A new methodology for optimal location and sizing of battery energy storage system in distribution networks for loss reduction

Journal of Energy Storage 29 (2020) 101368

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A new methodology for optimal location and sizing of battery energy storage system in distribution networks for loss reduction

T

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Zhi Yuana, , Weiqing Wanga, Haiyun Wanga, Abdullah Yildizbasib a Engineering Research Center of Renewable Energy Power Generation and Grid-connected Control, Ministry of Education, Xinjiang University, Urumqi, Xinjiang 830047, China b Department of Industrial Engineering, Ankara Yıldırım Beyazıt University (AYBU), Ankara, 06010, Turkey

A R T I C LE I N FO

A B S T R A C T

Keywords: Battery energy storage system Lithium-ion battery Optimal allocation Optimal sizing Coyote optimization algorithm Improved

In this study, a new methodology has been proposed for optimal allocation and optimal sizing of a lithium-ion battery energy storage system (BESS). The main purpose is to minimize the total loss reduction in the distribution system. The optimization process is applied using a newly developed type of Cayote Optimization Algorithm (COA). The proposed technique includes two different approaches. In the first approach, the optimization for allocation and the sizing are performed one by one and in the second approach, the optimization has been done simultaneously. To analyze the proposed system, four different scenarios have been analyzed which include different conditions without/with PVs and also using single/two BESS. The results showed that using two BESS can reduce the total error of the distribution system. the results also showed that using PVs can also decrease the total losses. Finally, the proposed approach based on ICOA is compared with Firefly Algorithm (FA), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO) to show the proposed method's prominence efficiency.

1. Introduction Nowadays, with the development of energy generation technologies, increasing attention to environmental issues and interest in improving the reliability of electric grids, the possibility and incentive to shift distribution networks from passive to active and relish in renewable energy generation at the distribution system level has been provided [1]. On the other hand, connecting distributed generation resources to current distribution networks has not met the technical and economic needs of investors [2]. While the expected increase in the diffusion coefficient of distributed generation sources was expected to improve the quality of power, due to power fluctuations due to the difference in voltage and frequency of different renewable energy sources, the opposite results were obtained [3]. While the use of distributed generation resources can potentially reduce the need for traditional power grids, controlling a large number of them along with controllable loads has created a new challenge in controlling and operating a secure and economical grid [4]. This challenge is partially mitigated by the micro-networks by reducing network control responsibility and maximizing economic returns [5]. Therefore, the appropriate solution is to build small networks independent of the main network or subnetworks [6].

⁎

One of the popular technologies for resolving the aforementioned problem is to use the Battery Energy Storage System (BESS) [7]. Due to different advantages of BESS such as improving the flexibility and the stability of the power systems, perform profitable energy and reducing the impact of oscillations made by renewable energy sources like wind and solar [8]. However, BESS has several advantages, the proper location and sizing of this technology have a significant effect on the economy installing it in the networks such that inappropriate and oversized BESS can make the additional burden on the accessories which increases investment cost [9]. Although, the optimal location and sizing of the BESS is a complicated NP-hard problem [10]. One of the popular approaches for solving NP-hard problems is to use soft computing such as meta-heuristics and artificial neural networks thanks to their straightforward and easygoing implementation features. It is important to note that however meta-heuristics give a simpler solution with global searchability, the solution for these types is not always guaranteed. Grisales-Noreña et al. [11] proposed a technique based on the nonlinear programming model for optimal locating of the BESS and the capacitors banks (CB) in distribution systems (DS). The main purpose was to minimize energy loss in the distribution system. The analysis

Corresponding author. E-mail address: [email protected] (Z. Yuan).

https://doi.org/10.1016/j.est.2020.101368 Received 14 January 2020; Received in revised form 7 March 2020; Accepted 9 March 2020 2352-152X/ © 2020 Published by Elsevier Ltd.

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(EPO) [27], and Rhino Herd (RH) Algorithm [28]. In 2018, Pierezan and Coelho [29] introduced a new bio-inspired optimization technique using coyote that lives mainly in North America. Coyote (scientific name: Canis latrans) is a mammalian category of carnivores. It is native to North and Central America. Coyote has brownish-yellow (plain) or brownish-gray fur. The fur under the belly of the beast is white. He has a black nose and yellow eyes with a shaggy tail. A number of Native American tribes have called Coyote a tricky. Coyote is one of the most intelligent and successful mammals in North America. While in the West all efforts were made to get rid of this animal, the coyotes expanded in the east and their population increased. The COA models the adaptation behavior of the coyote by the environment. This algorithm uses a new technique to make a trade-off between exploitation and exploration. The COA is a population-based algorithm that is categorized by both evolutionary heuristic and swarms intelligence. The COA simulated the social structure behavior and the coyote's experiences exchanging. By assuming the algorithm with NP number of populations, with Nc number of coyotes as the candidate solution. The social behavior of coyote is considered as the cost of the objective function. In this regard, the social behavior for coyote number c in a group p during time t can be assumed as a set of design variables as follows:

