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Projects: Projects for Investigator
Reference Number NIA_SPEN_034
Title NCEWS2 Network Constraint Early Warning System (Phase 2)
Status Completed
Energy Categories Other Cross-Cutting Technologies or Research(Energy Models) 5%;
Other Cross-Cutting Technologies or Research(Energy system analysis) 5%;
Other Power and Storage Technologies(Electricity transmission and distribution) 70%;
Other Cross-Cutting Technologies or Research(Other Supporting Data) 20%;
Research Types Applied Research and Development 100%
Science and Technology Fields PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 20%;
ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 80%;
UKERC Cross Cutting Characterisation Not Cross-cutting 70%;
Systems Analysis related to energy R&D (Energy modelling) 10%;
Systems Analysis related to energy R&D (Other Systems Analysis) 10%;
Other (Energy technology information dissemination) 10%;
Principal Investigator Project Contact
No email address given
SP Energy Networks
Award Type Network Innovation Allowance
Funding Source Ofgem
Start Date 01 October 2018
End Date 30 September 2022
Duration ENA months
Total Grant Value £150,000
Industrial Sectors Power
Region Scotland
Programme Network Innovation Allowance
Investigators Principal Investigator Project Contact , SP Energy Networks (100.000%)
  Industrial Collaborator Project Contact , SP Energy Networks (0.000%)
Web Site https://smarter.energynetworks.org/projects/NIA_SPEN_034
Objectives NCEWS2 will build on the LV Connectivity Platform developed through the NCEWS1 project, adding a range of functionality, as well as increasing the geographical scope of the analysis. Six (6) work packages are proposed, discussed in detail below:WP 1: Business-as-Usual investigation of NCEWS1 PlatformWP 2: LV / EV / DG Modelling Development / EnhancementWP 3: Platform Development / EnhancementWP 4: EV API DevelopmentWP 5: Data Analytics SupportWP 6: Stakeholder Engagement / DisseminationWP 1: Business-as-Usual Implementation of Innovation PlatformTaking output of NCEWS1 into Business-as-Usual Implementation of Innovation Platform for 20 users of NCEWS Platform and Web Portal. This will demonstrate scale and complexity of a BAU implementation, but without need to deploy full Enterprise Solution.WP 2: LV / EV / DG Modelling Development / EnhancementDevelopment of power system analysis export function(s) for PSSE tools (primarily DIgSILENT, but other PSSE tools may be considered). Includes changes to portal interface required to initiate export and potentially initiate PSSE runs.Development of improved LV network modelling capability. This will allow: scenario analysis of EV/DG penetration; and logging of EV/DG customer locations for enhanced network visibility. This data will be provided to the EV Working Group within SPEN, who are responsible for all EV modelling across the network.Development of EV connection methodology using real/modelled EV data annotated to network.WP 3: Platform Development / EnhancementAPI Development. Development of API that aligns with SPEN Enterprise Service Bus architecture for improved data transfer across multiple IT systems required for future DSO Operation.Connectivity Improvement.  Enhancement of network tree analysis, developed through NCEWS1, to create automated verification of network connectivity using Smart Meter (if available/accessible) and PowerOn data. Data Aggregation. Demonstration of how aggregated data cannot be disaggregated by any user. Tuning of GIS visualisations available to users through NCEWS2 Web Portal to ensure detailed data is not available to user, only aggregated data. Demonstration of how the use of project internal & external DBs shields core data from wider users, but ensures detail is available within NCEWS2 for quick aggregation of any new data received. This activity will be linked to NCEWS2 Privacy Impact Assessment (PIA).WP 4: EV API DevelopmentDevelopment of an API that will allow EV charging data to be taken from the Transport Scotland EV database and brought into the NCEWS2 Platform via EnergyIP (Siemens Innovation Project), which currently handles all SM data. This will allow annotation to the LV network for both modelling and analysis. Once annotated to the LV network, PSSE modelling will assess the impact of known and anticipated EV chargers on the network allowing LV network constraints to be developed. These constraints will identify periods when EV charging, if running at full capacity, could stress the network. In response to this, DSM profiles will be generated that will indicate EV charger throttle periods. These will be transmitted back to the chargers via the API, which will convey OCPP instructions to the relevant chargers to constrain them appropriately. High level tasks in this WP will include:- API Development- LV Profile Extraction- Profile Calculator- OCPP Instruction Generator- Privacy Impact AssessmentNote that this WP will be carried out in collaboration with the RUGGEDISED project (http://www.ruggedised.eu), which will be monitoring a number of chargers across Glasgow. This will allow SPEN to verify that any throttle instructions sent to EV chargers were successfully enacted.WP 5: Data Analytics SupportContinued support of Data Analyst. Tasks will include, but are not limited to:Connectivity improvement. Based on output of Voltage Analysis in NCEWS1 project, develop techniques to improve the connectivity.Phase identification using Voltage clustering & Aggregated Sum methods. [Note: these tasks should not be started until there is a good penetration of SMs on a number of circuits
Abstract Automated verification of network connectivityAn essential building block in the development of automated schemes to reduce CI/CML with associated quantifiable benefits. Understanding of the LV circuit and transformer each customer is fed from allows expected benefits of SM data to be realised. This potentially includes the ability to verify customer phase feeding arrangement, assuming sufficient SM data availability.Near real-time network connectivity understandingVisibility of running arrangements at configurable network split points (link boxes). This will provide verification of ongoing operational network changes in as near-to-real-time as possible. This is an enabler for the enhanced level of LV network control which is an expected future requirement for DSO operation. Improved LV network modelling capabilityDevelopment of capability to export LV network and associated network metrics (SM data, EV data, etc) into PSSE systems. Expected to become an essential tool as pressure on the LV network increases through the penetration of LCTs. Quantifiable benefits in terms of saving in design resources and reduced reinforcement through more accurate designs.Scenario analysis of investment requirements for EV penetrationDevelopment of real SPEN network topologies archetypes, including analysis of EV penetration density from understanding of customer EV charging requirements using GIS data, i.e. distributed on- & off-street parking and/or cluster EV charging locations. Supports the EV NIC proposal and informs SPEN reinforcement requirements for ED2.Platform for EV connection managementThrough the integration of LCT location data and improved LV circuit and transformer connectivity and rating understanding. This would be an overall LCT penetration visualization and access management platform for both internal designer and external customer use. Benefits would be achieved through streamlining the design process and improving customer service.
Publications (none)
Final Report (none)
Added to Database 15/12/22