A cost-efficient application of different battery energy storage technologies in microgrids considering load uncertainty

Journal of Energy Storage 22 (2019) 17–26

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A cost-efficient application of different battery energy storage technologies in microgrids considering load uncertainty

T

⁎

H. Bakhshi Yamchia, H. Shahsavaria, N. Taghizadegan Kalantaria, A. Safaria, , M. Farrokhifarb a b

Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran Center for Energy Science and Technology, Skolkovo Institute of Science and Technology, Moscow, Russia

A R T I C LE I N FO

A B S T R A C T

Keywords: Battery energy storage (BES) Monte Carlo simulations (MCS) Wind power Solar power Multi-objective particle swarm optimization (MOPSO) Active distribution networks

Dispersed application of Battery Energy Storages (BESs) can have many benefits in terms of voltage regulation and energy management in Active Distribution Networks (ADNs). The batteries are high-cost technologies, and they must be installed and managed optimally to benefit from their innumerable advantages. In addition, each battery technology has specific economic and technical attributes which can be appropriate or inappropriate in a particular condition and utilization. The purpose of this paper is that the optimal size and location of various battery technologies are specified in the distribution network to minimize total cost and maximize reliability index considering the uncertainty of load demand as well as the output power of the wind and solar. Also, multiobjective particle swarm optimization (MOPSO) algorithm is used to minimize two objective functions. Monte Carlo Simulation (MCS) is used to model the uncertainties of economic and technical characteristics of photovoltaic, wind power and load demand. The suggested planning scheme is tested on the modified IEEE 33-bus system. Finally, the different types of the battery technologies in one, five, ten, fifteen and twenty-year period are compared to present the optimum type.

1. Introduction The high cost of production, pollution, global warming and nature depletion are some of the main problems that are emerging owing to use traditional energy source. Therefore, eco-friendly and economical energy sources such as solar and wind energy are used to resolve these problems in generating electricity. These renewable resources, in spite of their numerous advantages, owing to their accidental manner are non-dispatchable. Using Energy Storage Systems (ESSs) in a power system can help significantly to solve these problems [1]. Battery Energy Storage (BES) is one of the various types of the ESSs which is the most pervasive and developing one in the power system because of its especial benefits, e.g., fast response, a large range of capacity, suitable efficiency and so on. To take advantage of the mentioned advantages of the BES in the distribution networks, depending on objectives, the optimal planning of the location, size and type of the BES should be specified [2–7]. The objectives in the BES planning may be voltage regulation [2], reliability indices improvement [3], peak load shaving [4], load management [5], and error reduction of wind power prediction [6]. In [7], an optimal planning approach of the BES is suggested to reduce wind power interruption. In [8], optimal planning for the different types of the BESs is introduced to enhance reliability indices and

⁎

manage the energy of Active Distribution Network (ADN), considering the uncertainty of plug-in electric vehicles, wind power and load. The uncertainties in this paper are modeled by point estimation approach. Ref [9] introduces a strategy to reduce the operational cost of the power system by informing the community energy management system about the shortage and surplus amounts and adjustable power. The energy management system will select among different available options, which include trading with the power grid, buying from a distributed generation plant or a community battery energy storage system, or controlling the adjustable power. In [10], a fuzzy logic-grey wolf optimization based intelligent meta-heuristic approach is suggested to specify battery size and manage energy in the isolated microgrid. The proposed energy management operation is applied by a grey wolf optimizer that is helped to set the membership functions and rules of the fuzzy logic expert system. Ref [11] proposes stochastic optimal energy management for the microgrid to minimize operation cost and emissions. The microgrid includes combined cooling, heating and power, plug-in hybrid electric vehicles, photovoltaic unit, and battery energy storage systems. In [12], lead-acid battery, wind turbine and photovoltaic cells are modeled and new control approach, using Lab VIEW Software, is introduced to control hybrid power system. Ref [13] investigated optimal allocation of the large-scale ESS in the power system

Corresponding author. E-mail addresses: [email protected] (A. Safari), [email protected] (M. Farrokhifar).

https://doi.org/10.1016/j.est.2019.01.023 Received 15 October 2018; Received in revised form 27 December 2018; Accepted 24 January 2019 2352-152X/ © 2019 Published by Elsevier Ltd.

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Nomenclatuure

Interest rate O&M cost function of battery Cost of purchased energy at s-th HV/MV substation in t-th hour Csell, t Price of sold energy in t-th hour AILR Annual increment load rate Annual outage time at load point i Ui i-th bus load power PLi j-th branch failure rate λj r Outage time s Particle position v Particle velocity c1, c2 Acceleration constants rand Random number between 0 and 1 The best position ever visited by the particle i pbesti gbest The global best position in the entire swarm iter Current iteration number itermax Max imumiteration number w Inertia weight Final weight wmin wmax Initial weight fi (x ) i-th objective function gj (x ) j-th equality constraint dk (x ) k-th inequality constraints x The decision vector representing a solution Pgmin The lower limit of generator active power Pgmax The upper limit of generator active power Pgt Generator active power Pit Active power generated at bus i Pext , t Sold power to the grid in t-th hour The output power of PV PPV ugt The generator status in time t(0:‘Off’, 1:‘On’). Charging/discharging power of ESSs during each period psin / out (kW h) Rsin, min / max Minimum/maximum charging rate of ESSs (kW/h) Rsout , min / max Minimum/maximum discharging rate of ESSs (kW/h) Binary decision variable for discharging status of ESSs (1 if kst it is discharging and 0 otherwise) Binary decision variable for charging status of ESSs (1 if it hst is charging and 0 otherwise) Esinitial Initial storage level of ESSs (kWh) Esmin / max Minimum/maximum storage level of ESSs (kWh) Esstored Energy storage level of ESSs at the end of each period (kWh) Charging/discharging efficiency of ESSs ηsin / out γ Decision variable Efficiency of PV panel (%) ηPV APV Area of PV panel (m2) Atmospheric temperature (°C) T0 I Solar irradiation (W/m2)

