 Open Access
 Authors : Mouli Moitra, Tuhin Shubra Das, Dr. Papun Biswas
 Paper ID : IJERTCONV9IS11044
 Volume & Issue : NCETER – 2021 (Volume 09 – Issue 11)
 Published (First Online): 16072021
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
A Decision Support System for Ranking the Different Battery Energy Storage Technologies using CRITIC and EDAS Method
Mouli Moitra
Electrical Engineering, JIS College of Engineering
Kalyani, West Bengal, 741235, India
Tuhin Shubra Das
Electrical Engineering, Kalyani Government Engineering
College, Kalyani West Bengal, 741235, India
Dr. Papun Biswas

of Electrical Engineering, JIS college of Engineering
Kalyani, Nadia, West Bengal, 741235, India
Abstract Electric Power Distribution Utility Companies (EPDUC) performing in the deregulated energy market always strive to provide a stable as well as steady power supply to its consumers cost competitive price. One of the latest approach to achieve the objective of providing costcompetitive reliable power supply is to integrate Battery Energy Storage Systems (BESS) with grids at both micro and macro level. However, due to involvement of multiple stakeholders in a power business there arises a problem of decision making to choose from a plethora of BESS technologies. An automated condition monitoring system (CMS) of a modern EPDUC deploys a combination of Internet of Things (IoT), Cloud computing, and BigData Analytics (BDA) based Decision support system (DSS) to make a choice for most technocommercially viable BESS suitable as per dynamics of the demand in its network. This paper presents a DSS applying a hybrid approach of two Multi criteria decision making (MCDM) strategies namely CRITIC and EDAS for selecting the most BESS technology available in the network.
Keywords Battery Energy Storage System (BESS), Decision Support System (DSS), Multicriteria Decision Making (MCDM), Power Distribution System (PDS).

INTRODUCTION
Modern Electric Power Distribution Utility Companies (EPDUC) has to perform in an environment which is not only competitive on economic front [1], but also challenging on the technical fronts like reliability and quality [2]. The essence of a successful power distribution system (PDS) business is to strike a balance between cost and reliability of supplied power [3]. However, integration of Distributed Energy Resource (DER) based microgrid in a PDS beyond a limit may not be beneficial; rather over penetration of such DER microgrids can harm the performance of PDS due to phenomena like Duckcurve [4].
Therefore, EPDUC around the globe are in constant search of tangible solutions which can address the problem effectively. In recent times, one of the most trending solution for addressing the technocommercial viability problems of PDS business is integration of Battery Energy Storage Systems (BESS) along with DER microgrids at various levels of PDS grid [5, 6]. The philosophy behind the integration is simple; the BESS will act as a buffer for the customer at the enduser level whenever there occur an unfavorable change in the dynamics of PDS.
However, one of the prime decision making problem faced by operator of EPDUC is to choose from a pallet of different BESS technologies integrated into the system by different companies. For example, owner of one microgrid may integrate Lead Acid Battery technology based Energy Storage System (ESS) while owner of other microgrid may integrate Compressed air energy storage technology based ESS. In other case, the EPDUC may create redundant ESS banks comprising of different technologies and deploy any particular technology which suits the dynamics of the business. Moreover, EPDUC are increasingly applying autonomous condition monitoring systems (CMS) to cut the cost of human workforce. The situation calls for development of robust Decision Support System (DSS) which can recommend BESS technology based on objectivity of data rather than depending on the subjective judgment by human observers.
This paper illustrates the application of a novel hybrid Multi criteria decision making (MCDM) based DSS, that can help an autonomous CMS to choose from different available BESS technologies based on their performance criteria. The MCDM techniques are Criteria Importance through Inter Criteria Correlation (CRITIC) [7] and Evaluation Based on Distance from Average Solution (EDAS) [8] methods which are applied in solving many MCDM problems [912] on individual levels, however their hybrid application for BESS technology selection is not observed till the development of this work.

