Multi-objective Optimal Hybrid Power Flow Algorithm for Integrated Community Energy System

Available online at www.sciencedirect.com

ScienceDirect Energy Procedia 105 (2017) 2871 – 2878

The 8th International Conference on Applied Energy – ICAE2016

Multi-objective Optimal Hybrid Power Flow Algorithm for Integrated Community Energy System Wei Lina, Xiaolong Jina, Yunfei Mua*, Hongjie Jiaa, Xiandong Xub, Xiaodan Yua a Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast BT9 5 AH, UK

b

Abstract A multi-objective optimal hybrid power flow algorithm was proposed for multi-objective scheduling and management of the integrated community energy system (ICES). Firstly, an energy conversion analysis model for the energy center was developed based on the energy hub model. Then, a multi-objective optimal hybrid power flow algorithm is proposed to minimize the operation cost and total emission of the ICES considering the constraints from unbalanced three-phase electric distribution network, the natural gas network and the energy centers. Numerical results showed that the proposed multi-objective optimal hybrid power flow algorithm can be further used in the optimal day-ahead scheduling for the ICES, which considers the ICES’s multiple operation needs in aspects of security, economy and environmental friendliness. © Published by ElsevierPublished Ltd. This is an access article ©2017 2016 The Authors. byopen Elsevier Ltd. under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). under responsibility Selection and/or peer-review Peer-review under responsibility of the scientific committee of theof 8thICAE International Conference on Applied Energy. Keywords: Integrated community energy system (ICES); energy hub; multi-objective optimal hybrid power flow; optimal day-ahead scheduling.

1. Introduction The increasing level of environmental pollution and energy crisis are the two main factors that restrict the development of future low-carbon cities [1]. In order to tackle these problems, more and more attention has been paid on the integrated community energy system (ICES) with couplings and interactions among various energy systems (e.g. electric power systems, natural gas supply systems, and heating systems) at the community level [2][3]. ICES is able to coordinate the above energy systems to provide new solutions for more secure, sustainable and economical energy production, distribution and consumption in the future low-carbon cites [4]. There are a number of ICES demonstration projects in China, such as the Langfang Eco-city, Sino-German Eco-park, Ubiquitous Energy Network in Zhaoqing New District, etc., which are in need of an effective method to schedule and coordinate the interrelated

* Yunfei Mu. Tel.: +86-1582-250-9583; fax: +86-022-27892809. E-mail address: [email protected].

1876-6102 © 2017 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 8th International Conference on Applied Energy. doi:10.1016/j.egypro.2017.03.638

2872

Wei Lin et al. / Energy Procedia 105 (2017) 2871 – 2878

energy systems of the ICES in an optimal way. Thus, the optimization, coordination and management of these various energy systems of ICES are of significant importance. The existing research works have made good contributions to the scheduling of ICES. However, the previous researches consider only the total cost of operation as the objective function of the problem [5]. However, there are other objectives that might be observed in an optimization process. One of them is the total emission of the system which is an attention-grabbing criterion these days. Therefore, a multi-objective optimal hybrid power flow algorithm was proposed to minimize the operation cost and total emission of the ICES. The proposed algorithm is helpful to find out all possible optimized operating points, called Pareto optimal curve, which provides a more flexible way for the ICES operators to coordinate the interrelated power, gas, and heating systems in the ICES for cost and total emission reduction.

