$0XOWL2EMHFWLYH(YDOXDWLRQ0HWKRGIRU'LVWULEXWHG ,QWHJUDWHG(QHUJ\6\VWHP Cong Chen
Yixun Xue
Xinwei Shen
Qinglai Guo
Tsinghua-Berkeley Shenzhen Institute
Hongbin Sun
Shenzhen, China
Dept. of Electrical Engineering, Tsinghua University
[email protected]
Beijing, China
Abstract—In this paper, a multi-objective mathematical
a / b / c1 / c2 Coefficient of the linear model in the CCHP.
model concerning the optimization of ecology, economy and
Q11
environment benefits in a distributed integrated energy system
Q6 / Q7 / Q8 Cooling/heating/power load of the system.
(IES) is proposed. Compared with the traditional evaluation methods, e.g. Analytic Hierarchy Process (AHP), this method is based on the Pareto Front (PF) of the multi-objective optimization model, in which the multi-objective potential variant is defined to evaluate the potential of the system making the evaluation more convincing and indisputable. Moreover, to avoid the phenomenon
quantity and ignoring the quality, we utilize the Exergy Efficiency to
amend
the
evaluation
based
on
theories
D 6 /D 7 'T Ti M in / M run
Conversion factor.
M inj
Cost of investment for equipment j in the system.
R M unitj
that we generally tend to overweight the significance of energy’s
(EE)
Power output of the grid.
Absolute value for temperature change. Temperature for the output water or vapor. Initial investment/Operation cost. Capital recovery factor. Equipment’s initial investment for unit capacity in unit time.
in
Pj
Rated capacity for equipment j.
Particle Swarm Optimization (MOPSO) algorithm is applied to
r
Annual interest rate.
the distributed IES. By this method, the PF of the multi-objective
n
Life-cycle of the facilities.
thermodynamics. During operation simulation, a Multi-Objective
M runj /M wj Equipment’s fixed operation costs/ variable
model can be easily found.
operation costs for unit capacity in unit time.
Index Terms—multi-objective evaluation; Pareto Front (PF);
M gas , elec ,i
Exergy Efficiency (EE); distributed integrated energy system (IES)
the subsidy for unit capacity of the devices in unit
NOMENCLATURE
time.
Indices:
i
P k Ck
Index for the evaluating time stage. Index for serial number
by the sequence of N O , SO2 ,
i
i
influence/ the greenhouse effect/ the dust
2
NOx , CH 4 , CO2 , PM 2.5 . j
emission effect of the gas k .
Index for serial number by the sequence of photovoltaic
K / M /W
power generations, wind power generations, the small
Efficiency/economy/environment objective.
Variables:
hydropower stations, electric storages, CCHPs, gas boilers,
Pl (i )
electric storages, heat pumps, heat storages, electric
Power output of the CCHP in unit time.
H l (i ) / Cl (i ) Heating/cooling output of the CCHP in unit time.
coolers, the distributed electric grids, the cooling grids and
Fl (i ) Qg i Fg (i)
the heating grids. Input Parameters and Functions:
Pl min / Pl max Minimum/maximum power output of the CCHP. Hl min / Hl max Minimum/maximum power output of the CCHP. 1
,(((
Emission factor/ amount of pollutant k .
w1 / w2 / w3 Weight coefficients. I / U / T i Factor denoting the atmospheric acidification
T Index for the evaluating time. k
Price of gas, the price of electricity in the grid or
Consume of gas in the CCHP in unit time. Heat output of the boiler in unit time. Consume of gas in the boiler in unit time.
Pdiselec,char/ heat i CEC (i )
Pwind (i)
Ppv (i)
Power input/output of the electric/heat storage in
Strength Pareto Evolutionary Algorithm (SPEA) algorithm [15]
unit time.
and some other Sequence Quadratic Programming (SQP) methods [16-17] are used to solve the multi-objective
Cooling output of electric cooler in unit time. Power output of wind power generation in unit
optimization problem by getting the Pareto Front (PF), and then
time.
analyze the results. That way, the PF can act as a perfect reference to evaluate the planning and operation of a system.
