Continuous Supply Of Electricity Research Articles

Optimal multiobjective design of an autonomous hybrid renewable energy system in the Adrar Region, Algeria

Extended power outages are not only a nuisance but a critical problem in the modern world, which demands a continuous supply of decent quality electricity. Hybrid renewable energy systems (HRES) within a microgrid (MG) play an important role in delivering energy to rural and off-grid areas and avoiding potential power outages. This research describes an in-depth study of the three phases, design, optimization, and performance analysis of a stand-alone hybrid microgrid for a residential area in a remote area in the province of Adrar in southern Algeria. The system is composed of photovoltaic (PV) modules and a wind turbine, a set of batteries as an energy storage unit, a diesel generator as a backup energy source, and an inverter. This paper investigates four recent methodologies based on Multi-objective Particle Swarm Optimization (MOPSO), Multi-objective Ant Lion Optimizer (MOALO), Multi-objective Dragonfly Algorithm (MODA), and Multi-objective Evolutionary Algorithm (MOGA) to identify the optimal sizing of a microgrid (MG) integrated with hybrid renewable energy sources (RES). The proposed methods are carried out to select the optimal system size, which is a multi-objective problem involving the minimization of the annual cost of electricity (COE), and the loss of power supply probability (LPSP) simultaneously. To achieve this, the proposed methods are combined with energy management strategy (EMS) rules that coordinate energy flows between the various system components. The findings reveal that the MOPSO method has the most efficient hybrid renewable configuration with an annual generation cost of electricity (COE) of 0.2520 $/kWh and loss of power supply probability (LPSP) of 9.164%, which dominates the performance of MOALO (COE of 0.1625$/kWh and LPSP of 8.4872%), MOGA (COE of 0.1577$/kWh and LPSP of 10%), and MODA (COE of 0.02425$/kWh and LPSP of 7.8649%). Furthermore, a sensitivity analysis is performed for the effect that COE variants may have on the design variables.

The Eectricity Crisis and the Feasibility of Nuclear Energy in Egypt

The power outage crisis is increasing in Egypt, negatively affecting many other sectors such as industry, agriculture, and trade. In light of the lack of sufficient capacity to meet the growing needs of consumers, which has led to regular power shortages and power outages. While Egypt needs additional energy, the production of gas and oil needed to generate thermal energy decreased in 2014, and Egypt moved from an exporter of natural gas to a net importer. In addition to this, the volume of subsidies allocated to energy increased, the rise in global prices for oil and natural gas, and the need for huge investments to create Power generation plants, development of transportation networks, and distribution of electrical energy, which led to pressure on the general budget. A continuous and reliable supply of electricity is needed for Egypt's social and economic development. Accordingly, the government encouraged the development of new energy, invited the private sector to participate in energy generation, and a number of government reforms were made in order to enhance competition in the energy market, which required the need to study the possibility of providing it using nuclear energy. Its stations have a high load capacity, operate almost at full capacity all the time, are characterized by the stability of the electrical network and energy independence, and avoid the risks of fuel price shocks, and the costs of generating electricity from them decrease with increasing production. The importance of this research lies in highlighting the challenges facing the structure of the electricity sector in Egypt, and studying the advantages enjoyed by nuclear energy as the most important renewable energy sources in the world. Its importance also lies in comparing nuclear energy with other energy sources, and trying to benefit Egypt from obtaining this energy for use. In economic development projects, rationalizing tariffs and supporting their access to the poor. <i>This research is divided into</i>:<i>The first topic</i>: The electricity generation crisis in Egypt and a development vision for the contribution of nuclear energy to its solution, <i>The second topic</i>: trends in development planning for establishing nuclear plants in Egypt. The electricity generation crisis in Egypt and a development vision for the contribution of nuclear energy to its solution.

