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The utilization of energy is on the rise in current trends due to increasing consumptions by households. Smart buildings, on the other hand, aim to optimize energy, and hence, the aim of the study is to forecast the cost of energy consumption in smart buildings by effectively addressing the minimal energy consumption. However, smart buildings are restricted, with limited power access and capacity associated with Heating, Ventilation and Air Conditioning (HVAC) units. It further suffers from low communication capability due to device limitations. In this paper, a balanced deep learning architecture is used to offer solutions to address these constraints. The deep learning algorithm considers three constraints, such as a multi-objective optimization problem and a fitness function, to resolve the price management problem and high-level energy consumption in HVAC systems. The study analyzes and optimizes the consumption of power in smart buildings by the HVAC systems in terms of power loss, price management and reactive power. Experiments are conducted over various scenarios to check the integrity of the system over various smart buildings and in high-rise buildings. The results are compared in terms of various HVAC devices on various metrics and communication protocols, where the proposed system is considered more effective than other methods. The results of the Li-Fi communication protocols show improved results compared to the other communication protocols.

The energy market is gradually changing from centralized trading to peer-to-peer trading due to the tremendous increase in a microgrid with green energy resources. When more generating units are included in the microgrid, the possibilities of more reactive power flows exist in the system that leads to high transmission loss which has to be optimized. The reactive power is one of the essential ancillary services in the microgrid towards preserving the voltage in the transmission and distribution line. The major contribution of the paper is towards managing the ancillary service in the distributed energy network economically and technically. This study aims to estimate and optimize the power loss, reactive power, and price management as well. Towards optimization, the self-balanced differential evolution algorithm (SBDE) is used in this study. A distribution system operator is involved in coordinating the sellers and buyers. The proposed layered microgrid architecture uses the blockchain technology for reactive power price management by providing transparency and security among peers. The process of converging various transactions into a block and adding in the distributed blockchain is illustrated. Multiple transactions are performed by using the proposed methodology, giving efficient energy transaction. The results show that the power loss is minimized using SBDE algorithm for different cases. Additionally, the study has demonstrated the price allocation of the optimal reactive power obtained from providers. The blockchain technology embedded in reactive power pricing will play a significant role in the evolution of traditional power distribution systems to active distribution networks.

The Smart Grid environment gives more benefits for the consumers, whereas the power quality is one of the challenging factors in the smart grid environment. To protect the system equipment and increase the reliability, different filter technologies are used. Even though, consumers’ expectations towards the power quality are not fulfilled. To overcome these drawbacks and enhance the system reliability, a new Custom Power Devices (CPD) are introduced in the system. Among different CPDs, the Dynamic Voltage Restorer (DVR) is one of the voltage compensating devices that is used to improve the power quality during distortions. When the distortions such as voltage swell and sag occur in the distribution system, the control strategy in the DVR plays a significant role. In this article, the DVR performance using Proportional Integral (PI), Proportional Resonant (PR) controllers are analyzed. A robust optimization algorithm called Self Balanced Differential Evolution (SBDE) is used to find the optimal gain values of the controllers in order to reach the target of global minimum error and obtain fast response. Then, a comparative analysis is performed between different controllers and verified that the performance of PR controller is superior than the other controllers. It has been found that the proposed PR controller strategy reduces the Total Harmonic Distortion (THD) values for all types of faults. The proposed SBDE optimized DVR with PR controller reduces the THD value less than 4% under voltage distoration condition. The DVR topology is validated in MATLAB/SIMULINK in order to detect the disturbance and inject the voltage to compensate the load voltage.

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The deregulated electricity sector needs an improvement in the Available Transfer Capability (ATC) towards the maintenance of power network at balanced condition and to utilize the system in effective manner. Independent System Operator (ISO) maintains the ancillary services by ensuring the reliability of the power system. One of the major functions of ancillary service provider is to maintain the voltage and power flow at stable level. To improve the ATC, both the line power flow and bus voltage profile have to be modified and it is taken care by the ISO. The major limiting criterion for ATC is bus voltage profile. It is well known that the device Thyristor Controlled Series Compensation TCSC which is one of the Flexible AC Transmission System (FACTS) devices can modify the line flow by adjusting the line reactance and Static VAR compensator (SVC) can improve the bus voltage profile by injecting reactive power to the bus. In this research, an Artificial Neural Network (ANN) based estimation of control parameter of FACTS devices such as TCSC and SVC for ATC enhancement is used. The proposed approach uses two different ANN network to find the different TCSC and SVC control parameters to improve the ATC values without violating its voltage constraints for real time transactions. The ANN algorithms such as Radial Basis Function (RBF) as well as Back Propagation Algorithm (BPA) were used to find the TCSC and SVC Parameters and the results are compared. The proposed methods are demonstrated through Reliability Test System (RTS) of IEEE 24 bus. The simulation output represents the suitability of the anticipated method for Real Time estimation of FACTS devices control parameter settings for ATC Enhancement.

<p>At a recent time voltage distortion is one of the foremost power quality problem at all level of power system. Power electronic controllers like various filtering technology have been used for more desirable solution of power quality problems to users. But still there is a voltage and frequency deviations problem in the power system leads to reduce in quality of power and thereby reduce the life of consumer equipment. To overcome this drawback and protects the sensitive loads from power quality problems, custom power devices are used. Among various custom power devices, Dynamic voltage restorer (DVR) is an ultimate solution to address the voltage correlated power quality problems. DVR is a usual three phase inverter which transforms DC to AC and vice-versa using DC link capacitor. Whenever utility is distorted by voltage correlated faults, DVR is dynamic and it shields the load from utility distortions. Whenever distortion arises, the control strategy for the DVR plays significant role to make the DVR in active condition. Among all control strategies, Synchronous reference frame theory is simple and suitable for DVR. The DVR topology is verified and validated using SIMULINK/MATLAB.</p>

The shift from regulated to restructured power system results in an increased competition among the electricity market. In restructured power system, the separation of transmission services from generation and distribution makes it necessary to find the contribution of power from individual generator to individual load. The power flow tracing method is used to obtain the generator power output to a particular load. The reactive power has to be maintained in order to sustain the voltage level throughout the system for reliable and secure operation. Hence the reactive power cost allocation has become imperative in the power system. In this paper, the tracing method is integrated with the optimal reactive power dispatch problem to trace the generator minimal reactive power for sustaining the real power transaction and enhancing the system security by meeting the demand. The Differential Evolution is used for optimal reactive power dispatch. The cost allocation to the generators for the reactive power service based on the opportunity cost method is obtained for 62 Bus Indian Utility Systems.