The transition from DNO to DSO will involve performing new functions. These new functions will in turn require new systems to support them. This project is to explore in detail the additional functionality required as a DSO, to evaluate the potential options and implement systems that provide that new functionality.
This will include;
- Creating weather adjusted forecasts for load and generation at different time-frames, in order to determine the nature, duration and frequency of expected constraints.
- Evaluating the suitability of flexibility services to resolve those constraints.
- Communicating flexibility services requirements to the market and creating commercial agreements for those services.
- Executing flexibility services including arming, execution, validation of delivery and payment.
- Sharing information with interested parties to avoid conflicts in flexibility service use
The project will consider the optimum degree of integration with existing systems and whether simplified alternatives to full optimised powerflow analysis can provide sufficiently reliable information.
Objectives
The project method is to deliver a robust DSO system capability, by:
1 – Forecasting Evaluation
Determine optimal forecasting arrangements for short term (2-5 days ahead) and near real time forecasting. This considers the sources of data, methods of forecasting, accuracy of forecasts and critical timings for other processes e.g. gate closure. It considers available load forecasts provided by National Grid, whether combining forecasts is beneficial and options for improvement such as within-day correction.
2 – Co-ordination
Determine optimal arrangements for co-ordination and conflict resolution with other parties using flexibility services. This could include different providers e.g. direct provision or via a third party (aggregator or supplier), or price signals for real-time trading, advanced notification, dynamic amendment of systems e.g ANM or merit
3 - Determine Requirements
Incorporate the learning from Methods 1 & 2 to determine and then specify the system requirements for:
- long term flexibility service assessments and cost benefit analysis, and
- short term assessment and implementation.
This will determine whether a single system is required for long and short term requirements or whether these are better managed separately. This will maximise the use of open standards and modular solutions using data available to all DNOs. DSOs may take on further responsibilities such as local network balancing on behalf of the TSO. Any proposed systems should be able to support the potential range of DSO activities and will involve data exchanges with third parties including triggering services.
4 – Implementation & Testing
The systems specified by Method 3 will then be built, implemented and tested to prove their suitability. Method 4 is anticipated to involve development of AMT Sybex Networkflow software suite.
Academic / independent experts will provide oversight and assurance for Methods 1 & 2.
Learnings
Outcomes
The Project has completed its lifecycle to time and budget. It has met its four objectives and completed the ten Project deliverables as required in the Project Direction. Despite operational difficulties and impacts on data due to the COVID-19 pandemic, EFFS has delivered substantial documentation and learning for WPD and other DNOs to utilise in their DSO transition journey.
The Project has delivered a documented process and method including developed tools, which could assist a DNO in operating the core activities required to deliver a DSO function. In particular, the EFFS Project has designed and delivered the component requirements to meet the ENA’s Open Networks World model B8 form of DSO. The EFFS system could be used to support system balancing by providing a means to identify, optimise and initiate the execution of flexibility services. It can select and enable flexibility services to operate across a network in a safe and secure manner. The EFFS method could allow more automated access to a range of flexibility services, subject to their availability within a constrained area of the network, via directly interfacing with local flexibility platforms, flexibility pools or enabling access to other flexibility service providers such as suppliers with available flexibility or ready access to it. Providers of such services could compete with one another and the EFFS process would be capable of selecting the cheapest or optimum service based on set criteria. By facilitating the selection of flexibility services, EFFS could assist the development of the flexibility services market. In addition, The EFFS forecasting capability is robust and operating in conjunction with a suitable power flow analysis tool such as PSS®E, the EFFS method is capable of identifying the level of services required.
The outcomes of the forecasting method have shown that shorter-term forecast horizons can help a DSO better procure and manage flexibility in addition, aid decision making. The ability for DSOs to forecast at shorter time-horizons provides relies on the adequately specified tooling making use of input data including historic demand and generation data, and weather data. The EFFS output demonstrated what would be needed in a function specification to avoid limitations demonstrated during the projects forecasting implementation. The ability to have daily feeds of forecast data at different time-horizons can aid control room engineers to make decisions on the network and better inform the volume of flexibility to procure using the latest up to date information.