intr OM CST CsSS ,t

Indices

K M nST nSS nb nbr Nobj Ni yr

Number Number Number Number Number Number Number Number Years

of of of of of of of of

all inequality constraints all equality constraints all installed storage units all HV/MV substations all buses all branches all objectives customers at load point i

Abbreviations ADN BES DER DG EMS ESS HV/MV MCS MOPSO O&M PSO PV SNC

Active distribution network Battery energy storage Distributed energy resource Distributed generation Energy management system Energy storage system High voltage/medium voltage Monte Carlo simulation Multi-objective particle swarm optimization Operation and maintenance Particle swarm optimization Photo voltaic Saft nickel capacitor

Variables

OF1 OF2 OC IC RCkST ENS SAIDI Losses D(t) TB w1 w2 INSC CST CAPkST REPC CST , yr

T OCyr TB yr infr

First objective function Second objective function Operation cost Investment cost Replacement cost of the k-th storage Energy not Supplied System average interruption duration index Total loss in the power system Total load demand Total benefit Weight of ENS Weight of SAIDI Installation cost function considering the storage capacity Capacity of the k-th storage Replacement cost function considering the storage capacity in yr-th year Period of the project Annual operation cost Annual total benefit Inflation rate

owing to their numerous advantages, are used in distribution systems widely [18]. Even though BESs have numerous benefits for power systems, their investment cost is high [19], therefore, the advantages of financial and technical features must be regarded in the utilization of the BESs. Ref [20] proposed a mathematical model for identifying the optimal size of wind/battery system while the variability of wind speed and uncertainty of load are taken into account. In [21], a stochastic methodology is proposed to determine the placement and size of batteries optimally considering conservation voltage reduction in the distribution network. The enhancement of voltage profile and reduction of total cost show the effectiveness of this method. In [22], various

with both wind turbine and conventional generators. In [14], optimal management strategy of ESSs is illustrated in medium voltage distribution grids. In [15] genetic algorithm is used to optimize the sizing of a grid-connected hybrid photovoltaic/ battery energy system with a home energy management system under the different charging/discharging scenarios of a plug-in electric vehicle. In [16], wind turbines and ESSs are used together in the power system, and finally, the advantages of the ESSs application are presented. In [13,17], different algorithms are suggested to determine the optimal placement of ESSs, and various power flow methods are used to calculate objective function in this locating. The BESs among the different types of the ESSs,

18

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technologies of energy storages and their structures are discussed. In addition, the authors evaluate the pros and cons of using these storages in the microgrid based on efficiency, the mechanism of energy conversion, lifetime and material choice. Ref [23] suggests a management system for battery storages in the microgrid that contains diesel generators and photovoltaic systems. In this approach, different battery features are taken into account to reduce costs. In Ref [24], a two-layer optimization is used to specify the optimal placement and size of distributed generations (DGs) and BESs in the distribution network. Ref [25] presented a stochastic model for optimal scheduling of security‐constrained unit commitment associated with demand response (DR) actions in an islanded residential microgrid. This model maximizes the expected benefit of microgrid operator and minimizes the total customers' payments for electricity consumption taking into account uncertainties of load and renewable energy sources. In [26], a risk-constrained stochastic framework is proposed to maximize the expected profit of a microgrid operator considering renewable resources, demand load and electricity price uncertainties. Authors in [27] apply genetic algorithm to optimize the location and size of Na-S BES for enhancing the reliability of system. The considered reliability index is energy not supplied (ENS) and the results reveal the improvement of this index in this paper. In [28], active-reactive optimal power flow is suggested to minimize total power losses and maximize the efficiency of the power system. The considered power system possessed wind turbines, BESs and the charge/discharge period of the BESs is constant in each day operation. In continuation of the paper [28], in [29] a pliable BES operation procedure is proposed and its preference in comparison with the fixed management method is shown. Without considering the benefits and drawbacks of the cited papers in operation and management strategies, a particular technology of BES has used in each paper regardless a strong financial and technical explanation. The BES technologies that have studied in recent papers are as follows: Lithium-ion battery [30], Sodium-sulfur battery [31], Zincbromine battery [32], Lead acid battery [33], a typical battery with predetermined specific characteristics [34] and Vanadium redox flow battery [35]. Since each type of the BES has itself economic and technical properties, it is necessary to specify which type of the battery is the optimal selection in the ADNs depending on power system circumstances and existing uncertainties. This point has not been considered in the papers which referenced already. Accordingly, in this paper, the planning of diverse BESs is optimized and these battery types are compared from different points of view. To possess an all-inclusive assessment, the output power of wind and PV sources and load demand are modeled with uncertainties. Monte Carlo Simulation (MCS) technique is used to model the uncertainties. The MCS carries out risk analysis by using random values that acquired from a probability distribution function for any problem which has uncertainty in its parameters. This approach mainly is applied to nonlinear and complex problems or models with more than two uncertain parameters. With these explanations, the method that includes the combination of the MCS and Particle Swarm Optimization (PSO) can reach the optimal solution faster and more reliable than the other approaches. In this paper, MOPSO algorithm is applied to optimize the power system costs, as well as the reliability indices like energy not supplied (ENS) and System Average Interruption Duration Index (SAIDI) subject to operational constraints. In the following, we summarize the points that distinguish this article from previous papers:

Table 1 The average value of parameters of the BES technologies. Battery technology

Capacity cost ($/kWh)

Replacement cost ($/kWh)

O&M cost ($/kWh-yr)

Overall efficiency (%)

Na-S Ni-Cd Zn-Br ZEBRA

298 780 195 509

180 525 195 182

4.4 11 4.3 5.5

82 66 65 87

- The MOPSO is applied to maximize the reliability indices and minimize the cost of locating and planning of BESs optimally. 2. Battery technologies The batteries contain accumulated cells in which electrical energy and chemical energy are transformed into each other. The intended values of current and voltage of the battery are caught by suitable series and parallel, electrically connecting of the cells [10]. The rechargeable batteries according to the used material in electrolytes and electrodes and the procedure of storing are categorized into different types. The batteries differ in features such as capacity, efficiency, operating temperature, lifetime, permissible depth of discharge (in percent) and energy density [10]. These differences in the characteristics of the diverse BESs cause the creation of considerations in the optimal planning of the BESs, which the most important of them are capacity investment cost (in $/kWh), cost for operation and maintenance (O&M) (in $/kW-year) and replacement cost (in $/kWh). In this paper, Sodium Nickel Chloride battery (ZEBRA), Zinc Bromine battery (Zn-Br), Nickel Cadmium battery (Ni-Cd) are Sodium Sulfur battery (Na-S) are discussed. Table 1 demonstrates the attributes of the mentioned technologies. This Table shows the average values. The complete economic and technical details are presented in [19]. According to Table 1, ZEBRA battery has the best efficiency. The lowest O&M and capacity costs belong to Zn-Br battery. The Ni-Cd battery has the highest replacement, O&M and capacity costs. Utilizing large-scale BESs in the real grid will be more useful and widespread in future smart grids and ADNs; however, optimal planning is a necessary component in the extensive application of the BESs in actual systems. Therefore, the features of each technology should be justified. In this paper, both of technical analysis and economic analysis, which are necessary for a practical network, are investigated. On the other side, the distribution network properties influence the optimal solution. Hence, both of distribution network requisites and battery features should be considered in the optimal planning. The electric utilities require three kinds of response assets including quick, medium and slow. These response assets are used to stabilize the system, follow the load and buffer the storage respectively. In addition, the BESs have the additional boon. They can not only deliver power immediately but also absorb power when the generation is more than the demand. The aggregation of these advantages makes the BESs ideal for widespread grid utilization. Fig. 1 represents the features of a range of ESSs and their appropriateness for the implementation of three main grid functions in general. It displays the power rating of each BES and the duration which this power can be maintained [36]. 3. Problem formulation The main purpose of the suggested planning approach is detecting appropriate type, capacity and location of battery for installing in the ADNs. The objective functions are minimized under the technical constraints. This formulation has two objective functions. The cost of investment (consists of the BESs installation and replacement costs) and the cost of operation (consists of the bought energy cost from HV/MV substation and the cost of the O&M of the BESs) are the components of the first objective function and the second one comprises the reliability

- The different technologies of the BESs are considered and these battery types are compared from the different points of view to specify which type of battery is the optimal selection in the particular conditions of the ADNs. - The load demand and generated power of wind and solar sources uncertainties are implemented using the MCS approach in the BES optimal planning. 19

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TB yr =

24

∑t=1 Csell,t ∗ Pext,t ∗ 365

3.2. The second objective function The reliability indices represent power system quality. The utilities generally use two indices to evaluate reliability: the SAIDI and ENS [37]. When a sudden failure occurs in the power system, one of the most fundamental concerns is the sum of loads that interrupted until the system returns to its ordinary situation. Therefore, the optimal solution must be suggested a way to minimize the ENS caused by outages. The ENS is formulated as follows [38]:

ENS =

Ui =

nb

∑t=1 PLi ∗ Ui

(8)

nb

∑ j = 1 λi ∗ r

(9)

The SAIDI indicates the average interruption duration in the grid which is remarkable in measuring the reliability index of the distribution network. The SAIDI is formulated as [39]:

Fig. 1. Applications and technologies of the grid-scale BES.

nb

SAIDI =

indices (i.e. energy not supplied and system average interruption duration index). The objective functions are formulated in Eqs. (1) and (2) as follows:

∑ Ui ∗ Ni sum of customer iterruption durations = i = 1nb total number of customers served ∑i = 1 Ni

(10)

OF1 = IC + OC − TB

(1)

In addition, the optimal planning method must satisfy the technical requirements.

