IEEE Power & Energy Magazine - May/June 2018 - 39

0.6

1.2

0.5

1.0
Demand (kWh)

Demand (kWh)

0.4
0.3
0.2
0.1
0.0

0.6
0.4
0.2
0.0

-0.1
-0.2

0.8

-0.2
0

10

20

30

40

0

10

Time (h)

20

30

40

Time (h)

Product: Score = 0.58, Start = 15, Duration = 19
Var = 0.03

Product: Score = 2.13, Start = 19, Duration = 13
Var = 0.08

(a)

(b)
2.0

1.5

Demand (kWh)

Demand (kWh)

1.5
1.0

0.5

0.0

1.0

0.5
0.0

0

10

20

30

40

0

10

Time (h)
Product A: Score = 3.07, Start = 15, Duration = 14
Product B: Score = 0.85, Start = 29, Duration = 6
Var = 0.12

20

30

40

Time (h)
Product A: Score = 2.69, Start = 24, Duration = 9
Product B: Score = 1.02, Start = 13, Duration = 11
Var = 0.15

(c)

(d)

figure 4. Four typical daily products extracted from annual PV generator output.

flexibility and supply reliability could be recognized, then the
market can be split into many segments with multiple supply/
demand interactions and equilibria.
By way of example, in Figure 5, we have illustrated how
flexible (0%-60%) demand would respond to energy prices.
For local generation, the supply curve slope is flatter than for
the central supply because it is intermittent and of lower reliability and, thus, less expensive. at the equilibrium points,
the customer with higher flexibility will enjoy a much lower
price by taking advantage of local low-carbon and low-cost
generation instead of relying on the central energy market.
Big data analytics will support the design of local energy
markets by way of the following.
may/june 2018

✔ a p2p local energy market would allow energy to

be traded in different time blocks (from seconds
and minutes to hours), in different quantities, and,
particularly, with different supply reliability. Big
data analytics would be critical to ensure a multitime, multiscale, multireliability energy trading
system optimized for tracking supply/demand levels
and their variability. potential applications include
the following:
*	Resource characterization: This is needed to understand the largest and smallest trading units, largest
and smallest time blocks, and highest and lowest
supply reliability.
ieee power & energy magazine

39



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
IEEE Power & Energy Magazine - May/June 2018 - 2
IEEE Power & Energy Magazine - May/June 2018 - 3
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IEEE Power & Energy Magazine - May/June 2018 - Cover3
IEEE Power & Energy Magazine - May/June 2018 - Cover4
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