Session Abstract
Faced with Israel’s 20% renewable mandate by 2020, The national grid of Israel IEC had several predictive challenges. First, they needed to determine how much renewable generation capacity needed to come on line by 2020 to supply 20% of the country’s power requirements, and then figure out how to optimize generation dispatch across their entire fleet to accommodate the weather-related intermittency of their own renewable capacity as well as growing capacity from independent power producers and distributed generation assets. The ultimate goal was to optimize unit commitment and dispatch taking into account weather, transmission capacity, congestion, and demand response by combining sensors, unstructured data and weather models
The first step was to create and deploy a weather simulation app that could interface with a proven and publicly available weather model, public weather databases, and IEC’s existing models for unit commitment economics. Working with App Orchid, developer of Artificial Intelligence apps , next generation visualization tools, and natural language processing to blend structured data with unstructured data to identify patterns, IEC was able to deploy a weather condition simulation app that made it simple for IEC to assess the impact of existing and planned solar and wind generation on load flow across the Israeli electrical grid. The app utilizes a gamified UX approach that allowed analysts to simply drag graphical representations of weather events (wind, various cloud types, rain, temperature ranges, date and time of day ranges, etc.) across a map of Israel and see the short term impact on load flow and unit commit economics, while providing the longer term predictive analysis to make better decisions on future infrastructure investments. The app even takes into account solar panel and wind turbine models, geographical locations and angle toward the sun at any given day or time. All this data is then modeled against operational generation dispatch, transmission and distribution systems to identify congestion hot spots, market weaknesses, the best candidates for proactive demand response programs, and priorities for grid infrastructure improvements and expansions.
- Assess the renewable and generation portfolio across the grid
- Analyze various demand profiles, contracts and devise new policy tools to manage demand and supply
- Identify the weak links in the electric grid from congestion arising out of Intermittency conditions on the electric grid.
- Plan and optimize Dispatch schedules to help plan long term scenarios around generation, load management policy decisions amongst other grid conditions.
Speaker:
Krishna Kumar
CEO App Orchid Inc
Krishna Kumar is an entrepreneur, innovator, visionary and architect with proven expertise in taking a concept to a market-leading industry recognized commercial product. Within a short period, he has commercialized the product, secured beachhead customers and made the company profitable with 300% Quarter over Quarter growth. Prior to App Orchid, Krishna was initially the founder and CEO of Space Time Insight and later CTO, a company with over 44M in VC financing. Under his leadership, Space Time Insight created a market called Situational Intelligence. He has extensive experience in building management teams, acquiring marquee customers and securing multiple multi-million dollar deals. He has also successfully envisioned and implemented product roadmaps. Krishna already has 2 patents on Big data and has filed for 7 more in the field of Cognitive Computing and ‘Internet of Things’. Prior to that he was the Vice President of Ness, a Nasdaq listed company spearheading their SAP practice.
Krishna has over 15 years of experience working in the field of analytics, artificial intelligence, and semantic web specifically in the area of critical infrastructure, infrastructure planning, smart grid and smart cities. He has worked with multiple energy and water based utilities that have used his expertise and technology in several areas including load management, improved asset management, predictive analytics around demand and supply, long term forecasting and energy conservation. As founder and CTO of Space-Time Insight, he pioneered a new field of analytics called Situational intelligence for reliable asset monitoring and predictability around critical infrastructure elements within an organization’ operating area.
Session Tags
Government, Enterprise, Small / Medium Enterprise,
Iot, Cognitive COmputing, Artificial Intelligence, Big Data, Natural Language Processing,
CxO, VP / Director, Middle Management, Business Line Management,
Intermediate,
Industrials, Government / Public Sector
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