METHODS AND MATERIALS
This paper presents seven different kinds of battery technologies with their sixteen different criteria [13]. The batteries which have selected for these MCDM processes are Lead Acid Battery (T1), Liion Battery (T2), Super capacitors (T3), Hydrogen Storage (T4), Compressed air energy storage (T5), Pumped Hydro storage (T6) and Thermal energy storage (T7). The sixteen different criteria along of these batteries are Area Intensity (K1), Material Intensity (K2), Energy Intensity (K3), CO2 Intensity (K4), Lifecycle of Green House Gas Emission (K5), Capital Intensity (K6), Operating Cost (K7), Current Installed Capacity (K8), Growth Rate (K9), Health and Safety (K10), Specific Energy (K11), Energy Density (K12), Specific Power (K13), Cycle efficiency (K14), Cycle Life (K15), Adaptable for Mobile (K16).

Summary of Performance Criteria
Area Intensity (K1): Area intensity refers that intensity of the energy which is transferred in the aspect of per unit area. Unit of the area intensity is watt per square meter.
Material Intensity (K2): Material intensity is universally identified parts of materials which are required for manufacturing, processing and destruction of a portion of a good or a material. It can be also classified as total resources of metals like energy or fuels are consumed in per unit of manufacturing or the production. The unit of the metal intensity is kg / MJ .
Energy Intensity (K3): Energy intensity is a part of the energy inability of a monetary system. Energy intensity can be optimized by calculating the ratio of usage of the energy to the gross domestic products (GDP). Energy intensity also describes how well the recession disciples energy into the monetary outcome. This is unit less as this intensity is described the ratio value of the energy supply to the GDP.
CO2 Intensity (K4): CO2 Intensity indicates that the radiation rate of the carbon di oxide which is ejected during the industrial production process. On the other hand, when it comes to the electricity or the power generation process then, it indicates that the amount of grams of CO2 is required to generate one unit of electricity which is measured as Kilowatt per hour (KW/hr).
Lifecycle of Green House Gas Emission (K5): Lifecycle of greenhouse gas emission associates with considering the global warming potential of the energy resources through the lifecycle estimate. However, this is occurred only by the electrical energy but sometimes it is also happened of heat which is evaluated.
Capital Intensity (K6): Capital intensity is classified as the total amount of capital which is related with various types of factors of the production of the energy especially the workers or the labors. Capital intensity plays a crucial role in the
Specific Energy (K11): Specific energy can be defined as the energy for every unit of mass. Furthermore, this is helped to measure the requirements of energy of each and every process.
Energy Density (K12): Energy density can be classified as the total quantity of the energy reserved in a particular space or the reservoir in per unit of the volume. Contrarily, the batteries which have a higher energy density may last for a longer time ad the weigh is less.
Specific Power (K13): Specific power of the battery energy system can be defined as the gravimetric power density and this can be asserted as watt per kilograms (W/kg).
Cycle efficiency (K14): Cycle efficiency is also called the Coulombic efficiency which can be described by the number of electrons are conveyed in the battery. Cycle efficiency is the ratio of the electrons squeezed from the battery to the total charge inserted to the battery over a complete cycle.
Cycle Life (K15): Cycle of a battery can be identified as the total number of the charge cycles as well as the discharge cycles that a battery energy storage system can complete before failing its conductivity.
Adaptable for Mobile (K16): Adaptable for mobile termed as the batteries which can be move as per the requirements and there are some batteries which have this advantage.

Summary of MCDM Methods

CRITIC Method: CRITIC method is one of those weighthing method which includes the intensity of the opposition and the dispute in the structure ogf the decision making problem. It uses the correlation to find out the differences between the criteria and utilizes the result to assign a weight to them.
Step 1. Make a choice matrix of PAs.
productivity of an industry which implies the economic
xa11 xa12 … xa1n
growth of the industry for a longterm process.
x x
… x
Operating Cost (K7): Operating cost is expressed as the
Xa xaij
a 21 a 22 a 2n
(i=1,2,………, m and j=1,2,…., n)
maintenance cost, resources cost and the workers cost of an
mn
x x … x
industry. This cost leans on the quantity of electricity that a plant generates. Operating cost is mainly two types one is fixed operating cost which includes with the capital cost,
am1 am2 amn
maintenance and the royalty. On the other hand, the other one is variable operating cost which is associated with the electricity, fuel and the feedstock which can be predicted by
Step 2. Make a standardized form of a decision matrix.
the latest financial data from the vendors quote.
Current Installed Capacity (K8): Installed capacity of a power generation plant means the highest capacity that the system is constructed to drive this plant. Current installed capacity is generally optimized by kilowatt or megawatt.
Xa
normal
N
aij
Growth Rate (K9): The chunk by which a variable expands beyond a particular span of time as a percentage of its prior measure. The growth rate generally indicates the percentage
of fraction over a period of year.
Where, Naij can be indicated as
Health and Safety (K10): Health and safety plays an important role in the battery energy storage systems.
x
a
a
N ij

