Responding to the challenge of RIIO-ED1 : a DNO perspective. 15 May 2014 AMT-Sybex conference Jonathan Booth

Transcription

1 Responding to the challenge of RIIO-ED1 : a DNO perspective 15 May 2014 AMT-Sybex conference Jonathan Booth

2 Introducing Electricity North West Regulated electricity distributor Distribute >23 million megawatt hours of electricity annually To 5 million people via 2.4 million premises At 99.99% reliability Using an 11bn network; 56,000km of network 96 bulk supply substations 363 primary substations 33,000 transformers 1

3 Introducing RIIO-ED1 New price control framework from Ofgem Sets the outputs that the 14 electricity Distribution Network Operators (DNOs) need to deliver for their consumers and the associated revenues they are allowed to collect for the eight-year period from 1 April 2015 to 31 March RIIO-ED1 is the first electricity distribution price control to reflect the new RIIO (Revenue = Incentives + Innovation + Outputs) model for network regulation. RIIO is designed to drive real benefits for consumers. It provides the companies with strong incentives to step up and meet the challenges of delivering a low carbon, sustainable energy sector at value for money for existing and future consumers. 2

4 Part of a process of increasingly complex price controls DPCR1 DPCR2 DPCR3 DPCR4 DPCR5 RIIO-ED Fixed income Five year review period Telephone response measures Losses incentive Uncertainty mechanisms Quality of Supply Incentives Innovation Funding Incentive Eight year period Broad Measure of Customer Satisfaction Low Carbon Network Fund Losses reporting Network Innovation Allowance and Competition Balancing of opex and capex incentives Incentives for workforce renewal Health indices and load Indices Fully fledged risk based health indices Cost benefit analysis Outputs Fast track

5 RIIO-ED1 challenges Greater uncertainty over more aspects for a longer period of time Greater need to tie investments to demonstrable outcomes Need to up the sector s game in terms of customer service Increased political influence on priorities and actions Greater use of incentives Focus on the non-average and non-typical Need to demonstrate much greater understanding of the asset base Need new approaches to deal with optionality & uncertainty 4

6 RIIO offers hopeful signs for the asset manager Asset management aims DPCR RIIO-ED1 Whole life consideration Evaluation of different intervention options Cost benefit analysis cheapest is not always best Customers are important Balanced incentives for costs and outcomes Short-term Silo assessment cherry picking Lowest cost Activity is key Underspending is rewarded Different efficiency models encourage long term evaluation Different efficiency models encourage evaluation of different options CBA models introduced Output metrics more important New incentives encourage best value 5

7 And puts checks & balances into the process Onus on DNOs to demonstrate that any changes justified Company makes case at price control Detailed assessment including analysis of past performance Asset or performance deteriorates Allowances generated through DPCR process Competing routes to contract outperformance incl. re-investment Company makes efficiencies by descoping Associated targets, incentives and sanctions for nondelivery 6

8 Challenges facing the business - systems Given the RIIO objectives it is imperative that we define and fully articulate an enterprise data architecture that ensures each activity contributes to the overall objectives relating to data management and data mastering Implementation of a single, centrally managed Customer dataset Implementation of an improved Network Management System (NMS) that includes substantial smart grid capabilities Mobilisation of field force to extend near real time access to data in the field as well as implement field data capture capabilities Improved work management capabilities, particularly for scheduling and dispatch and mobilisation of work management capabilities Point and linear asset data quality improvements Re-implementation of Back Office financial forecasting and planning capabilities Solutions to support the UK Smart Meter rollout Development of Criticality and Network risk modelling 7

9 The assets we operate Vast majority of our assets are still first generation Asset lives range from <five years to over a century Replacement costs range from 00s to 20M+ Which is more risky tens of these or thousands of these? 8

10 Consequences of failure Probability of failure varies from regular to extremely rare Impacts of failure vary from inconsequential to disastrous Failure can impact: Safety risk Environmental consequences Customer service Costs Electric fault causes pavement fire Burnley Market evacuated after power cut POWER CUTS HIT HOMES ACROSS THE LAKE DISTRICT 9

11 Key issues for an asset manager How do I manage the risks of network assets? How do I compare the risk of different asset types or groups? What level of risk is appropriate? How do I trade off different intervention options against each other? How do I ensure an appropriate balance to my investment programme? All require a common risk management framework, enabled by a common risk metric 10