Power output of photovoltaic power generation in
However, it’s rather difficult to get an accurate PF when the
unit time.
Phd (i)
problems are sophisticated especially in distributed IES.
Power output of hydropower station in unit time.
Moreover, how to utilize the PF to conduct a multi-objective
I. INTRODUCTION
evaluation is seldom researched.
Under the circumstances of globalized energy crisis and the
With considerations above, in this paper, we propose a
environmental problem, many countries have decided to
method to realize the multi-objective evaluation for a
upgrade the energy system and to include the clean energy in
distributed IES, in which the mathematic model of distributed
the way of distributed Integrated Energy System (IES), which
IES is simplified, a Multi-Objective Particle Swarm
is also known as Energy Hub and Multiple energy carriers [1-
Optimization (MOPSO) algorithm is utilized to get the PF and
2].To establish a distributed IES which is optimal in terms of
the multi-objective potential variant is brought about to
economic, environment friendliness and renewable energy
evaluate the system based on the PF.
integration is one of the major concerns in many researches.
The remaining content is organized as below: In Section II,
However, an accurate way to evaluate the distributed IES is still
the model formulation of distributed IES is introduce. Then, a
under discussion because of the complexity of the distributed
mathematical model for multi-objective evaluation method is
IES and the coupling between different energy flows [3-4].
presented in Section III, as well as the multi-objective potential
Taking all these factors into consideration, this study put
variant, which is defined to illustrate how to evaluate the
forward a multi-objective evaluation plan for the disposition of
system based on the PF. In Section IV, a MOPSO algorithm is
distributed IES based on the analysis of the distributed IES in
applied to search for the PF of the model. Case studies are
Guizhou Province, China.
carried out in Section V to demonstrate the feasibility and
Many scholars have done a lot of research on the single
improvement of the multi-objective evaluation method.
objective evaluation of the CCHP [5-13] and the chosen
II. MODELING OF DISTRIBUTED IES.
objective are paramount to the evaluation. The objectives like
A. Combined Cooling Heating and Power (CCHP) System
the energy efficiency, the exergy efficiency [5] and the
This paper mainly focused on the optimization of ecology,
Coefficient of Performance (COP) [6] are utilized to evaluate
economy and environment in a distributed IES, thus the output
the interest of ecology of a distributed IES system. In [7-9], the
and the electric-thermal coupling relationship are mainly
interest of economy is mainly concerned and the emission of
concerned in the mathematic model.
the CO2, the influence of the greenhouse effect, the pollution
Pl min d Pl i d Pl max
of the emission, etc. [10] are utilized to evaluate the interest of
(1)
H l min d H l i d H l max
environment. However, concerning the complication of the
aPl i bH l i
distributed IES single objective analysis is far from enough.
(2)
Fl i
Hl i
There are also some literatures on multi-objective
(3) c1 Pl i c2
evaluation for distributed IES [11-24]. Generally, there are two
(4)
ways. Firstly, the integrated evaluation [11], AHP-entropy
B. Gas Boiler, Storage, Electric cooler and Other Equipment
weight method [12], the hierarchal analysis [13], the fuzzy
When the power output of the CCHP is limited, the system
method [14], etc. are applied to transform multi-objective
may need the gas boiler to help fulfill the heat load. And in a
problem into single objective problem making the problem
distributed IES, the electric storage devices will charge and
much easier to solve. Nevertheless, it’s controversial to add
discharge for at least one time during one day. It can be charged
objectives with different properties together. Secondly, the
when the electricity price is low and discharged to meet the 2
need of the power load in system. The electric cooler’s cooling
The energy efficiency can be described as follow.
power is related to the power flow and the efficiency. The
K=
model for gas boiler, storage, electric cooler and other
Q6 Q7 Q8 Q2 Q3 Q4 Q11 Q5
(5)
equipment like photovoltaic power generation, wind power
In the calculation of the renewable energy, the formula leaves
generation and small hydropower station are illustrated in [11].