Importance of Nuclear Energy to Accomplish Net Zero Carbon

The world is entangled with the urgent need for existing energy modes transition to clean energy for mitigating global warming and climate change. It ultimately demands to set stringent goals for achieving net zero carbon for each country. In this direction different approaches are being stipulated and one of the important options emerges as nuclear power, a compelling option for sustainable development. Unlike renewable energy sources such as solar, wind and nuclear energy which provides a continuous and reliable supply of electricity with minimal greenhouse gas emissions. On the other hand nuclear power plants operate with high capacity factor compared to variable renewable sources that depend on weather conditions and its reliability makes nuclear energy a strong candidate for baseload power. Moreover, nuclear energy's low carbon footprint and high energy density output makes it an attractive alternative to fossil fuels. Although, it is crucial to acknowledge the challenges associated with nuclear energy, including the safe management of nuclear waste, the risk of nuclear accidents, and the high upfront costs of building nuclear power plants. Hence, a balanced perspective on the future of nuclear energy and its potential to contribute to a sustainable future is to be adopted carefully. However, a periodic advancements in technology and stringent safety measures, modern Nuclear reactors are designed to minimize environmental impact and enhance safety.

Efficiency evaluation of electricity demand prediction models in lower-income countries: a case study of panama

Abstract Electricity plays a crucial role in the development of countries, serving as the foundation for the functioning of modern societies and as a key indicator of a nation’s level of modernization and overall strength. Predicting a country’s electricity demand provides valuable insights into the evolving patterns of electrical energy provision and usage and helps to improve the flexibility of the energy distribution network. In addition, it provides early warnings and decision support for governments, businesses, and individuals about future electricity consumption. This plays a pivotal function in optimizing power distribution, adjusting the proportion of power generation sources, and ensuring a continuous and secure electricity supply. This paper uses the example of electricity demand in Panama and uses the LSTM model and Prophet model to compare their predictions for electricity demand in lower-income countries, with the aim of improving accuracy. Furthermore, it introduces a data set representing the electricity demand of higher-income countries, using the United Kingdom as a representative, to compare the forecast results between the two countries. The results indicate that within the Panama data set, the Prophet model exhibits better accuracy than the LSTM model. On the contrary, in the data set representing higher-income countries with the United Kingdom as a representative, the LSTM model outperforms the Prophet model. This leads to the conclusion that the selection of a predictive model is specific to each country, influenced by factors such as development status, geographic location, policies, energy markets, and more. A specific model may not be universally applicable due to these unique conditions, requiring tailored-modeling approaches for precise and efficient allocation and scheduling of electricity resources. This approach ensures the improvement of residents’ living standards and quality of life while meeting the typical demands of a country’s economic and social development.

Optimizing Concentrated Solar Power: High‐Temperature Molten Salt Thermal Energy Storage for Enhanced Efficiency

ABSTRACTMolten salts (MSs) thermal energy storage (TES) enables dispatchable solar energy in concentrated solar power (CSP) solar tower plants. CSP plants with TES can store excess thermal energy during periods of high solar radiation and release it when sunlight is unavailable, such as during cloudy periods or at night. This capability allows these plants to provide reliable, dispatchable power, ensuring a continuous electricity supply to the grid. This paper examines the challenges and opportunities of utilizing higher‐temperature molten salt formulations to enhance power cycle efficiency. Drawing on existing literature, performance analysis of existing power plants, and novel simulation results, we project the expected technological improvements by the end of this decade. By using 15 h of TES and a higher temperature MS formulation, with heat transfer fluid hot temperatures of 700°C, and a power cycle 350 bar 700°C of efficiency 48%, the annual electricity production from a 115 MW power plant in Daggett, California is 688 GWh, the total installed cost is $684 m while the 25‐year LCOE is 6.37 c/kWh.

Prototipe Sistem Monitoring, Proteksi, dan Kontrol Putaran pada Mesin Emergency Generator Menggunakan Lora untuk Mencegah Kerusakan pada Sistem di Kapal

In ship operations, the existence of a reliable emergency generator system is a critical factor to ensure continuity of electricity supply in emergency situations. Damage to the generator engine can cause significant disruption and even safety risks. To overcome this problem, a prototype monitoring, protection and rotation control system for emergency generator machines was developed using LoRa (Long Range) technology. This system is designed to monitor critical engine parameters, including speed, temperature and other operational conditions in real-time. Data collected from sensors is sent over a LoRa network that has wide coverage and low power consumption, enabling remote and continuous monitoring of Blynk's Internet of Think (IoT). This system is equipped with a protection algorithm that can detect anomalies and automatically activate corrective action or provide early warning to engineers and electricians. By implementing this prototype, it is hoped that it can reduce the risk of damage to the emergency generator engine and increase the reliability of the ship's electrical system, thereby supporting safer and more efficient operations.