x min
a
a
j
Batteries have the probability to be risky if they are not attentively constructed or if they are corrupted. Health condition of the battery is a normal condition of the battery.
aij
x max x
a
a
a
a
j j
min
For beneficial criteria (3)
Where, AVG represents the average value of the alternatives and the average value will be calculated in the
respect of the ith value.
a
a
x
N j
max x
a
a
ij
Step 3. Estimation of Positive distance (PD) and negative
aij
x max x
a
a
a
a
j j
min
distance (ND) from the normal arrangement grids are relying on the kind of the models either favorable or nonfavorable (cost).
For Nonbeneficial (cost) criteria
(4)
Step 3. Calculate the approaches of the information in each
When, the criteria are favorable:
max 0, xaij AVG
PDa
column.
C
m 1
ij
AVG
aj a j
i1 ij
ND
max 0, AVG xaij
a
a
Where, represents the value of the standard deviation of
j
aij
AVG
a
a
each column of [ N ],
ij
When, the criteria are nonfavorable (cost criteria):
And,
ij
represents the correlation coefficient of each
a
a
column of [ N ].
ij
Step 4. Estimate the objective weight criteria.
PD
max 0, AVG xaij
a
a
C
a
a
w j
j m C
aij
AVG
i1 ai


EDAS Method: The evaluation based on distance from

NDa
max 0, xaij AVG
the average solution or EDAS technique centers to embrace the best option dependent on a number of components, and the last positioning of the components is made by deciding the unity level of every component.
ij
AVG
Step 1. Demonstrate a decision matrix A by inserting the criteria and the alternative as interpreted.
Step 4. Estimation of the weighted amount of Positive
distance (SoP) and amount of negative distance (SoN) independently for all chose the alternatives, as portrayed.
x x
x x
… x
… x
SoP
SoP
w PD
w PD
xa11 xa12 … xa1n m
X x
a 21
a 22
a 2n
(i=1,2,………, m and j = 1,2,…., n)
a i i1 ai
aij
a aij mn
x x … x
a i1 a a
a i1 a a
am1
am2
amn
SoN m w ND
i i ij
Step 2. Calculate the average value of the alternatives in the aspect of the criteria.
a
a
Where, w is the weight of the criteria.
i
Step 5. Calculate the Standardizing values of the sum of
AVG
m
a
a
x
x
i1 ij
m
positive distance (SSoP) and the Standardizing values of the sum of positive distance (SSoN).
SSoP
SoP
a
a
i
i
i
a i max
i SoPa
SSoN
1 SoNa
i
i
i
i
i a
i a
a i max SoN
Step 6: Calculate the measure of conflict for each column of DM.
Step 7: Determine the quantity of relation in each column of DM. [Using the equation
(5)]
Step 6. Estimate the appraisement score (APS) for all the chosen alternatives.
Step 8: Created the weighted matrix of Positive Distance Average Solution (wPDA) and weighted matrix of Negative Distance Average Solution (wNDA).
[Using the equations (9), (10), (11) &APS
1 SSoP SSoN
a 2 a i a i
12]
Step 9: Estimate the summation value of each row in wPDA and wNDA matrix. [Using the equations (13) & (14)]
Step 10: Create the column vectors of wPDA and wNDA matrix row of summation. [Using the equations (15) & (16)]


