charging station installations.

An intelligent load management application for solar boiler system

A load management application is developed to maximize the utilization of solar boiler systems through the scheduling of household activities. This application uses an hourly and daily Global Solar Radiation prediction model and is fed live weather forecast data directly from a weather application program interface. Unlike Time-of-Use Price-scheduling, the proposed scheduling system is based on the amount of predicted generated solar energy and the behavior of the load. In the first phase of operation, live weather data are collected using a weather application program interface. In the second phase, an Artificial Neural Network (ANN) model is trained using five-year weather-solar radiation data recorded for a North American city (2000–2004). Then, the Global Solar Radiation is predicted for ten days and compared with actual values for 2005. The cumulative hourly and daily energy and optimal days in the ten days are also obtained. In the final phase, household activities that consume hot water, such as washing dishes, using the washing machine, and taking a shower or bath are rescheduled to utilize the generated energy, which maximizes savings and minimizes emissions.

Smart residential electricity distribution system (SREDS) for demand response under smart grid environment

The present research work attempts to develop a Smart Residential Electricity Distribution System (SREDS) for enabling Smart Residences (SR) to participate in Demand Response (DR) schemes of Power Distribution Company (PDC) with either Flat Rate tariff, Time of Use tariff or Real Time Pricing. SREDS includes a Smart Energy Meter (SEM) enabled with Internet of Things, an Intelligent Residential Load Management System (IRLMS) with Wi-Fi connectivity, distributed Wi-Fi enabled Smart Load Nodes (SLN) and Smart Battery Charing/Discharging Controller (SBCDC). The SEM receives the key parameters such as electricity pricing, maximum demand limit, etc. as per the DR schemes from the PDC through Meter Data Management System (MDMS) using Internet. IRLMS receives the required operational timings of the loads as per the desire and comfort of the user either through the user interface or from the SLN connected to the appliances participating in DR. If the SR is equipped with battery storage system, then the battery units are interfaced with the IRLMS through SBCDC. The present status of the battery is communicated to IRLMS by SBCDC. The communication between SEM, IRLMS, SLNs and SBCDC is established through Home Area Network using Wi-Fi. The IRLMS processes the PDC parameters and load requirements, and executes an optimization algorithm to find the best possible scheduling of appliances in order to get minimized electricity bill. The SEM communicates back the energy consumption data to the PDC through MDMS.

Load and energy management for factories through multi-stage production planning and optimization

The production phase has a major influence on the embodied energy of many products and, thus, their overall environmental performance. As a result, manufacturing enterprises are prompted to raise their energy efficiency and flexibility to improve their products’ sustainability. The simultaneous integration of renewable energy sources and decentral storages is a feasible option in this but also require consistent solutions for appropriately planning and controlling the operation of the entire factory system. This paper proposes an iterative multi-stage heuristic optimization for intelligent load and energy management. A load profile of the production plan, based on the minimized overall makespan, is checked against the available energy supply to identify demands and opportunities to shift single jobs based on calculated buffer times. Whenever infeasibility is determined, the process returns to the previous production planning stage for further alterations. This cycle is initiated regularly (e.g. weekly) or following certain events (e.g. long-lasting machine failure) and terminates once all restrictions are observed or if a termination threshold (e.g. calculation time) is reached. The experimental setup used for developing and validating this approach to energy-aware factory operation makes use of a probabilistic material flow simulation model. It provides information on tasks to be scheduled and executes production plans to evaluate the performance of the proposed solution. This new approach to energy-aware factory operation also provides opportunity to integrate additional ecologic criteria to improve the sustainability of manufactured products.

An Intelligent Load Management System With Renewable Energy Integration for Smart Homes

Demand side management (DSM) will play a significant role in the future smart grid by managing loads in a smart way. DSM programs, realized via home energy management systems for smart cities, provide many benefits; consumers enjoy electricity price savings and utility operates at reduced peak demand. In this paper, evolutionary algorithms-based (binary particle swarm optimization, genetic algorithm, and cuckoo search) DSM model for scheduling the appliances of residential users is presented. The model is simulated in time of use pricing environment for three cases: 1) traditional homes; 2) smart homes; and 3) smart homes with renewable energy sources. Simulation results show that the proposed model optimally schedules the appliances resulting in electricity bill and peaks reductions.

전기자동차 침투율을 고려한 피크 부하 저감용 스마트 기기의 적응적 제어

Electric utilities may face new threats with increase in electric vehicles (EVs) in the personal automobile market. The peak demand will increase which may stress the distribution network equipment. The focus of this paper is on an adaptive control of smart household appliances by using an intelligent load management system (ILMS). The main objectives are to accomplish consumer needs and prevent overloading of power grid. The stress from the network is released by limiting the peak demand of a house when it exceeds a certain point. In the proposed strategy, for each smart appliance, the customers will set its order/rank according to their own preferences and then system will control the household loads intelligently for consumer reliability. The load order can be changed at any time by the customer. The difference between the set and actual value for each load’s specific parameter will help the utility to estimate the acceptance of this intelligent load management system by the customers.

