Optimal Operational Scheduling of Renewable Energy Sources Using Teaching–Learning Based Optimization Algorithm by Virtual Power Plant

The necessity for Renewable Energy Resources (RES) has become a major issue due to the shortage of adequate fossil fuels recently. In addition to fossil fuel shortages, global warming is also an anxiety for a lot of countries and companies. These problems have led to the addition of a large number of renewable energy sources to modern distribution systems. However, due to the high integration of RES to these systems and the intermittent characteristic of sources such as wind and solar, it causes variable production and uncertainty in the power system. It is proposed as a solution to gather these resources together in order to solve the problems caused by the changeable outputs of these resources. Virtual Power Plant (VPP) is described as the management of distributed generation facilities, energy storage systems and controllable loads by an Energy Management System (EMS). In this study, the VPP model consists of renewable energy sources such as wind power plant (WPP), solar power plant (SPP), biogas power plant (BPP) and pumped hydro storage plant (PHSP) as an energy storage system (ESS). The aim of the VPP is to maximize the net daily profit in a 24 h time interval. The proposed mixed integer linear programming (MILP) optimization problem is modeled in GAMS software and solved using CPLEX.

Hippopotamus algorithm for optimal sizing of hybrid renewable energy system

Teaching learning-based optimization is one of the widely accepted metaheuristic algorithms inspired by teaching and learning within classrooms. It has successfully addressed several real-world optimization problems, but it may still be trapped in local optima and may suffer from the problem of premature convergence in the case of solving some challenging optimization problems. To overcome these drawbacks and to achieve an appropriate percentage of exploitation and exploration, this study presents a new modified teaching learning-based optimization algorithm called the fuzzy adaptive teaching learning-based optimization algorithm. The proposed fuzzy adaptive teaching learning-based optimization algorithm uses three measures from the search space, namely, quality measure, diversification measure, and intensification measure. As the 50-50 probabilities for exploitation and exploration in the basic teaching learning-based optimization algorithm may be counterproductive, the Mamdani-type fuzzy inference system of the new algorithm takes these measures as a crisp inputs and generates selection as crisp output to choose either exploitation or exploration based on the current search requirement. This fuzzy-based adaptive selection helps to adequately balance global search or exploration and local search or exploitation operations during the search process as these operations are intrinsically dynamic. The performance of the fuzzy adaptive teaching learning-based optimization is evaluated against other metaheuristic algorithms including basic teaching learning-based optimization on 23 unconstrained global test functions. Moreover, adaptive teaching learning-based optimization is used to search for near-optimal values for the four parameters of the COCOMO II model, which are then tested for validity on a software project of NASA. Analysis and comparison of the obtained results indicate the efficiency and competitiveness of the proposed algorithm in addressing unconstrained continuous optimization tasks.

Teaching Learning Based Optimization (TLBO) algorithm simulate the teaching learning peculiarity of a classroom to solve multidimensional, linear and nonlinear problems with appreciable efficiency. In order to accelerate the execution time of software implementation, the TLBO algorithm is implemented on hardware. Then the TLBO hardware is developed as TLBO Intellectual Property and it is interfaced as a peripheral to the System on Chip platform. We compared the performance of the floating point TLBO IP is realized by solving the benchmark functions, results 183–224X times faster than the software implementation of the same algorithm. As a case study, the same TLBO IP is used to solve the spectrum allocation problem by optimizing Max-Sum-Reward (MSR) function and it results 69–78X times faster than the software implementation of the same algorithm.

