An Improved UFLS Scheme based on Estimated Minimum Frequency and Power Deficit

In the event of a power system disturbance, it is important that the decision to implement under frequency load shedding is based on both the minimum frequency and the magnitude of the disturbance. In this paper, we propose the use of higher order polynomial curve fitting to estimate the minimum frequency. If the prediction shows that the minimum frequency threshold will be violated, the magnitude of the total disturbance is estimated using the swing equation. In addition, the minimum amount of load that must be shed to restore the frequency just above the minimum value can also be directly calculated. Simulations are carried out for the considered Taiwan power system and the results prove the efficiency of the proposed technique

Decarbonize Russia — A Best–Worst Method Approach for Assessing the Renewable Energy Potentials, Opportunities and Challenges

Russia is known to be a country with enormous energy resources both renewables and non-renewables. Much of the country's effort towards energy generation has been on the development of the non-renewables over the years. This study examined the opportunities and challenges in Russia's Renewable energy (RE) sector. By coupling both interviews and literature reviews, a total of 8 main opportunities and 7 key challenges were identified and discussed. The Best–Worst-Method was used to assign weights to the various factors using inputs of 30 experienced experts in Russia's RE sector. According to the obtained results, the most significant opportunity that the country would have to take advantage of is the opportunity to export RE outside the shores of the country, it recorded 27.7 percent. This is followed by the country's target for the RE sector which scored 18%, hydrogen production and need to meet local energy requirements followed with 12% each. The greatest challenge which also serve as a hindrance to the development of RE in the country is the low attention given to clean technologies from government, it recorded a weight of 31.4%. This is followed by unequal playing field, and strict local content requirements which recorded 17.9% and 13.5%, respectively. The study ended with some strategic recommendations to authorities for the development of the sector. © 2021 The Authors

Frequency response models and control in smart power systems with high penetration of renewable energy sources

The high penetration of renewable energy sources in modern power systems introduces opportunities and challenges to power system operators. The frequency, which is a global variable in power systems, is being affected due to the increase of the renewable energy shares. This paper suggests dynamic models of power systems for assessing the frequency response due to different types of disturbances and uncertainties from renewable energy sources. The introduced model is built based on the sub-transient model of synchronous generating units. Likewise, a simplified system frequency response model is presented, in which the demand-side participation in providing ancillary services is considered. Different types of power systems are considered to verify the introduced models. The results show the effectiveness of the presented models in evaluating the impact of different parameters on power system frequency response. The impacts of increasing the renewable energy shares and its uncertainties are also investigated

A Robust Mixed-Integer Programing Model for Reconfiguration of Distribution Feeders Under Uncertain and Variable Loads Considering Capacitor Banks, Voltage Regulators, and Protective Relays

Feeder reconfiguration is an effective way to reduce power losses of distribution network. In this way, configuration of distribution system is changed in order to achieve possible minimum losses, while electricity demand of consumers has to be provided. Consumers' power demand has an important role in feeder reconfiguration because any change in demand affects power losses directly. Whereas load demand has a variable and stochastic nature because of its dependency on consumption pattern and accuracy of forecasted load amounts. Accordingly, reconfiguration models should be enough robust against load uncertainty and variations. Thus, this paper presents an efficient robust model for reconfiguration of distribution feeders under uncertain and variable loads. The proposed reconfiguration model is enough robust and efficient, in which its implementation is relatively simple. The results show higher efficiency and lower complexity of the proposed model compared to existing robust reconfiguration approaches

Assessing the optimal generation technology mix determination considering demand response and EVs