was performed based on three operating scenarios. The optimization was done on a 69-node test feeder based on a genetic algorithm and the results showed the system efficiency. Balducci et al. [12] presented a methodology to consider the advantages to the ESS services for assigning the values and ranges of them and calculating different accessories utilized for estimating the value for specific ESS utilization. Zhu et al. [13] worked on an optimization design for the BESS locating and its controller parameters selection to develop the damping of the power system oscillation. A meta-heuristic based method was designed to solve the problem based on a mixed-integer Particle Swarm Optimization (PSO) algorithm. The case study was a 39-bus New England system. Final results were validated based on seasonal load variations and finally to determine the minimum number of BESS units. The controller was also compared with some controllers to verify the efficiency. Carpinelli et al. [14] proposed a combined technique for optimal sizing and placing of the BESS over an unbalanced low voltage microgrid. This method also used a mixed non-linear optimization problem by considering economic issues and technical limitations. The method was performed on a low voltage test network to show the performance of the method and its computational burden. Rui et al. [15] proposed an optimized fuzzy strategy for modeling the energy storage system (ESS) in active distribution systems (ADS). The paper analyzed how optimal placement can affect and be affected by ESS at different levels. The IEEE-33 bus was utilized as the case study to analyze the effect of the hybrid algorithm on the case study. The hybrid optimization problem was based on a chaos hybrid algorithm that is presented using PSO and differential evolution (DE). The final results declared good achievements for the algorithm. Generally, several meta-heuristics are applied for optimal locating and sizing the BESS. But there is a common drawback for all of them; most of these algorithms trapped in the local minimum which makes them with a slow convergence rate [16]. This problem can be resolved by improving the algorithms based on making a proper balance between exploitation and exploration of the meta-heuristics [17]. The main purpose of the present work is to improve a new bioinspired optimization algorithm based on the Coyote Optimization Algorithm (COA) to optimal allocation and sizing of BESS in a 48 bus distribution network with a nominal voltage of 11 kV for minimizing the overall system losses. The method is also compared with some popular and new meta-heuristic algorithms to show its better performance for optimal allocation and sizing of BESS.

SOCcp, t = x = [x1, x2, …, xD]

(1)

Like any other population-based bio-inspired methods, COA starts by a number of random candidates (cayotes)in the search space as follows:

SOCcp, j, t = LBj + δ × (UBj − LBj )

(2)

where, δ ∈ [0, 1] describes a random number, and LBj and UBj describe the lower and the upper bounds of the jth variable in the search space. The objective function for each coyote is considered as follows:

objcp, t = f (SOCcp, j, t )

(3)

An updating formulation for the COA is randomly placing them in the groups. Sometimes, they leave their group to another one. This leaving is determined by a probability formulation that is formulated by the following equation:

Pl = .005 × Nc2

(4)

By assuming Pl with values greater than 1 for Nc ≤ 200 , the coyotes’ number in each group is limited to 14 to improve the diversity of the COA between all the population that presents a cultural exchange among the cayotes crowd. The leader (alpha coyote) is considered as the best solution in the algorithm and is formulated as follows:

2. Coyote optimization algorithm 2.1. The standard coyote optimization algorithm Optimization is the art of discovering the best possible global optimum value for a given problem. In general, each problem with the objective to find a minimum, maximum, best, cheapest, shortest, and other related purposes are categorized in the optimization problems [18]. In some problems, using classic optimization algorithms fail to find the optimum value or it needs lots of time for solving which makes the method unappealing for these purposes [19]. In the last decade, using meta-heuristics as a new part of the optimization techniques family has been extremely increased [20]. Due to the random nature of these algorithms and neglecting the dynamic and differentiation, it turned into a popular member of optimization techniques [21]. Due to neglecting the dynamic and differentiation, bio-inspired algorithms have been accounted for as a kind of fast techniques that are introduced to solve different kinds of NP-hard problems [22]. there are several types of these algorithms, such as Quantum Invasive Weed Optimization (QIWO) algorithm [23], Variance Reduction of Gaussian Distribution (VRGD) [24], World Cup Optimization [25], Deer Hunting Optimization Algorithm (DHOA) [26], Emperor Penguin Optimizer

α p, t = soccp, t for min objcp, t

(5)

An interesting feature in COA is that it stores the common experiences among the coyotes over the groups which makes a culture transformation among different groups. This trend is formulated as follows:

cul jp, t =

p, t ⎧ Rh, j , Nc is odd number ⎨ 1 (Rgp,,jt + Rgp+, t1, j ) O. W . ⎩2 N g = 2c

h= p, t

Nc + 1 2

(6)

where R describes the coyotes, social condition ranking for group number p at time t for the variable j. In COA, the life cycle of coyotes, i.e. like birth and death taking into consideration. The birth life cycle considered a combination of the social behavior of the parent coyotes (which are selected randomly from the search space) and the 2

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Z. Yuan, et al.

environmental factor. This term is formulated by the following equation:

Blejp, t =

p, t ⎧ socr 1, j , ⎪ socrp2,,tj, r j

⎨ ⎪ ⎩

soccp, t + 1 + γ ⊗ f (soccp, t + 1), ,t+1 soccp, new =⎧ ⎨ soccp, t + 1 − γ ⊗ f (soccp, t + 1), ⎩

r j < prs or j = j1 O. W .

prs =

1 D

(8)

Pra =

1 (1 − prs ) 2

(9)

⎜

δ1 = α p, t − soccrp,1t

(10)

δ2 = cul p, t − soccrp,2t

(11)