The paper presents an application of Teaching Learning-Based Optimization (TLBO) algorithm to improve the performance of self-excited induction generators (SEIG).Two control methods of SEIG have been studied.The first method, the TLBO algorithm is applied to generate the optimal capacitance to maintain rated voltage with constant prime mover speed.The drawback of this method is the generator frequency decreases with load and to overcome this disadvantage, the other control method is proposed.In the proposed method, the TLBO is used to obtain optimal capacitance and prime mover speed to have rated load voltage and frequency.The Static VAR Compensator (SVC) of fixed capacitor and controlled reactor is used to control the reactive power.The parameters of SVC are obtained by using TLBO algorithm.The performance of the SEIG at different loads and prime mover speeds using TLBO algorithm is realized.A whole system of three phase induction generator and SVC is established under MatLab/Simulink environment.The performance of the SEIG is demonstrated on two different ratings (i.e. 10 hp and 2hp).An experimental setup is built-up using a 2 hp induction motor to confirm the theoretical analysis.Good agreement between results confirms and signifies the viability of the proposed TLBO-based methodology. ‫الع‬ ‫الولخص‬ ‫شبى‬‫ٔانرعهى‬ ‫انرعهيى‬ ‫خٕاسصييّ‬ ‫ذطثيق‬ ‫انثحث‬ ‫يقذو‬ ( ‫اساط‬ ‫عهى‬ ‫االيثم‬ TLBO ) ‫اداء‬ ‫نرحسيٍ‬ ‫انًٕنذ‬ ‫انحثى‬ ‫راذى‬ ‫ٔقذ‬ ‫انرغزيح.‬‫ان‬ ‫انحثى‬ ‫انًٕنذ‬ ‫اداء‬ ‫خٕاص‬ ‫فى‬ ‫نهرحكى‬ ‫طشيقريٍ‬ ‫دساسح‬ ‫ذًد‬ ً ‫عضٔل.‬‫تاسرخذاو‬ ‫انًثهى‬ ‫انًكثف‬ ‫قيى‬ ‫ذحذيذ‬ ‫فيٓا‬ ‫يرى‬ ‫االٔنى‬ ‫انطشيقح‬ ‫خٕاسصييّ‬ ( TLBO ) ‫خٓذ‬ ‫نرثثيد‬ ‫االحًال‬ ‫كم‬ ‫عُذ‬ ‫انًٕنذ‬ ‫عُذ‬ ‫انًٕنذ‬ ‫ذشدد‬ ‫ذغيش‬ ‫فى‬ ‫انطشيقّ‬ ‫ْزِ‬ ‫عية‬ ‫ٔيرًثم‬ ‫انًٕنذ.‬‫سشعّ‬ ‫ثثاخ‬ ‫تاسرخذاو‬ ‫انًٕنذ‬ ‫ٔسشعّ‬ ‫انًكثف‬ ‫يٍ‬ ‫نكال‬ ‫انًثهى‬ ‫انقيى‬ ‫ذعييٍ‬ ‫ذى‬ ‫حيث‬ ‫انثاَيّ‬ ‫انطشيقّ‬ ‫اقرشاح‬ ‫ذى‬ ‫انعية‬ ‫ْزا‬ ‫ٔنرالفى‬ ‫االحًال.‬‫ذغيش‬ ‫يع‬ ‫خٕاسصييّ‬ ( TLBO ) ‫عُذ‬ ‫انًٕنذ‬ ‫ٔذشدد‬ ‫خٓذ‬ ‫نرثثيد‬ ‫انساكُّ‬ ‫انفعانح‬ ‫انغيش‬ ‫انقذسج‬ ‫يعٕضاخ‬ ‫اسرخذاو‬ ‫ذى‬ ‫انثحث‬ ‫فى‬ ‫االحًال.