PROPOSED FRAMEWORK

IoTBDA Based DSS Architecture
The proposed architecture of DSS for BESS technology selection is simple, it consists of three main units. The first unit is the data collection and curation mechanism which employs IoT based sensors to collect data field as well as Battery Management Systems (BMS), the data is routed through a lowpower wide area network (LPWAN) [14] gateway to optimize the long term operational cost. The second unit consists of a combination of data ware house and a cloud based data strage system and finally the third unit consists of a hybrid MCDM engine which is hosted in cloud computing environment. Figure 1 shows the center segments of the DSS architecture.
LPWAN
IoT Gateway
Step 11: Normalized the obtained matrix.
Step 12: Estimate the average performance score.
[Using the equation(17)]
Step 13: Obtain the rank of the battery technologies i.e. the lowest average value is the best and the viceversa.
Fig. 2 presents the flowchart of the paper.
Big Data Warehouse
IoT Sensors
Battery Bank with
Battery management system
BigData Clouds
Supervision Data
Actuators response
Decision Support System
based on MCDM
Controls Security
Authentication
Cloud Computing
Fig. 1. DSS Architecture for Autonomous BESS Selection.

MCDM Hybridization
This segment presents the algorithm and the corresponding flowchart developed for the automated DSS.
Step 1: Collect and curate performance criteria of the Battery Technologies. [Using Bigdata Analysis]
Step 2: Create the decision matrix (DM) of the different types of performance criteria.
Step 3: Normalize the DM and determine the average solution of DM. [Using the equation (3), (4) & (8)]
Step 4: Calculate the value of the standard deviation of each column of DM and create the matrix of PDA and NDA.
Step 5: Create symmetrical correlation matrix of DM.
START
Collect & Curate Performance Criteria
criteria. Table 1 shows the positioning of battery technologies as per their performance scores.
TABLE I. RANKS OF THE BATTERIES BASED ON THEIR PERFORMANCE
Score
0.0042
0.0094
0.2266
0.2404
0.2789
0.3169
0.6943
Rank
1
2
3
4
5
6
7
Batteries
A7
A5
A1
A6
A2
A4
A3
Score
0.0042
0.0094
0.2266
0.2404
0.2789
0.3169
0.6943
Rank
1
2
3
4
5
6
7
Batteries
A7
A5
A1
A6
A2
A4
A3
of Battery Technologies
Create Decision Matrix (DM) of
Performance Criteria
Normalize the DM Determine
Average Soln. of DM
V. CONCLUSION
Maintaining a sustained balance between cost and reliability of the power distribution network is a major
Calculate Standard Deviation of each DM column
Create Symmetrical Correlation matrix of DM
Calculate Measure of Conflict for each column of DM
Determine Quantity of Relation in each column of DM
Determine Objective Weight by scaling Quantity of Relation
Create Matrix of
Positive Dist. Average Soln. (PDA), Negative Dist. Average Soln (NDA)
Create Weighted Matrix of Positive Dist. Average Soln. (wPDA),
Negative Dist. Average Soln (wNDA)
Find Summation of each row in (wPDA), (wNDA) matrix
Create column Vectors of (wPDA), (wNDA) matrix row Summation
Normalize the obtained Vectors
challenge to the power distribution companies. Hence, EPDUC acting in the liberated energy market consistently endeavors to give a steady energy supply to their consumers cost cutthroat price. Due to the involvement of the multiple vendors in the power business it creates a confusion to select the proper BESS from a stack of battery. Thus, this paper highlighted on the DSS by implementing a hybrid computational method of MCDM strategies namely CRITIC and EDAS for choosing the most adequate BESS technology. Hopefully, this paper will help the readers to select the proper battery technology from the batterystack in a simple way as per their performance criteria. Therefore, the power distribution utility companies have integrated the BESSs with micro as well as the macrogrid which will provide a budget friendly power supply to their consumers without any disruption. Moreover, EPDUC are progressively employing the autonomous CMS to reduce the expenses of the labor cost.
Estimate the Performance score from the Avg. of Norm. Vectors
Determine the Technology Ranks as per ascending order of Performance scores
STOP
Fig. 2. Flowchart of the proposed MCDM based DSS