This paper proposes a novel, generation technology mix determination method considering short term demand response, energy storage systems, and electric vehicles to provide more flexibility to the future power systems. In the proposed method, new models of both electric vehicles and energy storage technologies for contributing to generation mix determination studies are suggested. The integration of the different emerging technologies, i.e. demand response, energy storage systems, and electric vehicles into traditional generation mix determination is done firstly by adopting mix complementary programing method. Then, to overcome the problems of such integration and to avoid its complexity, it is converted to quadric complimentary programing model. The proposed generation mix determination framework is tested on the Spanish power system with real data. The outputs of the proposed method are the optimal capacities of the conventional generating units, the different types of energy storages and wind turbines. Simulation results demonstrate the effectiveness of the proposed method in determining the optimal generation mix of future power systems with high penetration level of wind energy resources. Moreover, the results verify the potential of the proposed method in providing better flexibility services to power system if compared with other methods

Comparative study of hysteresis controller, resonant controller and direct torque control of five-phase im under open-phase fault operation

The need for regulating the operation of unhealthy motor drives has motivated the researchers to modify the control techniques in order to be valid for the new drive state. The use of a fault-tolerant facility is an attractive feature of multiphase machines; therefore, the applicability of different controllers has been established for the operation under open-phase fault conditions. The considered control algorithms were utilized to analyze the operation of the unhealthy system and evaluating the capability of the control to regulate the speed and torque under the fault condition. However, the majority of these studies considered only one control algorithm to be tested with the faulty system without comparing its performance with other techniques. The performance comparison is a vital way to visualize the features and characteristics of each algorithm. For this purpose, this paper deals with the performance comparison of the hysteresis controller, RFOC based on resonant controller and direct torque control (DTC) control under open-circuit fault conditions. A detailed comparison between the three control techniques is presented to outline the main differences between the three control procedures and identify the most appropriate technique in between

A sequential hybridization of ETLBO and IPSO for solving reserve-constrained combined heat, power and economic dispatch problem

The explosive demand for electricity and ecological concerns has necessitated the operation of power networks in a more cost-effective approach. In recent years, the integration of combined heat and power units has presented a potential answer to these problems; nevertheless, a new difficult challenge has emerged: finding an optimal solution for simultaneous dispatch of power and heat. Therefore, to tackle this problem, this work presents an intelligent sequential algorithm based on a hybridization of an enthusiasm-aided teaching and learning-based optimization algorithm (ETLBO) with an improved version of particle swarm optimization (IPSO). The proposed method can simultaneously minimize total generating costs while considering a variety of physical and operational limitations. In addition, this research designed an adaptive violation constraint management approach combined with the formulated hybridized optimization algorithm to ensure system constraints' safe preservation during the optimization process. Finally, the performance of the proposed method is compared to the recently developed metaheuristic algorithms as well as Knitro and IPOPT (industrially used optimization packages), in which the ETLBO-IPSO outperforms all the other methods

A Novel Solution for Day-Ahead Scheduling Problems Using the IoT-Based Bald Eagle Search Optimization Algorithm

Advances in technology and population growth are two factors responsible for increasing electricity consumption, which directly increases the production of electrical energy. Additionally, due to environmental, technical and economic constraints, it is challenging to meet demand at certain hours, such as peak hours. Therefore, it is necessary to manage network consumption to modify the peak load and tackle power system constraints. One way to achieve this goal is to use a demand response program. The home energy management system (HEMS), based on advanced internet of things (IoT) technology, has attracted the special attention of engineers in the smart grid (SG) field and has the tasks of demand-side management (DSM) and helping to control equality between demand and electricity supply. The main performance of the HEMS is based on the optimal scheduling of home appliances because it manages power consumption by automatically controlling loads and transferring them from peak hours to off-peak hours. This paper presents a multi-objective version of a newly introduced metaheuristic called the bald eagle search optimization algorithm (BESOA) to discover the optimal scheduling of home appliances. Furthermore, the HEMS architecture is programmed based on MATLAB and ThingSpeak modules. The HEMS uses the BESOA algorithm to find the optimal schedule pattern to reduce daily electricity costs, reduce the PAR, and increase user comfort. The results show the suggested system’s ability to obtain optimal home energy management, decreasing the energy cost, microgrid emission cost, and PAR (peak to average ratio)