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In 2018, Navigant Consulting coined the term Energy Cloud that described a massive transition across the entire energy value chain caused by disruptive clean energy technologies and business models. The Energy Cloud will replace the existing linear value chain built over the last 100 years that delivers electricity unidirectionally from massive centralized generation sources to consumers. I share Navigant’s view that the Energy Cloud will be a multi-modal, multi-nodal, multi-directional system with new dynamic stocks and flows that include nascent consumer-centric models such as Integrated DER (iDER), Transportation-to-Grid (T2G), Building-to-Grid (B2G), Internet of Energy (IoE), Transactive Energy (TE), Smart Cities, and the Neural Grid. Disruptive clean energy technology solutions that mitigate Green House Gas (GHG) emissions have entered a new stage of commercialization in the application of artificial intelligence (AI), advanced materials, IoT, and potentially, blockchain to make production and use of energy more efficient, less costly, and better for the environment.
Startups around the world, in academic and independent labs, are discovering and developing new high-performance, low-cost clean advanced materials such as carbon fiber composites, new elements for batteries and solar cells, super capacitors and semiconductors, high performance coatings, advanced membranes, structural materials, and processes to capture and convert CO2 to useful outputs. This acceleration of advanced materials is being supported by a number of maturations of the clean energy ecosystem including growth of energy accelerators across the U.S. to more than 20, a deep pool of seasoned clean technology entrepreneurs, faster and cheaper early stage rapid prototyping resources, greater availability of outsourced manufacturing options to reduce scaling costs and increased involvement of corporate venture capital (CVC) units of major energy companies.
"Grid intelligence will be essential as both on the grid and “behind-the-meter” storage is introduced into power systems"
The use of artificial intelligence/machine learning (AI/ML) is having an extraordinary impact on the energy sector and is being widely applied to predictive and preventive maintenance of turbines, solar fields, industrial processes, as well as improved monitoring of transmission and distribution of power. AI/ML applications are being developed to make the power sources and grid delivery of electricity smart, adaptive, and resilient. Data analytic solution has become essential as electricity is generated from widely distributed sources and are being introduced to an already tenuous grid. Analytics with sensor data can identify anomalies and weaknesses in complex and opaque system to predict failures that can result in tragic losses such as the 2018 fires in California.
The U.S. has approximately 6,000 power generation plants that on average are 30 years old and are delivering power over three million miles of transmission and distribution lines. Huge power systems like PG&E are mostly monitored by humans trying to make decisions based on incomplete and untimely data. Utilities and the PUCs that regulate them can choose to spend billions to replace their legacy infrastructure or adopt technologies that provide timely and relevant information about the health of their systems, particularly with the radical changes associated with the impact of new intermittent and distributed sources of energy. It is most likely that the current U.S. infrastructure that consists of three massive grids will be replaced by many local micro-grids of power generation, delivery and use. The builders of the new micro grids may use AI/ML and blockchain to create systems that are easily adapted to a broader range of power supplies, locations and micro-flows, and which are resilient to disruptive events such as weather and capable of dynamically “self-optimizing”. Blockchain in microgrids can potentially allow any member of the microgrid system to supply or consume electricity with fast, secure and reliable peer-to-peer payments. AI/ML is also delivering intelligence to commercial, industrial and residential users with real-time data capture to allow consumers to manage their electricity consumption. Grid intelligence will be essential as both on the grid and “behind-the-meter” storage is introduced into power systems.
For AI/ML to optimize electricity systems, IoT sensors are critical to generate real-time data from source to use. IoT devices and sensors are currently being assimilated into a spectrum of loads such as circuit breaker boxes, electric and water meters, home appliances, electrical outlets, lighting, and HVACs. IoT devices will also be used to collect data of non-load devices such as windows, doors, insulation, roofs that will generate data to be used in combination with other variables such as weather, building occupancy, holidays and others external but easily predictable and monitored variables. Although it is still very early in the IoT deployment cycle, the trend points toward extensive integration into all public, commercial, industrial and residential spaces of our lives to make energy use more secure, reliable and efficient, provided cyber security can be adequately addressed.
Unlike the 2006-2011 bubble and bust of investment in clean energy, there is exponential growth in the integration of clean energy technologies into the operating models of diverse sectors such as residential, retail, transportation, manufacturing, cold chain, shipping, IT, telecom, media, agriculture, and others. The expanding demand for clean energy has propelled the entrance of strategic investors, increased mergers and acquisitions activity, introduced innovative financing mechanisms, and generated compelling investor returns for investing in clean energy technologies.
Check Out: Energy Tech Review
See Also : Top CleanTech Startups