OF2 = w1 ∗ ENS + w2 ∗ SAIDI

(2)

3.3. Power balance limit The generated power from all the committed units plus the amount of battery power must satisfy the load demand plus the system losses, which is defined as:

3.1. The first objective function The storage capacity determines the installation and replacement costs. Therefore, the investment cost is shown in the following:

IC =

nST

INSC [CST (CAPkST ) k=1

∑

+

RCkST ]

nb

∑ (uit Pit + Eistored,t ) = D (t ) + Losses (3)

i=1

(11)

Where,

RCkST =

T

ST ∑yr=1 CSTREPC , yr (CAPk )

∗ (

3.4. Real power generation limits

1 + inf r yr − 1 ) 1 + intr

In this paper, an important assumption is that each DG source works at its maximum capacity of power generation. The generated active power by each unit should be between the lower and upper limits as follows:

The battery life time specifies its total number of charge/discharge cycles. Therefore, when the number of cycles reaches the specified number, the replacement cost is considered. The cost of energy that is bought from HV/MV substations and the O&M cost of the BESs are two components of the operation cost [36]. The backward/forward sweep load flow is used for calculating the purchased electrical energy considering charge/discharge energy of the BESs. The cost of operation includes the yearly cost of operation, project period, interest rate and inflation rate, as shown in the following:

OC =

T

∑yr=1

1 + inf r yr − 1 OCyr ∗ ( ) 1 + intr

Pgmin ∗ ugt ≤ Pgt ≤ Pgmax ∗ ugt 3.5. Battery limits

The capacity and the nominal power of energy storage limit the charging and discharging power of a BES.

(4)

Where, 24

n

∑s=SS1 CsSS,t (AILR)] ∗ 365 t=1

OCyr = [ ∑

(5)

psout , t ≤ Rsout ,max ∗ kst

(13)

psin, t

(14)

Esmin

For a grid-connected microgrid, the total benefits that reached by selling power to the grid are as follows:

≤

Rsin,max

≤

Esstored, t

∗

hst

≤

(6)

T

∑yr=1 TByr

∗ (

1 + inf r yr − 1 ) 1 + intr

(15)

1 ∗ psout , t + γ ∗ Esstored, t − 1 + (1 − γ ) ∗ Esinitial ηsout (16)

0 γ=⎧ ⎨ ⎩1

- For a grid-connected microgrid:

TB =

Esmax

Esstored, t = ηsin ∗ psin, t −

- For an isolated microgrid:

TB = 0

(12)

;t=1 ; other

kst + hst ≤ 1 (7)

(17) (18)

The power that is charged and discharged at each time interval during the operation is brought in Eqs. (16), (18) prohibits the charging and discharging simultaneously at any time.

Where, 20

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Fig. 2. Flowchart of MOPSO technique for multi-optimizing of the placement, size and type of the different technologies of BESs.

Step 1. Develop a parameter-based model. Step 2: Determine the bounds for numbers which are generated randomly. Step 3. Produce the random numbers as inputs based on probability distribution function. Step 4. Evaluate the model with producing numbers. Step 5. Repeat steps 3 and 4. Step 6. Aggregate the result. In this paper, uncertainty is applied to load demand, solar PV, and wind power generation.

3.6. Uncertainty analysis In this paper, the MCS is applied for modeling the uncertainty. The MCS is a stochastic and statistical method that generates random numbers based on a distribution function to provide a proximate answer for a problem which contains a kind of uncertainty [40]. When the number of variables increases and is more than a couple, the interactions between them are complex and equations can hardly explain them. The MCS investigates the uncertainty of the problem successfully by using random numbers as inputs [41,42]. The comprehensive descriptions of this approach are given in [40,41]. The MCS that is used in the article principally contains the methods in which random numbers are produced. The steps of MCS can be expressed as follows: 21

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Fig. 6. Forecasted output power of wind with uncertainty.

Fig. 3. Distribution network for case studies.

Table 2 Properties of the distributed generators in the case study. DG types

Minimum Power(kWh)

Maximum Power(kWh)

Wind turbine Solar PV

0 0

150 150

Table 3 Zones properties.

Fig. 7. Output power of the PV in different temperatures.

Zone

λ (failure/yr)

r(h)

U(h/yr)

A B

1.052 1.052

0.131 0.053

0.138 0.055

Table 4 The optimal location of batteries and values of the objective function. Battery Type

Location (node no.)

Capacity (kWh)

OF1

OF2

Zn-Br

8 14 8 14 8 14 8 14

321 423 321 423 321 423 321 423

53.3 × 106

660

93.4 × 106

1046

170.5 × 106

785

137.5 × 106

895.8

Ni-Cd ZEBRA Na-S

Fig. 4. Domestic loads active power profile with uncertainty.

Fig. 8. Comparison of different storage technologies. Fig. 5. Three-level electricity price profile.

Minimize fi (x )

4. Multi-objective optimization

; i = 1, ..., Nobj

gj (x ) = 0 ; j = 1, ..., M subject to constraints:⎧ ⎨ d ⎩ k (x ) ≤ 0 ; k = 1, ..., K

Multi-objective problems occur when simultaneous optimization more than one objective function is required. This type of optimization entails maximizing or minimizing of the objective functions; in fact, any maximization problem can be converted to a minimization problem [43]. Some equality and inequality constraints accompany the multiobjective problem. This optimization is shown mathematically as follows:

(19)

(20)

The vector x represents the decision variables aboard the possible domain that is defined for vector x considering the constraints. Due to conflicting objectives, there is not one global optimum solution generally. Therefore, the Pareto dominance concept is applied to choose the optimal solution from the candidate solutions of multi-objective problems. A vector v dominates another vector u (in other wordsv < u) if: 22

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Fig. 9. Voltage profile before and after batteries placement.

fi (v ) ≤ fi (u) ∧ ∃ i ∈ {1, 2, ..., N } : fi (v ) < fi (u)

network is considered as two zones so that the properties of each zone differ from each other that are shown in Table 3. In this study, each kW load is considered as one customer at load buses of the network. The profiles of domestic load power considering uncertainty and electricity price are brought in Figs. 4 and 5, respectively. The profile of load is obtained based on time-varying probabilistic model [49].

