加藤研究室・大岡研究室・菊本研究室Kato Lab., Ooka Lab., and Kikumoto Lab.
Keywords: Batteries, Thermal energy storage, Heat source machine, Optimal control, Artificial intelligence, Annual optimisation, District heating and cooling, Heat-sharing network
1/4
It is hard to be used in complex systems2
Research objectives
Development of optimization methods for operating energy systems in buildings and districts
What is “operating optimization”?
It is to decide following issues:
1) Which machines should be worked?
2) When start and stop the machines?
3) How much heat should be generated?
1
It is not difficult to be applied to a simple system though,…
Building energy system District heating and cooling
What should we do?
1) Development of optimization methods for complex system
2) Incorporation of the methods in practical use
3
Development of optimization methods4
• Significant functions of the methods
1) Rapid calculation for real-time controls
2) High accuracy of a given result
3) Adaptability
i. Un-limitation of machine’s configurations
ii. User friendly to set some parameters
iii. Incorporation of unsteady calculations
Key technology: Artificial intelligence
Artificial intelligence, especially metaheuristic
optimization methods and neural networks, has
required characteristics mentioned above.
We focused on 𝜀DE-RJ(epsilon constrained differential
evolution with random jumping) in this study.
5Boiler
Boiler
CR
CR
AR
ART
E
S
BoilerT
E
S
CR
CR
AR
AR
T
E
S CHP
CHP
*TES: Thermal energy storage
*CR: Centrifugal refrigerator
*AR: Absorption refrigerator *CHP: Combined power and heat
Cooling
Heating
Hot water
Electricity
ex)
加藤研究室・大岡研究室・菊本研究室Kato Lab., Ooka Lab., and Kikumoto Lab.
Keywords: Batteries, Thermal energy storage, Heat source machine, Optimal control, Artificial intelligence, Annual optimisation, District heating and cooling, Heat-sharing network
2/4
Optimization method
What is 𝜀DE-RJ (Epsilon differential evolution with random jumping)?
Decis
ion v
ariable
1
Decision variable 2
Feasibledomain
1) Initialization of 𝜀(=1)
2) Calculation of constraint violation
(𝜑) and objective function (𝑓)
3) Replacement of individuals by
comparison of 𝜑 or 𝑓 as follows:
i) Comparison of 𝑓𝑖𝑔
and 𝑓𝑖𝑔+1
when 𝜑 < 𝜀.
ii) Comparison of 𝜑𝑖𝑔
and 𝜑𝑖𝑔+1
when 𝜑 ≥ 𝜀.
6) 𝜀 decreases exponentially
7) Return to 1) Conceptual diagram of ε constraint method
Differential evolution (DE)
Epsilon (𝜀) constraint method
+
Random jumping (RJ)
+Individual
-100 -100 0 100 100 150 150 400 200 370
0 -100 -100 50 150 100 300 500 350 250
Schedule of TES Schedule of machine No.1
Time (h)
Output
(kW)
Charge
Discharge
1 2 3 4 5 1 2 3 4 5
TES
100
200
Demand
-100Machine No.1
Machine No.2
400
1. Initialization: Individuals (𝑁𝑝𝑜𝑝 = 100) is randomly generated
2. Evaluation of each individual
3. Create new individuals by mutation and crossover methods
1) Iteration of individuals, 𝑥𝑖(𝑖 = 1,… ,𝑁𝑝𝑜𝑝)
2) Selection of two individuals (𝑥𝑚, 𝑥𝑛(𝑚 ≠ 𝑛 ≠ 𝑖))
3) Donor individual: 𝑣𝑖𝑔+1
= 𝑥𝑖𝑔+𝑀 𝑥𝑚
𝑔− 𝑥𝑛
𝑔, 𝑀: mutation rate(=0.5)
4) Crossover:
𝑥𝑖𝑔
𝑅𝑎𝑛𝑑 0.23 0.87
0.78 0.23
𝑣𝑖𝑔+1 0.82 0.23
Crossover rate=0.7
𝑥𝑖𝑔+1 0.82 0.87
The same values
Replace by
uniformly
random number First
variable
Second variable
0
𝑥𝑚𝑡
𝑥𝑛𝑡 𝑥𝑖
𝑔
𝑣𝑖𝑔+1
×𝑀
(Donor)
Search a solution
Solve constraints
Avoid local optimum
Applied random jumping
Algorithm of DE and RJ
Problem formulation
Algorithm of 𝜀 constrained method
Development of optimization methods for operating energy systems in buildings and districts
加藤研究室・大岡研究室・菊本研究室Kato Lab., Ooka Lab., and Kikumoto Lab.
Keywords: Batteries, Thermal energy storage, Heat source machine, Optimal control, Artificial intelligence, Annual optimisation, District heating and cooling, Heat-sharing network
3/4
Development of optimization methods for operating energy systems in buildings and districts
Optimal operation of complex energy system
Objective function: daily operating costs
Energy system configuration
Hot waterdemandWater
Boiler
GAS
DC/DCElectricitydemand
GridPV
Secondarysystem
Lowerside (80)
Higherside (85)
83
75
Battery
DC/AC
DC/DC
Power conditioner
CGSCoolingTower
CoolingTower
500 m2
12
12
12
127
7
7
7
88
88
83
20
12
Secondarysystem
7
CoolingTower
Lower side (7) Higher side (12)
127
HRAR
AR
AHP1
AHP2
TR
-10
0
10
20
30
40
50
-200
0
200
400
600
800
1,000
0 2 4 6 8 10 12 14 16 18 20 22
(yen
/kW
h)
(kW
h)
Time (h)
The price of purchased elec.