A study of XLPE insulation failure in power cables under electromagnetic stress

Underground cables with cross-linked polyethylene (XLPE) insulation are integral to medium voltage (MV) power transmission systems, ensuring continuous electricity supply amidst operational challenges and environmental conditions. However, the reliability of these cables can be compromised over time due to aging and installation-related factors, particularly at joints and terminations. This study offers a comprehensive analysis of how various defects, including spherical air voids, pinholes, and irregularities in semiconducting layers, affect electric field and potential distributions within cable end terminations using COMSOL Multiphysics software. Through detailed simulations, the study identifies significant variations in electric field strength caused by these defects, highlighting critical stress concentration areas. In this study, it assumed that the cable and termination have the same cross-section. By analyzing these simulations, the study provides insights into optimizing cable design and installation practices to enhance the reliability and lifespan of underground power transmission systems. This study introduces a novel approach by combining advanced COMSOL Multiphysics simulations with a detailed analysis of defect impacts on electric field distributions, offering new insights into stress concentrations and degradation at cable terminations. Simulation outcomes reveal significant variations in electric field strengths due to air voids and pinholes in cables and terminations: for 2 mm voids, up to 2.45×106 V mm−1 near the conductor and 56.5 V mm−1 at termination. These findings enhance the understanding of XLPE-insulated cable behavior under electromagnetic stress, providing a basis for mitigating failures and improving overall system performance.

АНАЛІЗ ПОКАЗНИКІВ ЯКОСТІ ПОСЛУГ ПОСТАЧАННЯ ЕЛЕКТРИЧНОЇ ЕНЕРГІЇ

The object of the study is the processes in distribution electric networks and their influence on technical and economic characteristics aimed at achieving the main indicators of reliability, which is characterized by the continuity of electricity supply, namely, indicators of the quality of services: the index of the average duration of long interruptions and the index of the average frequency of long interruptions ( SAIDI, SAIFI), as well as compliance with the guaranteed quality standards of electric energy supply services, which are established by the Resolution of the NCRECP. The problem to be solved is a set of factors aimed at reducing the index of the average duration of long interruptions and the index of the average frequency of long interruptions (SAIDI, SAIFI), taking into account and applying effective measures of stimulating tariff formation (Rab - tariff) aimed at providing quality services to consumers of electric energy in accordance with the guaranteed standards established by the Resolution of the NCRECP. The analysis of the main indicators of the quality of SAIDI and SAIFI services for the period from 01.01.2022 to 03.31.2022 was carried out on the example of the operator of the distribution system JSC "Kharkivoblenergo". The impact of natural and unnatural factors on the formation of these indicators is analyzed, taking into account force majeure circumstances and military aggression on the territory of the Kharkiv region. The changes in the quality indicators of SAIDI and SAIFI services and their compliance with the normative values established by the NCRECP were analyzed. The conducted analysis of SAIDI and SAIFI service quality indicators allows us to state that achieving the target values of electricity supply continuity indicators is a fundamental factor for improving the quality of electricity supply services to consumers. The provision of the necessary guaranteed quality standards for the supply of electric energy to consumers is substantiated taking into account the stimulating tariff formation (Rab - tariff). It is shown that the introduction and development of stimulating tariff formation lays a reliable foundation for a strong and competitive Ukraine on the territory of the European Union and introduces dynamic development for a strong and independent Ukrainian economy. Ref. 19, figure, table.

A Predictive Model Using Long Short-Time Memory (LSTM) Technique for Power System Voltage Stability

The stability of the operation of the power system is essential to ensure a continuous supply of electricity to meet the load of the system. In the operational process, voltage stability (VS) should be recognized and predicted as a basic requirement. In electrical systems, deep learning and machine learning algorithms have found widespread applications. These algorithms can learn from previous data to detect and predict future scenarios of potential instability. This study introduces long short-term memory (LSTM) technology to predict the stability of the nominal voltage of the power system. Based on the results, the recommended LSTM technology achieved the highest accuracy target of 99.5%. In addition, the LSTM model outperforms other machine learning (ML) and deep learning techniques, i.e., support vector machines (SVMs), Naive Bayes (NB), and convolutional neural networks (CNNs), when comparing the accuracy of the VS forecast. The results show that the LSTM method is useful to predict the voltage of an electrical system. The IEEE 33-bus system indicates that the recommended approach can rapidly and precisely verify the system stability category. Furthermore, the proposed method outperforms conventional assessment methods that rely on shallow learning.

