Self-adaptive fuzzy-PID controller for AGC study in deregulated Power System

The aim of this paper elucidates the AGC issues in a large scale interconnected power system incorporating HVDC link under the deregulated environment. The performance of the system is degraded under the influence of abrupt load change, and parameter variation. To perceive a reliable and quality power supply, secondary robust controllers are essential. A novel self-adaptive Fuzzy-PID controller is proposed to ameliorate the dynamic performance of both the conventional PID and Fuzzy-PID controller, employed in the restructured power system. In self-adaptive Fuzzy-PID controller unlike the Fuzzy-PID controller, the output scaling factors are tuned dynamically while the controller is functioning. These three controllers are designed by enumerating different gains and scaling factors, applying a budding nature-inspired algorithm known as Wild Goat Algorithm (WGA). The superior dynamic performance of frequency and tie-line power deviation under self-adaptive Fuzzy-PID controller in comparison to its' counterparts is investigated by dispatching the scheduled and unscheduled power under different contracts such as poolco based transaction, bilateral transaction and contract violation based transaction through different tie-lines. The dynamic response under parameter variation and random load perturbation confers the robustness of the proposed controller

Critical Aspects of AGC Emerging from Optimal Control to Machine Learning Techniques

With the emphasis towards renewable energy lot more advancement has been done in the field of electric energy system and it is expected that future energy system may have wind power dominating control areas or hydro power be it bulk hydro or micro hydro based power generations in order to cater the rising energy demands of the modern society. Hence, automatic generation control (AGC) plays a crucial role in the modern electrical energy system in order to maintain the frequency standards to nominal value besides maintaining the power interchange between the interconnected controls areas in order to distribute value and cost effective power. This paper presents the literature survey related to some of the critical aspects of AGC such as diverse sources power generations, hydro dominating control areas, wind power based power generations and applications of flexible alternating current transmission system (FACTS) in AGC. This paper also discusses the novel control designs based on the concept of optimal control, output vector feedback, model predictive control, robust control and finally the machine learning based AGC designs are explored and the critical gaps among the available research work are well presented and discussed

Frequency deviations stabilizations in restructured power systems using coordinative controllers

Modern restructured power system faces excessive frequency aberrations due to the intermittent renewable generations and persistently changing load demands. An efficient and robust control strategy is obligatory to minimise deviations in the system frequency and tie-line to avoid any possible blackout. Hence, in this research, to achieve this target, automatic generation control (AGC) is utilized as a secondary controller to alleviate the changes in interconnected restructured systems at uncertainties. The objective of AGC is to quickly stabilize the deviations in frequency and tie-line power following load fluctuations. This thesis addresses the performance of AGC in two-area restructured power systems with many sophisticated control strategies in the presence of renewable and traditional power plants. As per literature of research work, there are quite a few research studies on AGC of a restructured system using optimized coordinative controllers. Besides, investigations on advanced optimized-based coordinative controller approaches are also rare to find in the literature. So, various combinations of two degrees of freedom (2DOF) controllers are utilized as supplementary controllers to diminish the frequency deviations. Nevertheless, the interconnected tie-lines are typically congested in areas with huge penetration of renewable sources, which may reduce the tie -line capability. Therefore, distinct FACTS controllers and ultra-capacitor (UC) are integrated into two-area restructured systems for strengthening the tie-line power and frequency. Further, new optimization techniques such as cuckoo search (CS), bat algorithm (BA), moth-flame optimization (MFO) are utilized in this work for investigating the suggested 2DOF controllers and compared their performance in all contracts of restructured systems. As per the simulation outcomes, the amalgamation of DPFC and UC with MFObased 2DOF PID-FOPDN shows low fluctuation rate in frequency and tie-line power. Besides, the settling times (ST) of two areas are 9.5 S for ΔF1, 8.2 S for ΔF2, and 10.15 S for ΔPtie. The robustness of the suggested controller has been verified by ±25% variations in system parameters and loading conditions

Novel control design and strategy for load frequency control in restructured power systems

In restructured electric power systems, a number of generation companies and independent power producers compete in the energy market to make a profit. Furthermore, a new marketplace for ancillary services is established, providing an additional profit opportunity for those power suppliers. These services are essential since they help support the transmission of power from energy sources to loads, and maintain reliable operation of the overall system. This dissertation addresses regulation , a major ancillary service also known as the load frequency control (LFC) problem, and presents novel control designs and strategies for the LFC in restructured power systems.;A power system is an interconnection of control areas, which are operated according to control performance standards established by the North American Electric Reliability Council (NERC). LFC is a necessary mechanism in each control area because it maintains a balance between power demand and power generation while assuring compliance with NERC standards.;This dissertation first develops three new control designs that yield effective and robust load frequency control actions. All controllers developed here require only local measurements. The first control design is based on decoupling each area thru modeling of the interconnection effects of other control areas. The second control design relies on the robust H infinity theory in terms of linear matrix inequalities (LMIs). The third control design is achieved by the collaboration between genetic algorithms (GAs) and LMIs. The first two control designs result in high-order dynamic controllers. The third design requires only a simple proportional-integral (PI) controller while yielding control performance as good as those resulting from the previous two designs. Consequently, the third control design is the most preferable due to its simplicity and suitability for industry practice. Furthermore, a stability analysis method based on perturbation theory of eigenvalues is developed to assess the stability of the entire power system being equipped by the proposed controllers.;Second, to comply with NERC standards, two LFC strategies are developed to direct LFC\u27s actions. One strategy employs fuzzy logic to mimic a skillful operator\u27s actions so that all decisions are made efficiently. The other strategy treats the compliance with NERC standards as constraints while minimizing the operational and maintenance costs associated with LFC actions. Three new indices are introduced to assess economic benefits from the strategy compared to the conventional methods. Simulation is performed to demonstrate performances of all proposed methods and strategies