K AC

Nomenclature

Thermal energy conversion rate of the

Abbreviations

CAC

ICES

Integrated community energy system

CHP

Combined heat and power

CAC

Central Air-conditioning

K ge

CHP

Conversion efficiency of gas into electricity through CHP

K gh

CHP

Indices

through CHP

t

Index of time intervals

i,j

Indices of electric bus

m,k

Indices of natural gas nodes

N

Total

number

of

eelec polluting

Emission coefficient of polluting gas produced by electric network

gas

produced by electric network n

Conversion efficiency of gas into heat

Total number of the energy centers

F EC

Emission coefficient of energy center

min max Pelec , Pelec Limits of electricity purchase

Parameters and constants

U imin , U i

Limits of bus voltage magnitude

Celec ,t

Electricity price at time period t

Sijmax

Maximum capacity of electric feeder

C gas,t

Natural gas price at time period t

k min , kmax Limits of natural gas compressor ratio

k

Fraction parameter of pipelines

pmin , pmax Limits of natural gas node pressure

Tk

Temperature of pipelines

Pemin , Pemax Limits of electric power exchange of

D

Specific heat ratio

q gas

Gross heating value of natural gas

Le, Lh

Electric power and heat power output of the energy center

K T , K GB Efficiency of the power transformer and the gas-boiler

max

energy center Pgmin , P

max g

Limits of natural gas power exchange

of energy center Variables P, Q

Active and reactive power of electric feeder

2873

Wei Lin et al. / Energy Procedia 105 (2017) 2871 – 2878

V,

θ

p, F

Bus voltage magnitude and phase angle

kcp

Natural gas compressor ratio

of electric feeder

vAC

Electric partition coefficient

vCHP

Natural gas partition coefficient

Gas node pressure and gas pipeline flow

2. Model of the integrated community energy system (ICES) The ICES investigated in this paper consists of three parts, i.e., electric distribution network, natural gas network and the energy center. 2.1. Electric distribution network model The general equations for calculating active and reactive power of the network branch ij are shown in Eqs. (1) - (2). (1) Pij g si  gij Vi2  gijViV j sinθij  bijViV j cosθij Qij





 bsi  bij Vi2  bijViV j sinθij  gijViV j cosθij

(2)

2.2. Natural gas network model The general equations for calculating gas flow are shown in Eqs. (3) - (4) [6]. Fkn



k kn skn skn pk2  pn2 skn



(3)

­ 1 pk t pn ® ¯ 1 pk  pn

(4)

Compressor stations are installed on gas pipelines to provide the pressure needed to transport gas from one location to another. The model of a transmission link with compressor and pipeline is shown in Fig. 1. A gas-fired compression station connected between nodes m and k is mathematically represented by its power consumption and compression ratio as shown in Eq. (5).

pm

n

k Fmn Fcp

Compressor

m

Fkn Pipeline

pk

pn

Fig.1 Natural gas pipeline model

Pcp

D º ª «§ pm · D 1 » ¸ ¨ k cp FknTk «¨  1» ¸ » «© pk ¹ ¼ ¬

(5)

The volume flow of gas consumption by the compressor is given by Eq. (6). Fcp

Pcp / qgas

(6)

2.3. Energy center model The energy centers physically link different energy systems and provide possibility and flexibility to coordinate the different energy systems in an optimal way. In this paper, the energy conversion processes of the energy center under the hybrid thermal-electric load following mode are characterized in the energy hub model incorporating interactions among different energy systems and component constraints, as shown in Fig. 2 [7].

2874

Wei Lin et al. / Energy Procedia 105 (2017) 2871 – 2878

Power transformer

Power transformer Electricity

Electricity

Electricity

Outputs

Inputs

Electricity

CAC

Inputs

Outputs CHP

CHP

Gas-boiler

Heat

NG

Heat

NG

(a) Type I---CHP and CAC

(b) Type II---CHP and gas-boiler

Fig.2 Structure of the energy hub model

Two types of energy hub structure are considered in this paper, as shown in Fig. 2. The first type of energy hub is composed of a power transformer, a CHP and a CAC group. The input energy consists of electricity and natural gas while the output energy consists of electric and heat loads. The couple relationship between input and output energy can be expressed by Eq. (7). ª Le º « » Lh ¼ ¬, L

CHP º ª1  v AC K T K ge ªP º » e « AC CHP « P » K gh »¼ , «¬ v ACK ¬ h¼ 

(7)

