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An intelligent load-shedding system (ILSS) was designed and implemented in a paper mill to ensure power system stability after the steam turbine generator transitions from back-pressure operating mode to isochronous mode upon loss of the electric grid. The innovative approach taken to validate and optimize the operation of the system in a closed-loop environment by using dynamic simulation is presented. Modeling of the double-extraction steam turbine representing real-time processes was performed. The purposes for implementation of the ILSS were to maintain safe operating conditions and increase the reliability of the mill while minimizing operating costs and capital losses during unplanned events.

The authors examine the electrical energy usage in the pulp and paper industry for the continental United States. It is noted that the broad picture of energy required and fuel sources used gives a clear indication of a movement away from oil as an energy source. Internally generated fuel sources of spent liquor and hogged fuel have increased. The steam generated from boilers has increased internally generated electricity. It is pointed out that technologies such as high-yield pulping and impulse drying require less chemicals and reduced thermal requirements. The advanced thermal analysis technique of pinch technology focuses on optimum use of thermal requirements. It is concluded that all of these technologies point the way to more efficient thermal usage and increased demand for electricity. >

The ground fault is the most common short circuit on an electrical power system. The stability of a power system and its ability to recover from a ground fault is directly related to the neutral grounding method used. A summary is given of the common grounding practices used in paper mills for low and medium voltage systems and the advantages of resistance grounding are described.

This paper describes the strategy of a daily optimal operational schedule for cogeneration systems operating in a paper mill. The cogeneration systems consist of three generators, the several auxiliary devices which include three auxiliary boilers, two waste boilers and three sludge incinerators. One unit generates the electrical and the thermal energy using a back pressure turbine. The other two units generate both of these types of energy using an extraction condensing turbine. Auxiliary devices operate to supplement insufficient thermal energy to the thermal load with three units. The cogeneration systems have a large capacity which is able to supply enough thermal energy to the thermal load, however the electric power generated is insufficient to satisfy the electrical load. Therefore the insufficient electrical energy is supplemented by electrical energy buying from the utility. Simulation for operational schedule was carried out using Evolutionary Programming (EP). The operational cost shows that efficiency is improved by 10% compared with the past. The result reveals that the proposed modeling and the scheduling strategy can be effectively applied to cogeneration systems of another paper mills.

This review paper summarizes state-of-the-art energy management methods applied to electrical systems of large aircraft. An electrical load management based on fixed priorities of the loads is considered a conventional implementation as applied in today’s aircraft systems. It can cut and reconnect loads depending on their importance. The advantages and disadvantages of such a system are presented. Further implementations are depicted that are able to eliminate certain drawbacks of such a typical load management. Most promising is the exploitation of so-called slow responding loads which can be handled like an electrical storage. The optimization potential on future energy management functions is finally discussed and conclusions are drawn.

PT. Perusahaan Listrik Negara (PLN) Indonesia has a responsibility to provide and ensures the continuity of electricity on safety and reliability for all areas in Indonesia. An accurate electrical load forecasting is fundamental to deliver efficient energy scheduling management. This paper proposes a prominent technique called long-short term memory (LSTM) for short-term load forecasting. The load data for this research work is taken by each of 30 minutes in a day for each four weeks earlier in the past to predict the following week's load. To examine the efficacy of the proposed method, the Sulawesi and Java-Bali Sub System Load are utilized in this paper. The simulation result shows that the LSTM with 1-input has similar performance to ANN-PSO in MAPE value and slightly superior performance in RMSE at Java Bali Sub System Load. At Sulawesi Sub System Load, it is shown that LSTM with 1-input is superior to all performance parameters compared to 3-input LSTM, have a little lower performance to Java Bali prediction about 1.03% on MAPE value, but perform better performance up to 45.08 on RSME value. Although the performance of LSTM is different in each case, the simulation result shown the capability of LSTM as an additional reference in performing short-term load forecasting on the electricity system.

This paper proposes an advanced coordination of virtual inertia control (VIC), load frequency control (LFC), and automatic voltage regulator (AVR) to overcome the problem caused by high wind power plant plant integration. In this study, an energy storage system (ESS) is utilized as VIC. In this paper single machine infinite bus with AVR and LFC is used as the test system. To achieve the best performance of control scheme for frequency oscillation damping, the parameters of VIC, LFC, and AVR are optimized by sine cosine algorithm (SCA). Three load demand scenarios as of 100 MW, 200MW, and 150 MW are applied to the power system model with 1000 MVA of electric generator rating value. Moreover, wind power plant plant capacity is 40% of electric load peak that is allowed in injecting its power to the power system grid. Simulation results showed that the proposed method could decrease the overshoot and compress the settling time better than other control schemes utilized in this paper.

Short-term predictive analysis on electrical load is of utmost importance to the utility company. Load forecasting plays a key role in effective energy planning as well as managing finances. Although machine learning models are already tested for predictive analysis, this paper has employed a newly developed forecasting model known as ‘Prophet’ developed by the IT major Facebook Inc. Owing to lack of research using this model, this paper predicts electrical load consumption based on several factors viz. Time, Temperature, Humidity, and Weather forecast. This model gives an approximate assumption and thus used for predictive modeling.