NOVEL APPROACH TO DESIGN IOT BASED INTELLIGENT INDUSTRIAL LOAD MANAGEMENT SYSTEM

This paper introduces a novel approach to integrate IOT with an intelligent Industrial Load Management System. A virtual control system is designed to balance the supply of electricity on the network with the electrical load of an industry by recovering heat from hot chemical gases to produce electricity and by disconnecting the low priority loads automatically during power shortage periods. This approach takes advantages of the LabView’s better hardware support, rapid configuration, MATLAB’s ability to perform complex calculations and newer innovations helping in cheaper ways of realising internet connected devices. An intelligent power generating system which works on waste heat recovery mechanism comprising a boiler turbine system is modeled using MATLAB and a human machine interface is developed using LabVIEW. The system that is modeled in MATLAB can be simulated by LabVIEW Simulation Interface Toolkit (SIT) toolkit. The SIT provides a seamless integration between MATLAB/Simulink and LABVIEW.

How accurate are drivers’ predictions of their own mobility? Accounting for psychological factors in the development of intelligent charging technology for electric vehicles

Intelligent load management systems (ILMS) for electric vehicles (EVs) would make it possible to link EV use to renewable energy sources. ILMS require information about the departure time and length of EV drivers’ upcoming trips to optimize the charging process depending on the availability of renewable energy in the grid. Inaccurate information may lead to insufficient battery levels or inefficient charging processes. In a field test during two weeks 60 participants predicted the departure time and trip length of their upcoming trips after having arrived at home with their own gasoline-powered cars. Actual mobility behavior was assessed by means of logbooks and GPS tracking devices. The results show that participants are on average able to accurately predict their departure times and trip lengths although for some outliers their prediction errors would potentially have led to insufficient battery levels. The type of trip (work, leisure, shopping) significantly influenced the accuracy of mobility predictions.

Intelligent Load Management Scheme for a Residential Community in Smart Grids Network Using Fair Emergency Demand Response Programs

In the framework of liberalized deregulated electricity market, dynamic competitive environment exists between wholesale and retail dealers for energy supplying and management. Smart Grids topology in form of energy management has forced power supplying agencies to become globally competitive. Demand Response (DR) Programs in context with smart energy network have influenced prosumers and consumers towards it. In this paper Fair Emergency Demand Response Program (FEDRP) is integrated for managing the loads intelligently by using the platform of Smart Grids for Residential Setup. The paper also provides detailed modelling and analysis of respective demands of residential consumers in relation with economic load model for FEDRP. Due to increased customer’s partaking in this program the load on the utility is reduced and managed intelligently during emergency hours by providing fair and attractive incentives to residential clients, thus shifting peak load to off peak hours. The numerical and graphical results are matched for intelligent load management scenario.

TCP/IP Based Intelligent Load Management System in Micro-Grids Network Using MATLAB/Simulink

Computerized power management system with fast and optimal communication network overcomes all major dicrepencies of undue or inadequate load relief that were present in old conventional systems. This paper presents the basic perception and methodology of modern and true intelligent load management scheme in micro grids topology by employing TCP/IP protocol for fast and intelligent switching. The network understudy performs load management and power distribution intelligently in a unified network. Generated power is efficiently distributed among local loads through fast communication system of server in the form of source and clients in the form of loads through TCP/IP. The efficient use of information between server and clients enables to astutely control the load management in a power system of micro grids system. The processing time of above stated system comes out to be 10ms faster than others which ensure very less delay as compared to conventional methods. The Micro Grids system operating through TCP/IP control has been implemented in MATLAB/Simulink and results have been verified.

Load management for refrigeration systems: Potentials and barriers

As a strategy to deal with the increasing intermittent input of renewable energy sources in Germany, the adaptation of power consumption is complementary to power-plant regulation, grid expansion and physical energy storage. One demand sector that promises strong returns for load management efforts is cooling and refrigeration. In these processes, thermal inertia provides a temporal buffer for shifting and adjusting the power consumption of cooling systems. We have conducted an empirical investigation to obtain a detailed and time-resolved bottom-up analysis of load management for refrigeration systems in the city of Mannheim, Germany. We have extrapolated our results to general conditions in Germany. Several barriers inhibit the rapid adoption of load management strategies for cooling systems, including informational barriers, strict compliance with legal cooling requirements, liability issues, lack of technical experience, an inadequate rate of return and organizational barriers. Small commercial applications of refrigeration in the food-retailing and cold storage in hotels and restaurants are particularly promising starting points for intelligent load management. When our results are applied to Germany, suitable sectors for load management have theoretical and achievable potential values of 4.2 and 2.8GW, respectively, amounting to about 4–6% of the maximum power demand in Germany.

Intelligent Load Control For Frequency Regulation In Microgrids

Abstract During autonomous operation, MicroGrids may suffer large fluctuations in frequency due to the mismatch between load and local generation. The available spinning reserve might not always be enough to provide the required frequency support. Control of non-critical loads is able to provide the required primary and secondary frequency support. The use of Intelligent Load Management Controllers (ILMC) installed at consumer nodes is investigated. The ILMC units use fuzzy logic to determine the shedding and the restoration frequencies for non-critical loads based on the current power measurement and the history of disconnection. The fuzzy rules were chosen to achieve high reliability and equity between consumers.