Multi-objective optimization of a hybrid distributed energy system using NSGA-II algorithm

doi:10.1007/s11708-018-0594-7

Front. Energy

2018, Vol. 12

Issue (4) : 518-528 https://doi.org/10.1007/s11708-018-0594-7

RESEARCH ARTICLE

Multi-objective optimization of a hybrid distributed energy system using NSGA-II algorithm

Hongbo REN¹, Yinlong LU¹, Qiong WU¹(

), Xiu YANG², Aolin ZHOU¹

¹. College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
². College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China

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Abstract

In this paper, a multi-objective optimization model is established for the investment plan and operation management of a hybrid distributed energy system. Considering both economic and environmental benefits, the overall annual cost and emissions of CO₂ equivalents are selected as the objective functions to be minimized. In addition, relevant constraints are included to guarantee that the optimized system is reliable to satisfy the energy demands. To solve the optimization model, the non-dominated sorting generic algorithm II (NSGA-II) is employed to derive a set of non-dominated Pareto solutions. The diversity of Pareto solutions is conserved by a crowding distance operator, and the best compromised Pareto solution is determined based on the fuzzy set theory. As an illustrative example, a hotel building is selected for study to verify the effectiveness of the optimization model and the solving algorithm. The results obtained from the numerical study indicate that the NSGA-II results in more diversified Pareto solutions and the fuzzy set theory picks out a better combination of device capacities with reasonable operating strategies.

Keywords multi-objective optimization hybrid distributed energy system non-dominated sorting generic algorithm II fuzzy set theory Pareto optimal solution

Corresponding Author(s): Qiong WU

Just Accepted Date: 29 September 2018 Online First Date: 03 December 2018 Issue Date: 21 December 2018

Cite this article:

Hongbo REN,Yinlong LU,Qiong WU, et al. Multi-objective optimization of a hybrid distributed energy system using NSGA-II algorithm[J]. Front. Energy, 2018, 12(4): 518-528.

URL:

https://academic.hep.com.cn/fie/EN/10.1007/s11708-018-0594-7
https://academic.hep.com.cn/fie/EN/Y2018/V12/I4/518

Fig.1 Energy flow of the hybrid distributed energy system

Fig.2 Procedure of finding the optimal solution set

Fig.3 Flowchart of multi-objective optimization based on NSGA-II

Fig.4 Elite strategy in NSGA-II

Fig.5 Image of the crowding distance

Fig.6 Energy demand profiles on typical days

Tab.1 Electricity tariffs/($·kWh^–1)

Tab.2 Coefficients of energy supply equipment

Fig.7 Pareto efficient solutions for the case

Tab.3 Selected solutions in detail

Fig.8 Thermal balance on typical days

Fig.9 Electrical balance on typical days

Fig.10 Optimal results with different algorithms

AC	Absorption chiller
CCHP	Combined cooling, heating and power
CC-MOPSO	Co-constrained multi objective particle swarm optimization
CHP	Combined heating and power
COP	Coefficient of performance
CRF	Capital recovery factor
DER-CAM	Distributed energy resources customer adoption model
GA	Genetic algorithm
GB	Gas boiler
GE	Gas engine
MOP	Multi-objective programming
NDRC	National development and reform commission
NSGA-II	Non-dominated sorting generic algorithm II
PSO	Particle swarm optimization
PV	Photovoltaic
TOU	Time of use
A_pv	Capacity of PV in unit area/(kW·m⁻²)
C	Cost /USD
C'	Cooling/kW
Cap	Capacity of device/kW
E	Electricity/kW
e	Electricity tariff/($·kWh⁻¹)
ECI	Carbon intensity of electricity/(kg·kWh⁻¹)
F	Non-dominant set
$f C O 2 e q$	Function of annual CO₂ emissions
f_cost	Function of annual cost
f_i	Solution of i-th objective
GCI	Carbon intensity of natural gas/(kg·kWh⁻¹)
g_m	Gas price/($·kWh^–1)
H	Heating/kW
i, j	Index
k	Number of non-domination solutions
l	Lifetime of device/a
M	Dimension of the objective space
m	Number of the optimization objectives
N	Number of populations
n	Number of Pareto solution sets
P_t	A dominating set
Q	Subsidy of PV/($·kWh^–1)
Q_t	A dominating set
R	Solar irradiation/(kWh·m^–2)
r	Interest rate/%
S_max	Maximum installation areas of PV/m²
S_pv	Installation areas of PV/m²
t	Hour
u	A vector
UC	Unit cost of device/($·kW^–1)
v	A vector
ele	Electricity
gas	Natural gas
grid	Power grid
inv	Investment
load	Energy load
pur	Electricity purchased
sell	Electricity sold to grid
sub	Subsidy
U	Universe set
α	Efficiency of gas engine/%
β	Efficiency of boiler/%
η	PV conversion efficiency/%
λ	Heat-to-power ratio/%
τ	Dominance function