(21)

The PSO possesses a flexible and simple structure for exploring and finding the local and global solutions during a concise computing time. The major superiorities of PSO comparing other algorithms are summed up as understandable meaning, powerful and flexible in parameters control, high performance in computation and simplicity of implementation in comparison to other optimization algorithms [44]. Hence, these excellent features cause the PSO a proper selection to be applied for optimizing the multi-objective problems. The positions and velocities are updated by the following equations [45]:

vik + 1 = wvik + c1 rand1 ∗ (pbest i−sik ) + c2 rand2 ∗ (gbest − sik )

(22)

sik + 1 = sik + vik + 1

(23)

5.1. Uncertainty modeling of wind power The output power of wind turbines depends on the speed of the wind. The speed of the wind is gathered within five years. Therefore, the output power of the wind is obtained [50]. The probabilistic character of the wind speed is modeled using the Beta and Weibull probability density functions as indicated in [50]. Since the wind turbine output power is not constant, in this study it is supposed that the wind turbine output power varies in a region with ± 10% deviation from the nominal wind speed derived from probabilistic data. The output power of the wind turbine is shown in Fig. 6.

The MOPSO algorithm is applied to optimize the power system costs, as well as the reliability indices such as the energy not supplied and the system average interruption duration index subject to operational constraints. The MOPSO is appropriate to minimize several objective functions at the same time [46]. Due to lack of space, the steps of MOPSO algorithm are shown in the following: 1) Generate the initial positions and velocities randomly. 2) Compute fitness values for each particle of the population. 3) Determine non-dominated decision vectors and save them in the repository (non-dominated decision vectors archive). 4) Choose a leader for each particle from the archive as gbest and update the particle position and velocity based on Eqs. (22) & (23). 5) Compute fitness value according to new positions of particles. 6) Update the pbest value. 7) Add current non-dominated decision vectors to the archive. 8) Remove archive dominated decision vectors. 9) Stop the procedure if the algorithm is reached to maximum iteration or converged, else return to the step 4. The flowchart of MOPSO technique for multi-optimizing of the placement, size and type of the different technologies of the BESs is shown in Fig. 2 [47].

In this study, the output model which the PV field tests achieve is applied. These curves are brought in Fig. 7. In this case, also we assumed that the output power of the PV changes between 1 ± 10% of the nominal average value [51].

5. Simulation results

5.3. Numerical results

The suggested approach is tested on the IEEE 33-bus distribution system. The one-line diagram of the system and two types of DGs are shown in Fig. 3. In [48], more details about branches and loads can be found. The PV and the wind turbine (WT) are embedded in nodes 18, 33, respectively. The PV system is connected to the AC bus via DC-to-AC converter while the WT system is connected to the AC bus via AC-to-AC converter. The properties of these DGs are given in Table 2. The

In this section, the results of the optimal planning of the mentioned BESs are presented. The optimum locations are found for two batteries with one technology, and the objective functions are calculated for this embedding. Then, at the same locations, the technology of the batteries is changed, and the corresponding objective functions are calculated. The optimal placement of the BESs and capacities of them are shown in Table 4. By attention to Table 4, it is found that the BESs are placed in

5.2. Uncertainty modeling of solar PV The output power of a PV system depends on solar irradiation, area and efficiency of the PV array, angle of incidence, and atmospheric temperature. In this paper, it is assumed that a maximum power point tracker is installed to harvest the maximal of available power. Power generated by the PV panel can be calculated using the following equation. The weather conditions change the output power of the PV as well as the wind turbine output.

Ppv (t ) = ηpv Apv I (t )(1 − 0.005(T0 (t ) − 25))

23

(24)

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Fig. 10. Pareto front solutions acquired for two objectives of each battery technology.

using this technology. The minimum voltage after the placement of the BESs occurred in bus 18 with the amount of 0.9591 and the percent of improvement at the worst case is 4.18%. Therefore, the suggested methodology affects the improvement of the voltage profile significantly. The profiles of voltage before and after the BESs placement are shown in Fig. 9. The Pareto methodology is used to solve the multi-objective problem in the MOPSO algorithm. This

the far away nodes from the HV/MV substation and not certainly near the DGs. Installing the BESs in these nodes enhances not only the reliability indices but also the voltage profile. Fig. 8 shows the objective functions of the mentioned BESs and it reveals that the Zn-Br BES technology is the foremost selection for installing in the network. According to Fig. 1, due to the high discharge time at the rated power of the Zn-Br battery, it was expected that the ENS and SAIDI were less in 24

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Table 6 Optimal values of the objective functions in the various periods. Project period (year)

OF1

OF2

ENS (MWh/yr)

SAIDI (h/ Cus.yr)

IC (USD)

OC (USD)

TB (USD)

5

23.35 × 102

0.4980

49.07 × 10 4

32.18 × 107

26.25 × 106

10

51.29 × 102

0.9421

89.34 × 10 4

69.71 × 107

57.50 × 106

15

72.12 × 102

1.2090

11.31 × 105

11.29 × 108

84.75 × 106

20

97.80 × 102

1.8192

16.82 × 105

14.21 × 108

11.47 × 106

Fig. 11. Optimal hourly operation of Zn-Br battery in bus 8. Table 7 Optimal values of the objective functions. Battery type

Fig. 12. Optimal hourly operation of Zn-Br battery in bus 14.