Assessment of techno-economic analyzes of grid-connected nuclear and PV/wind/battery/hydrogen renewable hybrid system for sustainable and clean energy production in Mersin-Türkiye

The main novelty in the presented paper is that it presents an energy analysis for a hybrid system that integrates nuclear power plants with wind/solar power plants for sustainable and clean energy production. In addition, excess energy is used to produce hydrogen. A techno-economic feasibility assessment is performed to ensure continuous electricity supply for hourly and peak residential load requirements in Mersin located in Türkiye. In the first stage of the study, suitable areas and sizes for solar and wind energy for energy production were determined with ArcGIS and Multi-Criteria Decision-Making methodologies. In the second stage, six scenarios were investigated in terms of electricity production, and excess energy was used for hydrogen production. The results brought out that more electricity was produced than the city's electricity consumption (5395 GWh) in all 6 scenarios considered. The maximum electricity production at a rate of 11900 GWh/yr and the maximum hydrogen at a rate of 132675 tons was produced in Scenario 4 with the cost of energy (0.31 $/kWh) and cost of hydrogen (12.6$/kgH2). In this scenario, the percentages of solar, wind and nuclear energy in total production 1 %, 12 %, and 87 %, respectively. According to the results, it was concluded that 236621 tons of carbon emissions were reduced when the city's energy needs were met according to the 6 scenarios in this study.

Machine learning‐assisted anomaly detection for power line components: A case study in Pakistan

AbstractA continuous supply of electricity is necessary to maintain an acceptable standard of life, and the power distribution system's overhead line components play a crucial role in this matter. In Pakistan, identifying defective parts often necessitates human involvement. An unmanned aerial vehicle was used to gather a collection of 10,343 photos to automate this procedure. Using supervised and unsupervised machine learning methods, a number of automated anomaly detection systems were created. Support vector machine, random forest, VGG16, and ResNet50 were used as supervised machine learning models, and a convolutional auto‐encoder was used as the unsupervised machine learning model. VGG16 achieved the best accuracy of 99.00% while random forest achieved the worst accuracy of 72.49%. The convolutional auto‐encoder was successful in distinguishing between normal and abnormal components. The aforementioned machine learning models can be put on unmanned aerial vehicles to immediately identify defective parts.

A H2O2 Self‐Charging Zinc Battery with Ultrafast Power Generation and Storage

AbstractSelf‐charging power systems are considered as promising alternatives for off‐grid energy devices to provide sustained electricity supply. However, the conventional self‐charging systems are severely restricted by the energy availability and time‐consuming charging process as well as insufficient capacity. Herein, we developed an ultrafast H2O2 self‐charging aqueous Zn/NaFeFe(CN)6 battery, which simultaneously integrates the H2O2 power generation and energy storage into a battery configuration. In such battery, the chemical energy conversion of H2O2 can generate electrical energy to self‐charge the battery to 1.7 V through the redox reaction between H2O2 and NaFeFe(CN)6 cathode. The thermodynamically and kinetically favorable redox reaction contributes to the ultrafast H2O2 self‐charging rate and the extremely short self‐charging time within 60 seconds. Moreover, the rapid H2O2 power generation can promptly compensate the energy consumption of battery to provide continuous electricity supply. Impressively, this self‐charging battery shows excellent scalability of device architecture and can be designed to a H2O2 single‐flow battery of 7.06 Ah to extend the long‐term energy supply. This work not only provides a route to design self‐charging batteries with fast charging rate and high capacity, but also pushes forward the development of self‐charging power systems for advanced large‐scale energy storage applications.

A H2O2 Self-Charging Zinc Battery with Ultrafast Power Generation and Storage.

Self-charging power systems are considered as promising alternatives for off-grid energy devices to provide sustained electricity supply. However, the conventional self-charging systems are severely restricted by the energy availability and time-consuming charging process as well as insufficient capacity. Herein, we developed an ultrafast H2O2 self-charging aqueous Zn/NaFeFe(CN)6 battery, which simultaneously integrates the H2O2 power generation and energy storage into a battery configuration. In such battery, the chemical energy conversion of H2O2 can generate electrical energy to self-charge the battery to 1.7 V through the redox reaction between H2O2 and NaFeFe(CN)6 cathode. The thermodynamically and kinetically favorable redox reaction contributes to the ultrafast H2O2 self-charging rate and the extremely short self-charging time within 60 seconds. Moreover, the rapid H2O2 power generation can promptly compensate the energy consumption of battery to provide continuous electricity supply. Impressively, this self-charging battery shows excellent scalability of device architecture and can be designed to a H2O2 single-flow battery of 7.06 Ah to extend the long-term energy supply. This work not only provides a route to design self-charging batteries with fast charging rate and high capacity, but also pushes forward the development of self-charging power systems for advanced large-scale energy storage applications.