12 Risk-based modelling Risk = Probability of failure x Consequences of failure Consequences of failure = Criticality factor x Average consequences Need a scale for probabilities of failure, linked to discoverable and recordable condition metrics Also need a common, quantified framework for considering the consequences of asset failure Consequences considered in categories of; Repair cost, customer impact, environmental consequences, safety risk 11

13 Risk-based modelling DNOs introduced a 1-5 Health Index scale in DPCR5 Forthcoming RIIO-ED1 period introduces a Criticality Index scale Example for LV switchgear; Health Index representing the overall health of the asset Criticality Index representing the relative consequences of failure of the asset, eg C3 = >125% & <200% of average for asset type End of DPCR5 (31 March 2015) with investment HI 1 HI 2 HI 3 HI 4 HI 5 C ,251 C ,180 C ,560 C ,021 5,536 2,978 1,520 4,717 12

14 Risk-based modelling Framework allows modelling of; Deterioration of assets over time Impact of interventions Changes in circumstance Deterioration End of DPCR5 (31 March 2015) with investment C h a n g e HI 1 HI 2 HI 3 HI 4 HI 5 C ,251 C ,180 C ,560 C ,021 5,536 2,978 1,520 4,717 Intervention 13

15 Risk-based modelling As the consequences are calibrated in, we can add them together to form a view of total risk Criticality bands matched to % of average HIs correlated to Probabilities of failure End of DPCR5 (31 March 2015) with investment Average Consequence of Failure calibrated in HI 1 HI 2 HI 3 HI 4 HI 5 C ,251 C ,180 C ,560 C ,021 5,536 2,978 1,520 4,717 14

16 Risk-based modelling Allows expression of risk for; Individual asset types Overall network risk Modelled deterioration for selected asset groups 15

17 Possible strategic approaches Intervention options: 1. Output-driven how do I achieve a given level of risk? 2. Constraint-driven what risk can I get for a given constraint (funding, outages, delivery capacity etc.)? 3. What happens if I model a different approach? Our approach for RIIO-ED1: Model optimum whole-life strategies for each major asset group Derive optimum replacement/refurbishment mix Apply to each asset group Review impact of proposed intervention This effectively allows the network risk to float rather than be a constrained input or pre-specified output 16

18 Network risk management Detailed risk profiles for each asset group; Without intervention With different grades of intervention With different investment portfolios 400, , ,000 EHV Switchgear (GM) Without investment Baseline CBA Option 1 CBA Option 2 100,000 CBA Option 3 Also overall view of network risk Unconstrained movement to 128% of starting position by 2023 Replacement programme brings this to 109% Optimised replacement & refurbishment programme reduces to 103% - our target for RIIO-ED1 Constraining to 100% would have cost an additional 50m not judged as good value start

19 RIIO-ED1 plans Using the network risk model, we have submitted Output targets to Ofgem for RIIO-ED1 This supports a 350m programme Plan is to constrain but not completely mitigate deterioration in RIIO-ED1 We will report on our progress annually 18

20 Under the bonnet Asset Health data much recent work to; Improve data quality & scope in asset registers Link asset registers to modelling tools import/export Migrate offline data to corporate systems More rounded assessments of health Linkages to probability of failure assumptions Consequence of failure data Much more about understanding an assets role as part of a network More complex & dynamic Many attributes still held offline How to hold & master this data? Decision support Very immature area 19

21 Today s system landscape A range of disparate IT systems, mostly inherited from United Utilities Several Work Management systems with limited Ellipse integration Network topology/asset data duplicated across several systems requiring manual re-entry of data No single master Customer dataset There are different customer groups, eg occupants of premises supplied, Connections customers, etc Issues with Address Management and linking supply points to the network topology A number of standalone databases eg MS Access The above cause inefficiencies in today s processes and present challenges in realising efficient Smart Grid operations 20

22 Challenging times ahead Continued work to improve and refine base asset data Major cleanse programme on linear asset data as part of GIS project Increased integration of CBRM modelling with ellipse Major dependency on NMS replacement project Decision support tools being developed Current Asset data strategy project to underpin future developments 21