This paper helps us analyze three different MPPT techniques like Perturb and Observe, Incremental Conductance and Particle Swarm Optimization method. As the output characteristic depends on temperature and irradiance, therefore the maximum power point (MPPT) is not always constant. Hence it is necessary to ensure that the PV panel is operating at its maximum power point. There are many different MPPT techniques but, the confusion lies in selecting which MPPT technique is best as every algorithm has its own merit and demerit. In order to extract maximum power from PV arrangement, PSO algorithm is proposed. Algorithms are implemented using the DC-DC converter as well as SEPI converter. Results of simulations are presented in order to demonstrate the effectiveness of PSO algorithm, when compared to Perturb and Observe (P&O) and Incremental Conductance (INC). To simulate the proposed system MATLAB/SIMULINK power system tool box is used.

Now-a-days most of the industries want to earn more profit. It is possible with Five Phase Induction Machines. It attains the more efficiency because of its five phase winding arrangement. These machines are specially design for industries. Making the conversion of five phase supply from three phase supply with the help of transformer scheme which is specially designed. Generally, power quality problem like voltage sag occurs because of sudden changes in loads. In this paper, as a remedy, five phase DSTATCOM (Distribution Static Compensator) with PWM technique is opted to mitigate the voltage sag as power quality problem by injecting the currents in to the system. The MATLAB simulation results represent the effectiveness of five phase DSTATCOM with PWM technique.

Abstract

Reducing demand coincidence of customers with distribution system peak hours is very essential in the modern world energy sector. There is a great scope for peak demand coincidence reduction at the customer level, especially in the residential sector through Residential Demand Response programs. Through smart meter installations along with IT-enabled technologies, many of the distribution company’s initiatives like residential demand response programs can be taken to domestic customers with ease, less cost, and less technology deployment. Rate design is one such effective approach. Even though time-varying rates like Time of Use have been used as an effective approach for reducing peak electricity demand in different sectors around the world, the residential sector has not gained much attention due to a few challenges like externality problems, high on-peak and low off-peak prices, improper pricing mechanisms. Hence, considering the above challenges and constraints, a rate design, named consumer-centric time of use tariff is proposed in this article for domestic customers. The tariff is consumer-centric such that each customer gets a unique on-peak unit price and off-peak unit price based on their consumption during peak hours both at the house-level and utility level rather than common and fixed on-peak and off-peak prices for all the customers, thus addressing the above-mentioned constraints. For this, customers are classified into different clusters using the Machine Learning Algorithm K-Means. The proposed rate design model has been analyzed on synthetic smart meter data of 10 houses, and it is observed that the proposed tariff shows an increase in the monthly revenue by 4.3% for the utility and a variation of -0.4% to 7% of energy charge for different customers. This study and analysis show that the proposed consumer-centric time of use rate design provides a better pricing mechanism with a win-win strategy for both customers and utility, thereby avoiding windfall gains or losses to both. Furthermore, the proposed tariff influences each residential customer of different consumption levels to reduce peak demand coincidence as well as energy consumption for the power sector.