out the transformations from wind energy, solar energy and
III. MODELING OF MULTI-OBJECTIVE EVALUATION
hydro energy to power energy, for the transformation efficiency
This paper give a multi-objective model concerning the
is so low that the value will be too small to analysis. Besides,
interest of ecology, economy and environment. And the
Q1 , Q9 and Q10 , denoting power supply for electric storage,
evaluation are given based on the multi-objective potential
heat supply for the heat pump and heat supply for the heating
variant which is defined in this paper to illustrate to potential of
storage, are not included in the formula for their operational
the designing plan and operational plan in the system according
cycle time are far shorter than the evaluating time.
to the PF. This evaluation will be conducted every half year and
The exergy efficiency can be described as follow.
can bring about a revisionary plan.
K=
A. Model of ecology objective The distributed IES combines the power, heating and cooling
Q6 D 6 Q7 D 7 Q8 Q2 Q3 Q4 Q11 Q5 'T , i 6,7 Ti
Di
together, as is shown in Fig.1. It consists of several facilities,
(6) (7)
the model of which are illustrated in Section II. Q2 is equal to Fl . Q3 , Q4 and Q5 is respectively equal to Pwind , Ppv and Phd .
The electricity energy is of high quality, for which the
The green lines show the power flow of renewable energy, the
amend the plan and make high quality use of the energy.
orange lines show the heat flow, the blue lines show the cooling
B. Model of economy objective
conversion factor is 1. The exergy efficiency can be used to
flow, the grey lines show the other power flow and the dotted
The costs and subsidies of a distributed IES can be used for
lines show the energy supply inside the system, which are not
the economy quantitative analysis of a system. In this paper, the
included in Q6 , Q7 and Q8 .
model of economy objective consists of two parts, the investment cost and the operation cost.
M in M run
M 1.
(8)
The initial investments The initial investment can be calculated by: 10
¦M
M in
inj
(9)
j 1
M inj
R
2.
R M unitj Pj
r (1 r ) n (1 r ) n 1
(10) (11)
The operation cost The operation cost of a system consists of three parts:1) the
fixed operation costs include the fee to hire employee; 2) the variable operation costs such as the maintenance costs; 3) other
Fig.1 The energy flow in distributed IES
costs or subsidies like the fuel cost, the fee to buy electricity
Both the energy efficiency and the exergy efficiency can be
from the grid and the subsidies for renewable energy.
used to evaluate the interest of ecology in a system. Whereas,
10
the former focuses on evaluating the efficiency of energy
M run
10
runj
j 1
quantity and the later focuses on evaluating the efficiency of
Pj ¦ T M wj Pj + j 1
5
¦¦ M
energy quality. 1.
¦T M
i 1 T
The Energy Efficiency 3
gas ,elec ,i
Fj Pj
(12)
And the constraints of load can be illustrated below including the load balance of power, heating and cooling.
C. objective function about environment impact
¦ T
This paper is mainly concerned about the environment pollution of several gas like N O , SO2 , NOx etc., which can bring about atmospheric acidification and CH 4 , CO2 , PM 2.5 etc.,
l
l
EC
6
T
( Fl (i ) Fg (i ))
(13)
¶
{
TABLE I. THE VALUE OF EMISSION FACTOR (g/kWh)
Pk
7
T
T
The multi-objective potential variant can be calculated by:
T
Pollutant
heat dis , char
g
T
amount of the pollutants can be calculated by: k
(17)
¦ ( H (i) Q (i) P (i)) ¦ Q (i) (18) ¦ (C (i) C (i)) ¦ Q (i) (19)
which can bring about the greenhouse effect. The emission
¦P
Q8 (i) ¦ T
elec Phd (i) Pdis , char (i ))
2
Ck
( Pl (i) Q11 (i) Pwind (i) Ppv (i)
'x
,
'y
,
'z
xmax xmin ymax ymin z max z min
} (20)
CO2
CH 4
N 2O
SO2
NOx
PM 2.5
where x, y and z denotes three objectives. And 'x , 'y and
203.74
0.015
0.0004
0.011
0.202
0.0012
'z is the distance between this solution and the PF of whole
The influence of the pollutant can be divided by the influence
solution set in the direction of x, y and z, the formulas for which
of atmospheric acidification, the greenhouse effect and the dust
can be found.
