IEEE Power & Energy Magazine - May/June 2018 - 41

Flexible Demand
Facilitate

city

Network Capacity

p

up/S

Back

apa
are C

Secondary DNO
Fixed Demand
Utilized Cap

acity

Supply
Incumbent DNO

figure 6. The basic concept of the SNA DSO business model.

capacity. The independent parties who are licensed for Sna
would act as secondary dnos to provide flexible network services using the spare capacity in the network, thus substantially reducing the network access cost for flexible demand.
in the case of unforeseen contingencies, the spare capacity
would be returned to the incumbent dno to ensure security
of supply. Table 1 summarizes the differences between current models and sharing of energy systems.
it should be noted that there would be significant technical and regulatory challenges in aligning the objectives of
primary and secondary operators to ensure smooth coordination and transition. When the need arises to return the
spare capacity to the primary operator, the secondary operator is essentially required to interrupt the supply to its flexible
demand. For example, when necessary, the telecommunications industry typically stops all new calls from connecting
to the existing network, but ongoing conversations are not
interrupted. The technical and regulatory challenges must
be properly considered to give both primary and secondary
operators a higher degree of certainty in terms of capacity
availability so that they can optimize and coordinate their
networks in the interests of both fixed and flexible demand.
Failure to do so would lead to a much more complicated system as well as dissatisfied customers.
The key development required to achieve Sna is to make
visible the dynamically charging availability of network
spare capacity. generally, in the united Kingdom, the final
stage of system monitoring is on the outgoing 11-kV feeders
in a primary substation, where current and voltage are both
measured. The real-time loading of the distribution substations along the 11-kV feeders is not currently recorded or
visible without a site visit. Furthermore, measurements of
the maximum demand indicator taken during a site visit
exhibit poor accuracy across the loading range.
Without big data support, the dno often designs the
network according to passive "fit and forget" criteria. This
ensures that the system can operate within statutory limits
may/june 2018

and remain resilient under worst-case scenarios, such as the
evening peak during the coldest winter period. The approach
is based on the assumption that load growth for existing customers will be relatively small, uniform, and predictable.
Large-scale adoption of low-carbon technology on LV networks could change this assessment of the worst-case scenario and undermine current load growth assumptions. a
key design challenge is to identify when these changes are
likely to occur. Limited visibility of asset use and the state
of the network make it difficult for a dno to optimize its
network planning and operation. a better understanding of
how customer behavior is likely to impact the time-of-day
loading and voltage "headroom" available on different parts
of the LV network is crucial in planning the security of a
future network.
one solution under consideration for situational awareness is widespread monitoring. in practice, such a solution
could be prohibitively expensive: it has been estimated that
this would cost £2 billion to monitor all the LV networks in
the united Kingdom. inferential statistics, another feature
of big data, could be used to observe the whole system by
mining data from small populations. Such a process aims to
table 1. A comparative summary of differences
between the status quo and sharing energy systems.
Status Quo

Sharing System

Customer
flexibility

Very limited energy
products for customer
flexibility; assets are
severely underutilized
and not optimized for
the whole system

Horizontal P2P energy
trading between
prosumers

Network
flexibility

Reserved for
contingencies; idle
much of the time

SNA; spare capacity
can be leased to
accommodate more
flexible demand/
generation

ieee power & energy magazine

41



Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - May/June 2018

Contents
IEEE Power & Energy Magazine - May/June 2018 - Cover1
IEEE Power & Energy Magazine - May/June 2018 - Cover2
IEEE Power & Energy Magazine - May/June 2018 - Contents
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IEEE Power & Energy Magazine - May/June 2018 - Cover3
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