Transmission and Grid Planning

For millions of years before human activity, the extent of heat radiated away from the earth remained largely unchanged, thus ensuring global climate patterns remained stable. Lately, the effects of anthropogenic climate change have become increasingly obvious in the form of increased incidence of forest fires, icecap melting, and hurricanes. The drastic increase in the levels of greenhouse gases (GHGs) in the atmosphere due to the Industrial Revolution has reduced the level of heat radiated away, thereby retaining more heat within the global climate system.

The power sector is in the throes of a transition. This change is being driven mainly by renewable energy integration, energy storage technologies to support the renewables, and smart meters. By tracking electricity usage round the clock, smart metering facilitates dynamic pricing (raising or lowering the cost of electricity based on need), helping distribution companies (DISCOMs) cut down on commercial losses.

The Southern Region (SR) leads renewable energy (RE) deployment in India, having an installed capacity of about 43 GW as of December 2020. Recognising the immense RE potential of this region, the Ministry of New and Renewable Energy (MNRE), Government of India, has set an ambitious RE target of 59 GW for SR by 2022. However, the implications of injecting this additional power into the grid have to be understood. Because of the intermittent nature of renewables-based power, grid integration of RE has distinct technical implications.

The ongoing pandemic has spared none, including the power sector. According to the International Energy Agency (IEA), following the pandemic, the global energy demand has reduced by around 6%. This, along with the increase in the percentage share of renewable energy sources, indicates that carbon emissions this year will decrease by around 8%. 

Short-term variability in the power generated by utility-scale solar photovoltaic (PV) plants is a cause for concern for power system operators. Without quantitative insights into such variability, system operators will have difficulty in exploiting grid integrated solar power without negatively impacting power quality and grid reliability. In this paper, we describe a statistical method to empirically model the ramping behavior of utility-scale solar PV power output for short time-scales.

CSTEP organised a stakeholder consultation workshop on the wind energy sector at the request of the Ministry of New and Renewable Energy (MNRE) recently. The main objectives of this workshop were to discuss the results of the national wind potential reassessment study conducted by CSTEP, use geo-spatial analysis to locate high potential wind zones for further development, and discuss mechanisms to ease the problems currently plaguing the sector, such as inadequate site allotments, inefficient power purchase agreements, pricing of renewable energy etc.

CSTEP research report.

Short-term variability of utility-scale solar PhotoVoltaic (PV) plant is a significant issue for grid reliability. It is necessary to quantify the solar power variability in order to analyze the power variations on the electricity distribution network. In this paper, a Lorenz curve-based method is described to quantify the variability of power output from a megawatt-scale solar PV plant. The proposed method is used to analyze the power variability of Yelesandra PV power plant located in the state of Karnataka, India.

In Central Receiver Systems (CRSs), thousands of heliostats track the sunrays and reflect beam radiation on to a receiver surface. The size of the reflected image and the extent of reflection from the heliostats are one of the important criteria that need to be taken into account while designing a receiver, since spillage losses may vary from 2 to 16% of the total losses. The present study aims to determine the size of an external cylindrical receiver, such that the rays reflected from all the heliostats in the field are intercepted. 

In this paper, we present experimental results on specific energy consumption (SEC) in a number of manufacturing processes.