Industrial Automation Magazine India

Smart grid technology optimises energy flow, reducing losses

Published on : Saturday 04-02-2023

Sunil Khanna, Non-Executive Chairman, VERTIV.

Green Energy is a buzzword today. How can automation and control help to scale up the Green Energy solutions to industrial levels?
Automation and control can help scale up green energy solutions to industrial levels in several ways:
•    Increased efficiency: Automated systems can control and optimise energy production, reducing waste and increasing efficiency.
•    Predictive maintenance: Automated systems can monitor energy production and detect potential failures, allowing for proactive maintenance and reducing downtime.
•    Monitoring and data analysis: Automated systems can collect and analyse data on energy production, consumption and distribution, enabling improved decision-making and optimisation. Improved safety: Automated systems can reduce the need for human intervention in hazardous environments, increasing safety. 
•    Scalability: Automated systems can be easily scaled to meet increasing energy demands, making it easier to transition to renewable energy sources at an industrial scale.

Power and Energy are frequently used (incorrectly) as interchangeable, but they are different terms. Power is the instantaneous quantity of energy. Many applications use energy out of a storage system, most common being batteries. The process of capturing energy in a battery at a time of surplus and using the energy at a time of need is an old concept, both in stationary applications and also in mobile vehicles. The hitch is that, both the storage and withdrawal have many losses involved. What are recent advances which drastically reduce such losses?
Recent advances that have significantly reduced losses in energy storage and withdrawal include:
•    Improved battery technology: Advancements in lithium-ion and solid-state batteries have led to higher energy densities, longer lifetimes, and lower losses during charge and discharge cycles.
•    Smart grid technology: Smart grid technology optimises energy flow, reducing losses in transmission and distribution by dynamically balancing supply and demand.
•    Energy management systems: These systems use algorithms to optimise energy usage and storage, reducing losses and increasing efficiency.
•    Power electronics: The use of power electronics, such as inverters and converters, has improved the efficiency of energy storage systems, reducing losses during conversion from DC to AC and vice versa.
•    Advanced materials: The development of new materials, such as graphene and silicon, for energy storage devices has increased their energy density and reduced losses during charge and discharge cycles.
•    Renewable energy integration: Integrating renewable energy sources into the grid and using them to charge batteries during times of surplus reduces the need for energy storage, reducing losses associated with energy storage and withdrawal.

Generation of electricity from fossil fuels is attributed with generation of over 40% of CO2 emissions.  Automation can play a big role firstly in creating the dashboards and also helping with algorithms to reduce these emissions. What are the trends in this matter which is occupying the mind space of leaders the world over?
Automation can play a role in reducing CO2 emissions during power generation from fossil fuels in several ways:
•    Improved efficiency: Automated systems can optimise the combustion process and control emissions from fossil fuel-based power plants, reducing emissions per unit of energy produced.
•    Monitoring and control: Automated monitoring and control systems can continuously monitor emissions levels, allowing for rapid detection and correction of emissions anomalies.
•    Predictive maintenance: Automated systems can monitor equipment performance, detecting potential issues and allowing for proactive maintenance, reducing downtime and emissions.
•    Optimised operations: Automated systems can optimise the operations of fossil fuel-based power plants, reducing energy waste and emissions.
•    Carbon capture and storage: Automated systems can be used to monitor and control carbon capture and storage systems, reducing emissions and improving their efficiency. Example: NITI Aayog planning Roadmap for Carbon Storage project.
•    Digital twins: The use of digital twins can simulate the performance of fossil fuel-based power plants, allowing for optimisation and reduced emissions through virtual testing and experimentation.
•    Advanced analytics: Automated systems can collect and analyse data on energy production and emissions, providing insights that can be used to optimise operations and reduce emissions

Traditionally power transmission meant transporting energy generated from large utilities to consumption centres located well away. But today, with various alternate means of generation, who also have an ambition to export energy to the grid, the patterns have changed. There are multiple points of generation of various capacities with different constraints, which might impact grid stability. What steps are taken to induct the new technologies of AI and ML into this challenging area?
To improve grid stability with the integration of distributed energy resources and new technologies such as AI and ML, the following steps are being taken:
•    Real-time monitoring: Implementing real-time monitoring systems that use AI and ML to detect and respond to changes in energy generation and consumption, ensuring grid stability.
•    Predictive analytics: Using predictive analytics and machine learning algorithms to forecast changes in energy generation and consumption, allowing grid operators to proactively adjust supply and demand.
•    Dynamic resource allocation: Automated systems that use AI and ML to allocate energy resources dynamically based on real-time data, improving grid stability and efficiency.
•    Smart inverters: Smart inverters that use AI and ML algorithms to dynamically adjust power output from distributed energy resources, improving grid stability and reducing the risk of blackouts.
•    Virtual power plants: Implementing virtual power plants that use AI and ML to optimise the combination of energy generation and storage, improving grid stability and reducing emissions.
•    Energy management systems: Implementing energy management systems that use AI and ML algorithms to optimise energy usage and storage, reducing the risk of blackouts and improving grid stability.
•    Microgrid control: Developing AI-powered microgrid control systems that can manage distributed energy resources, improving grid stability and reducing the risk of blackouts.