The purpose of this paper is to solve the problem that the loading accuracy is affected by the surplus torque of electrical load simulator. The structure and working principle of electrical load simulator are introduced in this paper. Then a mathematical model of the electrical load simulator is established, and the cause of surplus torque is analyzed. We propose a compound control algorithm of restraining surplus torque. The results of the simulation indicate that the compound control algorithm can effectively restrain the surplus torque of the electric load simulator, reduce the interference that is caused by the surplus torque, and improve the control accuracy of electrical load simulator.

Most of the existing non-intrusive power load decomposition systems use the active power and reactive power of the electric load as the load characteristics. In the environment with a large load type and quantity, the decomposed effect is relatively poor, the accuracy is low, and the load characteristics are seriously lost. Aiming at this problem, this paper proposes a non-intrusive power load decomposition method using full current waveform as the characteristic of power load system. Firstly, in the data-driven framework, using the time series data of load voltage and current, the current model of each load is obtained by training RBF neural network based on the parameter optimization of support vector machine (SVM), and the load model library is formed. Then, genetic algorithm is used to find the combination of load model which is most similar to the actual current. The optimal combination of load model is the type and quantity of the actual load. In order to make the similarity evaluation more accurate, this paper establishes the weighted value of root mean square error, correlation coefficient and correlation entropy as the evaluated basis of similarity. The experimental results show that the load model library established by the decomposition method of this paper has better generalization ability and lower error, and the accuracy of decomposition load is 99.0%, which can effectively realize load decomposition.

Electrical energy consumption is affected by variations in temperature, seasons, humidity, weekdays and festive occasions. It affects electric load forecasting. This results in irregularity of production and distribution. Residential load forecasting can effectively help individual consumers to regulate their energy consumption and reduce cost. This in effect help utility to regulate the power consumed. This paper focuses on forecasting techniques for residential load. This paper compares the traditional load forecasting method with the neural network. The traditional forecasting models studied are ARIMA and ARIMAX. In the Neural Network, different models using feedforward backpropagation networks are studied. It is found that artificial neural networks give better results compared to traditional time series forecasting methods. The superiority of artificial neural network in enhancing load forecasting and the effect of transfer function used in the neural network is discussed in this paper.

The paper focuses exclusively on the analysis of the energy electric load balance and electricity consumption of a vessel belonging to the class of luxury charter yachts. Thoroughly examined throughout the paper are the primary characteristic consumers typical of the object under consideration and their respective proportion in the total energy mix.

With the extensive establishment of advanced metering infrastructure (AMI) and the development of big data technology, large volume of electricity load profile data collected from smart meter reveal information about customer's electricity consumption behavior. The precise knowledge of customer's load profiles clustering technology is helpful for power company to develop differentiated user services and reasonable power dispatching. In general, customers's load profiles exist several typical load profiles(TLPs) and a few abnormal or irregular load profiles, but the conventional clustering method can not distinguish them accurately. Therefore, this paper proposes an approach based on piecewise symbolic aggregation. Firstly, this method reduces the dimension of load profiles by a time series segmentation method based on Pearson correlation coefficient (PCC), and all load profiles are divided into several subsequences. Then, each sub-sequences is replaced by a character according to the size of each sub-sequence's mean value. After that, each load profile will be replaced by a symbolic string, and the load profiles with same symbolic string will be cluster into a group. Finally, this paper performs a case analysis with a Irish dataset, and the results show that the proposed approach can improve the clustering quality of electrical load profiles.

Pakistan’s power system is currently facing serious energy crisis issues as the supply of electric power is not increasing at the pace of its demand. Although conventional power system is experiencing a notable transition from the centralized supply-side energy management system to decentralized generation and demand-side load management system certainly due to the integration of hybrid energy systems to the local grids yet the shortfall in supply and energy demand gap resulted into large scale electric load-shading problem in all cities of Pakistan from last few years. This paper discusses a real-time load management system integrated with a hybrid energy system as a source i.e. grid-tied photovoltaic (PV) system, stand-alone PV system, wind-turbine, local grid and aims to have a customer control overload appliances in peak and off-peak hours, update them for hourly units consumption and also shed the unnecessary loads according to user-set priority and demand. The hybrid energy system and load end is interconnected to the programmable logic controller (PLC) and are also controlled by using PLC. According to the specified site and load demand discussed in the paper, it has been found that by implementing the proposed load management system approach average of 82 units of electricity can be saved in peak hours, and similarly the average amount of 4.875 Rs/KWh can also be saved for specified time dictating that proposed smart strategy can prove to be fruitful to combat electric load problem on large scale in Pakistan in future.

