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Fractional order PID controllers for collaborative energy management in IoT-Smart cities: Hybrid optimization algorithms for demand

Sunil Kumar Sharma, Rayed AlGhamdi, Sultan Alasmari, Naveen Kumar Sharma, Hasim Ali Khan, Fuzail Ahmad

2024Energy Reports11 citationsDOIOpen Access PDF

Abstract

Modern energy markets have undergone a revolution due to the development of smart grids, which allow users to take part in demand response (DR) initiatives and keep the balance between demand and power generation. This work presents a new method with a Fractional Order PID controller (FOPID) for optimizing the power consumption of appliances in smart homes. An improved Secretary Bird Optimization (ISBO) algorithm is presented in this research for better scheduling appliance power consumption under time-of-use (TOU) and critical peak pricing (CPP) schemes. This strategy uses TOU and CPP, two dynamic pricing schemes, to minimize peak demand and cut energy expenses. The proposed strategy creates an energy management system (EMS) that is more effective by integrating energy storage devices (ESD) and renewable energy sources (RES) with the electrical grid. Due to this integration with FOPID, the best possible scheduling of appliances is made possible by CPP and TOU pricing schemes, which are intended to shift load and lower the peak-to-average ratio (PAR). The FOPID had been incorporated with EMS to enhance the stability as well as precision. Reductions in energy consumption and electricity costs across a range of scenarios are evident in the simulation results conducted in the MATLAB environment.

Topics & Concepts

PID controllerOrder (exchange)Computer scienceEnergy (signal processing)Internet of ThingsAlgorithmMathematical optimizationControl engineeringEngineeringMathematicsTemperature controlEmbedded systemBusinessStatisticsFinanceSmart Grid Energy ManagementPower Line Communications and NoiseMicrogrid Control and Optimization
Fractional order PID controllers for collaborative energy management in IoT-Smart cities: Hybrid optimization algorithms for demand | Litcius