,t+1 f (soccp, worst )≠0

if

,t+1 f (soccp, worst )=0

(16)

,t+1 (soccp, best )

new δinew + βi × δinew + 1 = δi

(17)

new r1,newi + 1 = r1,new i + βi × r1, i

(18)

rinew +1

(19)

=

r2,new i

+ βi ×

r2,new i

where,

βi + 1 = 4 × βi (1 − βi )

(20)

where, βi represents the value for the i chaotic iteration, and the initial value βi ∈ [0, 1] is a random value. Fig. (1) shows the block diagram of the presented ICOA. th

2.3. Algorithm validation To validate the accuracy and the precision of the algorithm, some different benchmark functions are applied and compared with some

(12)

where r1 and r2 describe random numbers in the range [0, 1]. based on the updated behavior, the new objective value for the coyotes is achieved as follows:

p, t p, t p, t ⎧ nsocc , nobjc < objc ⎨ soccp, t , O. W . ⎩

if

and f describe the objective values for the where, f worst and best solutions for social behavior, respectively. This mechanism gives stronger exploration for the random moving to reduce the difference between the best and the worst solutions. The next utilized mechanism here is the logistic map mechanism. This mechanism is a type of chaos mechanism to resolve the premature convergence problem. The chaos mechanism is used for resolving the local optimum that makes a wrong solution with premature convergence. This problem is solved by employing pseudo-random values [31,32]. By applying this mechanism on implementing the development on the algorithm, the following equations have been updated to the algorithm:

where, δ1 describes the culture difference between alpha coyote and a random coyote (cr1) and δ2 determines the culture difference between group culture tendency and a random coyote (cr2). Updating the equation of the social behavior for a coyote by the leader with considering the impact of the group has been achieved by the following equation:

soccp, t + 1 =

⎟

,t+1 (soccp, worst ),

where D determines the dimension of the variable. 10% chance of dying is considered for Ble. The balancing process between the birth and death is as follows: where, i determines the number of coyotes in each group and ω describes the worst results of the coyotes. The culture transition over the groups is demonstrated based on the factors δ1 and δ2 as follows:

nobjcp, t = f (nsoccp, t )

(15)

2

⎧⎛ f ⎛soc p, t + 1⎞ ⎞ ⎪ ⎝ c, best ⎠ ⎪⎜ p, t + 1 ⎟ , γ = ⎜ f (socc, worst ) ⎟ ⎨⎝ ⎠ ⎪ ⎪1, ⎩

(7)

where, rj determines a random value in the range 0 and1, r1 and r2 describe random coyotes over the group p, j1 and j2 determine random design variables, σj describes a random value within the design variable limit and pra and prs represent the association and scatter probabilities, respectively that declare the coyote's cultural diversity from the group. The mathematical model for pra and prs is as follows:

nsoccp, t = soccp, t + r1 × δ1 + r2 × δ2

rand ≤ 0.5

where,

≥ prs + pra or j = j2

σj,

rand > 0.5

(13)

(14)

2.2. Improved COA (ICOA) Based on [29], COA has good results toward most of the newly introduced bio-inspired algorithms. instead, it has also a big shortcoming due to its premature convergence in some problems. This study introduces two improvement terms to develop this disadvantage. The first improvement is to do a self-adaptive weighting for controlling the algorithm propensity speed to obtain the best solution. For updating the social behavior of the coyotes, a random value is performed to the social behavior terms. For making a balance between exploitation and exploration in the algorithm, the exploration starts with high a divergence searching, and at the final iterations, the searching is turned to a local explore in the search space. There are different techniques that are used for improving the algorithm efficiency based on self-adaptive mechanism [30]. This study uses another different self-adaptive mechanism to Improve the COA. This improvement is applied to social behavior as follows:

Fig. 1. The flowchart diagram of the proposed ICOA. 3

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3. The optimization model for the studied system

Table 1 The balancing process between the birth and the death.

As the electricity sector is undergoing major changes, energy storage is a crucial choice to cover issues such as restructuring the electricity market, introducing renewable resources, helping to increase distributed generation, and improving power quality. Therefore, the use of the ESS, for power storage and network use is one of the most widely accepted solutions today. This paper uses a lithium-ion BESS using a real current injection. The model of current injection for the battery at ith iteration for the bus k and the ith iteration is considered as follows:

Determine i and ω if i = 1 Ble stays and coyote in ω has been eliminated else if i > 1 Ble stays and oldest coyote in ω has been eliminated else Ble has been eliminated End if

Iki =

Table 2 The details of the utilized benchmarks for algorithm validation. Type

Benchmark name

formulation

Unimodal

Rotated High Conditioned Elliptic Rotated Bent Cigar

F1 (x ) = f1 (M (x − o1)) + F1*

Rotated Discuss Multimodal

Rotated and shifted brock Rotated and shifted Ackley

F4 (x ) = f4 (M (

2.048(x − o4 ) ) 100

+ 1) + F4*

F5 (x ) = f5 (M (x − o5)) + F8*

(21) th

Vki

represents the voltage bus k for the i iteration, Pk and Qk where, describe the active and reactive power, respectively. The main purpose of this study is to optimal allocating and sizing of a lithium-based BESS. To do so, the optimal location of the BESS is first assigned and then, the optimal size of the proposed BESS has been selected. The optimization is based on minimizing the total power losses as follows:

F*

F2 (x ) = f2 (M (x − o2)) + F2* F3 (x ) = f3 (M (x − o3)) + F3*

1 (Pk + jQk ) Vki

100

200 300 400

M

Obj = min(∑k = 1 Rk × Ik 2 )

500

min max PBESS ≤ PBESS ≤ PBESS

V min ≤ Vk ≤ V max new bio-inspired algorithms such as Butterfly optimization algorithm (BOA) [33], Seagull Optimization Algorithm (SOA) [34], emperor penguin optimization (EPO) [27], and the standard COA [29].Table 2 illustrate the details about the utilized benchmarks for algorithm validation. The population size of the algorithms is considered 100, the dimension of the functions is set 30, and the stopping criteria is based on the maximum number of evaluated functions. Table 3 illustrates the validation results for the comparison between the presented ICOA and the literature algorithms. In the Table, “Median" determines the median value of the objective values, “std” stands for the standard deviation, and “minimum” and “maximum” represent the minimum and the maximum values for the algorithms, respectively. As can be observed from Table 3, the ICOA has the best results for the minimum, maximum, and the mean value which shows good accuracy with minimum values for the benchmark functions. It can be also observed that the value of std in the ICOA is the minimum compared with other algorithms that show its higher precision toward the compared algorithms.

(22)

where, PBESS represents the power for BESS, M describes the number of branches in the network, Vk is the voltage for bus k, and Rk and Ik are the resistance of the ith branch and the magnitude for the current, respectively. In this study, the dimensions of the coyotes in ICOA are based on the number of BESSs. For example, for a single BESS, the dimension is one that declares to the possible placing or sizing for the single BESS and for N number of BESSs, the dimension is N where the solutions show the possible location or sizing for the BESSs, respectively. During the optimization, the dimension is selected based on the employed BESSs. Because, for each BESS, the dimension is two, N = 2 × number of BESSs . During the optimization, the first term of the solution vector illustrates BESS allocation possibility and the second term describes the optimal size of the BESS. Based on the aforementioned explanations, for instance, for a model with two BESSs, the N is four such that the first two terms represent the possible locations for the two BESSs and the next two terms describe the possible sizing for them. Based on the aforementioned purposes, two scenarios have been utilized. The first scenario is based on the conventional electricity

Table 3 The results of validation for the ICOA compared with literature.

F1

F2

F3

F4

F5

Maximum Minimum Median std Maximum Minimum Median std Maximum Minimum Median std Maximum Minimum Median std Maximum Minimum Median std

BOA [33]

EPO [27]

SOA[34]

COA [29]

ICOA

6.58E + 07 5.37E+06 8.39E+06 2.80E+07 3.28E + 04 3.75E + 03 8.37E + 03 3.24E + 03 8.37E + 04 2.50E + 04 6.03E + 04 8.16E + 03 8.42E + 02 6.98E + 02 8.57E + 02 5.24E + 01 5.20E + 02 5.20E + 02 5.20E + 02 5.59E-03

8.34E + 07 6.98E+06 2.80E+07 3.35E+07 8.25E + 06 2.43E + 06 4.68E + 06 2.51E + 06 5.92E + 04 7.06E + 02 8.94E + 03 2.35E + 04 6.37E + 02 4.15E + 02 6.95E + 02 4.76E + 01 5.73E + 02 5.45E + 02 6.47E + 02 4.94E-04

3.87E + 06 3.35E + 05 2.80E + 06 5.94E + 05 4.52E + 04 6.29E + 03 2.33E + 04 7.28E + 03 2.45E + 04 3.93E + 03 8.15E + 03 4.96E + 03 5.83E + 02 3.96E + 02 5.39E + 02 3.84E + 01 6.38E + 02 5.98E + 02 5.81E + 02 4.65E-03

6.48E + 05 1.27E + 05 3.90E + 05 2.86E + 05 3.22E + 03 3.48E + 02 5.19E + 02 1.17E + 02 2.37E + 03 5.31E + 02 2.63E + 02 1.22E + 02 4.19E + 02 2.93E + 02 3.50E + 02 3.13E + 01 5.20E + 02 5.20E + 02 5.20E + 02 2.82E-04

5.40E + 05 6.18E + 04 1.05E + 05 1.38E + 05 1.38E + 03 2.17E + 02 2.64E + 02 3.11E + 02 1.42E + 03 2.16E + 02 5.30E + 02 1.19E + 02 3.85E + 02 2.17E + 02 2.34E + 02 3.40E + 01 6.89E + 02 3.80E + 02 5.20E + 02 5.76E-05

4

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Fig. 2. The proposed approach applied for optimal locating and sizing of different scenarios.

4. Simulation results

distribution system and the second is based on the electricity distribution system with solar photovoltaic (PV) systems. The results of these scenarios have been compared to show their effectiveness for power loss reduction. Each scenario includes two case studies where the first case is to optimal allocation and sizing of a single BESS and the second case study is to optimal allocation and sizing for two BESS. Fig. (2) shows the implementation of the proposed approach for optimal locating and sizing of different scenarios. The present study uses a 48 bus distribution system with 11 kV nominal voltage, 3.83 MW active load and 1.35 MVar reactive load, respectively. More information about the system is from [35]. Fig. (3) shows the studied system. For the case studies including the PV system, two PVs are considered at bus 30 with 35.13 kW power and bus 18 with 192.45 kW power.