Smart Meters in the recent years have led to the generation of large consumer data sets which have enabled more energy forecasting algorithms to be designed. Two such algorithms are discussed in this paper with their minor deficiencies and a hybrid approach is proposed. First algorithm being Autoregressive Integrated Moving Average (ARIMA) model which turns out to be futile in determining nonlinear relationships that are involved. Secondly, Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) can not correctly model seasonal variations in energy consumption. This paper blends Seasonal ARIMA (SARIMAX) with LSTM network by integrating their benefits for a improved electric load forecast. The major contribution is the implementation of the combining algorithm to form the hybrid network. The proposed hybrid implementations provides an almost 13.08% decrease in the mean absolute error when compared with the two algorithms. The slight superior performance of the proposed method in the power load forecasting application is highlighted in the results section.

This paper shows the electrical load forecast problem solution for gas industry enterprises, taking into account the comparative analysis of forecasting methods. The obtained information is an effective tool for influencing the optimization of the tariff policy and further corrective well-timed actions of personnel for improving contractual work and minimizing costs for consumed electricity. Artificial neural network models are developed taking into account electric energy consumption nature and parameters. This paper presents the dependences of changes in the coefficients of seasonality, cyclicity, and irregularity for the electric load at electricity metering points. The problem of forecasting electrical loads and predicting their accuracy is shown and solved in the context of the gas industry enterprises. The characteristics and architectures of multiple artificial neural networks comparative analysis is given.

This paper proposes a solution to recover part of the energy dissipated into heat in microstrip circuits, into useful DC energy. For that purpose, a multilayer system integrated by an embedded microstrip circuit and a thermo-electrical generator (TEG) subsystem is proposed. The aim of the paper is to enhance the amount of generated heat going towards the TEG subsystem to increase, in this way, the efficiency of the system, and consequently, the recovered power. Air channels, acting as thermal insulators, are inserted in the stackup configuration to guide the heat towards the TEG. For validation purposes, a proof-of-concept device is implemented consisting of a bandpass filter working at WiMAX band. Noticeable levels of DC power have been recovered and supplied to an electrical load for some of the case scenarios, in which a 2 W-CW RF in-band signal is applied.

Load forecasting is an interesting issue in which the electrical load demands are dynamic and non-linear in nature. This paper develops a forecasting model in which, the next hour load is predicted. The most popular machine learning algorithm called as Extreme Learning Machine (ELM) is implemented for the load prediction. The performance of ELM mainly depends on the output weights which are determined from the input weights and biases. In this paper both machine learning algorithm and optimization technique have been applied for optimal design of input weights and biases for getting a better result. Symbiotic Organism Search (SOS) algorithm has been implemented for determination of the optimal weights and biases.

Aiming at the problem of the lack of power load data in the forecasting of the delivery capacity of the sending end area and the load level forecasting of the receiving end during the clean energy delivery in the southwestern region, this paper gives the power load data recovery from the perspective of time series data characteristic analysis and modeling and estimation method. Based on the analysis of the multi-scale time series characteristics of the annual, monthly, and daily fluctuations of the electric load, the multi-scale time series characteristic modeling of the electric load is established. Spline interpolation is introduced to solve the non-parametric and variable coefficient problems of the load model, and the estimation method of the key parameters in the load model is given. According to the obtained load recovery model, a recovery method for missing data of weekly power load is proposed, and the idea of recovering daily load data is given. The actual calculation examples prove that the method proposed in this paper is accurate and effective.

Aiming at the combined heat and power optimal dispatching problem of electro-thermal system, this paper proposed a multi-time scale dispatching model electro-thermal system considering electric/thermal energy storage and flexible load. Firstly, an electro-thermal system with electric/thermal energy storage, flexible load, combined heat and power generation and heat pump is modeled, in which the flexible thermal load model is constructed based on the user's thermal comfort, the flexible electric load model is constructed based on the user's satisfaction with electricity consumption. Then a multi-time scale optimal dispatching model for electro-thermal system is proposed. Day-ahead optimal dispatching model minimizes the operation cost through multi-period collaborative optimization. Real-time feedback correction is based on the actual measured value of distributed renewable energy resources output and load demand to correct the day-ahead dispatching plan instructions and improve the reliability of dispatching. Finally, a simulation example verifies the effectiveness of the method proposed in this paper.

Optimal sizing of standalone hybrid renewable power supply for mobile telephony base stations is considered in this paper. This task is very complex due to stochastic nature of input variables such as wind speed, solar irradiance, ambient temperature and electric load. Further concern is modeling of the power consumed by cooling devices which is also uncertain variable dependent both on ambient conditions and power dissipation of telecommunication equipment. The realistic behavior of the station both in electrical and thermal terms can be analyzed only by a comprehensive probabilistic simulator. The basic idea of this paper is to systematically vary the main input parameters (rated powers of wind turbine and photovoltaic system along with battery capacity) and to run a Monte Carlo simulation for each considered variant observing the technical and economic indicators. Based on the minimum annual costs, it is possible to choose the best available investment solution. Simple optimization can be extended to multi-objective by adding some additional goals, such as minimizing the operating time of a diesel backup generator. The optimization method is tested on an illustrative example of a base station located in the Mediterranean area.