P

C

The second type of energy hub is composed of a power transformer, a CHP and a gas-boiler. The coupling relationship of input and output energy can be expressed by Eq. (8). CHP º ª Pe º ªK T vCHPK ge » « CHP GB « P » «¬ 0 vCHPK gh  1  vCHP K »¼ ¬, h¼ 

ª Le º « » ¬ Lh ¼ , L

(8)

P

C

3. Model of the integrated community energy system (ICES) 3.1. Objective function Two objective functions are considered in this paper, including the operation cost and total emission of ICES. The operation cost is depicted in Eq. (9), which consists of two terms: (1) the cost of purchasing electric power; (2) the cost of purchasing natural gas. (9) F1t Celec,t Pelec,t  Cgas,t Fgas,t The total emission of ICES is depicted in Eq. (10), which consists of two terms: (1) the emission of electric network; (2) the emission of energy centers. F2 t Eelec  EEC

¦

Eelec,i,t 

i 1

¦

n

N

n

N

EEC, j ,t

j 1

¦ i 1

eelec,i Pelec,t 

¦F

EC, j Lh, j ,t

(10)

j 1

3.2. Constraints 3.2.1Electric distribution network constraints min max Pelec d Pelec d Pelec

U imin

d U i d U imax

Sij d Sijmax

(11) (12) (13)

Eq. (11) is the electricity purchase constraint; Eq. (12) is the three-phase bus voltage constraint of the electric distribution network; Eq. (13) is the current constraint of the electric feeder. 3.2.2Natural gas network constraints kmin d kcp d kmax

(14)

2875

Wei Lin et al. / Energy Procedia 105 (2017) 2871 – 2878

(15)

pmin d pk d pmax

Eq. (14) is the compressor ratio constraint; Eq. (15) is the node pressure constraint of the natural gas system. 3.2.3Energy center constraints ­ Pemin d Pe d Pemax , Pgmin d Pg d Pgmax ° ° min max max Le  PCHP , Pemax Le  PAC / K AC ® Pe min max max CHP ° Pg 0 , Pg PCHP / K ge ° ¯

(16)

­ Pemin d Pe d Pemax , Pgmin d Pg d Pgmax ° max Pemin Le  PCHP , Pemax Le ° ° min Pg Lh / K GB ® CHP · ° § ° P max P max / K CHP  ¨ L  P max u K gh ¸ / K GB g CHP ge h CHP CHP ¸ ¨ ° K ge © ¹ ¯

(17)

3.3. Solution The Non-dominated Sorting Genetic Algorithm II (NSAG-II) is widely used in dealing with multiobjective optimization problems. The proposed multi-objective optimal hybrid power flow algorithm in this paper is based on NAGA-II and the flowchart is shown in Fig. 3. 4. Case studies A typical ICES in Fig. 4 is utilized to verify the effectiveness of the developed multi-objective optimal hybrid power flow algorithm. Electric network

Selection, cross over, mutation

Start

Generate the initial population and set iteration count n=1

Satisfy all constraints ?

Solving hybrid power flow

Reproduce the population

799

Solving hybrid power flow

N

Y

Calculate the objective functions

Satisfy all constraints ? Y

712

742

705

729

744

Hub 9 (Type Ȼ)

Produce the offspring generation

N

Hub 3 (Type І)

Convergence condition is satisfied ?

722 704

702

727

730

Hub 6

736

Output the Pareto Optimal Front

Hub 8 (Type ȻȻ)

End

3

707 720

4

706

6 5

7

725

2

1 Hub 4 (Type ȻȻ)

Hub 1 (Type І)

8

10

731 9

733 710

718

709

708

N

Y

714

Hub 5 (Type І)

703

728 (Type ȻȻ)

Hub allocation

2

701

732

Calculate the objective functions

724

713

n=n+1

ICES initialization

Gas network Gas node Gas Gas network pipeline Gas load 1

Electric bus Electric branch

External grid

775

Hub 2 (Type ȻȻ)

734

13

Electric bus No.

