B.P. Muni

Bio: B.P. Muni is an academic researcher from Bharat Heavy Electricals. The author has contributed to research in topics: Transmission system & Power (physics). The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Impact of photo voltaic cells on hybrid micro grid power distribution systems

Abstract: In spite of availability of natural sources, still people are now sitting on the dark zone. Because of the remote area, the grid networking system is very weak. As a result of which power stability may not be assured, which greatly effects to the day-today activities of the people in the reason. Fortunately, technology exist that can give the assurance of quality of electricity in an isolated area which is for away from the regular power network. The preset scenario is the coexistence of both AC and DC micro grids, because of the evolutions of different types of loads. The hybrid microgrid concept assume a cluster of different types of loads and distributed generatoroperating as a controllable system which maintain the quality of power and its stability to its local area. To make the world pollution free environment, renewable energy infrastructure is increasing particularly in the distribution network. It has the advantage that the power generated through distributed generation system is utilized in the grid for other users. The PV cell generates voltage at the distribution level. At the end of distribution line a 1 MVA three phase load is connected. In the absence of the generation from the PV cells, the voltages and currents at various lengths of the line are calculated with a single load of three phase 1 MVA. With the inclusion of PV cells, the improvement in voltage dip is also estimated. This estimate provides the power supplied by the PV cell and improvement in voltages. In order to determine the effect of additional load on voltage profile, a three phase load of 1 MVA was connected to intermediate point in the transmission system. This causes the dip in voltage at various nodes of the distribution line. Several combinations of source and loads have been examined to determine the effect on voltage and power. The above work has been carried out using Electro Magnetic Transient Program (EMTP) Software. In order to simulate the system, 8.98 kV, 5 km long line is divided into 5 sections of 1 km each. The sections are represented in the form of Pi-network by its series inductance, resistance and capacitance to ground. Loads are represented by R and L of appropriate magnitude and through EMTP program the calculations are done. The PV cell is represented by a source stepped up to 8.98 kV system. Both single phase and three phase lines have been examined through the above program. The results obtained for various conditions are presented in the paper.

Probabilistic estimation of hosting capacity and operating reserve including optimisation of PV installation capacity

Abstract: In recent times the need to deploy additional sustainable generation means has become more apparent due to the ever-changing landscape of the global energy generation sector. Australia’s changing consumer needs means new technologies like renewable generation sources such as solar PV systems have increased in popularity over time, though their full capability has not yet been met. Though their intermittent generation is cause for concern in maintaining a stable and quality power supply. This thesis aims to address the issues by developing a probabilistic methodology for the day ahead estimate of the maximum hosting limits capacity and minimum operating reserve requirements of a microgrid containing high levels of PV penetration. Before the commencement and development of the project, a wide range of methods from literature were analysed regarding microgrids and their use in this project. The comprehensive range of concepts of microgrids and their distributed generation were divulged and incorporated into the project methodology. To understand how to provide the probabilistic estimate of the maximum hosting capacity, three previously methods in literature were analysed, each providing more technically advanced approaches than the last. The same research approach was used to understand the methodology of developing a probabilistic estimate of the operating reserve. These methods range in methodology, from the Monte Carlo simulation method to advanced artificial neural networks. To provide the day ahead estimates, an artificial neural network is developed to generate the network parameter forecasts required, providing with it, a probabilistic range of input to a network model. The maximum hosting capacity limit will ensure the amount of renewable generation expected will not exceed the performance indexes required for, voltage level, line loading limits and generator reverse power flow. The minimum operating reserve will provide an estimate of the reserve generation required if there were to be a sudden drop in the renewable energy supply. The estimates are created by modelling the IEEE 13-bus network in PowerFactory containing high levels of renewable generation. The programming functionality in this package has been utilised to automate the immense simulation, calculation and data collection processes required on a case by case basis. Statistical analysis is performed to define the probability of these estates occurring. The probabilities of these estimates will help network operators in making decisions for the control of the microgrid. Adding to these estimates were the PV generation capacity optimisations to increase the maximum hosting capacity limit. Several test cases were created to analyse the performance of the modelling automation developed. Each of these cases created a different insight into the estimation and optimisation cases and their interaction with the performance indexes. The probabilistic estimations derived produced a normal distribution of values for each of the cases tested. Probability statistics are applied to provide the probability of such estimates occurring for the next day's operation. The optimisation successfully provided the maximised PV generation, setting a maximum hosting capacity within the performance index limits. The methodology developed was successful in providing the probabilistic estimation required and optimising the PV installed capacity. This method offered the use of advanced technology, such as artificial neural networks, to provide more reliable predictions into the network operation.