Optimal solutions for fixed head short-term hydrothermal system scheduling problem

In this paper, optimal short-term hydrothermal operation (STHTO) problem is determined by a proposed high-performance particle swarm optimization (HPPSO). Control variables of the problem are regarded as an optimal solution including reservoir volumes of hydropower plants (HdPs) and power generation of thermal power plants (ThPs) with respect to scheduled time periods. This problem focuses on reduction of electric power generation cost (EPGC) of ThPs and exact satisfactory of all constraints of HdPs, ThPs and power system. The proposed method is compared to earlier methods and other implemented methods such as particle swarm optimization (PSO), constriction factor (CF) and inertia weight factor (IWF)-based PSO (FCIW-PSO), two time-varying acceleration coefficient (TTVACs)-based PSO (TVAC-PSO), salp swarm algorithm (SSA), and Harris hawk algorithm (HHA). By comparing EPGC from 100 trial runs, speed of search and simulation time, the suggested HPPSO method sees it is more robust than other ones. Thus, HPPSO is recommended for applying to the considered and other problems in power systems

Data-Intensive Computing in Smart Microgrids

Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area

AGC of a multi sources power system with natural choice of power plants

This paper presents an application of optimal control theory in multi sources power system by considering natural choice of power plants participating in automatic generation control (AGC) scheme. However, for successful operation of large power system, the natural choices of generation suitable for AGC system are hydro and thermal power plants since gas and nuclear power plants are rarely participates in the AGC scheme. Therefore, this work presents design and implementation of proportional integral (PI) structured optimal AGC controller in the presence of hydro and thermal power plants by using state vector feedback control theory. Moreover, various case studies are identified to obtain: (i) Cost aspects of physical realization of optimal AGC controller, (ii) Closed loop system stability margin through patterns of eigenvalues and (iii) System dynamic performance. Further, results have shown that when optimal AGC scheme is implemented in power system, the dynamic performance of power system is outstanding over those obtained with genetic algorithms (GAs) tuned PI structured AGC controller. Besides, with optimal AGC controller, cheaper cost of control structure, increased in system closed loop stability margin and outstanding dynamic performance of power system have been found when lessening in hydro generation is replaced by generation from thermal power plants for various case studies under investigation

Control of Thermal Power System Using Adaptive Fuzzy Logic Control

Controlling thermal power systems increases the overall system efficiency and satisfies the desired requirements. In such a large system, fuel reduction of even a small percentage leads to large energy saving. Hence, power systems are gaining significant attention from engineers and scientists. In this thesis, the uncontrolled power system for single area, two area, and three area is modelled using state space representation. Frequency deviation is simulated using MATLAB and SIMULINK. PID control is added to the system to analyze the effect of conventional control on system output response. Adaptive fuzzy logic control is added to the uncontrolled system using MATLAB Fuzzy Inference System and its effect on the system output response is measured in terms of overshoot/undershoot percentage, settling time, and steady state frequency error. Effect of adaptive fuzzy logic control is analyzed on single area, two area, and three area power system. Tie-line power exchange among areas is investigated before and after implementation of PID and adaptive fuzzy logic control. For the purpose of comparison in this thesis, a conventional PID control and an adaptive fuzzy logic control is applied to two different thermal power systems. The simulations demonstrate that adaptive fuzzy logic control is proved to be more efficient and reliable than conventional PID control in power system control problem

On the contribution of wind farms in automatic generation control: Review and new control approach

© 2018 by the authors. Wind farms can contribute to ancillary services to the power system, by advancing and adopting new control techniques in existing, and also in new, wind turbine generator systems. One of the most important aspects of ancillary service related to wind farms is frequency regulation, which is partitioned into inertial response, primary control, and supplementary control or automatic generation control (AGC). The contribution of wind farms for the first two is well addressed in literature; however, the AGC and its associated controls require more attention. In this paper, in the first step, the contribution of wind farms in supplementary/load frequency control of AGC is overviewed. As second step, a fractional order proportional-integral-differential (FOPID) controller is proposed to control the governor speed of wind turbine to contribute to the AGC. The performance of FOPID controller is compared with classic proportional-integral-differential (PID) controller, to demonstrate the efficacy of the proposed control method in the frequency regulation of a two-area power system. Furthermore, the effect of penetration level of wind farms on the load frequency control is analyzed