This paper report the proposed of an optimization process of power dispatch in a DC microgrid using principles of linear programming. The microgrid used has as microsources: a wind turbine, a solar PV plant, a battery bank, an electric load and an electrical interconnection point with utility network. The microgrid only allows the energy input from utility network. Additionally, every component is configured to offer/request a fixed nominal power of production/demand during periods of five minutes of duration; this is a new operating strategy: a constant power generation and consumption is defined in each microsources, loads and storages. To optimization process search the minimum cost of production, distributing adequate the demand between microsources and utility network under the proposed operating strategy. In this paper has been assumed that costs vary randomly in every period by each microsource and utility network. The optimization process has been development, implemented and compared with its equivalent in Matlab.

Electric Load Power Load Forecasting (ELPF) is an important activity in any power distribution utilities. Most of the literatures debates that an Artificial Neural Network (ANN) based load forecasting technique always yields a higher accuracy as related to traditional statistical learning and regression techniques especially in short term load forecasting. Hence one such ANN i.e. cascade forward neural network (CFNN)-based technique is attempted here in this paper. This paper mainly deals with study the impact of weather in ELPF models developed using CFNN in a day-ahead time horizon.

This paper presents the modelling of an off-grid micro-grid situated in a remote rural village in Eastern Cape province in South Africa. The modelling looks at the optimization studies for control dispatch strategies for the integration of wind power into the existing micro-grid which comprises an electric load supplied by photovoltaic power, battery bank for energy storage and a diesel generator. The optimization studies are performed using HOMER Pro® and aims to identify the best possible dispatch strategy for the dispatchable generation Le. (diesel generator and battery storage) that minimizes load shedding and excess energy production in a system where the all the generation has been sized already. Four dispatch strategies built into HOMER Pro®, namely Cycle Charging, Load Following, Combined Dispatch and HOMER Predictive Strategy are tested. The aim of this paper is therefore to test and find which of the above dispatch strategies is the best fit for the rural village. Once the best-fit strategy has been determined, it is further customised and optimised for the rural village load profile.

Non-induction-motor loads are represented by static load models in existing electric load models. The electric loads supplied by switching power supply are typical non-induction-motor loads and occupy a large proportion of the nonindustrial electric load. In this paper, a simulation model of a switching power supply with a full-bridge structure is built, and the existing static load model is applied to fit the dynamic characteristics of the switching power supply. Simulation results show that the accuracy of the power function static load model is higher than that of the polynomial static load model, but the power function model still cannot accurately describe the dynamic response of switching power supply under voltage sag and voltage recovery, and cannot describe the impact of voltage sag degree on the dynamic characteristics. Therefore, a simplified dynamic model is proposed in this paper to describe the dynamic characteristic of the switching power supply precisely. In this simplified model, two transfer functions are employed to represent the amplitude and phase relationships between the output positive sequence current and the input positive sequence voltage, respectively. Additionally, two coefficients related to the degree of voltage sag are used to describe the influence of fault severity on the dynamic characteristic of the switching power supply. Simulation results reveal the accuracy of the proposed simplified dynamic model of the switching power supply is higher than that of the existing static load model at varying degrees of voltage sag faults.

The problem of the output voltage stabilization of buck converter is considered in the paper. It is assumed, that the stabilizer serves as power supply for inductive and resistive load, which can be described by differential equation with time-varying coefficients. The value of the equivalent inductance and resistance are unknown, but they belong to the class of bounded functions with several bounded derivatives with known bounds. According to these bounds, the influence of the external load is described as additive disturbance, which excites the right hand side of the differential equation of the control plant. By using the, so-called, "vortex" algorithm, the control input synthesis is developed in the paper, which theoretically provides asymptotical stabilization of the output voltage error to zero. The formal conditions for the parameters of the converter and the electrical load are derived. The theoretical results may be used for designing of the buck converters with the desired output characteristics. The proposed control scheme is developed for further usage in the electromechanical systems, especially for control of the input voltage of the electric drives. The simulation results show the efficiency of the basic control law.

With the rapid development of electric-vehicles at home and abroad, as an electric load, electric-vehicles have an increasing impact on the power grid. Therefore, it is of great significance to study the charging optimization strategies of electric vehicles. This paper proposes an electric vehicle charging optimization strategy based on electricity price demand response. First, based on the driving characteristics of electric vehicles, the load model of electric vehicles in disordered charging mode is established. Then, considering the real-time electricity price, a multi objective charge-discharge model of electric vehicles under different weight combination modes is established. Finally, through a comparative example, the charging and discharging schemes of electric vehicles in different scenarios are analyzed and compared, and the effectiveness of the multi objective optimal control strategy for electric vehicles proposed in this paper is verified, which is conducive to coordinating the impact of load fluctuation regulation on consumer interests, to achieve optimization of multi-objective cars.