Quantum support vector machine for forecasting house energy consumption: a comparative study with deep learning models

The Smart Grid operates autonomously, facilitating the smooth integration of diverse power generation sources into the grid, thereby ensuring a continuous, reliable, and high-quality supply of electricity to end users. One key focus within the realm of smart grid applications is the Home Energy Management System (HEMS), which holds significant importance given the fluctuating availability of generation and the dynamic nature of loading conditions. This paper presents an overview of HEMS and the methodologies utilized for load forecasting. It introduces a novel approach employing Quantum Support Vector Machine (QSVM) for predicting periodic power consumption, leveraging the AMPD2 dataset. In the establishment of a microgrid, various factors such as energy consumption patterns of household appliances, solar irradiance, and overall load are taken into account in dataset creation. In the realm of load forecasting in Home Energy Management Systems (HEMS), the Quantum Support Vector Machine (QSVM) stands out from other methods due to its unique approach and capabilities. Unlike traditional forecasting methods, QSVM leverages quantum computing principles to handle complex and nonlinear electricity consumption patterns. QSVM demonstrates superior accuracy by effectively capturing intricate relationships within the data, leading to more precise predictions. Its ability to adapt to diverse datasets and produce significantly low error values, such as RMSE and MAE, showcases its efficiency in forecasting electricity load consumption in smart grids. Moreover, the QSVM model’s exceptional flexibility and performance, as evidenced by achieving an accuracy of 97.3% on challenging datasets like AMpds2, highlight its distinctive edge over conventional forecasting techniques, making it a promising solution for enhancing forecasting accuracy in HEMS.The article provides a brief summary of HEMS and load forecasting techniques, demonstrating and comparing them with deep learning models to showcase the efficacy of the proposed algorithms.

Using genetic algorithm for optimal sizing of stand-alone hybrid energy system

When planning a hybrid energy system (HES) that incorporates both renewable and non-renewable energy sources—those that rely on fossil fuels—the primary considerations are the total cost of the system and the CO? emissions. In this paper, we will investigate the typical hybrid energy system (HES) that incorporates both renewable and non-renewable energy sources involving a detailed simulation process that may require specific inputs, models, and data. Then, we employed dual optimization methods: genetic algorithm (GA) and particle swarm optimization (PSO). The consequences of GA and PSO execution in the bus timetabling problem depict that the GA algorithm is better at finding the optimal solution in terms of accuracy and iteration. Additionally, the GA algorithm is also superior to the straightforwardness of the techniques used. So, in this work, we employed a Genetic Algorithm Optimization (GA)–-based optimal sizing technique for HES configurations that include sustainability wind turbines (WTs), battery storage (BS), and diesel generators (DGs). HES improved power delivery to a rural community in the Wasit Province, Iraq, situated at 46° - 36° and 32° - 31° in the country's southeastern central region. Throughout the project's 25-year lifespan, the optimization primarily aims to minimize the total cost (CT) and total CO? emissions (ECO2T). The outcomes demonstrate that the GA algorithm may, with continuous electricity supply, minimize the objectives while meeting the load demand.

RANCANG BANGUN PROTOTIPE SISTEM ATS-AMF BERBASIS MIKROKONTROLER DI PERUMDA AIR MINUM TIRTA SANJIWANI UNIT PRODUKSI BLANGSINGA

The continuity of electricity supply from generators to consumers cannot always be guaranteed due to various factors such as planned outages, distribution failures, and the impacts of natural disasters. The Regional Public Water Company (Perumda) Tirta Sanjiwani has adopted generators as Backup Power Sources to ensure the availability of water supply to consumers. However, the Blangsinga Production Unit of Perumda Tirta Sanjiwani faces challenges in switching electricity supply between PLN and the generator set (genset), especially in operations requiring uninterrupted continuity. To overcome the instability of the electricity supply from PLN, the use of automatic systems such as ATS (Automatic Transfer Switch) and AMF (Automatic Main Failure) is necessary. The function of ATS is to switch from PLN to the backup power source (genset) when there is a disturbance or outage in PLN. AMF is responsible for detecting failures in PLN and initiating the genset as well as transferring the load to the backup power source when there is a failure in the main power supply. The ATS-AMF system prototype is designed using voltage sensors, frequency sensors, current sensors, and relays to ensure optimal performance and responsiveness to environmental conditions and power supply conditions.