1	International Energy Agency. Energy, Climate Change & Environment: 2016 Insights. Technical Report, 2016
2	World Economic Forum and Accenture. Digital Transformation of Industries Electricity Industry. Technical Report, 2016
3	MLiu, Y Shi, FFang. Combined cooling, heating and power systems: a survey. Renewable & Sustainable Energy Reviews, 2014, 35: 1–22 https://doi.org/10.1016/j.rser.2014.03.054
4	YQiu, J Jiang, DChen. Development and present status of multi-energy distributed power generation system. In: IEEE Power Electronics and Motion Control Conference, Florence, Italy, 2016
5	LJu, Z Tan, HLi, Q KTan, X BYu, X HSong. Multi-objective operation optimization and evaluation model for CCHP and renewable energy based hybrid energy system driven by distributed energy resources in China. Energy, 2016, 111: 322–340 https://doi.org/10.1016/j.energy.2016.05.085
6	REvins. Multi-level optimization of building design, energy system sizing and operation. Energy, 2015, 90: 1775–1789 https://doi.org/10.1016/j.energy.2015.07.007
7	E DMehleri, H Sarimveis, N CMarkatos, L GPapageorgiou. Optimal design and operation of distributed energy systems: application to Greek residential sector. Renewable Energy, 2013, 51(2): 331–342 https://doi.org/10.1016/j.renene.2012.09.009
8	GCardoso, M Stadler, M CBozchalui, RSharma, CMarnay, ABarbosa-Póvoa, PFerrão. Optimal investment and scheduling of distributed energy resources with uncertainty in electric vehicle driving schedules. Energy, 2014, 64(1): 17–30 https://doi.org/10.1016/j.energy.2013.10.092
9	PVoll, C Klaffke, MHennen, ABardow. Automated superstructure-based synthesis and optimization of distributed energy supply systems. Energy, 2013, 50(1): 374–388 https://doi.org/10.1016/j.energy.2012.10.045
10	B HBakken, H I Skjelbred, O Wolfgang. Transport: Investment planning in energy supply systems with multiple energy carriers. Energy, 2007, 32(9): 1676–1689 https://doi.org/10.1016/j.energy.2007.01.003
11	TFalke, S Krengel, A KMeinerzhagen, ASchnettler. Multi-objective optimization and simulation model for the design of distributed energy systems. Applied Energy, 2016, 184:1508–1516
12	ZDuan, Y Yan, XYan, QLiao, W Zhang, Y TLiang, TXia. An MILP method for design of distributed energy resource system considering stochastic energy supply and demand. Energies, 2017, 11(1): 22 https://doi.org/10.3390/en11010022
13	MDi Somma, B Yan, NBianco, P BLuh, GGraditi, LMongibello, VNaso. Multi-objective operation optimization of a Distributed Energy System for a large-scale utility customer. Applied Thermal Engineering, 2016, 101: 752–761 https://doi.org/10.1016/j.applthermaleng.2016.02.027
14	MHu, H Cho. A probability constrained multi-objective optimization model for CCHP system operation decision support. Applied Energy, 2014, 116(116): 230–242 https://doi.org/10.1016/j.apenergy.2013.11.065
15	RZeng, H Li, LLiu, XZhang, GZhang. A novel method based on multi-population genetic algorithm for CCHP–GSHP coupling system optimization. Energy Conversion and Management, 2015, 105: 1138–1148 https://doi.org/10.1016/j.enconman.2015.08.057
16	L KGan, J K H Shek, M A Mueller. Optimised operation of an off-grid hybrid wind-diesel-battery system using genetic algorithm. Energy Conversion and Management, 2016, 126: 446–462 https://doi.org/10.1016/j.enconman.2016.07.062