IMPLEMENTATION AND RESULT

In this work, all calculations are performed employing MS Excel 2016. For assessing the weight applying the CRITIC technique first the engaging insights of the MDV are counseled to test for the standard deviation. This is trailed by the methodology referenced in segment 3.2 to appraise the loads, the consequences of the system are as per the following K1 is 0.050845566, K2 is 0.065671, K3 is 0.047908, K4 is 0.047212, C5 is 0.080340, K6 is 0.049437, K7 is 0.061592, K8 is 0.077503, K9 is 0.083922, K10 is 0.069380, K11 is 0.067289, K12 is 0.071749, K13 is 0.049280, K14 is 0.055863, K15 is 0.050072, K16 is 0.071936. Finally, EDAS is
used for determining the final ranks from the weighted
ACKNOWLEDGMENT
I might want to offer my thanks and earnest on account of my counsels Dr. Papun Biswas and Mr. Tuhin Shubra Das for permitting me incredible adaptability during my examination work and for their consistent help and oversight on my exploration, through which I refined my reasoning abilities that are fundamental for an analyst. Their eagerness, exhaustive information, and guarantee for great examination have been helpful to me. Likewise, their extraordinary endeavors in reconsidering my paper are really valued.
REFERENCES

X. Yuqin, Z. Beibei, H. Qianli, Z. Ying, C. Xin, and C. Yuan, The economic benefit evaluation of distribution network planning based on the LCC theory, in 2016 China International Conference on Electricity Distribution (CICED), Xian, China, Aug. 2016, pp. 15.

P. Xiao, L. Wenjuan, Z. Xiaotian, and J. Song, The influence of green development idea on power supply reliability and power quality of distribution network, in 2016 China International Conference on Electricity Distribution (CICED), Xian, China, Aug. 2016, pp. 14.

S. Gao, H. Jia, and C. Marnay, TechnoEconomic Evaluation of Mixed AC and DC Power Distribution Network for Integrating Large Scale Photovoltaic Power Generation, IEEE Access, vol. 7, pp. 105019105029.

Md. A. Rahman, I. Rahman, and N. Mohammad, Implementing Demand Response in The Residential Energy System to Solve Duck Curve Problem, in 2020 2nd International Conference on Advanced Information and Communication Technology (ICAICT), Dhaka, Bangladesh, Nov. 2020, pp. 466470.

L. Ai Wong and V. K. Ramachandaramurthy, A Case Study on Optimal Sizing of Battery Energy Storage to Solve Duck Curve Issues in Malaysia, in 2020 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE), Kuching, Malaysia, Oct. 2020, pp. 14.

L. A. Wong, V. K. Ramachandaramurthy, S. L. Walker, and J. B. Ekanayake, Optimal Placement and Sizing of Battery Energy Storage System Considering the Duck Curve Phenomenon, IEEE Access, vol. 8, pp. 197236197248, 2020

Diakoulaki, D., Mavrotas, G. & Papayannakis, L. Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research 22, 763770 (1995).

Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L. & Turskis, Z. MultiCriteria Inventory Classification Using a New Method of Evaluation Based on Distance from Average Solution (EDAS). Informatica 26, 435451 (2015).

H.W. Wu, J. Zhen, and J. Zhang, Urban rail transit operation safety evaluation based on an improved CRITIC method and cloud model, Journal of Rail Transport Planning & Management, vol. 16, p. 100206, Dec. 2020.

Z. Li et al., An improved approach for water quality evaluation: TOPSISbased informative weighting and ranking (TIWR) approach, Ecological Indicators, vol. 89, pp. 356364, Jun. 2018.

D. Asante, Z. He, N. O. Adjei, and B. Asante, Exploring the barriers to renewable energy adoption utilising MULTIMOORA EDAS method, Energy Policy, vol. 142, p. 111479, Jul. 2020,

Y. Liang, An EDAS Method for Multiple Attribute Group Decision Making under Intuitionistic Fuzzy Environment and Its Application for Evaluating Green Building EnergySaving Design Projects, Symmetry, vol. 12, no. 3, p. 484, Mar. 2020.

M. Albawab, C. Ghenai, M. Bettayeb, and I. Janajreh, Sustainability Performance Index for Ranking Energy Storage Technologies using MultiCriteria DecisionMaking Model and Hybrid Computational Method, Journal of Energy Storage, vol. 32, p. 101820, Dec. 2020.

K. Anoh, D. Bajovic, A. Ikpehai, B. Adebisi, and D. Vukobratovic, Enabling Peer to Peer Energy Trading in Virtual Microgrids with LP WAN, in IEEE EUROCON 2019 18th International Conference on Smart Technologies, Novi Sad, Serbia, Jul. 2019, pp. 15.