Electricity + Control July 2018

ENERGY MANAGEMENT + ENVIRONMENTAL ENGINEERING

Smart Meter & Customer Facing Apps

Distribution Intelligence Apps

Renewable Apps

Tarrif Systems, Billing, POS, Customer Service, and Scheduling Systems

Cross- Functional 'Tribal- Knowledge' Apps

Industrial IoT Apps

Asset Management Apps

Are there concerns with the future smart grid? One of the major concerns with the smart grid is the increased use of Information and Commu- nication Technology, which relies on the Internet as well as computing and processing power to run. This industry has become a large contributor of greenhouse gas emissions in recent years as companies shifted to machine-run operations, and the use of the Internet has increased by 30-40 per- cent per year. To process the amount of data nec- essary to run the smart grid, additional machines and computing power will be needed, and the im- pact of energy consumption on the environment from further greenhouse gas emissions is sure to increase. Therefore, players in the AI energy grid industry need to address this problem. Fortunately, industry leaders are aware of the challenge and are already taking steps in the right di- rection. The three leading greenhouse gas emitters in this industry – computer makers, data centres, and telecoms – are looking to reduce emissions in many ways. For example, computer makers are in- vesting in new hard drives and screens; Fuel cell data centres are monitoring temperatures, pooling resources and researching cloud computing; Tele- coms are looking into network optimisation pack- ages, solar-powered base stations, and fibre optics. If the smart grid is able to use fossil fuels in the most efficient way possible through increased incorporation of renewable resources as those technologies advance in sophistication and capa- bility, the entire system may be able to reduce its carbon footprint. Despite this uncertainty associ- ated with future technological innovation, we can be optimistic in expecting the smart grid system to lower electricity bills and prevent catastrophic blackouts by optimising supply and demand at lo- cal and national levels. For those looking to make a difference in shaping the future of society, the interface between AI and energy is a great place to start.Technological innova- tion is drastically changing the way we think about these two industries and their integration is in its early stages. Their synergy may change the world in ways we could never have imagined, and they are primed for innovative thinkers to make their mark. Acknowledgement Courtesy Harvard's Science in the News: sitnbos- ton.com

Energy Management Value Chain

collect and synthesise overwhelming amounts of data from millions of smart sensors nationwide to make timely decisions on how best to allocate ener- gy resources. Additionally, the advances made from ‘deep learning’ algorithms, a system where ma- chines learn on their own by spotting patterns and anomalies in large data sets, will revolutionise both the demand and supply side of the energy economy. As a result, large regional grids will be replaced by specialised micro-grids that manage local ener- gy needs with finer resolution.These can be paired with new battery technologies that allow power to continually flow to and between local communi- ties, even when severe weather or other outages afflict the broader power system. On the demand side, smart metres for consum- ers, including homes and businesses, and sensors along transmission lines will be able to constant- ly monitor demand and supply. Further, brief- case-sized devices known as ‘synchrophasers’ will measure the flow of electricity through the grid in real time, allowing operators to actively manage and avoid disruptions. These sensors will com- municate with the grid and modify electricity use during off-peak times, thereby relaxing the work- load of the grid and lowering prices for consum- ers. Google recently applied this AI technology to reduce its total data centre power consumption, which translated to millions of dollars in savings. On the supply side, AI will allow the U.S. to transition to an energy portfolio with increased renewable resource production and minimal dis- ruptions from the natural intermittency that comes with these sources owing to variable sunlight and wind intensity. For example, when renewables are operating above a certain threshold, either be- cause of increases in wind strength or sunny days, the grid will reduce its production from fossil fuels, thus limiting harmful greenhouse gas emissions. The opposite would be true during times of be- low-peak renewable power generation, thus allow- ing all sources of energy to be used as efficiently as possible and only relying on fossil fuels when necessary. Additionally, producers will be able to manage the output of energy generated from mul- tiple sources to match social, spatial, and temporal variations in demand in real-time.

Franklin Wolfe is a graduate student in the Earth and Planetary Science program at Harvard University.

18 Electricity + Control

JULY 2018