Intelligent Coordination of Traditional Power Plants and Inverters Air Conditioners Controlled With Feedback-Corrected MPC in LFC

Demand response programs have been receiving more serious attention as alternatives for participating in load frequency control. Inverter air conditioners (IAC) are acknowledged as suitable devices for demand response due to their increasing contribution to network consumption. Despite their potential, their use presents challenges, including delayed responses, variable interference, and the absence of coordination with traditional generation units, which may affect control performance. Also, existing control strategies fail to consider operational and physical constraints, resulting in possible model mismatches. In this paper, a model predictive control with feedback correction (MPCFC) is proposed to dispatch control signals to the IACs so they can effectively participate in the frequency control of an interconnected power system. The feedback correction method is presented to enhance prediction accuracy in the MPC and weaken the influence of model parameter mismatches and external disturbances. Furthermore, to minimize the impacts of communication delays on frequency overshoot/undershoot, this study introduces an intelligent supervisory coordinator based on an artificial neural network to coordinate the reaction of traditional generation units and IACs to correct significant frequency variations brought on by the time delays. The effectiveness of the developed control scheme is verified through numerical studies by comparing it with the IAC with PI and MPC controllers (without coordinator) and the system without IACs. Case studies are investigated on a two-area power system in MATLAB/Simulink environment, and the OPAL-RT real-time simulator is used to validate the results.</p

Model predictive-based secondary frequency control considering heat pump water heaters

The extensive development of renewable energies in power systems causes several problems due to intermittent output power generation. To tackle the challenge, demand response contribution to ancillary service is currently well recognized under the smart grid infrastructure. The application of the heat pump water heater (HPWH) as a controllable load in primary frequency control is well presented in the literature; however, the motivation of this paper is to use HPWHs for secondary frequency control. To this end, a model predictive control (MPC) method for a two-area power system incorporating HPWHs to contribute to secondary frequency control is proposed in this paper. A detailed model of HPWH is employed as a controllable load to control the power consumption during water heating. The MPC method predicts the future control signals using a quadratic programming-based optimization. It uses the system model, past inputs and outputs, as well as system control signals to predict the next signals. The effective performance of the proposed method for the two-area power system with HPWH is demonstrated for different scenarios of load changes, intermittent renewable power generation and parameter variations as the sensitivity analysis

An intelligent coordinator design for GCSC and AGC in a two-area hybrid power system

This study addresses the design procedure of an optimized fuzzy fine-tuning (OFFT) approach as an intelligent coordinator for gate controlled series capacitors (GCSC) and automatic generation control (AGC) in hybrid multi-area power system. To do so, a detailed mathematical formulation for the participation of GCSC in tie-line power flow exchange is presented. The proposed OFFT approach is intended for valid adjustment of proportional–integral controller gains in GCSC structure and integral gain of secondary control loop in the AGC structure. Unlike the conventional classic controllers with constant gains that are generally designed for fixed operating conditions, the outlined approach demonstrates robust performance in load disturbances with adapting the gains of classic controllers. The parameters are adjusted in an online manner via the fuzzy logic method in which the sine cosine algorithm subjoined to optimize the fuzzy logic. To prove the scalability of the proposed approach, the design has also been implemented on a hybrid interconnected two-area power system with nonlinearity effect of governor dead band and generation rate constraint. Success of the proposed OFFT approach is established in three scenarios by comparing the dynamic performance of concerned power system with several optimization algorithms including artificial bee colony algorithm, genetic algorithm, improved particle swarm optimization algorithm, ant colony optimization algorithm and sine cosine algorithm

Intelligent coordinators for automatic voltage regulator and power system stabiliser in a multi-machine power system

This study presents the design of intelligent coordinators for the automatic voltage regulator (AVR) and power system stabiliser (PSS) in a multi-machine power system. The intelligent coordinators are designed to update the gains of AVR and PSS in severe disturbances to guarantee the stability of the studied power system. Three potent intelligent coordinators are proposed: (a) fuzzy logic coordinator, (b) artificial neural network coordinator, and (c) brain emotional learning coordinator. Since the intelligent coordinators are based on the knowledge of the experts, desirable scaling factors are considered in the output signals of the coordinators to achieve optimal results. The scaling factors are optimised using a new evolutionary approach known as the sine–cosine algorithm. To evaluate the efficiency of the proposed intelligent approaches, the performances of coordinators are analysed on a two-area four-machine power system. A range of power system signals, such as rotor speed, terminal voltages, acceleration power and rotor angle of generators are demonstrated to approve and compare the performance of the intelligent coordinators. The simulation results indicate that the intelligent coordinators can guarantee the stability of the power system and satisfy performance objectives, such as desired transient and steady-state errors

Concept, definition, enabling technologies, and challenges of energy integration in whole energy systems to create integrated energy systems

In recent years, the vector coupling of energy systems is in progress by integrating different energy vectors such as power, heat, and gas and also integration of different sectors such as residential, transportation, commercial, and industry parts for efficient utilization of energy sources such as natural gas, coal, diesel, and renewable energy sources. The energy crisis, air pollution issues, increasing trend of energy consumption, reliability issues, and especially increasing the share of variable renewable energy sources are the major drivers of this transition. Interaction among energy systems is achieved through a real or physical node or even a virtual node so-called the energy hub or multi-energy node. Enabling technologies for integrating energy systems are energy conversion systems (such as cogeneration and trigeneration systems, heat pumps, diesel generator, and boilers), energy storage systems (such as battery, thermal, cold, and hydrogen storage), information and communication technologies, and particularly decarbonizing components. Demand-side management is also essential for energy integration since it contributes to energy conservation and flexibility of energy demands. In this chapter, the concept and definition of vector-coupling concept in the whole energy systems is discussed. In addition, enabling technologies and challenges associated with integrating energy vectors are discussed