The safe and economical operation of a power grid is not possible without knowing the future load. For this reason, the first step in terms of productivity and proper management of a system will be to predict the electric load in the future. In this paper, the features that exist in the history of electric load consumption are examined and they are used as a guide for designing the proposed method. We try to predict short-term electric load by extracting the characteristics of the electric load history using deep neural networks. Long Short Term Memory (LSTM), are able to hold short and long-term memory for extracting relationships between the load values from time series. On the other hand, convolution neural networks are capable of automatically learning features and can directly generate a vector for prediction. The Correlation based Convolution Recurrent Network (CCRN) which is proposed in this paper calculate the autocorrelation coefficients of the load series and obtain an quasi-periodic of hourly loads with the highest correlations. Then, by using this quasi-periodic, the load series is converted into a two-dimensional matrix (image) and the two-dimensional convolutional neural networks is used to extract the load features. Finally, the load time sequence information is extracted using the LSTM network. Experimental results on both the Toronto and ISO-NE datasets show reduced prediction error as well as reduced training time compared with LSTM networks.

With the further development of smart grids, lots of communication devices such as sensors and actuators are dramatically equipped into power grids. At the same time, social behaviors, investment, trading, management and user selection have become increasingly important in energy system research. The modern power system has evolved from the Cyber-Physical-Systems (CPS) which combines the power network and the cyber network to the Cyber-Physical-Social Systems (CPSS) composed of the cyber network, the power network and social network. For CPSS system, how to reflect its social characteristics is one of the biggest difficulties. Based on the social policy data, this paper constructs a statistical model reflecting the load law of power system under the framework of social physical information system, then it uses the improved long and short-term memory (Long Short-Term Memory, LSTM) deep learning network to train the model. Finally this paper realizes the integration drive based on data, model and social factors.

Electrical load forecasting is of great significance, which can guarantee the system stability under large disturbances and optimize the distribution of energy resources in the smart grid. Load forecasting of distribution is a very active research field and an important aspect, as the load power of distribution transformer is closely related to the health and reliable of the power grid. Deep Learning technology have shown to be very powerful in load forecasting of distribution. Based on the deep learning technology, this paper uses the historical load data of distribution transformer to establish an appropriate prediction model to predict the future load of transformer. In this paper, the long short-term memory(LSTM) model is used to predict the distribution transformer load. The idea of integrated learning is used to improve the accuracy of LSTM network in load forecasting. The input dimension of the model is enriched to solve the problem that the model cannot be built due to the lack of historical data. Finally, the experimental results show the superiority of the proposed method.

This paper analyse the performance investigation on electrical power generation using Gravitational force. The main figment is to practice pollution free power to all. Electrical power is generated with the help of gravitational force with reduced cost. A detailed design calculation is elaborated in this paper. In the proposed work the fly wheel, DC Generator and Boost converter fed electrical load is implemented in hardware. The experimental results are observed and the results are presented.

In the smart grid, huge amounts of consumption data are used to train deep learning models for applications such as load monitoring and demand response. However, these applications raise concerns regarding security and have high accuracy requirements. In one hand, the data used is privacy-sensitive. For instance, the fine-grained data collected by a smart meter at a consumer's home may reveal information on the appliances and thus the consumer's behaviour at home. On the other hand, the deep learning models require big data volumes with enough variety and to be trained adequately. In this paper, we evaluate the use of Edge computing and federated learning, a decentralized machine learning scheme that allows to increase the volume and diversity of data used to train the deep learning models without compromising privacy. This paper reports, to the best of our knowledge, the first use of federated learning for household load forecasting and achieves promising results. The simulations were done using Tensorflow Federated on the data from 200 houses from Texas, USA., ICC 2020-2020 IEEE International Conference on Communications (ICC)

The paper describes some initial evaluations concerning the possible use of SFRA based method of analysis to the pattern of energy use in the domestic residential sector. The paper illustrates the SFRA techniques of investigation that are well-consolidated tools in various research sectors as for the identification of faults in transformers and induction motors. The SFRA techniques are then applied to the very current interesting problem of domestic appliance signature identification in non-intrusive approaches of load research. Some first results of the applications of the techniques as obtained from laboratory tests conducted on a domestic electric installation simulator

Load forecasting in smart grid is the process of predicting the amount of electrical power to meet the short, medium and long term demands. Accurate load forecasting helps electrical utilities to manage their energy production, operations, control and management. Most of the state-of-the-art forecasting methodologies utilize classical machine learning algorithms to predict the electrical load. There is a need that big data platforms and parallel distributed computing are utilized to their potential in the available solutions. In this paper, the Apache Spark and Apache Hadoop are utilized as big data platforms for distributed computing in order to predict the load using available big data. In this paper, MLib, Spark library for machine learning algorithms, is utilized for distributed computing. Using MLib allows testing the classic regression algorithms such as linear regression, generalized linear regression, decision tree, random forest and gradient-boosted trees in addition to survival regression and isotonic regression. The obtained results show that Spark produces high accuracy while parallelizing the process of load forecasting in highly competent training and test times. Actual big data are used in the load forecasting process.

