Account of additional factors for damping torsional oscillations.

Some parameters are influenced by the turbine unit's torsional oscillations. The fundamental comes from damping these oscillations, which are brought on by a departure in the turbine blades' speed from the device's prediction of the steam volume, and attenuation of fluctuations due to the distribution of energy in the turbine's productive components. The usual single-machine infinite bus system is used for the analysis. For various turbine-generator shafts and various generator operating situations, rotating mass mechanical system evaluations for small-signal stability and large disturbance are conducted. It is demonstrated that the shaft's "structural' damping (H) and "steam' damping (Kn) coefficients have a considerable impact on the damping of torsional modes. The goal of this work is to determine the effect of changing the damping factors in the mathematical model of the steam turbine shaft on the system's static stability, as well as the extent to which these variables' limits on damping rotational oscillations on the maximum torsional torques generated in the shaft masses. The mathematical model of the steam turbine shaft with a single machine and transmission line to an infinite bus system was simulated using Dymola software, and the static and dynamic effects of damping factors (H) and (Kn) on system stability were demonstrated. By evaluating the best case for parameters with the least influence on the system's stability, the results were obtained by changing the factors (Kn) from 0.005 to 0.5 and (H) from 0.005 to 0.2 and the extent of its effect on the maximum torque of the steam turbine masses and reducing it by 8.4 %, as well as by reducing the settling time of the system after disturbances occur and reaching to Steady state by about 90 %.

Practical prototype for energy management system in smart microgrid considering uncertainties and energy theft.

The conventional electrical grid faces significant issues, which this paper aims to address one of most of them using a proposed prototype of a smart microgrid energy management system. In addition to relying too heavily on fossil fuels, electricity theft is another great issue. The proposed energy management system can simultaneously detect electricity theft and implement demand response tactics by employing time-of-use pricing principles and comparing real electricity consumption with grid data. The system uses the Al-Biruni earth radius (BER) optimization algorithm to make smart choices about how to distribute the load, intending to reduce energy consumption and costs without sacrificing comfort. As a bonus, it considers limitations imposed by battery charging/discharging and decentralized power generation. Incorporating sensors and SCADA-based monitoring, the system provides accurate measurement and management of energy usage through load monitoring and control. An intuitive mobile app also helps consumers connect, allowing for more active participation and better control over energy use. Extensive field testing of the prototype shows that by moving loads from peak period to another off-peak period, electricity expenditures can be reduced by up to 48.45%. The energy theft value was calculated to be 1199 W, proving that the system's theft detection model was effective.

Energy management system in smart buildings based coalition game theory with fog platform and smart meter infrastructure.

This paper proposes a central energy management system (EMS) in smart buildings. It is based on the coalition method for optimal energy sharing between smart buildings. Game theory is applied to obtain an optimal allocation of the building's surplus energy on the deficient energy buildings using the Shapley value, which enables the unequal energy distribution based on the energy demand. The main objective is reducing energy waste while preserving the generation/demand balance. The fog platform with memory storage is applied, which handles all the measured data from the smart buildings through Wi-Fi-based communication protocol and performs the EMS program. The smart meter links the smart buildings with the fog-based EMS central unit. Two scenarios are implemented based on the difference between total deficient and surplus energy. Coalition game theory is applied for optimal surplus energy allocation on deficient buildings when the total energy surplus is lower than the total energy deficient. Also, there is a one-to-one relationship between the surplus and deficient building; if the surplus energy is larger than the deficit, the extra surplus energy is stored for further usage. The proposed EMS is applied and tested using a smart city with 10 buildings in the MATLAB program. A comparison between the result obtained with and without applying the proposed method is performed. The performance of the fog platform is introduced based on the run and delay time and the memory size usage. The results show the effectiveness of the proposed EMS in a smart building.

A Novel Internet of Energy Based Optimal Multi-Agent Control Scheme for Microgrid including Renewable Energy Resources.

The increasing integration of Renewable Energy Resources (RERs) in distribution networks forms the Networked Renewable Energy Resources (NRERs). The cooperative Peer-to-Peer (P2P) control architecture is able to fully exploit the resilience and flexibility of NRERs. This study proposes a multi-agent system to achieve P2P control of NRERs based Internet of Things (IoT). The control system is fully distributed and contains two control layers operated in the agent of each RER. For primary control, a droop control is adopted by each RER-agent for localized power sharing. For secondary control, a distributed diffusion algorithm is proposed for arbitrary power sharing among RERs. The proposed levels communication system is implemented to explain the data exchange between the distribution network system and the cloud server. The local communication level utilizes the Internet Protocol (IP)/Transmission Control Protocol (TCP), and Message Queuing Telemetry Transport (MQTT) is used as the protocol for the global communication level. The effectiveness of the proposed system is validated by numerical simulation with the modified IEEE 9 node test feeder. The controller proposed in this paper achieved savings of 20.65% for the system, 25.99% for photovoltaic, 35.52 for diesel generator, 24.59 for batteries, and 52.34% for power loss.

A Novel Robust Smart Energy Management and Demand Reduction for Smart Homes Based on Internet of Energy.

In residential energy management (REM), Time of Use (ToU) of devices scheduling based on user-defined preferences is an essential task performed by the home energy management controller. This paper devised a robust REM technique capable of monitoring and controlling residential loads within a smart home. In this paper, a new distributed multi-agent framework based on the cloud layer computing architecture is developed for real-time microgrid economic dispatch and monitoring. In this paper the grey wolf optimizer (GWO), artificial bee colony (ABC) optimization algorithm-based Time of Use (ToU) pricing model is proposed to define the rates for shoulder-peak and on-peak hours. The results illustrate the effectiveness of the proposed the grey wolf optimizer (GWO), artificial bee colony (ABC) optimization algorithm based ToU pricing scheme. A Raspberry Pi3 based model of a well-known test grid topology is modified to support real-time communication with open-source IoE platform Node-Red used for cloud computing. Two levels communication system connects microgrid system, implemented in Raspberry Pi3, to cloud server. The local communication level utilizes IP/TCP and MQTT is used as a protocol for global communication level. The results demonstrate and validate the effectiveness of the proposed technique, as well as the capability to track the changes of load with the interactions in real-time and the fast convergence rate.