In this section, the results of optimal allocation and sizing of the BESS on the four aforementioned scenarios have been analyzed. As explained before, the optimization is based on a new improved version of COA, ICOA. To show the prominence features of the proposed method, it is also compared with some popular algorithms including Particle Swarm Optimization (PSO) [36], Whale Optimization Algorithm (WOA) [35], and Firefly Algorithm (FA) [37]. Table 4 illustrates the parameter values for the compared algorithms. The values are extracted based on trials and errors, the number of iterations for all the algorithms are considered 100 and the population size is selected 50.

5

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Fig. 3. The single-line diagram of the studied 48 buses distribution system [35].

allocated, the optimal sizing of the BESS has been performed [38]. After applying ICOA on single BESS, the optimal size is obtained 1.83 MW with 62.56 kW total system loss that is the minimum loss among the compared method. It is also found that by applying the algorithm on two BESS, the optimal BESS sizes are obtained as 0.97 MW and 0.66 MW with 63.60 kW total system loss at bus 19 and bus 24, respectively. Table 5 illustrates the optimization results. At a glance in Table 5, it is observed that the total capacity for the case with two BESS is 1.63 MW that consequently is smaller than the capacity size of the case with single BESS (1.83 MW); this means that using two BESS reduces the total power losses. Besides, for the method optimization based on FA, the optimal size of the single BESS for the first case is obtained at bus 19 with 1.76 MW with the total system losses of 62.59 kW.

Table 4 The parameter values for the compared algorithms. Parameter WOA

→ Vector a b PSO Cognitive coefficient Social coefficient r1 r2

value

Parameter FA

value

2

Randomization parameter

0.2

1

Decreasing factor γ ICOA Nc Np Maximum no. iterations

0.96 1

2 2 0.5 0.5

10 10 1000

4.1. Scenario (1–1) Based on the aforementioned explanations, in this condition, the purpose is to optimal locating and sizing of a BESS in an electric distribution system with no PVs. By applying the optimization to the scenario (1–1), the optimal locations for the single BESS are achieved at bus 19 and the optimal locations for the two BESS are achieved at bus 19 and bus 24. The optimal allocation of BESS is based on considering the buses with high priority of load demand. Once the BESS has been

4.2. Scenario (1–2) The purpose of this scenario is to optimal location and sizing the BESSs for a distribution network including two PVs of 0.5 MW at Buses 18 and 30. The optimization results for allocation by different algorithms are illustrated in Table 6 [39]. As can be observed, the optimal location for the single BESS based on optimization is obtained on bus 6

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Table 5 Optimal location and sizing for BESS/Scenario (1–1). # Case

# BESS

Optimal location

– Bus 19 Bus 19 Bus 24

0 1 2 2

1 2 2

Optimal size/Power loss ICOA

WOA

FA

PSO

– 1.83 MW/62.56 kW 0.97 MW/63.60 kW 0.66 MW/63.60 kW

-/131 2 kW 1.82 MW/62.57 kW 0.98 MW/63.60 kW 0.65 MW/63.60 kW

– 1.85 MW/62.58 kW 1.03 MW/63.45 kW 0.58 MW/63.45 kW

– 1.82 MW/62.57 kW 0.97 MW/63.60 kW 0.67 MW/63.60 kW

Table 6 Optimal location for BESS/Scenario (1–2).

Table 8 The optimal location for BESS/Scenario (2–1).

# Case

# BESS

PV location

PV size

1 1 2 2

1 1 2 2

Bus Bus Bus Bus

2 2 2 2

18 30 18 30

× × × ×

0.5 0.5 0.5 0.5

BESS location MW MW MW MW

Bus Bus Bus Bus

# Case

24 24 14 24

1 2 2

24. The table also declares that this optimal allocation for two BESS happens at bus 14 and bus 24. After achieving the optimal location, the ICOA is applied to the system to obtain the best size for the BESS. The results of size optimization for the single BESS are obtained as 1.17MW with 59.70 kW of total system losses and the results for the two BESS are found as 0.64 MW and 1.27 MW with a 46.54 kW reduced total system losses at buses 14 and 24, respectively. Table 7 illustrates the size optimization results. For the present scenario, the ICOA gives better loss reduction toward the firefly algorithm for the case with two BESS while the optimal sizes of BESS based on ICOA are a little higher than the results of FA. More details can be shown in Table 7.