emission effect [11], which can be illustrated by this formula:
W
6
6
6
i 1
i 1
i 1
w1 ¦ Ii Ci w2 ¦ Ui Ci w3 ¦Ti Ci
(14)
TABLE II. THE VALUE OF POLLUTION FACTOR Pollutant
U (g CO -
CO2 CH 4 N 2O SO2 NOx PM 2.5
equiv./g pollutant) 1 21 310 -
i
2
I
(g SO2 i equiv./g pollutant) 0.7 1 0.7 -
T i (g PM 2.5
equiv./g pollutant) 1.9 0.3 1
Fig.2 The multi-objective potential variant
D. Model of Multi-Objective and Multi-Objective Potential
The multi-objective potential variant can be utilized to
Variant 1.
evaluate the potential of the plan and find out the best way to
The Multi-Objective Model
improve the plan, which means if the potential variant on one
The multi-objective model including all the objectives above,
direction is larger, the plan should be adjusted on that interest.
which is a vector:
IV. SOLUTION STRATEGY
F {K , M ,W }
The algorithm to multi-objective evaluation includes the
(15)
following steps: Step1) Multi-Objective optimization:
This can be used to compare different plans of planning and
Calculate the optimal solution sets of the multi-objective
operation of a system. 2.
problem with (15). Step2) Pareto Front: Calculate the PF of
The Multi-Objective Potential Variant
the system by algorithms like MOPSO if the model is non-
The multi-objective potential variant is defined in this paper
convex; and many more simple algorithms can be used if the
to efficiently make use of the Pareto Front (PF) in the procedure
model is convex. Step3) Multi-Objective Potential Variant:
of evaluation, which can evaluate the potential waiting to be
Calculate the potential variant with (20). Step4) Exergy
improved in the plan.
Efficiency (EE): Calculate the EE to amend the plan if the plan
The PF can be found by solving this multi-objective
need to make high quality use of the energy. Step5) Evaluation
optimization problem. The objective function is: min{K ( x ), M ( x ), W ( x )}
Outcome: Evaluate and improve the plan based on F, σ and EE (16)
respectively gotten in step1), 3) and 4). The improvement can
The constraints consist of two parts, the constraints of load
be conducted by analysis the relationships between different
and the constraints of the equipment, illustrated in Section II.
decision variables and different objective functions. 4
can be thoroughly evaluated and the improvement direction of the plan are clearer.
V. CASE STUDIES
B. Evaluate and Improve the Energy Quality Efficiency
This paper use the distributed IES being constructed in
EE
Guizhou Province, China as a case.
In some circumstances, the system need to be planned and
Firstly, this paper evaluates the plan and brings up an
operated with high energy quality efficiency making it
improved plan without the amend of EE like in most real case
necessary to supervised the improved plan with EE. After
the energy efficiency is enough for evaluate ecology property,
amended, in the supervised plan E=0.9915 and F={0.8001,
thus making the evaluation method feasible and illustrating
3.900×107, 2.472×109}, while in the improved plan EE=0.9716
how to improve the plan based on the multi-objective potential
and F={0.883, 3.560×107, 2.094×109}. Apparently, to improve
variant. Then, another plan amended by EE is proposed to show
EE, the other objectives will be slightly lowered. Nevertheless,
how the EE criteria can be effectively utilized to evaluated and
the EE can be improved to almost 100%, which is of great
improve the energy quality efficiency. The initial plan is not
significance to energy conservation and emission reduction.
located on PF and the evaluation time ranges from May to Nov.
