Interview with Mostafa Farrokhabadi, BluWave-ai

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As part of our ongoing coverage of the upcoming Decentralised Energy Forum, we interviewed  Mostafa Farrokhabadi, Director of Grid Analytics & Technology at BluWave-ai .

The Energy Bit would like to thank Mostafa for providing us with excellent content and expect that our readers will enjoy this as much as we did!

Give us a little back story about yourself and the company beginnings?

Mostafa Farrokhabadi, BluWave-ai
Mostafa Farrokhabadi, Director of Grid Analytics & Technology, BluWave-ai

Here is my biography: Mostafa Farrokhabadi is the technology and innovation leader at BluWave-ai, an internationally award-winning start-up offering AI-enabled solutions to accelerate the adoption of renewable energy resources and electric transportation. BluWave-ai’s intelligent platform optimizes the cost and reliability of modern grids with various energy resources, leading the movement towards a smarter and greener electric grid. BluWave-ai’s customers have reported up to 40% reductions in their grid emissions, effectively mitigating their adverse impact on climate change.

Mostafa obtained his Ph.D. in Electrical and Computer Engineering from the University of Waterloo. He has more than eight years of experience in designing mission-critical grid solutions for industry and academia. Mostafa has (co)authored several articles in high-impact energy journals, conference proceedings, and magazines, and has filed multiple patent applications pertaining to the control and optimization of smart renewable-penetrated grids. Mostafa has also led an international coalition of 21 researchers from 14 institutions investigating stability issues in small-scale electrical systems with a high penetration of renewable energies. During the course of his career, Mostafa has received multiple business, research, and teaching awards, including the prestigious Ottawa’s Forty Under 40.

BluWave-ai was founded in 2017, initially with a founding circle of two people. The team has quickly grown to 20 people today. The company HQ is in Ottawa, Ontario. BluWave-ai uses artificial intelligence to accelerate the adoption and use of renewable energy sources and electric transportation by communities, corporations, and utilities. Our machine learning platform optimizes the cost, availability, and reliability of different energy sources – renewable and non-renewable – with energy demand in real-time. This lets our customers improve their energy decisions for sustainability, reliability, and affordability. BluWave-ai is pleased to work with innovators in the energy industry including Tata Power, Hydro Ottawa, Summerside, and Natural Resources Canada to realize a greener, more sustainable grid for all.

What has been the most challenging aspect of BluWave-ai Edge development? Are you planning on integrating into additional areas?

Our platform is data-driven; consequently, we have been facing two major challenges so far. First is the quality of available historical data for a customer, and the quality of the data being metered in real-time across the customer system. In view of these challenges, we have developed innovative solutions to mitigate issues around data quality, including systems and methods for warm-starting a product developed for one customer and deploy it for another customer with similar operational requirements.

We currently have ongoing projects in Canada, the US, and India, and we are looking to extend our footprint into the UK and European markets.

Has it been hard to communicate your offering to new customers? Has there been a good uptake?

Generally speaking, players in the energy and electricity domain are reluctant to changes as the system has been operating with minimal changes for the past 50 years, if not more. On the other hand, driven by climate change mitigation policies, decreasing cost of distributed energy resources and energy storage systems, and the need for higher reliability of supply, conventional power systems are moving towards decentralization, digitalization, and decarbonization. In particular, local distribution companies are starting to re-think their business model, transforming from distributing energy to consumers to facilitators of dynamic transactions among prosumers.

In view of these market changes, the majority of utilities approached by our sales team have shown interest in our solution, including Hydro Ottawa, Summerside Electric, Tata Power, Con Edision, and New York Power Authority (NYPA). On the other hand, the path to revenue is generally long in this domain, due to the systematic and traditional conservatism among the players in the domain. Thus, our team at BluWave-ai has been focused on a business model and technical approach that makes it easier for our customers to put a pen on the contract, hence minimizing the path to revenue compared to some of our competitors.

What kind of hardware is needed? How does it control the grid and appliances?

We have a SaaS business model, i.e., providing or implementing hardware is not our core business. To further elaborate, our target customers are those who already have a functioning metering and monitoring system in place, where we can use APIs to establish a connection between our platform and controllable assets. Saying that, in case a customer does not have the adequate hardware in place, we have partners that can help the customer to implement the system necessary for full observability and controllability.

Our platform consists of Centre and Edge modules. The Centre module is hosted in the cloud and interfaces with external data streams and the Edge modules. The Edge modules can be logically hosted in the cloud or be physically located on the customer’s site. In the latter case, we deploy Linux boxes that are specifically designed for the customer’s computational and communication bandwidth requirements.

How is the data collected, handled, and used? Security?

Our BluWave Edge server connects via IoT interfaces. Today we support MQTT, but have a few others on our roadmap. We would add relevant protocol support based on the opportunity. Generally, we prefer to interface through a traditional SCADA or BMS software system. Our servers support a cloud service model, with the possibility of onsite server capability at an additional cost. Data security is considered from end to end, including MQTT with TLS, encrypted storage, and secure hosting.

How does your platform stand out from its competition? What are BluWave-ai’s main advantages?

We offer an AI-enabled, self-learning, and fully automated distribution optimization platform. We activated the first-ever AI-enabled energy dispatch at a Canadian Utility, giving us the edge to be the market leader in this domain. We have one USPTO granted and four others in the pipeline.

Our platform is unique in the following ways:

  1. It trains itself to recognize shifting patterns in data and continuously improves its performance
  2. It delivers executable commands to the grid control platform in quasi-real-time, thanks to the fast inference of our state-of-the-art predictive optimizer.
  3. It ingests data from the Internet of Things (grid sensors and meters) and external sources following a variety of protocols
  4. Our Centre-Edge architecture allows for continuous deployment of models among processing locations, easier scaling and superior resiliency

What size is the company currently and what kind of growth is expected?

The company’s current headcount is 20, having raised $5M+ in investments and grants. Our 2023 vision is to turn into a global premier smart grid company, with 100+ customers and a projected revenue of $66M.

Is the outlook bright?

So far, we have received significant attention from investors to contribute to our Series A round, and we are on track with our customer acquisition roadmap.

Are there any limitations where your platform can be applied – Smart cities, smart grids, smart houses, etc?

Technically speaking, there is no limitation in where our technology can be applied. On the other hand, for the technology to bring value and be economically feasible, there should be the right mix of operation paradigm, available assets, and metering infrastructure. Generally speaking, we are interested in a system with at least 25% penetration of renewable energy resources, with a comparable distributed or central electrical and thermal energy storage systems.

How has BluWave-ai planned for the future of smart cities, autonomous electric vehicles, and majority renewables?

BluWave-ai is founded with the vision to accelerate the adoption of distributed renewable energy resources and electrified transportation. Our technology roadmap is designed to address major impediments towards renewable-penetrated electrical grids with a high share of electric vehicles and smart assets.

How much time does the system need to run before it can make accurate and profitable predictions?

It depends on the amount of quality of historical data available. In a worst-case scenario, in which there is zero historical data available, our simulations have shown that we can beat naïve benchmarks as fast as within a week of operation.

Do you have any other messages for the readers?

The movement towards decentralization, digitalization, and decarbonization of electric grids requires participation from all the players in various levels, from policymakers to system operators, from large enterprises to residential customers. While we at BluWave-ai are working hard to be a key enabler of the transition, it is important for individuals to feel responsible in fighting climate change and moving towards smarter greener electricity and transportation sectors.


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