# BESS

0 1 2 2

Optimal location ICOA WOA

FA

PSO

– Bus 18 Bus 7 Bus 18

– Bus 19 Bus 16 Bus 35

– Bus 18 Bus 7 Bus 18

– Bus 18 Bus 7 Bus 18

sizing in the presence of two numbers of PVs of 0.5 MW connected to bus 18 and bus 30 as illustrated in Table 10. Table 11 illustrates the optimal sizing of the system. as can be seen, for the single BESS, the optimal size for the selected bus (Bus 15) has been achieved as 1.63MW. In this scenario, the system overall losses for the single BESS and the two BESS are reduced to 55.17 kW and 32.71 kW, respectively. 4.5. Overall analysis The results show that proper optimal allocation and sizing decrease the total system losses. This reduction has been increased by increasing the number of BESS. This is because by increasing the number of BESS, the need for a high amount of transmitted power has been decreased and consequently, the system total loss in local power supply has been reduced. Fig. (4) shows the optimal total loss achieved for all the scenarios based on ICOA. It is clear that using simultaneous optimization improves design efficiency and reduced the total loss of the system. On the other hand, it is observed that using PVs in the distribution network also reduces the total loss of the system. Fig. (5) to (7) show he optimal total loss achieved for all the scenarios based on WOA, FA, and PSO, respectively. the results of these algorithms confirmed the aforementioned explanations. For more clarification of the excellence of the proposed ICOA toward the compared algorithms, all the results for the total loss in single and two BESS are illustrated in Figs. (8) and (9). As can be seen from the figures, the created system total losses during the ICOA is so better than FA. It is also clear that although the results of the ICOA are close to WOA and PSO, the system overall losses of ICOA are less than the others. This prominence of the algorithm becomes more explicit when the number of BESS has been increased. Fig. (10) shows the comparison of the convergence characteristics of the analyzed algorithms for the best scenario, i.e. in simultaneous optimal allocation and sizing in the presence of two numbers of PVs of 0.5 MW. As can be observed from the results, it Is clear that using the mechanisms helped in increasing the convergence speed of the

4.3. Scenario (2–1) Unlike the previous scenarios that do the optimization process step by step, this scenario presents a simultaneous optimal allocation and sizing for BESS for a typical distribution network with no PVs. The optimal allocation and sizing for single BESS and also two BESS cases have been illustrated in Tables 8 and 9, respectively. Table 8 shows that the optimal location for the proposed ICOA, WOA, and PSO both WOA and PSO are obtained at Bus 18 for single BESS and Bus 7 and Bus 18 for two BESS, respectively. Based on Table 9, it is observed that the total power loss for single BESS has been decreased to 61.38 kW, which is a 69.82 kW reduction than the case without BESS. As is clear from Tables 8 and 9, the optimal sizing for the selected bus 7 and bus 18 are achieved as 0.65 MW and 1.23 MW, respectively with 48.56 kW power loss that shows a reduction of 13 kW 82.64 kW compared with the single BESS and the case without BESS, respectively. The results showed that using two BESS has been located with high load demand. 4.4. Scenario (2–2) The final scenario is another simultaneous optimal allocation and Table 7 Optimal sizing for BESS/Scenario (1–2). # Case

1 2 2

# BESS

0 1 2 2

Optimal size/Power loss ICOA

WOA

FA

PSO

– 1.17 MW/59.70 kW 0.64 MW/46.54 kW 1.27 MW/46.54 kW

-/131.2 kW 1.18 MW/60.72 kW 0.67 MW/47.43 kW 1.13 MW/47.43 kW

– 1.20 MW/61.35 kW 1.63 MW/46.20 kW 0.57 MW/46.20 kW

– 1.18 MW/60.72 kW 0.67 MW/47.43 kW 1.15 MW/47.43 kW

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Table 9 Optimal sizing for BESS/Scenario (2–1). # Case

# BESS

0 1 2 2

1 2 2

Optimal size/Power loss ICOA

WOA

FA

PSO

– 1.88 MW/61.38 kW 0.65 MW/47.32 kW 1.23 MW/48.56 kW

-/131.2 kW 1.87 MW/61.56 kW 0.67 MW/48.56 kW 1.23 MW/48.56 kW

– 1.85 MW/62.58 kW 1.95 MW/55.51 kW 0.48 MW/55.51 kW

– 1.87 MW/61.56 kW 0.67 MW/48.56 kW 1.23 MW/48.56 kW

Table 10 Optimal location for BESS/Scenario (2–2). # Case

# BESS

1 1 2 2

PV location

1 1 2 2

Bus Bus Bus Bus

PV size

18 30 18 30

2 2 2 2

× × × ×

0.5MW 0.5MW 0.5MW 0.5MW

BESS location ICOA

WOA

FA

Bus Bus Bus Bus

Bus Bus Bus Bus

Bus Bus Bus Bus

15 15 7 15

15 15 7 15

PSO 14 14 7 22

Bus Bus Bus Bus

15 15 7 15

Table 11 Optimal sizing for BESS/Scenario (2–2). # Case

1 1 2 2

# BESS

1 1 2 2

PV location

Bus Bus Bus Bus

18 30 18 30

Optimal size/Power loss ICOA

WOA

1.63 1.58 0.33 2.73

1.67 1.67 0.35 2.23

MW/55.17 MW/55.17 MW/32.71 MW/32.71

kW kW kW kW

MW/56.28 MW/56.28 MW/35.48 MW/35.48

FA kW kW kW kW

1.94 1.94 1.25 0.62

PSO MW /58.62 kW MW /58.62 kW MW/37.32 kW MW/37.32 kW

1.67 1.67 0.36 0.96

MW /56.28 kW MW/56.28 kW MW/35.48 kW MW/35.48 kW

Fig. 4. The optimal total loss achieved for all the scenarios based on ICOA.

