The rise of distributed energy sources (DER) is changing the way power is generated and distributed in the power grid. Although the customer adoption of DERs, such as local solar photovoltaic (PV) and electrical vehicles (EV), is an important element in the energy transition, it also imposes challenges and contributes to a higher complexity in the distribution grid. The pace of the transformation is accelerating and onwards it is crucial for distribution system operators to quickly gain control over the impact and understand existing constraints to provide the needed capabilities to accommodate for such adoption.

Besides PV and EV, other significant DERs includes energy storage and wind power, and in this post, we will briefly discuss the challenges in integrating DERs and how our dInsight Analytics Platform has helped our user community.

The ever-increasing need and dependency of electricity in our everyday life makes the society vulnerable to outages and poor power quality, leading to a critical need for effective outage management strategies. And it all starts with collecting real high-resolution data for a better assessment of the grid health including which parts of the grid are exposed to disturbances and other power quality issues – where are the congestions, where are reverse power flows, etc.

dLab not only collects data, but we also analyze, classify, and further process the data automatically, making it useful and understandable. We give you the power to act.

Challenges specifically in integrating DERs in the distribution system includes:

• Thermal rating violations
• Voltage increase, or fluctuations
• Protection issues
• Wear and tear of circuit apparatus
• Reverse power flows

The whole is greater than the sum of its parts

The bottom line is, the advent of DERs increases the need to monitor the influences from these assets, and monitoring this from a primary/distribution substation perspective, provides a holistic view – which is what dLab contributes with. To monitor these effects only from other nodes further down in the distribution grid (e.g. secondary substations) will not provide you with an accurate historical overview, and even trying to do the math and strive for an aggregation will soon prove to be complicated, not least due to the change of (the grid’s) switching state over time. You will end up with complex equations with unknowns that at best will provide an approximation, which will not be sufficient moving forward.

Even in cases when the DER is behind the meter, disturbances and other anomalies propagates further up in the distribution grid, which emphasizes the importance of surveillance even higher up in the grid.

What will dLab contribute with?

Through a continuously 360-degree supervision of the distribution grid in high resolution, dLab facilitates an unprecedented collection of smart grid analytics enabling improved grid resilience. The platform from dLab is substation agnostic and does not affect any other ongoing processes, which makes it easy to get started.

The platform supervises all transient events thanks to its high sampling rate, regardless of where the events occur. It could be a matter of voltage dips, short circuits, earth faults including high ohmic earth faults, etc.

Through the dInsight Analytics Platform, our user community gets a quick and easy-to-understand overview to:

• stay in control of power quality
• understand load, active and reactive power
• improve monitoring of wear and tear of substation assets
• work proactively with grid disturbances to avoid outages
• reduce outage times

Grid reliability, or security of supply, has become increasingly important as our society is becoming more and more dependent on electricity. A reliable supply of electricity is a basic requirement in our modern society; without it nothing would work properly. In many countries, this is and has been a central issue for the DSOs for decades, but the increasing demands on the electrical grid – in the form of more renewable and intermittent energy, the development of prosumers, decentralized energy production and so on – is turning security of supply into a central challenge.

Knowing the weaknesses of the grid and keeping up a proactive maintenance work is key in avoiding outages and securing a reliable distribution. The traditional way of monitoring the grid does not allow proactive measures, as only actual outages are registered, and can be acted on. Digitalization of the grid with dLab’s solution enables collecting and analysing grid data in real time, which can then be used to find impending faults or deviations in quality and take action on these early warnings.

In the incremental transformation throughout the digitalization process, value from the dLab solution can be obtained during several steps. Many of our customers are successfully using the information directly provided by our products, but the complete transformation for a company is a long journey where one mission is to move to a predictive state.

To achieve this state, there is a need to change internal processes and adopt a new data-driven way of working. An evolution that requires a tighter connection between analytics and the organisation, essentially putting insights and knowledge into meaningful contexts,

This is what the last step in the dInsight Analytics Platform encapsulate. The final outcome is what generally referred to as Actionable intelligence, or as in e.g. Industry 4.0 referred to as the contextualization phase. The previous obtained information is transformed to something that can be followed-up on, with the further implication that a strategic plan should be undertaken to make positive use of the information gathered.

Exactly how the process is carried out can vary case-by-case, and the platform is flexible. As a basic first step, dLab offers a forum for cooperation where the knowledge and experience from dLab meets the experience and knowledge from the customer. And step-by-step understand how insights from the dLab platform relates to customer internal processes.

The transformation from yesterday’s grid – defined as a one-way energy transmission between producer and consumer – to the grid of the future, where there is a need for a two-way communication of data and energy, requires a whole new set of innovative solutions.

Transforming the grid into a vast network of intelligent nodes will require a significant number of sensors, which inevitably will lead to a huge amount of data collection resulting in a data lake without comparison. The natural next step is to extract information and gain insight from this vast amount of data. That means using analysis based on advanced AI software to tackle the velocity, variety, and volume of data. There is a need for advanced algorithms that will be able to detect complex patterns facilitating for the possibility to anticipate upcoming situations in the grid.

Therefore, AI will be essential when creating the smart grid, but that will not be enough on its own. We need to add the organizational aspect to the equation. We need to talk about working processes and behaviours, and not only information delivery. Information originating from the data lake needs to be contextualized, its meaning transformed into meaningful knowledge, allowing for further implementation in different parts of the organisation and its internal processes. We need less dashboard design and more data-driven behaviour. The contextualization of data and the actions based on that knowledge, are critical steps in the necessary transformation.

The transformation of the electrical grid is a step-by-step process, starting with connecting and collecting grid data, then gaining insights through AI powered analysis, and ending by converting information to meaningful knowledge. And that is what dLab’s dInsight Analytics platform is set up to do for you.

The power sector is facing major challenges today; among the most pressing are access to reliable and affordable energy and a decentralized production from renewable energy sources. This requires the utilities to move from a traditional, one-way distribution of electricity from producer to consumer, to a modern distribution grid with prosumers and where the customer and environment are in focus. These challenges can only be met by a digitalized grid – in other words, a smart grid.

A crucial step in this transformation is to start collecting data from different points in the grid. The collected data is analysed to provide smart grid analytics – which is essentially the base for moving toward data-based decision making.

Smart grid analytics can be used throughout the energy distribution process; analysing data in energy production, in the transmission and distribution in the grid and finally in consumption. dLab targets the distribution grid segment with dInsight Analytics Platform, a collection of products and services installed in the primary substations, analysing the medium voltage grid, 10-40 kV.

Smart grid analytics is estimated to grow significantly over the coming years; a 2019 report from Frost & Sullivan indicates a CAGR (19-25) equal to 10,4%, reaching a market value of $2,31 bn USD by 2025^(*).

(*) Frost & Sullivan, Global Smart Grid Analytics Market, Forecast to 2025 Big Data Analytics to Drive Utility Energy Management, Cost Reduction and Equipment Reliability, July 2019.