The outcomes of the constraint analysis method demonstrated that it is possible to identify constraints on our network using an automated tool. This then allowed for forecast data to be used, in combination with other network properties, to see a view of the network in the future and appropriately select the flexibility services to mitigate the constraints. This can better inform flexibility procurement methodologies and be used to aid selection of service. The EFFS projects constraint analysis methods demonstrate that it is technically possible to carry out automated analysis using existing computing equipment, and therefore network operators are not limited to basic power flow tools when carrying out assessment on their network or making decisions on the procurement of flexibility.
- Procurement and Selection of Services
The outcomes of the Project have shown that it’s achievable to manage flexibility and select services under the ENA Open Networks World B model. Using forecast and power flow analysis data generated closer to real time can better enable DSOs to procure the right level of flexibility from parties. The method can enable DSOs to:
- Create flexibility requirements from constraints identified in power flow analysis rather than existing crude assumptions;
- Publish these to a multitude of different market participants and platforms;
- Optimise the lowest cost bid based on multiple selectable parameters, and;
- Validate that dispatching a new flexibility service does not create addition constraints elsewhere on the network.
The outcomes of the market platform integration proved that different market platforms can communicate with the DSO. The ability to have different market participants submitting bids to flexibility requests gives greater opportunity to obtain a commercially viable bid providing liquidity is available in the network area. Platform integration enabled interoperability with the flexibility market that as the market grows provide a better liquid pool for DSOs to procure services.
- Technology Readiness Level
The EFFS Project began at Technology Readiness Level 6 as the technology and key software at that time had already been proved previously in a relevant environment. The design had not been completed as a finalised solution and would have required modification to make it operational. Through the testing and trial phases the Project achieved Level 7 as the technology was tested at or near full throughput and used simulations comparable to that expected during operations. Further developments and design work would be needed before any aspects of the EFFS system could be implemented but the outputs of the project have provided learning on how a BAU ready system would operate.
Lessons Learnt
The following points of learning apply to work to be carried out within future innovation projects, and therefore relate to areas including project management, procurement, implementation and timescales.
- The duration and structure of the EFFS Project meant that BAU activity carried out alongside the Project within this area was often able to be more agile and have a faster response to changes within the industry. For this reason, non-infrastructure projects lend themselves to a shorter sprint delivery timescales, allowing for outputs to be worked on and transitioned to the business over the course of the Project.
- During the later stages of the Project, WPD Innovation resource working on the Project increased to ensure a project manager and technical lead were engaged. This approach improved efficiency on the Project significantly. This improvement came as review times were able to be reduced and outputs of the Project were overseen by both parties.
- One challenge in the Project phases included designing a solution across multiple software solutions, provided by different vendors. During the requirements and design phase, it was difficult to ensure that the solution hung together across different systems and that the integration points were aligned and agreed upon. To resolve this the Project appointed a solution architect to oversee the solution across all systems, and be responsible for agreement of integration points and protocols and rationalising the solution to ensure consistency / remove duplication. This benefitted the Project greatly and enabled the delivery of a functional pragmatic solution with which to carry out the EFFS Trials.
- When the EFFS Project was originally planned and mobilised a waterfall methodology was agreed in terms of project management, design and delivery. Namely that a set of requirements would be defined, this would then cascade into a design, the build, and finally the trials. However, in practice this approach did not cater for the innovative nature of the Project, specifically, that thinking, and requirements would be fluid due to external industry developments and internal learnings within the Project. The waterfall nature of the Project meant it was difficult to return to the early phases to amend requirements, design, and approach. The barriers within this approach were governance, deliverable, and phase structure. Also, re-visiting earlier phases often led to rework. To avoid this if running the Project again we would consider the use of an agile approach.
- Originally it was expected that the key Project role of Forecasting Partner might be attractive to many academic institutions as well as commercial service providers - such as the party selected Smarter Grid Solutions - with expertise in this field. However, when the tender was issued in November 2018, in order to keep to Project timescale and the closeness to the Christmas break, only four weeks was available for interested parties to prepare and respond to the tender. Of the tenders received only one involved an academic party and this was received as a joint bid in conjunction with a commercial organisation. A wider and increased field of bidders might have been achieved if:
- A longer prequalification process had been possible;
- More time had been allowed for parties to prepare their bids; and
- The process had not been as close as it was to the end of the year.
