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 convergence from the electric grid to the smart microgrid motivates the incorporation of the intrusion detection system to identify intruders and mitigate the resultant damages to ensure system stability. It is planned to employ the Deep Belief Network (DBN), which is one of the deep learning techniques with some improvement to detect the attacks in a microgrid. To improve the accuracy of the detection, a rule-based detection technique is added to enhance the detection of intruders using DBN. The proposed technique is supported with the layered micro-grid architecture that makes the system flexible and simple toward the implementation. The proposed Enhanced DBN (EDBN) performance is measured in different bus representations for identifying the higher hit rate and rejection rate, lesser miss rate and false-positive rate. Two attacks, such as False Data Injection and Denial of Service attacks, are generated by Greedy Algorithm and are detected by the proposed technique. Compared to the existing detection and control system, the proposed EDBN technique provides accuracy higher than 92%, false alarm rate less than 1%. Thus, the experimental results show that the proposed technique accuracy is higher than the existing intrusion detection techniques in a microgrid.

Presently power control and management play a vigorous role in information technology and power management. Instead of non-renewable power manufacturing, renewable power manufacturing is preferred by every organization for controlling resource consumption, price reduction and efficient power management. Smart grid efficiently satisfies these requirements with the integration of machine learning algorithms. Machine learning algorithms are used in a smart grid for power requirement prediction, power distribution, failure identification etc. The proposed Random Forest-based smart grid system classifies the power grid into different zones like high and low power utilization. The power zones are divided into number of sub-zones and map to random forest branches. The sub-zone and branch mapping process used to identify the quantity of power utilized and the non-utilized in a zone. The non-utilized power quantity and location of power availabilities are identified and distributed the required quantity of power to the requester in a minimal response time and price. The priority power scheduling algorithm collect request from consumer and send the request to producer based on priority. The producer analysed the requester existing power utilization quantity and availability of power for scheduling the power distribution to the requester based on priority. The proposed Random Forest based sustainability and price optimization technique in smart grid experimental results are compared to existing machine learning techniques like SVM, KNN and NB. The proposed random forest-based identification technique identifies the exact location of the power availability, which takes minimal processing time and quick responses to the requestor. Additionally, the smart meter based smart grid technique identifies the faults in short time duration than the conventional energy management technique is also proven in the experimental results.

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.

Reactive power plays an important role in supporting active power transfer. Reactive power is essential for secure operation of power system and it is important to compute the cost of reactive power. Marginal cost theory is used for calculation of the cost of reactive power at each bus in electric markets. In conventional power flow methods, normally the reactive power production cost is ignored. To obtain an accurate reactive power marginal cost, production cost of the reactive power is considered in the objective function. The model is tested with IEEE-14 bus system for several load changes in order to price the reactive power of the generator.