Grid and analytics – geoinformation systems (GIS) and digital twins for low-voltage power grids in the energy industry

Geoinformation systems play a major role in the energy industry. They are primarily used by distribution grid operators to plan their network infrastructure, for power connections and to create digital twins of low-voltage electricity networks. As a model that provides a virtual representation of the real world conditions, a digital twin enables energy suppliers to record, document, analyse and optimise geographical and technical data from the grid infrastructure. This in turn allows them to increase the efficiency and reliability of the grid and utilise the grid infrastructure even more effectively. As the volume of data being generated by an ever-increasing number of measuring devices and sensors grows and owing to the complex changes taking place due to the energy transition, these models are taking on a progressively large role in strategic decision-making and planning.

Geoinformation systems (GIS)

Seeing as geodata is the information on which a digital twin is built, we will first explore the topic of geographic information systems (GIS) and possible applications for them as a basic introduction.

In combination with additional facts like the population, type of use or utilisation rates, geodata presents the position of locatable objects such as cities, buildings or electricity grids. A GIS is used to manage, aggregate and analyse this geoinformation and present it in the form of a map, for example. A variety of visual information can be superimposed and combined in a GIS as layers (layer 1 for roads, layer 2 for electricity grids and so on). Combining the information in this way helps the user to understand spatial patterns and relationships between the layers and to use this information, for example, as the basis for making a decision.

A frequently cited example is the different map systems on a smartphone. These apps provide geographical data in the form of maps and allow the user to view, navigate and search for a specific place or site (such as the nearest charging station) in relation to their current location. Even if they do not offer the full range of functions that a specialised GIS does, these apps are nevertheless an indispensable tool in daily life for many.

The main functions of commonly used GIS solutions:

• 1. Search bar for specific places that can be located using geographical coordinates or their names
• 2. Calculation and selection of a route, for example, based on the user’s current location
• 3. Configuring of a layer (map) to be used as the basis
• 4. Visualisations, among others, of a route as a line element between a start and an end point
• 5. Output of additional information, such as visualisation of the risk of traffic jams on a specified route or the estimated travel and arrival time

Geoinformation systems (GIS) and network information systems (NIS) for distribution grid operators

Geoinformation systems (GIS) have become an indispensable tool for grid operators. Network information systems (NIS) are special GIS solutions for the utilities and waste management sectors that contain information on operating resources, much like (analogue) paper plans. For a distribution grid operator’s perspective, one of the main use cases for a GIS is to document routes and the grid topology in order, for example, to facilitate the efficient digitalisation and automation of processes relating to the provision of (external) information on power lines or line maps. The precise routes of the lines and their properties, including the diameter, depth (underground lines) or height (overhead line), material, age and closest grid node are especially important for grid analyses and building work. After carrying out structural modifications, for example, it must be possible to update (geo)data in GIS solutions quickly and efficiently. GIS solutions are needed to create grid development plans, right-size the infrastructure and expand the grid. An increasingly important use case for GIS is grid connection testing for new PV systems, for example. Others include maintenance, servicing and asset management.

Digital twins for electricity distribution grid operators

Part of a digital twin is based on geodata, which visualises the geographical components of the electricity distribution grid. These are augmented, for example, by measurement data and time series of producers and consumers obtained from the measurement data management (MDM) system and measuring devices. The digital twin is not ideally suited for real-time operational data used in incident response, for example (out-of-scope). The planned scope of the digital twin lies primarily at the strategic and planning level. It enables distribution grid operators to effectively and efficiently manage and optimise the distribution grid and meet the challenges of an ever-changing energy sector.

In addition, it is also necessary to continuously expand and modernise the distribution grid. To ensure optimum deployment of capital, potential sites and locations for new grid elements must be identified in advance, taking into account geographical, topographical and environmental factors. Digital twins are becoming increasingly crucial to strategically planning and effectively managing the integration of renewable energy, which also include the assessment of current and future grid capacities and the simulation of distributed energy feed-in. In digital twins, data from a wide variety of domains can be superimposed and evaluated in seconds thanks to layers. Digital twins can be of help in modelling and calculating an array of scenarios and assessing their impact on the grid. Beyond that, comprehensive data management can be performed using a digital twin both as a single source of truth (SSOT) and as a central platform for storing, updating and analysing geographical and attribute data.