In this paper, self-tuning fuzzy logic controllers have been designed for load frequency control of power systems using the integral of time-absolute error (ITAE) and integral of time multiply squared error (ITSE) as optimization criteria and the maximum of area control error (ACE) as error signal. The optimal control parameters are obtained using the teaching–learning-based optimization algorithm which has a high accuracy and a high convergence speed. All simulations and coding were done MATLAB software in order to optimize the design of the controller. The effectiveness of the proposed method was shown on a two-area three-power plant power system under area load disturbances. After evaluating and comparing the performance of the optimized controller with the non-optimized controller, it was concluded that the optimized self-tuning fuzzy logic controller (OSTFLC) has a better and more robust performance than a STFLC in a wide range of electrical load variations occurred in both areas. To reach the above mentioned goal, optimization of the membership function, error and it’s derivative are done simultaneously. At the, same time the fuzzy PID controller and UPFC system parameters are also optimized, through the use of TLBO algorithm. An optimal control system and an optimal modelling of the system are the main contributions of this paper.

The application of artificial neural network to load forecasting can overcome the problem of dynamic load change, and its ability to adapt to nonlinear relationships makes the prediction result satisfactory. This paper firstly reviews and introduces the concepts and basic principles of load prediction, discusses various methods for load forecasting, and then selects artificial neural network to establish a predictive model. In this paper, the European electric load is predicted with a BP neural network. From the prediction results, it is feasible to use BP neural network for load forecasting, and its accuracy can meet the needs of real-life engineering work. However, BP neural networks have the problem of slow convergence and easily falling into local minimum points. Therefore, this paper also uses three other neural networks for load forecasting, which are Radial Basis Network (RBF), Elman Network, and Long-Short Term Memory Network (LSTM). In the experiment, the four neural networks achieved expected prediction results, and the LSTM network had the best prediction effect. Scientific discussions are offered.

The paper introduces energy storage systems (ESSs) and carbon emission trade (CET), gradually constructs joint scheduling optimization model for wind power and thermal power considering ESSs and CET. Firstly, the paper takes the maximum system benefit as the objective function considering load supply and demand balance constraint, unit operation constraint and system reserve constraints, establishes the basic scheduling mathematical model for wind power and thermal power. Secondly, the paper respectively builds carbon emission cost model of thermal power generation considering CET, ESSs’ operation profit model and puts forward system scheduling comprehensive objective function considering CET and ESSs. Thirdly, the paper constructs jointly scheduling model for wind power and thermal power considering ESSs’ operation constraints, CET’s constraint and newly system reserve constraint. Finally, 10 thermal power units and 2800MW wind power turbine are taken as simulation system. The result shows: the large-scale grid connection of wind power needs rely on thermal power units to provide reserve, which could result in the reduction of unit utilization efficiency and correspondingly increase coal consumption of units’ power generation, but system overall coal consumption rate will reduce. CET can transform wind power cleaning characteristics into economic benefits, promote system to consume wind power and reduce abandoned wind power. ESSs can gentle the electric load curve and improve the grid-connected space by adjusting its charge and discharge characteristics, but system overall benefit reduces subjected to high ESSs’ fixed costs. System overall benefit will reaches maximum when ESSs and CET are simultaneously introduced, which indicates that they have synergistic optimization effect.

The development of intelligent home has promoted the residents' attention to intelligent power management, the electric load of its mainly includes electric vehicles, electric lights, televisions, computers, air conditioners, water heaters, washing machines etc., while, this urbanization has led to the continuous emergence of commercial buildings, and the proportion of energy consumption in commercial buildings has continued to rise, the electric load of its mainly includes electric vehicles, lighting load and fixed electric load. The charging and discharging characteristics of electric vehicles have also made their role in the optimal management of intelligent power management for residential and commercial users. After plug-in electric vehicle grid, the power consumption characteristics of different users show significant differences. Therefore, this paper studies the intelligent power management framework considering electric vehicles for different users. For residential users, this paper studies the intelligent power management problem of electric vehicles, establishes the electric vehicle model of residential users, and proposes an intelligent power management framework for residential users considering electric vehicles. For commercial users, the model of electric vehicle for commercial users is established and the framework of intelligent power management system for commercial users considering electric vehicles is proposed. The framework of different user intelligent power management systems considering electric vehicles proposed in this paper lays a theoretical foundation for the study of intelligent power optimization strategies.

This paper constructs the photo-voltaic storage home energy management system (PV Storage HEMS) including PV power generation, battery energy storage, and electrical load. Based on the analysis of load working state model, the paper founds an integrated objective function considering electricity charge and user satisfaction, a coefficient is used to adjust the proportion of user satisfaction in the function. Furthermore, a coordinated optimization scheduling strategy for the system is proposed under operation constraints. Finally, the case study is carried out to analyze electricity usage and satisfaction for different scheduling modes, as well as verifying the application value of the strategy.

