The traditional approach to decision making on network reinforcements is done on an individual circuit by circuit basis i.e. System Planners will review the supply and demand characteristics of a circuit to determine if reinforcement is necessary based on future demand growth expectations. At this point it may be possible to reinforce via a traditional method e.g. install new overhead lines or possibly utilise a smart solution such as demand side response (DSR).
Objectives
This project has three high level objectives:
1. Understand how System Planners can utilise the Grid OS system in order to make more effective investment decisions in a future DSO world from an economic perspective.
2. Understand what information needs to be provided to DER players in order to make more effective investment decisions in a future DSO world from an economic perspective.
Understand what information needs to be provided to control engineers in order to make more effective operational decisions in a future DSO world from an economic perspective.
Learnings
Outcomes
Below are the main significant outcomes of the MERLIN project.
1. Opus One’s GridOS Integrated Distribution Planning PSA tool has been developed so that it can perform both economic and physics-based modelling. We tested the tool on a section of the Fort William network, which allowed us to compare traditional reinforcement vs flexible service investment. The tool was able to show high level costs of both solutions, but it was still necessary to use a CBA to verify the accuracy of the results and to also make a final investment decision. This is because the CBA created in Excel has more functionality than the IDP tool. Interestingly the economic modelling outputs suggested traditional reinforcement was recommended over flexible services for this section of network. The main reason for this was due to low costs of traditional reinforcement, as lots of back-feed is in place to support this specific circuit. There were also high requirements for flexible services, due to constraints lasting long periods of time. This meant the price per MWhr that we could offer for flexible services on this circuit was too low to be commercially viable for flexible service providers. The tool has been taken up by the Transition project to assist with the DSO Whole System Co-Ordinator (WSC) work, which is pushing it closer to becoming more commercially ready in the UK. More details on the WSC can be found in the following link:
https://ssen-transition.com/wp-content/uploads/2019/09/Whole-System-Coordination-Requirement-Specification-v10.pdf
2. Opus One’s GridOS Transactive Energy day ahead tool was initially developed to run day ahead markets in simulated conditions. However, the TE tool was adopted by the Transition Project part way through MERLIN, which led to a change in scope. Instead of focussing on simulating markets (in a desktop environment), the tool was developed so that it could be used operationally (in the real world). MERLIN focussed on developing and testing the TE tool and Transition on trialling and further evolving the tool for real world flexibility market deployment.
The TE tool is being explored by the Transition project to assist with the DSO Neutral Market Facilitator (NMF) and Whole System Co-ordinator work in the area. More information can be found in the following links:
https://ssen-transition.com/wp-content/uploads/2019/05/Neutral-Market-Facilitator-Requirement-Specification-v14.pdf
3. OGS’s CIMphony tool was initially developed to provide CIM conversion and export services of SSEN’s data to Opus One’s IDP and TE tools. As the project progressed and MERLIN aligned closer with Transition, the model requirements changed from creating models that were suitable for Network/System Planners to models that were suitable for Control Room operators. This involved processing and converting significantly more data types than was initially planned. This additional work completed by OGS led to significant improvements in model building efficiency that allowed both Transition and MERLIN to meet tight project timescales that would not have otherwise been possible. The CIMphony tool has proven that network modelling efficiency can be significantly improved vs current methods i.e. we are able to create far more models within a given time period. This is hugely significant and has led to interest in adopting it within the business as part of the SSEN Digital Strategy and NeRDA project.
Lessons Learnt
The key lessons to date, are as follows:
1. Before network modelling simulations can be performed the models need to be built. Building network models requires a large variety of data types from a large number of sources. We contracted Open Grid Systems (OGS) to process this data and provide it in Common Information Model (CIM) format, which was then provided to the Grid OS tools as a ready built network model. This exercise has demonstrated that we are able to build network models significantly quicker than current methods and is being explored further to assist with our Open Data and Digital Transformation Strategy.
2. The Grid OS IDP tool required additional manual work by System Planners in order to perform network modelling simulations e.g. scripting required to input load forecasts before the model can be run to view network constraints. Opus One have since addressed this issue with the work being performed on the TRANSITION project.
3. The Grid OS IDP tool is able to perform high level economic modelling but does not provide the granular level of detail required to make final investment decisions i.e. Excel is still required before a final decision can be made. However, Grid OS IDP can still provide significant time savings when comparing high level economic outputs from multiple power flow model studies, which will be important to narrow down best-case options more quickly.
4. The Grid OS TE tool is able to perform day-ahead flexible service trading by utilising SSEN data. However, the tool data needs to be provided closer to real time in order to maximise effectiveness. This requires additional investment by SSEN in data digitalisation that is planned for RIIO-ED2.
