IEEE Power & Energy Magazine - May/June 2020 - 31

Table 3 lists the day-ahead peak load forecast accuracy
across different voltage levels using the ARIMA models. The
mean absolute percentage error (MAPE) is used as the measurement of the forecast accuracy by calculating the relative
scale of the error as a percentage. A lower MAPE indicates a
better forecasting performance. Testing 100 LV substations/
customers and 249 MV substations resulted in MAPEs of
55.78 and 12.83%, respectively. As the voltage level increases
to high, the MAPE drops to 4.21%, indicating that magnitude
uncertainties can be reduced at aggregated levels.

0.4
0.3
Density

Potential Pricing Schemes That
Reduce Future Uncertainties
and Risk Investment

investment, which would lead to high use of network charges.
For customers not requiring high certainty in network access,
their needs may be better served via intermittent and lessexpensive network access, which could be supplemented by
local customer resources and/or network energy storage.

0.1
0
0

2

4

6

8 10 12 14 16 18 20 22 24
(Hour)
(a)

0.5
0.4
Density

There is a huge diversity in energy usage across a wide customer base. Uncertainties vary substantially among customer
types, both in terms of the magnitude and time of peak use.
Likewise, uncertainties across different voltage levels vary
significantly, with lower voltage levels having much higher
uncertainties. However, traditional network pricing methodologies do not distinguish between load uncertainties and
future projections for different loads, which could lead to
unnecessary network investments, as is currently experienced
in parts of Great Britain's network. As a result, the new round
of reforms in network charges that began in 2019 in the United
Kingdom focuses on achieving a greater efficiency in existing
network use by reducing future uncertainties and managing
said uncertainties through network sharing.
Reducing uncertainties can be achieved by improving
the definition of network access for customers (see Table 4).
For customers requiring high certainty in network access,
their needs may still be served by traditional costly network

0.2

0.3
0.2
0.1
0
0

2

4

6

8 10 12 14 16 18 20 22 24
(Hour)
(b)

0.4
0.4
0.3
Density

Density

0.3
0.2

0.2
0.1

0.1
0
0
0

2

4

6

figure 2. The temporal distribution of SME consumers'
peak demand.
table 2. The forecasting accuracy for different
customer types.
Customer Type

R1

MAE

0.0608 0.0602 0.1178

may/june 2020	

0

8 10 12 14 16 18 20 22 24
(Hour)

R2

R3

R4

SME

0.2624 0.0673

2

4

6

8 10 12 14 16 18 20 22 24
(Hour)
(c)

figure 3. The peak demand temporal distribution at (a) HV,
(b) MV, and (c) LV levels.
table 3. The forecasting accuracy across different
voltage levels.
Voltage Level

LV

MV

HV

MAPE (%)

55.78

12.83

4.21

ieee power & energy magazine 	

31



IEEE Power & Energy Magazine - May/June 2020

Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - May/June 2020

Contents
IEEE Power & Energy Magazine - May/June 2020 - Cover1
IEEE Power & Energy Magazine - May/June 2020 - Cover2
IEEE Power & Energy Magazine - May/June 2020 - Contents
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IEEE Power & Energy Magazine - May/June 2020 - Cover3
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