Examples of these implementations can be found in various countries around the world, such as the virtual power plants developed by Sonnen in Germany, the smart inverters developed by Enphase Energy in the US, and the microgrid control systems developed by ABB in Switzerland.

One of the major challenges in power distribution is disruption to power supply due to faults in the distribution system. This forces many entities to resort to captive power generation systems, which are of a smaller capacity, and then usually of lower efficiency. One attempt all along has been to localise and repair faults rapidly. What new technologies have entered this field?
To localise and repair power distribution faults rapidly, several new technologies have been introduced, including:
•    Advanced sensors: Advanced sensors, such as smart meters and phasor measurement units (PMUs), are being deployed to monitor the power grid in real-time, allowing for the rapid detection of faults.
•    Predictive maintenance: Predictive maintenance technologies, such as artificial intelligence (AI) and machine learning (ML), are being used to analyse sensor data and predict potential faults, allowing for proactive maintenance and reducing downtime.
•    Drones: Drones equipped with sensors and cameras are being used to inspect power lines, reducing the need for manual inspections and allowing for rapid fault localisation and repair.
•    Smart grid management systems: Smart grid management systems, using AI and ML algorithms, are being developed to optimise the distribution of power and quickly redirect power to other parts of the grid during a fault, reducing the impact on customers.
•    Mobile response teams: Mobile response teams equipped with advanced tools and technologies are being deployed to quickly respond to power outages and repair faults, reducing downtime.
•    Examples of these technologies can be found in several countries around the world, such as the smart grid management systems developed by Siemens in Germany, the predictive maintenance technologies developed by GE in the US, and the drone-based inspection systems developed by UAV Systems in Australia

In a smart city, metering of electricity consumed needs smart meters. These meters need to do more than just measure the flow of power, they may also need to track time-of-day consumption, maximum demand and such parameters. What are the latest techniques in this field?
The latest techniques to track time-of-day consumption, maximum demand, and other parameters in smart cities include:

Advanced Metering Infrastructure (AMI): AMI systems use smart meters to collect real-time data on energy consumption, including time-of-day usage and maximum demand, and communicate it to the utility through a secure communication network. Example: Landis+Gyr, Itron, Sensus.

Time-of-Use (TOU) Billing: TOU billing is a pricing mechanism that charges consumers different rates for electricity depending on the time of day they consume it. This encourages consumers to shift their energy usage to off-peak times when electricity is less expensive. Example: Pacific Gas & Electric.

Load Profiling: Load profiling uses smart meter data to create a detailed profile of a customer's energy consumption patterns, including peak demand periods, and helps utilities to better manage their distribution networks. Example: GE Digital.

Demand Response: Demand response is a program that incentivises consumers to reduce their energy consumption during peak demand periods by offering them financial incentives or by adjusting their energy usage automatically through smart appliances. Example: EnerNOC, OpenADR.

Home Energy Management Systems (HEMS): HEMS are systems that allow consumers to monitor and control their energy usage through a user-friendly interface, helping them to reduce their energy consumption and manage their energy costs. Example: Nest, Ecoisme, Sense.

These techniques help utilities to improve their operational efficiency, reduce costs, and better understand and serve their customers' energy needs, while also helping consumers to reduce their energy consumption and costs.

Sunil Khanna, Non-Executive Chairman, VERTIV, completed his B.Tech in Electronics Engineering from IIT (BHU) in 1976 and M. Tech in Electrical Engineering from IIT Kanpur in 1978. Mr Khanna is former President and Managing Director of VERTIV (formerly Emerson Network Power India Ltd.). Prior to this, he was the Managing Director of Emerson Process Management India Ltd between 2006 and 2011. Prior to joining Emerson, Sunil spent 22 years with ABB in various countries and rose to the position of Vice President – Automation Technology.
 
Mr Khanna is a founding member of Automation Industry Association (AIA) and was its President from 2008 to 2011. He is also a key member of Confederation of Indian Industry (CII), elected as CII National Council Member between 2008 and 2010. He was nominated Vice Chairman of the CII Maharashtra State council in 2015, and subsequently as Chairman in 2016. Until recently Sunil was the Chairman of Skill Development Taskforce for CII Western Region.
 
Since 2005 onwards, Mr Khanna has been actively involved in academia-industry interface initiatives. He also champions rural, social and agricultural development of our country, particularly in the State of Maharashtra. Under CSR initiative, he took up “Restoration of Assi River” at Varanasi which was completed in March 2019. 
 
Sunil Khanna was conferred with Distinguished Alumnus Award by IITBHU in 2017 and Centenary Award in Feb 2019.