Solar photovoltaic system is playing an important role to mitigate greenhouse impact on the planet. Also solar energy has the potential to support electrical loads and ensure higher network stabilities. In many countries, due to electrical generation limitation, regular outages are planned on a daily rotational sequence. The house owners rely on fossil fuel generator to support the electrical load during the planned outages. The work presented in this paper highlights the potential that PV system can play to support the load and eliminate the need for fossil fuel generator. Also the paper includes valuable information regarding the orientation and tilt for PV system to yield adequate output Lebanon. The case study which is based on real project installed in Tripoli Lebanon is also included.

Energy management systems are developed to control, monitor and optimize the smart grid energy market. Energy balance has to be maintained and demands of electrical loads has to be met at all the times. This paper presents an efficient power management system that has the battery connected in parallel to the ultra-capacitors. Batteries have very good energy density, (total amount of energy a device can hold) whereas ultra-capacitors have very good power density (speed at which power can be discharged). Hence in this paper with the use of the time series forecasting algorithms such as ARIMA, the next load requirement hence the peak requirement of the load is predicted. Extracting the advantages of both the devices the demands of electrical load that is by using ultra-capacitors during peak power need and battery during regular power need the lifetime of the model also increases and needs are also satisfied hence in the name efficient. The analysis of battery and ultra-capacitors is done using Simulink, a MathWorks product and using python the algorithm of time series forecasting is implemented. In this paper the algorithm ARIMA is proved to be the best suitable for the suggested load pattern with the lowest root mean square error.

This paper describes an important topic regarding with stability of the electrical grid, it is the load frequency control (LFC) problem. At any interconnected-areas of power systems, LFC problem must be solved to ensure system stability without any disturbance. When the amount of generating power is insufficient to meet the electrical load needs, frequency deviation is appeared with instability in power system. Doubly fed induction generator (DFIG) has the ability to support the power system with an additional inertia by supplying it with an extra active power. The studied system in this paper is a two-area thermal-thermal power plants with different participation levels of DFIG at the first area. The exchanged power is flowing between neighboring areas in the studied system through a tie-line. The two power plants in each model are subjected to 2% load perturbations in each area. These load perturbations are applied for examining the ability of Grey Wolf Optimizer (GWO) to restore the stability of studied systems. The optimum performance of power system is achieved when DFIG participation level is high. All time domain simulation results are carried out using MATLAB (R2015a) program for investigating the effectiveness of this method.

This paper deals with the design of a grid-con-nected hybrid system consisting distributed energy resources (DERs) as PV, wind generator, fuel cell electrolysis system with fuel cell, battery banks to provide to supply the electric load demand of the family house in Sofia area, Bulgaria. The main idea is to evaluate the operation of the proposed system and how it can meet the energy needs of a single-family home. At this stage the optimization of the proposed hybrid system is not intended. The grid-connected power system design is based on a simulation model developed using HOMER software. In this context, a standardized load demand profile for the Sofia urban area and household consumers was applied. In this paper, the wind and solar meteorological data for Sofia (Bulgaria) were used to design grid-connected hybrid power system for providing necessary electricity. The design of such hybrid systems and the literature review justify such a decision to be considered one of the most effective in reducing greenhouse gas emissions.

Distribution network planning is the basis to guarantee reliable operation of ‘Coal to Electricity’ (‘C2E’) distribution network. While, as a kind of incremental load, there is no standard about transformer capacity for each ‘C2E’ consumer. As a result, ‘C2E’ distribution network planning and design lacks basic rules. Aimed at this problem, a method named ‘Heat load to Electricity load’ for calculating ‘C2E’ incremental load and average transformer capacity per consumer is proposed in this paper, which provides basis to ‘C2E’ distribution network and electrical equipment selection. With this method, ‘C2E’ electrical load and average transformer capacity is determined by heating load of building in demand side. An example with typical residential building in Beijing is taken in this paper. Heating load of building is calculated using DeST (Designer’s Simulation Toolkits) firstly. Then, with the kind of electrical heating equipment, average transformer capacity for each ‘C2E’ consumer in Beijing rural area is given.

Voltage stability is very important in a power system operation. This paper presents a detailed study on voltage stability and line losses of a radial distribution system incorporating Renewable Distributed Generation (RDG) using a two method approach of Index Based planning (IBP) and Genetic Algorithm (GA). RDGs have been largely adopted in most power systems due to their ability to meet increased electrical load faster and cheaply without expanding the transmission network, they have minimal environmental effects and they cannot be depleted. Formulation and solution of distribution voltage stability problem of a distribution network with RDG is very critical in the modern power system operation and control. Researchers have previously solved the voltage stability problem by obtaining optimal placement of Flexible AC Transmission Systems (FACTS), Static VAR Compensators (SVC) and static capacitors to provide reactive power to the system and improve the voltage profile. Other researchers have used Under Frequency Load Shedding (UFLS) and others have used optimal sizing and siting of DG units to improve the distribution network's voltage profile. This paper outlines a detailed research of the effect of interconnecting RDGs on the voltage stability and line loss of the IEEE 14 bus radial distribution system using Index Based planning and Genetic Algorithm. The results indicate the total voltage deviation and the total line loss for Photovoltaic DG, Wind DG and Static compensators (STATCOMS). In addition it gives a detailed formulation of the problem. The results obtained shows that appropriate sizing and location of RDGs improves the voltage profile and reduces the line losses of the radial distribution system.

