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How Can Energy Companies Balance Short-Term Wins with Long-Term Goals with Big Data? Exclusive Interview with Bogdan Giubega of Transelectrica

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Embracing the new world of data that is staring utilities in the face, newly available thanks to smart grid technology and the Internet of Things, is high on the priority list of decision-makers. However, some of the technologies are so new and the troves of data newly available so vast that tackling it can be intimidating. Especially when utility executives are expected to stick to long-term goals related to this new digital utility, it’s quite a lot to take in.

These utilities can still take big data and push to show the value it brings and demonstrate the how worthwhile the investment is to the company. That’s the perspective of Bogdan Giubega, a project manager with Transelectrica. Bogdan is set to speak at the upcoming conference Smart Grid Big Data 2019  from SmartGrid Forums. Taking place September 17 to 19 in Berlin, Bogdan will share with attendees his presentation entitled "Demonstrating ROI – Balancing short-term wins with larger long-term goals to build a credible big data businses case to secure ongoing investment.”

To get a sneak peek into this critical topic, Energy Central was able to secure an exclusive interview with Bogdan:

 

Matt Chester: To start with the basics, you’re presentation deals with how to balance short-term wins with long-term goals when it comes to big data business. Why is this such an important topic in the utility business? Have some energy companies fallen behind in this balance in notable ways?

Bogdan Giubega: I think that the need for long term goals is quite obvious – for both technical and economic reasons, it is important to have an Enterprise Architecture, to have a "big picture”, and to understand the destination of the digital transformation process. A lack of clarity regarding the destination will make planning and resource allocation extremely difficult. Of course, given the pace of technology advancement, the long-term goals must be broad enough and flexible enough to allow space for updates, but we still need them. In fact, most companies I know do have long term goals included in their strategies. Some might lack an Enterprise Architecture, but almost all of them have pretty clear goals.

The nevralgic spot, most often than not, are the short-term wins. Short-term wins are important for two very different reasons. First, from a financial perspective a short term win means that some benefits are reaped early, and thus they ease the financial burden for future investment. Even for projects with a good ROI, companies are reluctant to invest if there is a long waiting time until they can see the benefits, as they keep in mind that long term projects have a larger degree of uncertainty.

Secondly, from a psychological perspective it is hard to maintain the engagement of the management--and of the team-- without short-term wins. Those quick results give a strong motivation to the management, and also increase the morale of the team.

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MC: The Internet of Things, smart grid tech, and even smart home products have all created a wealth of data in the utility industry where it didn’t previously exist. What are the main challenges that you’ve seen from utilities as they adjust to this new world where the data is so ubiquitous and valuable?

BG: I would like to start by explaining one thing: IoT doesn’t change the behavior of the equipment, it just gives us a lot of information regarding this behavior, so that we can make better decisions, and react faster to whatever is happening. So the first question, and the first challenge is this: what data do I need to accomplish my goals – better decisions and faster response – because, as you mentioned, we can get a wealth of data, which might be useful, or not.  Data is not cheap to process, store, or transport, so if we gather data that we do not really use, that data is going to be a burden, not a tool. In order to address this issue, the most important thing is to develop more and more precise algorithms able to put data in context, and make sense of it. That is why mathematicians and data scientists are harder to get than programmers nowadays. In fact, if you will search IT certifications that lead to wages increase, you will find that the top are data science and cloud architecture certifications. According to Glassdoor, even at entry-level, salaries for data scientists are in six figures.

The second question is where do I process and store the data – it can be done locally, where it is produced, it can be done centrally, on company’s servers, or it can be done in a cloud. The answer depends mostly on results when comparing costs, on preferred architecture, and also on comparing relative risks. While there is no good and bad answer, it is important to establish this early, and plan accordingly. The third challenge is related to cybersecurity – data can be stolen or compromised, the communication channels can be accessed to sabotage equipment, and so on and so forth. In energy, and utilities to a larger extent, maintaining the grid functionality is the foremost concern, so there are legitimate concerns about the risk of implementing IoT. In my opinion, the benefits are too important to ignore, so the digital transformation of the industry will continue, but we will hear more often of disruptions and attacks. In this context, it is important to focus on resilience and on managing the incidents, not just on prevention. 