The price ofsold elec.
CGS to ED
G to ED
Charging elec. B to EDPV to ED
Remaining B ED
-30
-10
10
30
50
70
90
-3,000
-1,000
1,000
3,000
5,000
7,000
9,000
0 2 4 6 8 10 12 14 16 18 20 22
(yen
/kW
h)
or
(yen
/m3
)
Time (h)
Remaining TES
CD
Charging thermal energy
TR
AR
HRAR
AHP1
AHP2
(kWh)
-20
0
20
40
60
80
100
120
-200
0
200
400
600
800
1,000
1,200
0 2 4 6 8 10 12 14 16 18 20 22
(yen
/kW
h)
or
(yen
/m3
)
(kW
h)
Time (h)
The priceof gas
RemainingTESh
HWD
Charging thermal energyDischarging thermal energy
CGS to HWD
BB
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0 2 4 6 8 10 12 14 16 18 20 22
Part
ial l
oad
rat
e (-
)
Time (h)
AHP2
AHP1
❖ Although the number of decision variables was 336 with nonlinear
configuration, computation costs was just 6 min on an ordinal PC.
❖ Powerful advantage: computational complexity of 𝜀DE does not
depend on the number of decision variables.
❖ 𝜀DE could incorporate variations of water temperature.
❖ CHP worked during daytime because of high electricity costs.
❖ Partial load rate of AHP1 and AHP2 were almost the same.
❖ Above result was optimal solution based on the mathematical
theorem such as Lagrange multiplier method.
Optimal operation: Electricity system Cooling system Hot water system CHP Partial load rate of AHPs
Benefits: rapid calculation of 𝜀DE1
Optimal operating schedules2
加藤研究室・大岡研究室・菊本研究室Kato Lab., Ooka Lab., and Kikumoto Lab.
Keywords: Batteries, Thermal energy storage, Heat source machine, Optimal control, Artificial intelligence, Annual optimisation, District heating and cooling, Heat-sharing network
4/4
3
4
5
6
7
8
9
3 4 5 6 7 8 9
MLRR2=0.927
3
4
5
6
7
8
9
3 4 5 6 7 8 9
m-PSOR2=0.981
3
4
5
6
7
8
9
3 4 5 6 7 8 9
CFR2=0.995
3
4
5
6
7
8
9
3 4 5 6 7 8 9
ANNR2=0.997
モデル温度 (ºC) モデル温度 (ºC)
モデル温度 (ºC) モデル温度 (ºC)
予測温度
(ºC
)予測温度
(ºC
)
予測温度
(ºC
)予測温度
(ºC
)
Development of optimization methods for operating energy systems in buildings and districts
Optimization of a district cooling system using ANN and 𝜀DE-RJ
PlantOffice 1
Office 2 Hospital
HotelCommercial 1
Commercial 2
Pipe 1
Pipe 2 Pipe 5
Pipe 3
Pipe 4
Pipe 6
50
m Pipe 7
50m
Modeling
Office 1
Office 2
Commercial 1
Commercial 2
Hospital
Hotel
Conceptual diagram of a district
Location of buildings
CR1
AR1
ASHP2
GHP1
GHP2
CR2
CR3
ASHP1
Pipe 1
AR2
Pipe 1
Thermal energy
storage
Energy system configuration
4 °C4 °C
4 °C
14 °C
4 °C~6 °C
Comparison of results of each regression model
TES operations
• Neural network is used to predict bottom temperature, the same meaning as outlet temperature of TES, to reduce computation costs instead of physical model of stratified TES. 19
蓄放熱なし
Maximum stored
energy, 𝐻𝑀(kWh)
Stored energy at
time step t, 𝐻𝑅𝑡 (kWh)
Bottom temperature, 𝑇20𝑡 [°C]
Input data No1.
Input data No.2
Output data
00.30.60.91.21.51.82.12.42.73
0 4 8 12 16 20 24
CR1 CR2 CR3 AR1 AR2
ASHP1 ASHP2 GHP1 GHP2
0
0.3
0.6
0.9
1.2
1.5
1.8
2.1
2.4
2.7
3
0 4 8 12 16 20 24
Time (h)
Prim
ary
energ
y b
ased C
OP
0
0.3
0.6
0.9
1.2
1.5
1.8
2.1
2.4
2.7
3
0 4 8 12 16 20 24
Time (h)
(a) Case 1 (b) Case 2
Comparison of COP in two cases
Conventional operation Optimal operation
Target [°C] Target [°C]
Target [°C] Target [°C]
Pre
dic
ted [°C
]
Pre
dic
ted [°C
]
Pre
dic
ted [°C
]
Pre
dic
ted [°C
]
Sto
red e
nerg
y [
kW
h]
Time [h]