Integration of dispersed renewable energy generating in power distribution network

As traditional sources of energy recede and demand for energy rises at an unprecedented pace, it is difficult for the available sources to satisfy demands. As a result, this research focuses on combining conventional generation with distributed generation, each of which can be used independently. For both industrial and residential electricity suppliers, providing a secure, continuous, and safe electrical supply is a critical necessity. As we progress toward a more sustainable approach to generating electricity (both renewable and nuclear) from the currently prevalent non-renewable sources, a hybrid supply system is an intermediate phase. This project aims to simulate a three-phase power plant that incorporates a conventional steam turbine and a DC-AC inverter generation device powered by solar to produce a total of 100kW of power with a power factor of 1. The input torque to the synchronous generator and the DC voltage to the distributed system vary depending on the output power potential of each system. Both devices achieve a constant voltage at the load. When the two systems work independently, the voltages produced by both are in phase and have the same amplitude.

Preventive framework for resilience enhancement in networked microgrids: A focus on hydrogen integration and optimal energy management

AbstractThe integration of distributed energy resources and smart grid technologies has increased the vulnerability and complexity of modern power systems, especially in the face of extreme events. Enhancing the resilience of power systems is crucial to ensure continuous and reliable electricity supply during and after such events. This paper proposes a preventive framework to enhance the resilience of networked microgrids (NMGs) during extreme events. The framework integrates optimal energy management strategies and preventive resilience enhancement measures, focusing on the coordination and utilization of hydrogen (H2) energy systems and controllable distributed generators (CDGs). The framework leverages capacity‐based signals to prompt H2 system owners and CDG operators to prepare for disruptions by fully charging H2 storage tanks and pre‐scheduling CDG commitments. By utilizing this proactive energy scheduling, critical loads can be supplied through stationary fuel cells and CDGs, improving overall network resilience. The proposed resilient energy management approach combines optimization techniques to improve the resilience of NMGs and reduce their dependence on the main grid. Numerical simulations demonstrate the effectiveness of the proposed linear model in enhancing the resilience of NMGs and improving their operational performance during extreme events. The contributions of this paper include advancements in understanding NMGs, the development of a preventive framework, integration of H2 energy systems and CDGs, and proposal of effective signalling models.

Development of an intelligent solution for the optimization of a hybrid system using renewable energy sources

Renewable energy technologies offer the promise of clean and abundant energy harvested from natural resources, self-renewable sources such as sun, wind, water, earth, and plants. In this work, we optimize the hybrid system using Homer power program, where the hybrid system is composed by solar panels, wind turbines with batteries to supply 20 homes that are not equipped with electricity in Ouzera area (Medea, Algeria), and by taking the results presented by the Homer program for Ouzera region, we obtained the cost of each day, of each season and the cost of energy ($/kWh), as well as the optimal number and characteristics for each solar panels and wind turbines with storage batteries. The homer software allows us to obtain real results in taking into account the constraints cost and variations in off-grid weather data. The most important criterion of this technique for optimizing renewable energy systems was the cost, seeking to minimize the expenses, while ensuring optimum quality and continuity of electricity supply.

Applications of Artificial Intelligence in Power Systems

A continuous and reliable supply of electricity is necessary for the functioning of today’s modern and advanced society. Since the early to mid 1980s, most of the effort in power systems analysis has turned away from the methodology of formal mathematical modeling which came from the areas of operations research, control theory and numerical analysis to the less rigorous and less tedious techniques of artificial intelligence (AI). Power systems keep on increasing on the basis of geographical regions, assets additions, and introduction of new technologies in generation, transmission and distribution of electricity. AI techniques have become popular for solving different problems in power systems like control, planning, scheduling, forecast, etc. These techniques can deal with difficult tasks faced by applications in modern large power systems with even more interconnections installed to meet increasing load demand. The application of these techniques has been successful in many areas of power system engineering.