Another bid deal is data privacy; as you mentioned, more and more home appliances are connected to Internet, and they are producing a lot of information about how they are used, and hence, about the lifestyle, behavior and activity of their owners. This data reaches the Internet Service Provider of the said home, but also the Distribution and System Operator, depending on the DSO’s systems. There is a growing concern that this data can be used for marketing purposes by third party companies, by political and social campaigns aiming to manipulate people one way or another. The current GDPR regulations do not cover much of the subject, mainly because the technology is not yet that widespread, but as I said, it is becoming more and more of a concern.

 

MC: The goal of your presentation is how to maximize the ROI on these data sets—can you give some examples where the ROI has been massive and projects incredibly successful with relative ease? How does that differ from the types of projects that sounded good on the surface but didn’t deliver the ROI?

BG: I have seen a mistake often repeated – when calculating the benefits, many consultants take into account only the revenues that are being generated, or increased. In truth, a decrease of expenditure, a risk avoided or diminished are benefits that can and should be quantified in money.

Energy companies depend on the functioning of their infrastructure. Thus, they have significant losses in case of an outage, and therefore significant maintenance costs.

Enterprise Asset Management platforms and Outage Management Systems have spectacular ROI if implemented and used correctly, through sheer cost and risk reduction. EAM allows a transition to predictive maintenance, and maintenance based on the condition of the asset, rather than the classic, and very expensive, preventive maintenance. OMS decreases both the likelihood of an outage, and the time to restore service, thus preventing big losses. This is especially true for Transport and System Operators, where outages tend to affect larger areas, and last longer.

However, as you have correctly observed, this benefits are great on paper; in real life, their realization depends on two things. First, te use. People resist change. If they are not involved in the project, if they don’t understand the reasons, they will not switch to using the platform or system, and as good as the system might be, it will bring no benefits if it is not used.

Second, the value of the data. All these apps are using data and if the data is generated by machines and sensors, there is a higher likelihood of precision. If, however, the data is manually inserted by humans, the possibility of errors, of incomplete data, can viciate the results affecting the performance of the platform and therefore the realization of benefits.

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MC: Are there institutional barriers within the utility industry—whether that’s typical investment knowledge, behaviors and preferences of executives, uncertainty regarding customer reaction, or otherwise—that are making this transition particularly challenging?

BG: I would not say "institutional barriers”, but there is a culture that has to be considered. Energy companies are highly risk-adverse, and highly focused on operational activities. Decisions are taken slowly, after much consideration, by higher level authorities. While this approach has payed dividends in operational context, it has harmed their agility, their adaptability, and their capacity to change. There is no such thing as minimum viable product in energy, and you will find that initiative and creativity are not valuable traits looked for in their employees. This culture needs to change. Companies need to create an environment where they can safely test new technologies, new ways increase revenues or generate new revenue streams, new ways to decrease risk and increase resilience, new ways to cut operational costs. In order to do that, they need to increase R&D budgets, to de-centralize decision, to foster a climate where employees spontaneously associate to work on a project that they themselves feel is important. Automation and data can give you new insights, but in the end, it comes back to human decision.

 

MC: Aside from presenting at the conference yourself, are there any particular topics or speakers you’re eager to listen in on as an attendee? What do you see as the most exciting topics you want to learn more about?

BG: There are, in fact, quite a few. It promises to be captivating. Topics related to Asset Management, GIS based analytics, Grid Planning and AI will probably be the highlights, even moreso given the quality and experience of the speakers.

I think the organizers did an outstanding job first by developing a program that manages to be both interesting and complete, and secondly by assembling a panel of speakers with real hands on experience in the field. I follow most of the other speakers on Linkedin, and I can tell you that I really look forward to be seeing them live.

I just hope that the audience will be as hyped and enthusiast as I am, and that we will really interact, because we have a lot to learn from each other!


If you're interested in learning more about how utilities should be attacking big data, be sure to check out Bogdan Giubega 's presentation this topic at the Smart Grid Big Data 2019 conference this upcoming September in Berlin, Germany. You can learn more about the agenda and register for the conference here.

 

 

 

Matt Chester's picture

Thank Matt for the Post!

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Todd Greenwood-Geer's picture
Todd Greenwood-Geer on Aug 5, 2019 4:57 pm GMT

It's early days, but I'm creating an open-source time series repository for energy sector data. I've started by pulling publicly available data from CAISO OASIS and publishing them as sqlite3 databases on github. See the project for further details and jupyter notebooks that render data from these data sources at: 

https://github.com/energy-analytics-project/energy-dashboard

-Todd

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