View
2
Download
0
Category
Preview:
Citation preview
Smart energy management for unlocking demand response in residential applications
Xiangping Chen, Kui Weng, Fanlin Meng, Monjur MourshedBRE Trust centre for sustainable engineering, Cardiff University, B24 3AB , UK
Smart energy management for unlocking demand response in residential applications
Abstract
This paper presents a smart energy management system which is primarily
designed for the residential applications in the UK. First of all, a 24-hour
ahead demand profile is scheduled by shafting potential flexible load. And
then both one-hour energy demand and supplying flow are estimated. The
real-time electrical demand is satisfied by the combined renewable energy
sources/grid electricity/batteries with minimal cost.
Utility information/
Electricity & gas prices
• Forecasted load consumption,
• PV generation
• Battery capacity (exchangeable energy amount)
• Estimation on the controllable load
• Forecasting weather data
One-hour ahead PV generation information Battery charge/discharge schedule Electrical Appliance calculation
Optimal scheme for whole day operation
Comfort evaluation and temperature setpoint calculation
Model predictive thermal control
Electrical demands calculation
Demand=<local
supply
Record batteries SOC
Battery SOC low
Buy electricity from the grid No
No Yes
24 hours
Yes
Day-ahead
Prediction
One hour-
ahead
schedule
Real-time
operation
and control
Flow chart of energy management strategy
Smart energy management for unlocking demand response in residential applications
Methodology:
• First of all, we forecast weather and predict the overall energy consumption, state of energy
storage, electricity price. The load are categorised into non-shiftable, time shiftable and
power shiftable load groups afterwards. Therefore, day-ahead facility operation scheme is
decided;
• One-hour ahead supply estimation and comfort setting are defined to configure the energy
consumption profile;
• Dynamic programming framework is used to optimise the real-time operation where PV,
battery and grid electricity are combined to configure optimal supplying flow to balance the
real-time electrical demand under the objective proposed.
Smart energy management for unlocking demand response in residential applications
Load
Shiftable Non-shiftable
Time shiftablePower
shiftable
Wash machine Dish washer Water pumpElectrical vehicle
cooker ovenRefrigera
tor
Smart energy management for unlocking demand response in residential applications
0
1
2
3
4
5
1 81
52
22
93
64
35
05
76
47
17
88
59
29
91
06
11
31
20
12
71
34
14
11
48
15
51
62
CO
NSU
MP
TIO
N (
KW
)
TIME (HOUR)
Electrical consumption profile (7 days)
0
1
2
3
4
5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
One-day ahead consumption forecasting
0
0.5
1
1.5
2
2.5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
CO
NSU
MP
TIO
N (
KW
)
TIME (HOUR)
Shiftable load
0
0.5
1
1.5
2
2.5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
CO
NSU
MP
TIO
N (
KW
)
TIME(HOUR)
Non-shiftable load
Smart energy management for unlocking demand response in residential applications
Energy supply
0
500
1000
1500
2000
2500
3000
35001 6
11
16
21
26
31
36
41
46
51
56
61
66
71
76
81
86
91
96
10
1
10
6
11
1
11
6
12
1
12
6
13
1
13
6
14
1
14
6
15
1
15
6
16
1
16
6
PO
WER
(W
)
TIME (HOUR)
Solar power
Grid electricity price
Time (hour)
£
Smart energy management for unlocking demand response in residential applications
• By re-scheduling the usage of shiftable appliances, the energy
cost can be reduced to some extent. However, it could
deteriorate the users’ conform. Furthermore, the high
electrical demand takes place between 17’oclock and
21’oclock when grid price gradually increase to its peaks.
• There is a need to adopt better adjustable method to satisfy
both comfort and economic requirement for the domestic
users.
Smart energy management for unlocking demand response in residential applications
• Energy storage systems, such as batteries are the good option
for domestic electricity demand shifting/shedding/peak
reduction. They can be regarded as either time
shiftable/power shiftable loads or power supply resource.
• In this study, batteries are used for adjustment method to
balance demand and supply which in turn minimize both the
discomfort and the cost.
Smart energy management for unlocking demand response in residential applications
• Real-time operation
0 < 𝑤𝑝𝑣 < 𝑤𝑚𝑎𝑥
𝑆𝑂𝐶𝑚𝑎𝑥 < 𝑆𝑂𝐶 < 𝑆𝑂𝐶𝑚𝑖𝑛
൯𝑤𝑙𝑜𝑎𝑑 = 𝑤𝑔𝑟𝑖𝑑 𝑡 + 𝑤𝑝𝑣 𝑡 + 𝑤𝑏𝑎𝑡𝑡𝑒𝑟𝑦(𝑡
𝐶𝑔𝑟𝑖𝑑𝑚𝑖𝑛 < 𝐶1 < 𝐶𝑔𝑟𝑖𝑑𝑚𝑎𝑥
S.t.
𝐽 = 𝑚𝑖𝑛 σ(𝐶1 ∙ (𝑤𝑔𝑟𝑖𝑑(t)) ∙ ℎ1(𝑡) + 𝛼 ∙ (𝑤𝑏𝑎𝑡𝑡𝑒𝑟𝑖𝑒𝑠 𝑡 ∙ ℎ2 𝑡 + 𝛽 ∙ (𝑤𝑃𝑉 𝑡 ) ∙ ℎ3 𝑡 )
Smart energy management for unlocking demand response in residential applications
• Case Study
The optimal strategy is validated via the energy system in a detached 3-
bedroom house. The house is located in London with 101.25 m2 space.
Simulation is executed within 168 hours in a summer week. A daily energy
consumption and supplies are evaluated as follows,
Smart energy management for unlocking demand response in residential applications
Case Study
A daily demand and supplies (a) without optimisation (b)with optimisation
-2000
-1000
0
1000
2000
3000
4000
5000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Po
we
r (W
)
Time (hour)Scheduled demand solar power batteries power grid power
-1000
0
1000
2000
3000
4000
5000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Po
we
r (W
)
Time (hour)
Electrical demands(W) PV power (W) Grid electricity (W)
Smart energy management for unlocking demand response in residential applications
Case Study
without optimisation with optimisation
Demand(kWh) 43.59 43.59
Electricity bought(kWh) 21.49 19.18
Electricity sold(kWh) 2.71 0.4
PV energy(kWh) 24.81 24.81
Peak demand (kW) 4.21 2.84
Electricity cost(£) 2.98 1.95
Smart energy management for unlocking demand response in residential applications
Conclusion:
This study developed an optimal operation scheme for a energy system in
a domestic house. The highlights are summarised as follows:
1. Load shifting/load shedding are realised through a day-ahead
scheming;
2. Energy storage assistants the realisation of peak shafting;
3. The costs for buying electricity are reduced;
4. CO2 emission will be considered in the future research.
Smart energy management for unlocking demand response in residential applications
Thanks
Recommended