An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems

Integrating non-dispatchable renewable energy sources (RES) such as wave farms into electric power systems presents inherent challenges. Specifically, the variability of the resource and its non-dispatchable) nature introduces power imbalances and power system stability problems in the system over both short and medium terms. This issue is particularly relevant for isolated power systems, such as those found on several European islands, where interconnection with the mainland is lacking. Therefore, although wave energy has significant potential for integration into island power systems due to resource availability, determining the maximum RES penetration that such systems can sustain while maintaining stability is not straightforward. Power system stability is closely tied to frequency. A well-established solution for improving the stability of isolated systems when integrating non-dispatchable RES is to install devices capable of responding to over- or under-frequency events, such as energy storage systems (ESS). The literature extensively discusses ESS control techniques for frequency regulation, with one of the most straightforward approaches being to operate the ESS according to a droop control strategy (similar to a statism). In this method, the power dispatched or stored by the ESS increases proportionally to the deviation from the frequency reference value. This paper presents a case study analyzing the frequency stability of an island power system equipped with an energy storage system (ESS) when integrating a wave farm. Frequency stability is evaluated based on two variables: wave energy penetration and the ESS droop control parameter. The case study is based on the island of El Hierro (Canary Islands, Spain), an isolated electrical system with a very high penetration of RES. The island’s electrical generation system consists of a wind farm, a pumped-storage hydroelectric power plant, and conventional generation from a diesel power plant. The analysis is conducted through simulations in MATLAB Simulink. A wave farm is modeled to evaluate the generated power and its associated oscillations, based on the wave energy resource at various locations along El Hierro’s coast. Additionally, an aggregated inertial dynamic model of the electrical power system is employed to assess the impact of the wave energy farm's generation on the system’s frequency and the aging that the frequency excursions introduce in the elements of the system. The Spanish Grid Code is considered to ensure compliance with frequency regulation mechanisms in isolated systems. Simulations are performed across different scenarios, varying both the nominal power of the wave farm and the ESS droop control parameter, in order to evaluate the ESS capacity. The results identify the optimal droop control parameter for each wave farm size to ensure frequency remains within acceptable thresholds. Furthermore, the minimum ESS capacity required for each wave farm size is determined from the simulations. These findings provide a basis for ESS and wave farm dimensioning, which could be translated into economic terms to maximize revenue (this aspect is beyond the scope of the present work).

Active Distribution Networks (ADNs) are modern and intelligent power distribution systems that integrate renewable energy sources and advanced control technologies to provide reliable, secure, and efficient electricity supply. The traditional power distribution networks are designed to deliver electricity in a one-way flow from the transmission system to the customers. In contrast, ADN enables two-way power flow between the distribution network and customers, allowing them to become active participants in the electricity system. The key technology behind ADN includes advanced metering infrastructure (AMI), Distribution Automation (DA), and energy storage systems (ESS). AMI provides real-time data on electricity consumption, generation, and distribution, enabling efficient load management and demand response. Other key technologies of ADN include renewable energy sources such as solar photovoltaic (PV), Wind turbines and small-scale hydroelectricity. These sources are connected to the distribution network through power electronic interfaces that convert the DC output of the renewable sources into AC for distribution. Smart grid technologies such as microgrids, virtual power plants, and energy management systems are also integrated into ADN to improve grid stability and resilience. ADNs have the potential to transform the electricity system by enabling more efficient, sustainable, and reliable energy systems that can better serve the needs of consumers and society. Thus, ADN is expected to become a critical technology in achieving a cleaner and more sustainable energy future.

In the last decade in Europe, the power generated by Renewable Energy Sources (RES) has significantly increased. For the electric power system, the integration of a high amount of power generated by RES represents a big challenge. Energy Storage Systems (ESS) may help to solve problems related to the grid integration of RES. In this study, an analysis of the benefits that a planned and dedicated Adiabatic Compressed Air Energy Storage (A‐CAES) system may provide to a distribution network with a high penetration of RES will be carried out. The A‐CAES system, taken into account in this investigation, is based on the proposed German study so‐called “ADELE project”. The technical benefits as well as the corresponding costs that A‐CAES brings to the system which were analyzed using the load and RES generation time series for 2010 and 2020, and were based on technical data from a selected part of a German distribution power network, were evaluated and results of those broad investigations are presented in this study. The result of the investigations shows that the direct technical benefits are quite limited for the A‐CAES technology as it exists today.