17	SGhaem Sigarchian, M SOrosz, H FHemond, AMalmquist. Optimum design of a hybrid PV–CSP–LPG microgrid with Particle Swarm Optimization technique. Applied Thermal Engineering, 2016, 109: 1031–1036 https://doi.org/10.1016/j.applthermaleng.2016.05.119
18	SFazlollahi, P Mandel, GBecker, FMaréchal. Methods for multi-objective investment and operating optimization of complex energy systems. Energy, 2012, 45(1): 12–22 https://doi.org/10.1016/j.energy.2012.02.046
19	JWang, Z Zhai, YJing, CZhang. Particle swarm optimization for redundant building cooling heating and power system. Applied Energy, 2010, 87(12): 3668–3679 https://doi.org/10.1016/j.apenergy.2010.06.021
20	SSoheyli, M H Shafiei Mayam, M Mehrjoo. Modeling a novel CCHP system including solar and wind renewable energy resources and sizing by a CC-MOPSO algorithm. Applied Energy, 2016, 184: 375–395 https://doi.org/10.1016/j.apenergy.2016.09.110
21	EZitzler, L Thiele. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 1999, 3(4): 257–271 https://doi.org/10.1109/4235.797969
22	EZitzler, L. ThieleAn Evolutionary Algorithm for Multiobjective Optimization: The Strength Pareto Approach. TIK-Report, 1998
23	KDeb, A Pratap, SAgarwal, TMeyarivan. A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182–197 https://doi.org/10.1109/4235.996017
24	HWang, C He, YLiu. Pareto optimization of power system reconstruction using NSGA-II algorithm. In: Asia-Pacific Power and Energy Engineering Conference, Chengdu, China, 2010
25	MFarina, P Amato. A fuzzy definition of optimality for many-criteria optimization problems. IEEE Transactions on Systems, Man, and Cybernetics. Part A, Systems and Humans, 2004, 34(3): 315–326 https://doi.org/10.1109/TSMCA.2004.824873
26	WSheng, Y Liu, XMeng, TZhang. An Improved Strength Pareto Evolutionary Algorithm 2 with application to the optimization of distributed generations. Computers & Mathematics with Applications (Oxford, England), 2012, 64(5): 944–955 https://doi.org/10.1016/j.camwa.2012.01.063
27	CWeber, J Keirstead, NSamsatli, NShah, D Fisk. Trade-offs between layout of cities and design of district energy systems. In: Proceedings of the 23rd international conference on efficiency, cost, optimization, simulation and environmental impact of energy systems, Lausanne, Switzerland, 2010
28	Shanghai Municipal Development & Reform Commission. Notice on implementing linkage adjustment of coal and electricity prices by Shanghai Price Bureau. 2016–01–05,
29	National Development and Reform Commission. Notice on giving full play to price leverage to promote the healthy development of photovoltaic industry by the State Development and Reform Commission. 2013–08–26,
30	Shanghai Municipal Development & Reform Commission. Notice on issuing the special supporting fund for renewable energy and new energy development in Shanghai. 2016–11–16,
31	X LHuo, W G Zhou, R Ying-Jun. The integrated optimization of sizing and operation strategy for BCHP (Buildings Cooling, Heating and Power) systems. Natural Gas Industry, 2009, 29(8): 119–122
32	HRen, Q Wu, WGao, WZhou. Optimal operation of a grid-connected hybrid PV/fuel cell/battery energy system for residential applications. Energy, 2016, 113: 702–712 https://doi.org/10.1016/j.energy.2016.07.091

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[2]	Ramin ROSHANDEL,Majid ASTANEH,Farzin GOLZAR. Multi-objective optimization of molten carbonate fuel cell system for reducing CO₂ emission from exhaust gases[J]. Front. Energy, 2015, 9(1): 106-114.
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