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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


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


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.


Load

Shiftable Non-shiftable

Time shiftablePower

shiftable

Wash machine Dish washer Water pumpElectrical vehicle

cooker ovenRefrigera

tor


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


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)

£


• 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.


• 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.


• 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 )


• 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,


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)


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


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.


Thanks