Edge-of-grid voltage control in distribution networks

As the electric power supply systems are undergoing major changes with the integration of renewables, the issues related to voltage regulation and system protection are arising. In this scenario, advanced voltage regulation technologies that provide voltage control at the grid-edge, that is at the low-voltage secondary side of the distribution circuit, have emerged as a potential solution to address the shortcomings of traditional voltage control practices in distribution systems. In this work, these technologies are modeled and algorithms are developed to strategically deploy them, tune their control parameters, and evaluate their voltage regulation performance. A two-stage optimization framework is proposed for optimal placement and real-time control of the low-voltage static var compensators to minimize the energy losses while maintaining the voltage regulation. Integration of high levels of distributed generation such as photovoltaic (PV) systems impacts the voltage regulation by causing steady-state voltage variations and transient voltage fluctuations. This work further develops a procedure to tune the control parameters of PV smart inverters to mitigate these voltage issues. Furthermore, the PV penetration levels in a distribution network can be increased without creating voltage problems by dynamic controlled reactive power absorption at several strategic buses. This concept is modeled and demonstrated in this work. Furthermore, the high levels of PV generation can interfere with the overcurrent protection schemes prevalent in distribution networks. An analytical approach is proposed in this work to estimate the distribution feeder PV accommodation limits with respect to overcurrent protection issues as the impact criteria, without needing to simulate numerous PV screening scenarios to assess the impactElectrical and Computer Engineerin

Increasing Feeder PV Hosting Capacity by Regulating Secondary Circuit Voltages

Voltage rise is one of the major concerns that limits the photovoltaic (PV) hosting capacity or the maximum amount of PV generation that a distribution circuit can accommodate. This paper examines the effectiveness of low-voltage distribution static compensators (LV-DSTATCOMs) in increasing the PV hosting capacity of distribution circuits by mitigating voltage rise. Stochastic analysis framework is used to determine the PV hosting capacity while an iterative placement technique is used to identify effective device locations. To provide insights on the optimal device size, number, and control settings, sensitivity analysis is carried out. The results show that, with appropriate size and control settings, installation of few LV-DSTATCOMs in a distribution circuit can significantly increase its PV hosting capacity. For the circuit under consideration, a set of 23 devices has increased the PV hosting capacity from 15% to 100% of the median day time peak load

IOT based solar energy prophecy using RNN architecture

It is the 21st century and scientists say that by the end of this century, resources will be replenished and the only way the future generations can access energy is through renewable resources— those which are inexhaustible. One such source is sunlight, which has a guaranteed stay in the long run. The energy thus given is termed as solar energy. In the present paper it is tried to solve the issue of limited resources and their adverse effects. Since the power generated from solar energy systems is highly variable, due to its dependence on meteorological conditions, an efficient method of usage of this fluctuating but precious energy source has to come in picture. This requires the scope of reliable forecast information as the development of predictive control algorithms for efficient energy management and monitoring for residential grid connected photovoltaic systems. The paper has given an overview of different applications and models for solar irradiance and photovoltaic power prediction, including time series models based on live measured data from rooftop solar power plant located at 17.5203° N, 78.3674° E. For experimentation, data collected over four years from the solar power plant was used in order to the train machine and understand the characteristics of the solar power plant and gives the predicted energy as the result. The use of Deep Learning is done where LSTM is used for the training and keras and tensorflow are used for obtaining the result. The mean square error thus obtained is 0.015

Peak Demand Management and Voltage Regulation Using Coordinated Virtual Power Plant Controls

The aggregation of distributed energy resources (DERs) enables them to provide various grid services as a virtual power plant (VPP). Utilities use enterprise control solutions, such as advanced distribution management systems (ADMS) and distributed energy resource management systems (DERMS), to efficiently integrate DERs and realize the benefits of a VPP. These control solutions can complement each other to offer additional benefits. This paper evaluates the coordinated operation of an ADMS and a DERMS that collectively implements a VPP to provide peak demand reduction and voltage regulation through the simulation of an actual distribution feeder. A commercial ADMS reduces the peak demand through conservation voltage reduction (CVR). A prototype DERMS dispatches residential battery energy storage systems (BESS) based on real-time optimal power flow to provide additional peak demand reduction. The DERMS also maintains voltage regulation across the feeder by controlling both residential batteries and rooftop PV systems. The results from the controller-hardware-in-the-loop (CHIL) real-time simulations conducted in a realistic laboratory environment show that the coordinated operation of the ADMS and the DERMS effectively achieves peak demand reduction while enforcing voltage regulation across the feeder. Specifically, the ADMS dynamic voltage regulation (DVR) application and DERMS working together achieved a peak demand reduction of nearly 500 kW, whereas the ADMS DVR application alone obtained a reduction of approximately 100 kW. The DERMS VPP control in this work relies on the residential BESS for the demand reduction; the demand reduction accomplished depends on the BESS capacity available in the distribution system