In order to meet the functional requirements of variable speed diesel generators and inverters, this paper proposes a speed regulation, rectification and inverter scheme. And the overall scheme is designed. Then the modelling, simulation and hardware design are carried out. Through the digital intelligent control system, the diesel generator set can change its speed according to the magnitude of the electric load, which can reduce the consumption of oil, save energy, and reduce the loss. Firstly, this paper analyzes the function requirements of diesel generator and inverter and lays out the scheme of adjusting speed, the rectifier and the inverter. Besides, the paper designs the topology structure for inverter. According to the overall design program, the system of inverter is modeled and simulated in this paper. And the strategy of booster and inverter is determined. Secondly, the design of hardware for inverter system is finished, and the power and control boards are developed. What’s more, the units of hardware are debugged. With the development of hardware, this paper introduces a program which can adjust the speed according to the change of load. In the end, DC bus voltage regulator can be controlled in different combinations of speed and load, which can output the power reaching the quality requirements by SPWM’s control.

This paper presents methods for creating time-varying, end-use electric load models for use by advanced model-based volt-VAR optimization (VVO) control schemes, which have been shown to reduce energy consumption and peak demand in the electric distribution system. Many modern VVO systems use power flow models to inform control decisions, which require accurate end-use load models for optimal performance. The methods presented in this paper use active power, reactive power, and voltage magnitude measurements from advanced metering infrastructure (AMI) to create time-varying load models for each customer meter. Simulated AMI data and distribution systems are used to validate the effectiveness of the presented methods. The results show that the load models derived from AMI data can accurately represent end-use load behavior such as total energy costs, but lose accuracy when considering more dynamic system behavior such as voltage violations over short time intervals. This work advances the state-of-the-art model-based VVO by providing data-driven load modeling techniques which acknowledge that optimal distribution system control requires a treatment of the system which is dependent on time and voltage.

In this paper, the thrust density and thrust fluctuation of tubular C-core modular linear permanent magnet synchronous motor (TC-core MLPMSM) are optimized. In this paper, the characteristics of magnetic density distribution in the core of a tubular linear motor are analyzed. Based on the analysis, this paper proposes that trapezoidal stator slot will lead to a much larger thrust density than rectangular stator slot under the same volume and magnetic and electric load. Aiming at the problem of large force fluctuation in linear motor, modular structure is adopted and the detent force and thrust fluctuation are reduced. 2D finite element method (FEM) results are obtained to verify validity of the methods. Results show that the thrust density increases and the force fluctuation decreases after optimizations in TC-core MLPMSM.

Aiming at the shortcomings of low prediction accuracy due to the randomness and complexity of power load data, this paper bring up a power load prediction method on the strength of VMD and dynamic adjustment BP. Firstly, for the redundant information and trend components contained in the original data of the power load, the VMD decomposed component reconstruction is used to remove the trend component and the redundant information. Secondly, after the VMD detrended, there is a disadvantage that the fixed points in traditional BP neural network prediction may cause low accuracy, the dynamic adjustment of nodes is designed to achieve the optimal prediction. Finally, based on the electric load data provided by Chongqing Tongnan Electric Power Co., Ltd., the prediction model put forward in this paper is used to estimate the electric load. The comparison of the example simulation results shows that the predicted values of the VMD and the dynamically adjusted BP cooperative electric load forecasting method are closer to the real one. The load value and the prediction error are lower, which is a better short-term power load forecasting method.

The 115/22 kV transformer is a significant equipment to transmit the energy to the customers. The power outage occurred, if there was a transformer tripping. The control center operator has to identify the severity of alarms on SCADA while the event occurred, by indicating the transformer tripping and how many events of the power outages, in order to quickly transfer the electrical load to other transformer. The process explained above, the operator would require less time to reduce the impact of power outage. Therefore, this paper proposes the Artificial Intelligence (AI) Fault Tree Analysis on Python Platform. It was developed to be as a substitute for operators work in analyzing the message alarm. This paper also presents the determination of the previous loads by Weighted Moving Average technique. This technique was used in planning to transfer the load from the transformer outage to another and can prevent the power overload event on the power system. Moreover, this article presents an automatically tripping event reported via Line application. All information was sent to distribution company control center and maintenance staff. As a result, AI can replace control center operator work. AI can more accurately identify the severity of alarms that occurred simultaneously than operator. AI also spends only 12 seconds which is less than the operator spends at388 seconds.