losses in the electricity distribution network. The optimization process was applied using a newly developed model of the Coyote Optimization Algorithm (COA). The method was applied to four different scenarios. The scenarios are different based on their number of BESS and the presence and absence of PVs. The optimal allocation of BESS is based on considering the buses with high priority of load demand. The results showed that by increasing the number of BESS, the total loss of the distribution has been decreased. On the other hand, it was clear that using two PVs in the distribution network can reduce the system overall losses. by assuming these considerations, the results of the proposed method based on ICOA were compared with Firefly Algorithm (FA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA) to show the proposed method's excellence in decreasing the distribution system losses.

proposed ICOA. The results also show that however WOA and PSO have good accuracies after the proposed ICOA algorithm for optimization, WOA has faster speed than PSO for optimization. Table 12 illustrates a comparison of the adopted methods timewise. As can be observed, although FA has the fastest time of optimization, it has weaker accuracy than the other algorithms with premature convergence. After FA, the proposed ICOA gives the fastest results for the optimization.

5. Conclusions This study proposed a new methodology for optimal allocation and sizing of the Battery Energy Storage System (BESS) in the distribution system. The main purpose here is to achieve the minimum value of the 8

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Fig. 5. The optimal total loss achieved for all the scenarios based on WOA.

Fig. 6. The optimal total loss achieved for all the scenarios based on FA.

Fig. 7. The optimal total loss achieved for all the scenarios based on PSO.

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Fig. 8. The optimal total loss achieved by the algorithms for single BESS.

Fig. 9. The optimal total loss achieved by the algorithms for Two BESS.

Data curation, Writing - original draft, Writing - review & editing. Haiyun Wang: Conceptualization, Data curation, Writing - original draft, Writing - review & editing. Abdullah Yildizbasi: Conceptualization, Data curation, Writing - original draft, Writing review & editing. Declaration of Competing Interest No conflict of interest exists. Fig. 10. Comparison of the convergence characteristics for the analyzed algorithms.

Acknowledgements This research was supported by the Open Project Program of Xinjiang Uygur Autonomous Region Key Laboratory(2018D03005), the Xinjiang Uygur Autonomous Region Tianshan Cedar Plan(2017XS02), the Tianchi Doctor Project of Xinjiang Uygur Autonomous Region 2017and the Scientific Research Staring FoundationProject for Doctor of Xinjiang University 2017.

Table 12 Comparison of the adopted methods timewise. Algorithm

Elapsed Time (s)

ICOA PSO WOA FA

360 372 366 312

References [1] A. Ahadi, N. Ghadimi, D. Mirabbasi, Reliability assessment for components of large scale photovoltaic systems, J. Power Sources 264 (2014) 211–219. [2] Y. Liu, W. Wang, N. Ghadimi, Electricity load forecasting by an improved forecast engine for building level consumers, Energy 139 (2017) 18–30. [3] A.R. Gollou, N. Ghadimi, A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets, J. Intell. Fuzzy Syst. 32 (6)

CRediT authorship contribution statement Zhi Yuan: Conceptualization, Data curation, Writing - original draft, Writing - review & editing. Weiqing Wang: Conceptualization, 10

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[22] H.A. Bagal, et al., Risk-assessment of photovoltaic-wind-battery-grid based large industrial consumer using information gap decision theory, Sol. Energy 169 (2018) 343–352. [23] M. Gheydi, A. Nouri, N. Ghadimi, Planning in microgrids with conservation of voltage reduction, IEEE Syst. J. 12 (3) (2016) 2782–2790. [24] M.H. Firouz, N. Ghadimi, Concordant controllers based on facts and FPSS for solving wide-area in multi-machine power system, J. Intell. Fuzzy Syst. 30 (2) (2016) 845–859. [25] Mahdiyeh Eslami, et al., A new formulation to reduce the number of variables and constraints to expedite SCUC in bulky power systems, Proc. Nat. Acad. Sci., India Section A 89 (2) (2019) 311–321. [26] M. Saeedi, et al., Robust optimization based optimal chiller loading under cooling demand uncertainty, Appl. Therm. Eng. 148 (2019) 1081–1091. [27] G. Dhiman, V. Kumar, Emperor penguin optimizer: a bio-inspired algorithm for engineering problems, Knowl. Based Syst. 159 (2018) 20–50. [28] G.-G. Wang, X.-Z. Gao, K. Zenger, L.D.S Coelho, A novel metaheuristic algorithm inspired by rhino herd behavior, Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016, Linköping University Electronic Press, 2018, pp. 1026–1033 no. 142. [29] J. Pierezan, L.D.S. Coelho, Coyote optimization algorithm: a new metaheuristic for global optimization problems, 2018 IEEE Congress on Evolutionary Computation (CEC), IEEE, 2018, pp. 1–8. [30] A.A. Moghaddam, A. Seifi, T. Niknam, Multi-operation management of a typical micro-grids using particle swarm optimization: a comparative study, Renew. Sustain. Energy Rev. 16 (2) (2012) 1268–1281. [31] D. Yang, G. Li, G. Cheng, On the efficiency of chaos optimization algorithms for global optimization, Chaos Solitons Fractals 34 (4) (2007) 1366–1375. [32] C. Rim, S. Piao, G. Li, U. Pak, A niching chaos optimization algorithm for multimodal optimization, Soft Comput. 22 (2) (2018) 621–633. [33] S. Arora, S. Singh, Butterfly optimization algorithm: a novel approach for global optimization, Soft Comput. 23 (3) (2019) 715–734. [34] G. Dhiman, V. Kumar, Seagull optimization algorithm: theory and its applications for large-scale industrial engineering problems, Knowl. Based Syst. 165 (2019) 169–196. [35] L.A. Wong, V.K. Ramachandaramurthy, S.L. Walker, P. Taylor, M.J. Sanjari, Optimal placement and sizing of battery energy storage system for losses reduction using whale optimization algorithm, J. Energy Storage 26 (2019) 100892. [36] J.C. Bansal, Particle swarm optimization, Evolutionary and Swarm Intelligence Algorithms, Springer, 2019, pp. 11–23. [37] X.-.S. Yang, Firefly algorithms for multimodal optimization, International Symposium on Stochastic Algorithms, Springer, 2009, pp. 169–178. [38] J. Dulout, B. Jammes, C. Alonso, A. Anvari-Moghaddam, A. Luna, J.M. Guerrero, Optimal sizing of a lithium battery energy storage system for grid-connected photovoltaic systems, 2017 IEEE Second International Conference on DC Microgrids (ICDCM), IEEE, 2017, pp. 582–587. [39] A. Anvari-Moghaddam, T. Dragicevic, J.C. Vasquez, J.M. Guerrero, Optimal utilization of microgrids supplemented with battery energy storage systems in grid support applications, 2015 IEEE First International Conference on DC Microgrids (ICDCM), IEEE, 2015, pp. 57–61.

