Learnings

Outcomes

The key learning outcomes from the project, as documented in the Work Package 3 & 4 report: Pragmatic Security Assessment Method: Refinement, Validation, and Implementation, are:

1.                   The first, and potentially most far reaching, conclusion is that the capacity value of smart solutions is heavily influenced by the network in which they are installed.  This is something that is absent from the existing network security of supply standards which allow alternatives to conventional, network-based reinforcement: ENA Engineering Report 130 contains F-Factors for distributed generation, but these are based only the property of the generation itself, and ignore the network topology and reliability and the demand shape.  This suggests that the F-Factors are only appropriate in very simple cases (for example, securing a primary substation against an EHV fault without considering alternative supply), and that using them within an HV network would require analysis of the impact of network factors.
2.                   The key second conclusion is that the prevailing network planning standards have a significant impact on the way networks have been designed, and this has a direct impact on the capacity value of smart-solutions.  For example, because ENA Engineering Recommendation P2 Class of Supply A does not require an alternative supply for demand groups below 1 MW, Pollington and similar networks have a large proportion of customers on teed HV feeders which result in high Expected Energy Not Supplied (EENS) values corresponding to certain network outages, and which none of the smart-solutions studied in this project can rectify. 

3.                   In the existing regulatory framework, quality of service incentives are in place to reduce the frequency and duration of supply failures experienced by customers, while planning standards enforce a minimum level of redundancy.  In fact, when taking a risk-based approach to security of supply there is significant interaction between these factors.  Installing automated switching to reduce CMLs after a fault has a substantial impact on network risk (risk is probability multiplied by consequence, and the automation significantly reduces the consequence).  This means, in a risk-based planning standard, this type of network intervention should allow additional demand to be connected, since it can be used to retain the same overall level of risk.  However, the existing planning rules do not allow this.

4.                   The capacity value of Demand Side Response (DSR) and Electricity Storage (ES) solutions examined within the project are low, with the exception of a small number if feeders.  These values are close to the expectations set out within the literature, and are largely a function of the specific modelling assumptions used within the project (i.e. no islanding for ES, short peak reduction for DSR).  These technologies either require different assumptions (ES) or more complex, data-driven modelling (DSR) to realise their full potential.

5.                   The capacity value of Enhanced Thermal Rating (ERT) solutions within the project are lower than may have been initially expected.  This is because ETR helps to resolve thermal issues however most feeders also have switching and repair related issues; as the loads increases thermal, switching and repair related issues increase, although ETR can only address thermal issues so the Equivalent Load Carrying Capacity (ELCC) associated with ETR is less than would be expected.

6.                   Real distribution networks are generally more complex than reference networks often used to assess the viability of novel network solutions.  Risk modelling of real network involves extensive complex modelling.  If this approach is to become more widely adopted, then accessibility to the required network data needs to improve, proprietary network analysis packages need to accommodate novel network and non-network solutions, and have sufficient computational resources to carry out the analysis in reasonably short periods of time.

7.                   The work carried out in this project, the primary aim of which was to create the pragmatic estimation tool described in this report, has yielded a wide range of learning around the nature of network risk and the drivers of unreliability within electricity distribution networks.  It has also yielded a substantial data set of reliability study results which have substantial value for future analysis, as well as a set of real network models which can be used to create additional data should further studies require it (for example, investigation into the impact of network topologies on generator derating factors).

The estimation tool has been delivered as envisaged, however it is probably more of use to engineers establishing design policy rather than to design engineers involved in the design of individual network development schemes.  To some extent this is because further consideration is required in relation to the two most promising solutions; the application of Remote Control and Automation is traditionally seen as a means of reducing the duration customer interruptions rather than providing additional capacity, and the application of enhanced thermal ratings yields lower ELCC that its naïve capacity.  As previously noted the ELCC values for Electricity Storage and Demand Side Response are relatively low.

Lessons Learnt

In terms of considerations for future projects there were two main learning points:

·                     One of the initial tasks was to collate network modelling data from the Northern Powergrid modelling platform, transfer it to the Newcastle and Imperial College platforms and validate the transferred models.  Transferring modelling data was not straightforward and the development of more streamline data transfer approaches, potentially via a Common Information Model, approach may be helpful.

·                     Understanding the complex nature of the network and the associated complexities of the risk analysis was a significant part of the project and proved to be more complex and time consuming than originally envisaged.  This is one reason why the project took longer than originally envisaged.

The wider learning points that could be exploited further are set out in detail in the project reports and are summarised in the following sections of this report.