With the increased generation from renewable sources, the number of producers is increasing – both decentralised and also in the private sector. At the same time, the number of conventional power producers is decreasing due to the phase-out of nuclear energy and coal-based power generation. With the ramp-up of electromobility, new delivery points are being created that could also serve as storage facilities. Driven by the increasing decentralisation of the generation landscape as well as the mixing of generation and consumption in the private environment (“prosumer”), the requirements for safe and efficient grid operation are increasing.
Due to the strong increase in market players, combined with the use of new technologies and systems, the volume of data is also increasing massively. With this data, grid operators can create added value - beyond the operation of prescribed and required processes.
Digital Grid Operation
The current and future challenges for grid operators can only be tackled through increasing digitisation. The goals of further digitisation are grid stability, security of supply, cost optimisation, improved protection of operating resources, optimised maintenance and servicing planning, increased reliability, process security, increased efficiency and the avoidance of occupational accidents.
Possible fields of digitisation of grid operation:
- Process automation in the grid control systems:
- Congestion management and containment of redispatch costs at the electricity transmission system operators.
- For the distribution service operators’ mapping of the requirements from NABEG 2.0 / Redispatch 2.0, establishing the observability of the grid situation and the controllability of flexibilities in the medium and low voltage level.
- IoT technologies, LoRaWAN and Blockchain:
- Increasing the degree of automation in the distribution grid down to the low-voltage level and intensifying communication between grid operation and market players.
- Real-time monitoring and status assessment of critical grid elements over the entire life cycle.
- Artificial intelligence:
- Decentralisation of functions and intelligence from power plant and grid control technology in local grid stations.
- Mobile end devices, such as smartphones or handhelds:
- Creation of measurements and material parts lists for fault clearance management and for planned maintenance work in the grid.
- Simultaneous alignment with GIS and asset management systems up to the digital construction file in workforce management
- AR glasses and VR applications:
- Use of AR glasses to support maintenance and switching interventions in fault clearance management.
- Use of VR applications for simulation training in the education of fitters and technicians, especially in conjunction with prior digital capture by means of 3D scanning.
- Drones can be used to support processes in maintenance and servicing.
- Monitoring of operating resources
We know both the core processes of the grid operators and the starting points for digitisation. We know how to "digitally think" processes end-to-end in order to identify and avoid inconsistencies and system discontinuities. A close cooperation with the suppliers of the grid control systems is an important prerequisite for this. This is where we know our way around. This is how we help you to digitise the right processes in your company and to achieve optimal effects.
The amendment to the German Grid Expansion Acceleration Act (NABEG) also redefines the grid congestion management ("redispatch"), which is to be carried out more efficiently and cost-effectively in the future. From October 1st, 2021, all generation plants from 100 kW or plants that can be steered at any time by the grid operator, respectively, will be included in the redispatch. This will then also include renewable energy plants, CHP plants and electricity storage facilities.
The electricity distribution system operators (DSOs) are significantly affected by these new regulations. With the new regulations, DSOs themselves will make use of the Redispatch instrument. The extension of the Redispatch requires new coordination processes, data messages and unified data exchange channels between grid operators and market participants. In order to meet the necessary data provision obligations and to be able to take coordinated measures if necessary, DSOs are faced with the task of setting up new processes and IT systems.
In particular, the following new responsibilities must be mapped:
- Redispatch planning aligned and coordinated between transmission system operators (TSOs), DSOs and plant operators
- Data supply for model calculations and identification of flexibility potentials in the own grid area
- Therewith grid condition analysis and modelling of measures
- Infrastructure for steering generation plants during the execution of redispatch measures
- Processes for accounting and financial settlement
- Generation and load forecasting as well as schedule management
We do understand the impact of the new regulations on the existing core processes of grid operators. With our extensive experience in energy trading, we know how forecasts are made. Our close contacts with the leading manufacturers of grid control systems help us to work with you to integrate the new processes efficiently and in compliance with the rules.
Advanced Analytics enables the use of new analytical techniques and empowers the specialist department to conduct its own analyses:
- The specialist department formulates questions, e.g.: How can the schedule forecast be improved on the basis of empirical values?
- Iterative data mining process to answer the questions
- Data structures can be defined dynamically (schema-on-read)
- Horizontal scalability of the infrastructure (scale-out)
- The specialist department carries out analyses independently and is the data user - IT is the data/platform provider
Typical use cases for advanced analytics in the grid sector:
- Predictive maintenance
- Day-ahead schedule forecasting
- Consumer segmentation using smart meter data
- Gas storage optimisation
Use Case - Predictive maintenance:
- Questions: How can maintenance costs be reduced? How can a shutdown of equipment be avoided / reduced? How can we predict when equipment will fail?
- Implementation: Use existing data sources to move from a reactive or preventive maintenance strategy to a prediction-based one. Historical data plays a key role in modelling. The model learns from past failures which factors or variables indicate that failure is imminent.
Four typical steps in processing advanced analytics use cases:
- 1. Data import:
- The source data is loaded into the base directory.
- This allows source-independent work, and also increases the processing speed
- 2. Data preparation:
- The loaded data needs to be cleaned to be a valuable source of information
- Data consistency is also critical for further processing
- 3. Modelling:
- In this step, the actual problem is modelled
- There are numerous algorithms for this, and their use depends on the problem
- 4. Post-processing:
- Some modelling requires post-processing in terms of optimisation or cost evaluation
For the realisation of your data potential, you benefit from our partnership with Rapidminer.