Among the various diseases that threaten human life is heart disease. This disease is considered to be one of the leading causes of death in the world. Actually, the medical diagnosis of heart disease is a complex task and must be made in an accurate manner. Therefore, a software has been developed based on advanced computer technologies to assist doctors in the diagnostic process. This paper intends to use the hybrid teaching learning based optimization (TLBO) algorithm and fuzzy wavelet neural network (FWNN) for heart disease diagnosis. The TLBO algorithm is applied to enhance performance of the FWNN. The hybrid TLBO algorithm with FWNN is used to classify the Cleveland heart disease dataset obtained from the University of California at Irvine (UCI) machine learning repository. The performance of the proposed method (TLBO_FWNN) is estimated using K-fold cross validation based on mean square error (MSE), classification accuracy, and the execution time. The experimental results show that TLBO_FWNN has an effective performance for diagnosing heart disease with 90.29% accuracy and superior performance compared to other methods in the literature.

In the event of a fault isolation process, all loads located downstream from the faulted point become out of service, and as a consequence, the power interruption affects a greater portion of the radial distribution system. This paper proposes an optimal Service Restoration (SR) method that entails changing the network topology configuration via optimal tie-switch and section switch combinations. However, when the network topology configuration is performed, it results in increased load currents. As a result, some Protective Devices (PDs) can operate undesirably and some network branches may become unprotected. Therefore, it is essential to consider protection constraints in the SR problem to maintain service continuity during power interruptions. The proposed method aims at optimal SR with minimum out-of-service loads, minimum power loss, and improved voltage profiles and at the same time ensures PDs operate correctly during the normal and overloading conditions. The proposed method was carried out on the Debre Markos distribution networks, using the Teaching Learning Based Optimization (TLBO), Particle Swarm Optimization (PSO), and Differential Evolutionary (DEV) algorithms. The proposed SR was carried out considering and without considering protection constraints. The obtained SR topology was not feasible for SR without considering protection constraints, since some PDs fail to operate properly in normal loading conditions. After executing the proposed SR algorithms by considering protection constraints for a single fault case, the power loss reductions in TLBO, DEV, and PSO were 64.9073%, 45.9073%, and 55.358 %, respectively. The minimum voltage profiles obtained in each proposed TLBO, DEV, and PSO algorithm were 0.96%, 0.95%, and 0.96%, respectively. In each algorithm, except for the branch under fault, all healthy out-of-service branches were restored. When the protection constraints were considered in an optimal SR, load current did not exceed the rating of the fuses. The results show the importance of considering protection constraints during SR to prevent dysfunction of the PDs in the network. Comparative analyses were carried out on each algorithm and TLBO algorithms performed better than PSO and DEV for search functions.

A Solid-State Transformer as Enabling Technology for Microgrids for Integration into the Power Distribution System: An Overview

An improved teaching–learning-based optimization algorithm with a modified learner phase and a new mutation-restarting phase

Automatic generation control of multi-area power systems with diverse energy sources using Teaching Learning Based Optimization algorithm

Image segmentation has been used widely for detection and extraction of objects in digital images. Thresholding is one of the effective image segmentation techniques. Fuzzy 2-partition entropy is used effectively for the selection of threshold value. Fuzzy 2-partition entropy required the optimization of some parameters for the selection of threshold value. Teaching-Learning-Based Optimization (TLBO) algorithm has been applied on fuzzy 2-partition entropy to find these parameters and subsequent threshold value. Normally, fuzzy 2-partition Shannon entropy is used for thresholding. In the proposed research work, fuzzy 2-partition Kapur entropy is explored for the evaluation of its potential for selecting optimal threshold value using TLBO algorithm. The present work measures the performance comparison of fuzzy 2-partition Kapur entropy using TLBO with fuzzy 2-partition Shannon entropy using TLBO. The experiments are carried out on standard test images from benchmark dataset. Peak signal to noise ratio, uniformity and structural similarity index are the three different measures which have been used to compare the performance. Results demonstrate that the performance of fuzzy 2-partition Kapur entropy using TLBO is quite promising.