Gas node No.

Hub 1

725

2

Hub 2

731

3

Hub 3

742

5

Hub 4

730

7

Hub 5

713

8

Hub 6

708

10

Hub 7

741

12

Hub 8

710

13

Hub 9

729

14

740

735 737

11 12

Hub No.

738

711

Fig.3 Flowchart of the multi-objective optimal hybrid power flow algorithm

741

Hub 7 (Type І)

14

Fig.4 Scheme of the ICES case

Electricity price

1

3

5

7

Natural gas price

9 11 13 15 17 19 21 23 Hour of day (h)

Fig.5. (a) Energy prices;

60 55 50 45 40 35 30 25 20

electirc load

100

heat load

90

Load of energy center (kW)

130 120 110 100 90 80 70 60 50

Natural gas price ($/MWh)

Electricity price ($/MWh)

The energy prices [8][9] and the electric and heat load of all energy centers are shown in Fig.5. The bus voltage is subject to the constraint of 0.9 dV d 1.1 and the pressure of natural gas pipelines is subject to the constraint of 0.2 d p d 1.3 . The emission coefficient of CO2, CO, SO2 and NOx are 0.8647, 0.008, 0.039 and 0.0309, respectively. The emission coefficient of the energy center is 0.04 [10]. 80 70 60

50 40 30 20 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour of day (h)

(b) Electric and heat load of energy centers

4.1. Single-objective optimization for operation cost minimization

2876

Wei Lin et al. / Energy Procedia 105 (2017) 2871 – 2878

Energy price is an important factor for the optimization operation of ICES, so this paper compares two typical time periods (1 and 18) whose electricity prices are quite different. The optimal operation cost for time period 1 and 18 are 210.186 $ and 408.028 $, respectively. Tab.1 shows the operation of each energy center. Tab.1 Operation of energy centers Time period 1

Time period 18

Electric power (KWh)Natural gas power (KWh)Electric power (KWh)Natural gas power (KWh) EC1

65.375

0

84.250

0

EC2

55.750

64.167

1.750

225

EC3

65.375

0

84.250

0

EC4

55.750

64.167

1.750

225

EC5

65.375

0

84.250

0

EC6

55.750

64.167

1.750

225

EC7

65.375

0

84.250

0

EC8

55.750

64.167

1.750

225

EC9

65.375

0

84.250

0

As shown in Tab.1, for the first type of energy center, it tends to consume electricity only to meet the load in both the two time periods. For the second type of energy hub, it tends to consume both electricity and natural gas in time period 1 and consume natural gas mainly in time period 18. There are two main reasons for this phenomenon. Firstly, the primary energy efficiency of CAC for generating heat in the first type of energy hub is higher than that of the CHP for generating electricity and heat [11] [12]. Therefore, almost all heat loads are satisfied by CAC and electric load is satisfied by power transformer. Secondly, for the second type of energy center, it has more heat load and less electric load (high heat to power ratio) in time period 18, which matches the relative high heat to power ratio of CHP [13]. Therefore, all natural gas is utilized by CHP to meet electric and heat load. The electric bus voltage magnitude for time period 1 and 18 are shown in Fig. 6. The pressures of natural gas pipeline for two time periods are shown in Fig. 7. 1.02

0.98 0.96

C-phase

0.94

A-phase

0.92 0.9

0.98

C-phase

0.96 0.94

A-phase

0.92

1 0.9999 0.9998

0.9997 0.9996 0.9995 0.9994 0.9993

0.9

0.9992

799 775 702 713 727 704 720 712 725 724 708 732 710 736 741 718 734 738 729

799 775 702 713 727 704 720 712 725 724 708 732 710 736 741 718 734 738 729

Electric network buses number

Electric network buses number

Fig.6. (a) Electric bus voltage magnitude for time period 1

Time period 18

Time period 1

1.0001

B-phase

1

Node pressure/p.u.

B-phase

1

Voltage magnitude/p.u.

