13. June 2023 By Andreas Essl, Wolfgang Weber and Tim Bunkus
Grid and analytics – data innovations and data use case analytics for electricity distribution grid operators to speed up the energy transition
Electricity distribution gird operators face a multitude of challenges
The energy transition presents a number of challenges for grid operators in their roles as key players. For example, there are more and more grid connection applications for new feeders and consumers, such as photovoltaic systems, heat pumps and charging stations. At the same time, regulations, changes in the energy market and adjustments in market communication mean electricity grid operators have to take on more and more tasks and responsibilities (such as metering point operators, redispatch and so on). This can push the operators’ employees, processes, IT systems and IT program integration to their (organisational) limits.
In addition, existing renewable energy plants are partly leading to a load flow reversal and to electricity being fed back into higher-level grids. What is more, new generators and consumers can have repercussions on the electricity grids, which means that it can be a challenge to keep the quality of the voltage in sinusoidal wave form. In the worst case, neighbour A impacts on the quality of neighbour B’s electricity. In addition, the increasingly demanding technical situation can lead to grid operators overloading. Many of these challenges can push electricity grid network operators to their (technical) limits.
The energy grid transition is the foundation for the energy transition
People often cite expanding the grid as the solution to everything. There is no question that expanding the distribution grids is important for the energy transition. However, endless grid expansion (in the broadest sense up to the last metering point/home connection) cannot be the only and most efficient means of speeding up the energy transition, if only because of the high economic costs and the time approval procedures take to complete. If the electricity distribution grids are not powerful enough to handle the many photovoltaic systems, charging stations and heat pumps that the energy transition has brought about, then they cannot be connected and operated (even if they have already been purchased). This means that we will need to make better technical use of the existing electricity grid infrastructure and to manage it. There are different ways and methods to do this. Data analytics use cases are an essential way to leverage realisable efficiency potentials at distribution grid operators.
Electricity distribution grid operators have a lot of data
The progressive development of smart grids – especially the greater coverage provided by sensors in substations, transformer stations, cable distribution cabinets or directly using smart meters installed generators’ or consumers’ sites, as well as new data transmission technologies, new IT programs and better data storage options – is creating more and better usable data volumes for electricity grid network operators.
Examples of data and (stored) technical time series from the electricity grid
- Voltage of the three phases in volts (V)
- Current of the three phases in amperes (A)
- Effective power in kilowatts (kW)
- Apparent power in kilovolt amperes (kVA)
- Reactive power in volt amperes (var)
Many distribution grid operators are sitting on a mountain of data, some of which has not been cleansed or evaluated. This means that for a lot of data, the electricity grid system operator does not even know whether this data has any value at all or not. As a result, the value that some of the data may have (for the distribution grid operator) often goes used. This means some synergy effects, efficiency potentials and savings opportunities cannot be leveraged. In addition, electricity distribution grid operators have multiple instances of many similar types of data available across several different IT programs and lists, some of which are up to date and some of which have not been updated in a long time.
Consistent, integrated and clean processes, systems and data are THE foundation for data analytics and for data analytics use cases
- 1. Process and system integration: A key challenge for electricity distribution grid operators is the interlinking of processes, IT systems and data streams. This means that before use cases for data analytics can even be deployed, it is often necessary to first integrate a system, process or data interface using end-to-end processes.
- 2. Data consolidation and data cleansing: Next, a data strategy, data structuring, data cleansing and data consolidation can substantially improve efficiency for the distribution grid operator. Uniform data is becoming increasingly important and forms the basis for further IT systems and data analytics use cases, especially with regard to the roll-out and transformation of new and existing IT programmes and platform solutions.
- 3. Data analytics use case evaluation and implementation: Once it is clear which data can be found in which IT system and how good that data is, you can assess which data analytics use cases can be implemented using which data within the company.
Data analytics use cases for electricity distribution grid operators
Deep and machine learning techniques and artificial intelligence can be used for forecasting and to detect anomalies in large, complex datasets. Ongoing research into and live application of these types of models has seen their efficiency and accuracy increase drastically in recent years. This allows relatively reliable analysis results to be generated with comparably little (data) effort.
This means that potential strategically critical network element failures can be detected in advance as a use case. The diagram below contains other examples of use cases. Ideally, an individual data use case is economically feasible. This means that the costs of implementing a small IT project, for example, can be recouped by improving efficiency or reducing costs, and in the medium term, data analytics use cases can lead to grid operators being able to use their budget more efficiently.
Possible data analytics use cases for electricity distribution grid operators
Data use case analytics with innovative moderation formats from adesso
A working meeting helps to create transparency about where you stand with your data and to determine the feasibility of innovative use cases in your company.
The relevant stakeholders can meet in a one-day workshop, which is supported and moderated by experienced industry experts from adesso, and identify the possible data sources and use cases for the distribution grid operator.
- Joint coordination sessions are held before the working meeting to define the group of participants, the relevant topics and key framework conditions (such as the IT landscape). This helps make sure that the discussions that take place during the workshop are focused on getting results.
- During the workshop, the customer’s existing data types are analysed, for example, in which unit and which granularity which data is transmitted (to which system) and stored. adesso can compile a detailed data map of the data architecture for this purpose.
- Then, the participants analyse which use cases are feasible from the current point of view for the customer with their data within a time period to be defined. The main focus here is on the topics that were already defined before the workshop. adesso supports this section of the workshop with experienced industry experts who contribute use case ideas and expertise.
- Lastly, the participants evaluate the use cases and put them into a possible order of implementation (recommendation). The evaluation criteria, such as implementation costs, staff availability, cost savings, implementation duration, duration, feasibility and so on are agreed in advance.
Ideally, the result of the workshop is a prioritised and evaluated list of use cases that can be transferred into individual IT projects. The electricity distribution grid operator’s internal IT departments and teams can implement some of the use cases, should they need to. In addition, adesso can help the customer to implement the use cases after the working meeting and/or continue to provide consultancy if the customer so wishes. The main goal of data analytics use cases is to relieve the burden on processes and employees through digitalised and simplified processes in order to become faster and more efficient.
By using new data analysis methods, the energy management of the future can be put in place now for the energy transition. adesso is at your side as a partner for the transformation to a data-driven company and supports you with a vast amount of industry and technology experience.
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