(2017) 4031–4045. [4] F. Mirzapour, et al., A new prediction model of battery and wind-solar output in hybrid power system, J. Ambient Intell. Humaniz Comput. 10 (1) (2019) 77–87. [5] H.F. Mansour, N. Ghadimi, Optimal preventive maintenance policy for electric power distribution systems based on the fuzzy AHP methods, Complexity 21 (6) (2016) 70–88. [6] M. Hamian, et al., A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on mixed integer genetic algorithm, Eng. Appl. Artif. Intell. 72 (2018) 203–212. [7] H. Leng, et al., A new wind power prediction method based on ridgelet transforms, hybrid feature selection and closed-loop forecasting, Adv. Eng. Inf. 36 (2018) 20–30. [8] P. Akbary, et al., Extracting appropriate nodal marginal prices for all types of committed reserve, Comput. Econ. 53 (1) (2019) 1–26. [9] H. Ebrahimian, et al., The price prediction for the energy market based on a new method, Econ. Res.-Ekon. Istraž. 31 (1) (2018) 313–337. [10] A. Anvari-Moghaddam, T. Dragicevic, L. Meng, B. Sun, J.M. Guerrero, Optimal planning and operation management of a ship electrical power system with energy storage system, IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2016, pp. 2095–2099. [11] L. Grisales-Noreña, O.D. Montoya, W. Gil-González, Integration of energy storage systems in AC distribution networks: optimal location, selecting, and operation approach based on genetic algorithms, J. Energy Storage 25 (2019) 100891. [12] P.J. Balducci, M.J.E. Alam, T.D. Hardy, D. Wu, Assigning value to energy storage systems at multiple points in an electrical grid, Energy Environ. Sci. 11 (8) (2018) 1926–1944. [13] Y. Zhu, C. Liu, K. Sun, D. Shi, Z. Wang, Optimization of battery energy storage to improve power system oscillation damping, IEEE Trans. Sustain. Energy (2018). [14] G. Carpinelli, F. Mottola, D. Proto, A. Russo, P. Varilone, A hybrid method for optimal siting and sizing of battery energy storage systems in unbalanced low voltage microgrids, Appl. Sci. 8 (3) (2018) 455. [15] L. Rui, W. Wei, C. Zhe, W. Xuezhi, Optimal planning of energy storage system in active distribution system based on fuzzy multi-objective bi-level optimization, J. Mod. Power Syst. Clean Energy 6 (2) (2018) 342–355. [16] Y. Cao, Y. Li, G. Zhang, K. Jermsittiparsert, N. Razmjooy, Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm, Energy Rep. 5 (2019) 1616–1625. [17] Y. Cao, Y. Wu, L. Fu, K. Jermsittiparsert, N. Razmjooy, Multi-objective optimization of a PEMFC based CCHP system by meta-heuristics, Energy Rep. 5 (2019) 1551–1559. [18] N. Razmjooy, F.R. Sheykhahmad, N. Ghadimi, A hybrid neural network–world cup optimization algorithm for melanoma detection, Open Med. 13 (1) (2018) 9–16. [19] X. Fei, R. Xuejun, N. Razmjooy, Optimal configuration and energy management for combined solar chimney, solid oxide electrolysis, and fuel cell: a case study in Iran, Energy Sources Part A (2019) 1–21. [20] M. Khalilpuor, N. Razmjooy, H. Hosseini, and P. Moallem, "Optimal control of DC motor using invasive weed optimization (IWO) algorithm," in Majlesi conference on electrical engineering, Majlesi town, Isfahan, Iran, 2011. [21] N. Razmjooy and M. Khalilpour, "A robust controller for power system stabilizer by using artificial bee colony algorithm," vol. 5, no. 3, pp. 106–113, 2015.

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