Voltage magnitude/p.u.

1.02

1

2

3

4

5

6

7

8

9

10

11

12

13

14

Natural gas network nodes number

(b) Electric bus voltage magnitude for time period 18

Fig.7 Natural gas network node pressure

4.2. Single-objective optimization for total emission minimization The optimal total emission of polluting gas for time period 1 and 18 are 1.929 ton and 1.966 ton, respectively. Tab.2 shows the operation of each energy center. Tab.2 Operation of energy centers Time period 1

Time period 18

Electric power (KWh)Natural gas power (KWh)Electric power (KWh)Natural gas power (KWh) EC1

-143.655

570.082

-129.066

581.772

EC2

12.452

EC3

-143.655

144.348

1.797

224.914

570.082

-129.066

581.772

EC4

12.452

144.348

1.797

224.914

2877

Wei Lin et al. / Energy Procedia 105 (2017) 2871 – 2878

EC5

-143.655

570.082

-129.066

581.772

EC6

12.452

144.348

1.797

224.914

EC7

-143.655

570.082

-129.066

581.772

EC8

12.452

144.348

1.797

224.914

EC9

-143.655

570.082

-129.066

581.772

As shown in Tab.2, two types of energy centers tend to consume more natural gas and less electricity. And the first type of energy center injected extra electric power back into electric network. The reason is that the emission coefficient of energy center is lower than that of electric network. Therefore, all energy centers tend to purchase natural gas mainly. The electric bus voltage magnitudes for two time periods are shown in Fig. 8. The pressures of natural gas pipeline for two time periods are shown in Fig. 9. 1.02

1.02

0.99

C-phase

0.98

A-phase

0.97

1.01

1

B-phase

1 0.99

Node pressure/p.u.

Voltage magnitude/p.u.

Voltage magnitude/p.u.

B-phase

1

C-phase

0.98

A-phase

0.97

799 775 702 713 727 704 720 712 725 724 708 732 710 736 741 718 734 738 729

799 775 702 713 727 704 720 712 725 724 708 732 710 736 741 718 734 738 729

Electric network buses number

Electric network buses number

Fig.8. (a) Electric bus voltage magnitude for time period 1

0.998

0.996 0.994 0.992 0.99 0.988

0.96

0.96

Time period 18

Time period 1

1.002

1.01

0.986 1

2

3

4

5

6

7

8

9

10

11

12

13

14

Natural gas network nodes number

(b) Electric bus voltage magnitude for time period 18

Fig.9 Natural gas network node pressure

4.3. Multi-objective optimization The Pareto optimal curves of multi-objective optimization considering both the operation cost and total emission of ICES for time period 1 and 18 are shown in Fig. 10. The operation cost reduction and total emission reduction are two opposite objectives that decreasing one of them would increase the other one and vice versa. Furthermore, the results of single-objective optimization lie in the edges of the Pareto optimal curve, as shown in Fig. 10. Energy utilizations of the ICES for two time periods are shown in Tab. 3 and Tab. 4, from which the conclusion can be drawn that optimal schemes based on the proposed algorithm can balance both the operation cost and total emission of the ICES. The multi-objective optimal day-ahead scheduling schemes in a whole day for the ICES are shown in Fig. 11. Tab.3 Energy utilization of ICES for time period 1 Object

Electric power/KWh

Natural gas power/KWh

Cost optimization Emission optimization Multi-objective optimization

3061.800 2023.900 [2023.900,3061.800]

257.397 3425.300 [257.397,3425.300]

Object

Electric power/KWh

Natural gas power/KWh

Cost optimization Emission optimization Multi-objective optimization

2955.300 2050.500 [2050.500,2955.300]

912.219 3796.800 [912.219,3796.800]