Fig. 14. State of charge for Zn-Br battery in bus 14. Table 5 The optimal location of batteries and values of the objective functions in the various periods. Optimal technology

Location (node No.)

Capacity (kWh)

OF1

OF2

5

Zn-Br

[20,23]

[452,370]

29.61 × 107

3180

10

Zn-Br

[17,18]

[514, 412]

64.04 × 107

5420

15

Zn-Br

[14,18]

[632,560]

10.45 × 108

9230

20

Zn-Br

[18,20]

[780,654]

108

OF2 IC (USD)

OC (USD)

TB (USD)

ENS (MWh/ yr)

SAIDI (h/ Cus.yr)

Without batteries Zn-Br

1230 540

0.41 0.105

–

–

–

35.55 × 10 4

57.20 × 106

4.25 × 106

Ni-Cd

960

0.320

97.09 × 10 4

98.61 × 106

6.18 × 106

ZEBRA

690

0.290

29.00 × 10 4

181.03 × 106

10.82 × 106

Na-S

835

0.265

51.40 × 10 4

142.12 × 106

5.134 × 106

methodology selects the actual optimal answer to the problem from the Pareto solutions (non-dominated solutions). Therefore, the optimal type, placement and size of the BESs are found by applying the MOPSO method. The Pareto solutions that are acquired for two objective functions of each battery technology are presented in Fig. 10. The dominated solutions are shown with blue circles and the non-dominated solutions are shown with red ones. The optimal response of each BES technology is selected from these Pareto solutions. Figs. 11 and 12 show the optimal hourly operation of the batteries with the Zn-Br technology in the case study. According to Figs. 11 and 12, the charging of the BESs often occurs in the off-peak hours when the energy price is low and the discharging mostly happens at the peak hours when the power cost is high. Figs. 13 and 14 show the state of charge (SOC) of batteries which may be helpful to understand this concept. It should be noted that the initial charge of batteries (at t = 0) are considered 20% of the battery capacities. The charging/discharging in this manner makes the load profile smoother, so the cost of operation will be reduced a lot. In this study, the impacts of the wind and solar power uncertainties on the optimal hourly operation of the given BESs are taken into account. Considering simulation results, it is perceptible that the peaks of the DGs output power curves don’t coincide with the peaks of price and load profiles. For this reason, it seems more capacity of the BESs can feed and support the distribution network at the peak hours. Also, it is worthy to note that the first period of the project is assumed one year. After discussing this case, the results of five, ten, fifteen and twenty-year periods are presented in Tables 5 and 6. It should be noted that in this study the interest rate and inflation rate are equal to 10 percent and 7 percent, respectively. The reliability indices are given in Table 7. It is evident that the SAIDI and the ENS have been lowered by installing the BESs.

Fig. 13. State of charge for Zn-Br battery in bus 8.

Project period (year)

OF1

6. Conclusions

14.11 ×

In this paper, the MOPSO was used to compare four types of the battery technologies in the ADNs planning. The capacity and location of these batteries were optimized to specify the optimal type of technology. The uncertainty was applied to the load demand, solar and wind resources power. The MCS was utilized in applying the uncertainties. The objective functions of this paper were minimizing the

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H. Bakhshi Yamchi et al.

BESs cost and maximizing the reliability indices while satisfying the system operational constraints. First, the Pareto-front results were acquired from the Pareto-front non-dominated sorting-based multi-objective optimization algorithm, and then an optimal decision has been applied to trade-off the solution set. The proposed model is tested on the standard IEEE 33-bus radial distribution system. By installing the different batteries on the network in one, five, ten, fifteen and twentyyear period, the total cost of the system has been reduced impressively and also the profile of voltage and the reliability indices of the system were improved. The calculation results showed that the suitable size and location of the battery units will have a profound effect in maximizing the reliability indices. In the future work, the dynamic behavior of the different type of battery technologies in distribution network can be considered.