TABLE III. THE CONSTRUCTION OF EQUIPMENT AND LOADS IN
A. Evaluate and Improve the Plan without EE
THE SYSTEM
The construction of facilities and loads in the distributed IES is give in table III. The evaluation can be conducted by
Equipment &Loads
the algorithm in Section IV and as the model is non-convex,
CCHP1
the MOPSO is utilized to find PF efficiently. The evaluation
CCHP 2
outcome of the initial plan is F={0.8474, 7.330×108, 2.472×109} and σ={0.231,0.563,0.245}, which shows the initial plan can be improved by 23.1% in ecology objective, 56.3% in economy objective and 24.5% in environment objective. Based on the evaluation above, the economy interest of the initial has a huge potential to be improved. After improvement the evaluation outcome is F={0.883, 3.560×107, 2.094×109}
with
CCHP 3
Capacity
Remarks
1.2MW /0.6MW 1.6MW /0.8MW 2MW /1MW 4MW
The max power output is 1.2MW; the max heating/cooling output is 0.6MW The max power output is 1.6MW; the max heating/cooling output is 0.8MW The max power output is 2MW; the max heating/cooling output is 1MW Being Under planning
Wind Farm Small Hydropower 12MW Station Electric Storage 2MWh
Fixed capacity in the system
PV
4MW
Power Load Heating Load Cooling Load
8395MW 1080MW 55500MWh
and σ={0.012, 0.185,0.04}, which shows the improved plan is
The max storage capacity is 2MWh The max power output of the distributed PVs is 4MW The max power load is reached in summer The max heating load is reached in winter The max cooling load is reached in summer
VI. CONCLUSION
nearly on PF, which can be seen on Fig.3.
A multi-objective model and a multi-objective potential variant is brought up in this paper to evaluate the interest of ecology, economy and environment of a distributed IES. As is shown in case studies, this method based on the PF of the multievaluation model are more convincing and indisputable, which can evaluated the initial plan thoroughly and point out a clearer improvement direction of the plan. Moreover, to fulfill the need to make high quality use of the energy, this paper utilizes the EE to amend the evaluation, which is of great significance to energy conservation and emission reduction. Based on the proposed multi-objective model, several
Fig.3 The values of the decision variables And by comparing the values of σ, the potential of the system
interesting directions are open for future study. How to find the
has be dug out by 21.9% in ecology objective, 37.8% in
PF of highly complicated distributed IES model more
economy objective and 20.5% in environment objective.
effectively with guaranteed convergence is still a note-worthy
Apparently, with the multi-objective model and the multi-
topic. The analysis of the accuracy of the PF and the
objective potential variant model in this paper, the initial plan
quantitative analysis of the relationships between different
5
decision variables and different objectives are worth further
[11] LI Miao, Integrated Evaluation of Distributed Energy System Based on 3E
investigating.
Benefit, Dalian: Dalian University of Technology, 2015. [12]
ACKNODEGMENT
DONG
Fugui,
ZHANG
Ye,
SHANG
Meimei.
Multi-criteria
Comprehensive Evaluation of Distributed Energy System [J]. Proceedings of the CSEE,2016,36(12):3214-3222.
The authors gratefully acknowledge the National Natural Science Foundation of China (51537006) and the Shenzhen
[13] HUANG-FU Yi, WU Jing-Yi, WANG Ru-Zhu, HUANG Xing-Hua. Study
Municipal Development and Reform Commission, Shenzhen
on Comprehensive Valuation Model for Combined Cooling Heating and Power
Environmental Science and New Energy Technology
System(CCHP)[J].Journal of Engineering Thermophysics, 2005,26(9):13-16.
Engineering Laboratory, Grant Number: SDRC [2016]172
[14] Youyin Jiang, He Bai, Jiangjiang Wang. A fuzzy multi-criteria decision
that provided the funding.
making model for CCHP systems driven by different energy sources[J]. Energy policy, 2012(42): 286-296.
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