Transparency in the low-voltage grid

Broadly speaking, the low-voltage grid can be mapped in a digital twin from the medium-voltage level (such as a transformer station) to the electricity meter in the home. In current grid models – among others in a control room for grid operation – the low-voltage grids are currently mapped in detail in rare exceptions only; this is in many cases reserved for high- and medium-voltage grids. This means that the distribution grid operator is to a certain extent working ‘blind’ on the low-voltage level. The energy transition, however, is having many effects on this level. Consumers that use a lot of power (such as heat pumps or charging stations) and energy generators (like photovoltaic systems) are often connected at the end of the grid, which is often not designed for this. Sometime in the future, data from smart meters could be included in the digital twin and, ideally, there will then be next to no ‘blind spots’ left in the electricity distribution grid. Until full and complete data from smart meters (for many power connections) is available, simulations in digital twins are extremely important because they allows you to better assess the behaviour of the electricity distribution grids (in non-measured areas).

A step-by-step approach to creating a digital twin

A step-by-step approach, which includes creating a minimum viable product (MVP) based on existing data, is recommended when it comes time to develop a digital twin. Feedback is gathered on this product for further visualisations and in order to obtain additional data. After this, a second iteration of the upgraded product is developed, with further iterations to follow. The advantage of this approach is that it brings deep insights on the relevant customer and target group and that ideally only functions that really deliver added value and are needed are developed.

In one of the first steps, it is necessary to choose a software solution. Open-source and commercial software can be used in the potential technical implementation of a digital twin. The open-source community is especially helpful with it comes to software updates and questions. The open format makes it easy to make changes in the system, and the software can be customised to meet individual requirements. Ideally, interfaces to other programmes, such as to an ERP system, should be possible. In the steps to follow, you will need to import geodata from a GIS in order, for example, to keep it up-to-date and visualise the data. It will also be necessary to import and visualise technical data such as measurement series from producers and consumers. As part of an initial step to streamline the process, districts and parts of municipalities can be modelled as a single grid node, or as a grouping of several consumers and generators, in order to be able to calculate the initial simulation results more easily, quickly and cost-effectively.

A step-by-step approach that is recommended for creating a digital twin looks as follows:

• 1. Software selection: Commercial or open-source software with or without extensions
• 2. Data: Check the data required as well as the existing data available, the data formats and the data sources. This could include:

  • a. Vector data and attributes (points, lines, areas): x-, y- and z-coordinates of power lines, for example: ‘How deep in the ground was a line laid?’
  • b. Grid data and aerial photographs of houses, streets and so on

    • i. Image data formats such as PNG or JPEG such as from WMS services
  • c. Technical data and time series, including readings from measuring devices and meters of feeders and consumers in kWh, active power in kW, voltage in V, current in A, temperature in Celsius and so on

• 3. Feasibility study: Is data missing or is the available data sufficient for a viable product?
• 4. Visualisation of:

  • a. Geodata, if necessary on several layers
  • b. Technical data, such as power consumption and so on

• 5. Carry out simulations and calculations
• 6. Obtain feedback and improve the product

Use cases for digital twins for electricity distribution grid operators

• 1. Process power connection applications faster for photovoltaic systems, heat pumps, wallboxes and the like
• 2. Perform analysis to determine the ideal number of and ideal locations for measuring devices (which ones offer the greatest added value?). To give an example, this could then be extrapolated to the rest of the low-voltage network based on the data generated in the analysis.
• 3. Identify critical grid elements after simulating expected future electricity generation/consumption and making recommendations regarding strategic grid planning (which grid areas will be blocked for photovoltaic systems for the time being and so on).

Outlook

Digital twins for distribution grid operators can be augmented by adding new layers as required in order to include further data and analyses. Examples include sector coupling, smart grids and the optimisation of multiple parts of the infrastructure such as electricity, heat, gas, hydrogen, water or waste water. In addition, there are many other possible uses for digital twins in the energy industry, including in simulations of wind farms or for predictive maintenance. Digital twins in the building sector have a crucial role to play in property management, energy management and the construction industry. Data and the combination of (multiple) digital twins are taking on an increasingly important role with regard to municipal heat planning, land use planning and smart cities.

You can find more exciting topics from the adesso world in our previously published blog articles.

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