3536 (1) (2018). [23] K. Thirugnanam, S.K. Kerk, C. Yuen, S. Member, N. Liu, Energy management for renewable microgrid in reducing diesel generators usage with multiple types of battery, IEEE Trans. Indus. Elect. 65 (8) (2018) 6772–6786. [24] A. Saif, V. Ravikumar Pandi, H.H. Zeineldin, S. Kennedy, Optimal allocation of distributed energy resources through simulation-based optimization, Electr. Power Syst. Res. 104 (2013) 1–8. [25] M. Vahedipour‐Dahraei, H.R. Najafi, A. Anvari‐Moghaddam, J.M. Guerrero, Security‐constrained unit commitment in AC microgrids considering stochastic price‐based demand response and renewable generation, Int. Trans. Electr. Energy Syst (2018) e2596. [26] M. Vahedipour-Dahraie, A. Anvari-Moghaddam, J.M. Guerrero, Evaluation of reliability in risk-constrained scheduling of autonomous microgrids with demand response and renewable resources, IET Renew. Power Gener. 12 (6) (2018) 657–667. [27] E. Naderi, I. Kiaei, M.R. Haghifam, NaS technology allocation for improving reliability of DG-enhanced distribution networks, Probabilistic Methods Applied to Power Systems (PMAPS), 2010 IEEE 11th International Conference on, (2010), pp. 148–153. [28] A. Gabash, P. Li, Active-reactive optimal power flow in distribution networks with embedded generation and battery storage, IEEE Trans. Power Syst. 27 (4) (2012) 2026–2035. [29] A. Gabash, P. Li, Flexible optimal operation of battery storage systems for energy supply networks, IEEE Trans. Power Syst 28 (3) (2013) 2788–2797. [30] F. Marra, G. Yang, C. Træholt, J. Ostergaard, E. Larsen, A decentralized storage strategy for residential feeders with photovoltaics, IEEE Trans. Smart Grid 5 (2) (2014) 974–981. [31] D.Q. Hung, N. Mithulananthan, R.C. Bansal, Integration of PV and BES units in commercial distribution systems considering energy loss and voltage stability, Appl. Energy 113 (2014) 1162–1170. [32] M. Sedghi, M. Aliakbar-Golkar, M.R. Haghifam, Distribution network expansion considering distributed generation and storage units using modified PSO algorithm, Int. J. Electr. Power Energy Syst. 52 (1) (2013) 221–230. [33] O. Ekren, B.Y. Ekren, Size optimization of a PV/wind hybrid energy conversion system with battery storage using simulated annealing, Appl. Energy 87 (2) (2010) 592–598. [34] A. Roy, S.B. Kedare, S. Bandyopadhyay, Optimum sizing of wind-battery systems incorporating resource uncertainty, Appl. Energy 87 (8) (2010) 2712–2727. [35] L. Johnston, F. Díaz-González, O. Gomis-Bellmunt, C. Corchero-García, M. CruzZambrano, Methodology for the economic optimisation of energy storage systems for frequency support in wind power plants, Appl. Energy 137 (2015) 660–669. [36] B. Dunn, H. Kamath, J.-M. Tarascon, Electrical energy storage for the grid: a battery of choices, Science 80 (6058) (2011) 928–935 334. [37] I. Hernando-Gil, I.-S. Ilie, A.J. Collin, J.L. Acosta, S.Z. Djokic, Impact of DG and energy storage on distribution network reliability: a comparative analysis, IEEE Int. Energy Conf. Exhib. (2012) 605–611. [38] A. Soroudi, M. Ehsan, H. Zareipour, A practical eco-environmental distribution network planning model including fuel cells and non-renewable distributed energy resources, Renew. Energy 36 (1) (2011) 179–188. [39] H.R. Baghaee, M. Mirsalim, G.B. Gharehpetian, H.A. Talebi, Reliability/cost-based multi-objective Pareto optimal design of stand-alone wind/PV/FC generation microgrid system, Energy 115 (2016) 1022–1041. [40] M. Mohammadi, Y. Noorollahi, B. Mohammadi-Ivatloo, H. Yousefi, S. Jalilinasrabady, Optimal scheduling of energy hubs in the presence of Uncertainty-a review, J Energy Manage. Technol. 1 (1) (2017) 1–7. [41] R.C. Eberhart, J. Kennedy, A new optimizer using particle swarm theory, Proceedings of the Sixth International Symposium on Micro Machine and Human Science 1 (1995) 39–43. [42] Y. Shi, Particle swarm optimization: developments, applications and resources, Proceedings of the 2001 Congress on Evolutionary Computation 1 (2001) 81–86. [43] H. Yousefi, R. Tavakkoli-Moghaddam, M. Oliaei, M. Mohammadi, A. Mozaffari, Solving a bi-objective vehicle routing problem under uncertainty by a revised multichoice goal programming approach, Int. J. Ind. Eng. Comput. 8 (3) (2017) 283–302. [44] J. Kennedy, R. Eberhart, Particle swarm optimization, Proc. IEEE Conf 4 (1948) 1942–1952 1942. [45] H. Latifi, M. Farrokhifar, A. Safari, S. Pournasir, Optimal sizing of combined heat and power generation units using of MPSO in the Besat Industrial Zone, Int. J. Energy Statistics 4 (1) (2016). [46] C.A.C. Coello, G.T. Pulido, M.S. Lechuga, Handling multiple objectives with particle swarm optimization, IEEE Trans. Evol. Comput. 8 (3) (2004) 256–279. [47] S. Abdi, K. Afshar, Application of IPSO-Monte Carlo for optimal distributed generation allocation and sizing, Int. J. Electr. Power Energy Syst. 44 (1) (2013) 786–797. [48] V. Kumar, I. Gupta, H.O. Gupta, DG integrated approach for service restoration under cold load pickup, IEEE Trans. power Deliv. 25 (1) (2010) 398–406. [49] M. Daghi, M. Sedghi, A. Ahmadian, M. Aliakbar-Golkar, Factor analysis based optimal storage planning in active distribution network considering different battery technologies, Appl. Energy 183 (2016) 456–469. [50] M. Kumar, P. Nallagownden, I. Elamvazuthi, Optimal placement and sizing of renewable distributed generations and capacitor banks into radial distribution systems, Energies 10 (6) (2017) 811. [51] B.P. Dougherty, A.H. Fanney, M.W. Davis, Measured performance of building integrated photovoltaic panels: round 2, ASME 2004 International Solar Energy Conference, (2004), pp. 421–430.

