Price-sensitive home energy management method based on Pareto optimisation

Figures & data

Figure 1. Component and household appliances classification of Prosumer

Table 1. Assignment method with vague description

Vague description	Assignment[λ1, λ2]
Cis as important as J	[0.5, 0.5]
Cis slightly more important than J	[0.6, 0.4]
Cis obviously more important than J.	[0.7, 0.3]
Cis much more important than J	[0.8, 0.2]
Cis extremely more important than J	[0.9, 0.1]

Figure 2. Flow chart of solving model

Table 2. Household appliances parameters

Name of household appliances	Rated power (kW)	Working interval	Working hours (h)	Minimum continuous working hours (h)
Washing machine	1	12:00–23:00	0.75	0.75
Electric cooker	0.8	6:00–7:0010:00–12:0016:00–18:00	3	1
Microwave oven	1	17:00–18:00	0.5	0.5
Vacuum cleaner	0.8	18:00–22:00	0.5	0.25
Electric kettle	1.2	7:00–9:0017:00–18:00	0.5	0.25
Air blower	1.2	21:00–23:00	0.25	0.25
Electric vehicle	6.3	19:00–7:00 the nextday	/	/
HVAC	2	19:00–6:00the nextday	/	/

Figure 3. Predicting photovoltaic power and load

Table 3. Household batteries

Capacity	SOCmin	SOCmax	Pch/Pdisch	ηloss	ηself
4 kW⋅h	0.1	0.9	3 kW	5%	2%

Table 4. Electricity price in Guangzhou (yuan/kWh)

Classification	Time frame	Purchase price	Sale price	Subsidise price
Peak period	14:00 ~ 17:0019:00 ~ 22:00	0.96	0.34	0.18
Flat period	8:00 ~ 14:0017:00 ~ 19:0022:00 ~ 24:00	0.58	0.34	0.18
Valley period	0:00 ~ 8:00	0.29	0.34	0.18

Figure 4. PSHEMS effect curve for oneday

Figure 5. Energy storage charge and discharge power

Figure 6. The purchase of electricity in the summer household nano-grid system under various operating modes

Table 5. Cost, benefit, user comfort and PAPR in different modes

Mode	Expenditure(yuan)	Income(yuan)	Comfort(pu)	Peak-to-average power ratio (pu)
No energy storage, no HEMS	28.15	10.06	0.89	13.22
Considering HEMS	14.94	10.06	0.81	4.74
PSHEMS	13.41	10.06	0.81	3.95

Figure 7. Income, cost and PAPR of load curve under different weights of the system objectives