Cost optimization

2.6 2.4 2.2 2 1.8 200

Emission optimization 220

240

260

Operation cost/$

280

300

Fig.10. (a) The Pareto optimal curve for time period 1

3

Total emission of polluting gas/ton

3 2.8

Total emission of polluting gas/ton

Total emission of polluting gas/ton

Tab.4 Energy utilization of ICES for time period 18

2.8

Cost optimization 2.6 2.4 2.2 2 1.8 405

Emission optimization 410

415

420

425

Operation cost/$

430

435

(b) The Pareto optimal curve for time period 18

440

3 2.5 2 1.5 500 30

400

20

300

Operation cost/$

10 200

0

Time/h

Fig.11 Day-ahead optimal scheduling scheme

2878

Wei Lin et al. / Energy Procedia 105 (2017) 2871 – 2878

5. Conclusion A multi-objective optimal hybrid power flow algorithm for the ICES considering both the operation cost and total emission of the ICES was developed in this paper. The constraints from electric distribution network, the natural gas network and energy centers were considered in the proposed algorithm. The proposed algorithm is helpful to find out all possible optimized operating points, called Pareto optimal curve, which provides a more flexible way for the ICES operators to coordinate the interrelated power, gas, and heating systems in the ICES for cost and total emission reduction. Acknowledgements This work was financially supported by the National High-tech R&D Program of China (863 Program with No. 2015AA050403), the project National Natural Science Foundation of China (Grant No. 51307115, 51377117, and 51277128), Science and Technology Project of Tianjin Electric Power Corporation (Forecast and Coordination Control Technique of Smart Grid Park). References [1] Dong R,Yu Y,Zhang Z. Simultaneous optimization of integrated heat, mass and pressure exchange network using exergoeconomic method. Appllied Energy 2014;136:1098–1109. [2] Keirstead J, Jennings M, Sivakumar A. A review of urban energy system models: Approaches, challenges and opportunities. Renewable and Sustainable Energy Reviews 2012; 16(6):3846–3866. [3] Zhang X, Karady G G, Piratla K R, et al. Network capacity assessment of combined heat and power-based distributed generation in urban energy infrastructures. IEEE Trans Smart Grid 2013; 4(4):2131–2138. [4] Mendes G, Ioakimidis C, Ferrao P. On the planning and analysis of integrated community energy systems: A review and survey of available tools. Renewable and Sustainable Energy Reviews 2011; 15(9):4836–4853. [5] Zhou R, Li S, Chen R, et al. Combined cool and heat and power multi-objective scheduling considering carbon emissions trading using algorithm of fuzzy self-correction particle swarm optimization. Proceedings of CSSE 2014;34(34):6119-6126. [6] Jiang M, Xu Y, Wang S, et al. Simulation and analysis of gas transmission and distribution network. Beijing: Petroleum Industry Press, 1995. [7] Xu X, Jia H, Jin X, et al. Study on hybrid heat-gas-power flow algorithm for integrated community energy system. Proceedings of CSSE 2015; 35(14):3634-3642. [8] (2015, Jan) New Yorker Independent System Operator. Available: http://www.nyiso.com. [9] Pacific Gas& Electric Tariffs. A-10 TOU, 200-500kW. Available: http://www.pge.com/tariffs/electric.shtml. [10] Wei X, Zhou H. Evaluating the environmental value schedule of pollutions mitigated in China thermal power industry. Research of Environmental Science 2003; 16(1): 53-56. [11] Adnot J, Riviere P, Marchio D, et al. Energy efficiency and certification of central air conditioners. Study for the DG transportation-energy of the Commission of the EU-final report 2003. [12] Kaikko J, Backman J. Technical and economic performance analysis for a microturbine in combined heat and power generation. Energy 2007; 32(4): 378-387. [13] Huang Y, Mcllveen D R, Rezvani S, et al. Comparative techno-economic analysis of biomass fuelled combined heat and power for commercial buildings. Applied Energy 2013; 112:518-522.

Biography Wei Lin received the B.S. degree in electrical engineering from Tianjin University, China, in 2016. His research interests include the operation optimization and energy management of integrated energy systems.