References [1] S.A. Alavi, A. Ahmadian, M. Aliakbar-Golkar, Optimal probabilistic energy management in a typical micro-grid based-on robust optimization and point estimate method, Energy Convers. Manage. 95 (2015) 314–325. [2] M. Farrokhifar, Optimal operation of energy storage devices with RESs to improve efficiency of distribution grids; technical and economical assessment, Int. J. Electric Power & Energy Syst. 74 (2016) 153–161. [3] S. Bahramirad, W. Reder, A. Khodaei, Reliabil. Constrained Optim. Sizing Energy Storage Syst. Microgrid 3 (4) (2012) 2056–2062. [4] S.X. Chen, H.B. Gooi, M. Wang, Sizing of energy storage for microgrids, IEEE Trans. Smart Grid 3 (1) (2012) 142–151. [5] A.S.A. Awad, T.H.M. El-Fouly, M.M.A. Salama, Optimal ESS allocation for load management application, IEEE Trans. Power Syst. 30 (1) (2015) 327–336. [6] F. Luo, K. Meng, Z.Y. Dong, Y. Zheng, Y. Chen, K.P. Wong, Coordinated operational planning for wind farm with battery energy storage system, IEEE Trans. Sustain. Energy 6 (1) (2015) 253–262. [7] Y. Zhang, Z.Y. Dong, F. Luo, Y. Zheng, K. Meng, K.P. Wong, Optimal allocation of battery energy storage systems in distribution networks with high wind power penetration,” IET renew, J. Nucl. Energy Sci. Power Gener. Technol. 10 (8) (2016) 1105–1113. [8] M. Sedghi, A. Ahmadian, E. Pashajavid, M. Aliakbar-Golkar, Storage scheduling for optimal energy management in active distribution network considering load, wind, and plug-in electric vehicles uncertainties, J. Renew. Sustain. Energy 7 (3) (2015) 33120. [9] V.-H. Bui, A. Hussain, H.-M. Kim, A multiagent-based hierarchical energy management strategy for multi-microgrids considering adjustable power and demand response, IEEE Trans. Smart Grid 9 (2) (2018) 1323–1333. [10] K.S. El-Bidairi, H.D. Nguyen, S.D.G. Jayasinghe, T.S. Mahmoud, I. Penesis, A hybrid energy management and battery size optimization for standalone microgrids: a case study for Flinders Island, Australia, Energy Convers. Manage. 175 (2018) 192–212. [11] M. Sedighizadeh, M. Esmaili, N. Mohammadkhani, Stochastic multi-objective energy management in residential microgrids with combined cooling, heating, and power units considering battery energy storage systems and plug-in hybrid electric vehicles, J. Clean. Prod. 195 (2018) 301–317. [12] S. Aissou, D. Rekioua, N. Mezzai, T. Rekioua, S. Bacha, Modeling and control of hybrid photovoltaic wind power system with battery storage, Energy Convers. Manage. 89 (2015) 615–625. [13] M. Farrokhifar, S. Grillo, E. Tironi, Optimal Placement Of Energy Storage Devices For Loss Reduction In Distribution Networks, IEEE PES ISGT Europe, Lyngby, Denmark, 2013. [14] M. Farrokhifar, S. Grillo, E. Tironi, Loss minimization in medium voltage distribution grids by optimal management of energy storage devices, IEEE Grenoble Conference (2013). [15] J. Abushnaf, A. Rassau, Impact of energy management system on the sizing of a grid-connected PV/battery system, Electr. J 31 (2) (2018) 58–66. [16] H. Pandžić, Y. Wang, T. Qiu, Y. Dvorkin, D.S. Kirschen, Near-optimal method for siting and sizing of distributed storage in a transmission network, IEEE Trans. Power Syst. 30 (5) (2015) 2288–2300. [17] D. Gayme, U. Topcu, Optimal power flow with large-scale storage integration, IEEE Trans. Power Syst. 28 (2) (2013) 709–717. [18] M. Aneke, M. Wang, Energy storage technologies and real life applications–a state of the art review, Appl. Energy 179 (2016) 350–377. [19] B. Zakeri, S. Syri, Electrical energy storage systems: a comparative life cycle cost analysis, Renew. Sustain. Energy Rev. 42 (2015) 569–596 no. August. [20] J.M. Lujano-rojas, R. Dufo-lópez, J.L. Bernal-agustín, Optimal sizing of small wind / battery systems considering the DC bus voltage stability effect on energy capture, wind speed variability, and load uncertainty, Appl. Energy 93 (2012) 404–412. [21] Y. Zhang, S. Ren, Z.Y. Dong, Y. Xu, K. Meng, Y. Zheng, Optimal placement of battery energy storage in distribution networks considering conservation voltage reduction and stochastic load composition, IET Gener. Transm. Distrib. 11 (15) (2017) 3862–3870. [22] M. Faisal, M.A. Hannan, P.J. Ker, A. Hussain, M. Mansur, F. Blaabjerg, Review of energy storage system technologies in microgrid applications, Issues Challenges

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