hydrogen hybrid system

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Long-term optimization based on PSO of a grid-connected renewable energy/battery/ hydrogen hybrid system ~ o a, Francisco Llorens-Iborra a, Pablo García-Trivin zquez a, Antonio J. Gil-Mena a, Carlos A. García-Va ndez-Ramírez a,*, Francisco Jurado b Luis M. Ferna a

Research Group in Electrical Technologies for Sustainable and Renewable Energy (PAIDI-TEP-023), Department of Electrical Engineering, University of Cadiz, 11202 EPS Algeciras, Algeciras, Cadiz, Spain b Research Group in Research and Electrical Technology (PAIDI-TEP-152), Department of Electrical Engineering, University of Jaen, 23700 EPS Linares, Linares, Jaen, Spain

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abstract

Article history:

This paper presents and evaluates three energy management systems (EMSs) based on

Received 21 March 2014

Particle Swarm Optimization (PSO) for long-term operation optimization of a grid-

Received in revised form

connected hybrid system. It is composed of wind turbine (WT) and photovoltaic (PV)

7 May 2014

panels as primary energy sources, and hydrogen system (fuel cell eFCe, electrolyzer and

Accepted 11 May 2014

hydrogen storage tank) and battery as energy storage system (ESS). The EMSs are

Available online xxx

responsible for making the hybrid system produce the demanded power, deciding on the energy dispatch among the ESS devices. The first PSO-based EMS tries to minimize the ESS

Keywords:

utilization costs, the second one to maximize the ESS efficiency, and the third one to

Hybrid renewable energy system

optimize the lifetime of the ESS devices. Long-term simulations of 25 years (expected

Energy storage system

lifetime of the hybrid system) are shown in order to demonstrate the right performance of

Hydrogen

the three EMSs and their differences. The simulations show that: 1) each EMS outperforms

Energy management system

the others in the designed target; and 2) the third EMS is considered the best EMS, because

Particle swarm optimization

it needs the least ESS devices, and presents the lowest total acquisition cost of hybrid system, whereas the rest of parameters are similar to the best values obtained by the other EMSs. Copyright © 2014, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

Introduction Factors such as the energy consumption increase, the depletion of the fossil fuel reserves and the need of reducing the

greenhouse gas emissions have enhanced the use of hybrid renewable energy systems (HRESs) for distribution generation, integrating various renewable energy sources, mainly PV solar panels and WTs [1,2]. However, the fluctuating nature of the

* Corresponding author. Tel.: þ34 956028166; fax: þ34 956028001. ~ o), [email protected] (F. Llorens-Iborra), carloandres.garcia@uca. E-mail addresses: [email protected] (P. García-Trivin  zquez), [email protected] (A.J. Gil-Mena), [email protected] (L.M. Ferna  ndez-Ramírez), [email protected] es (C.A. García-Va (F. Jurado). http://dx.doi.org/10.1016/j.ijhydene.2014.05.064 0360-3199/Copyright © 2014, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

~ o P, et al., Long-term optimization based on PSO of a grid-connected renewable Please cite this article in press as: García-Trivin energy/battery/hydrogen hybrid system, International Journal of Hydrogen Energy (2014), http://dx.doi.org/10.1016/ j.ijhydene.2014.05.064

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solar radiation and wind speed implies that the energy productions from the PV panels and from the WTs are not always synchronized with the energy consumption. Integrating ESSs, such as batteries, supercapacitors and hydrogen systems (composed by FC, electrolyzer and hydrogen tank), allows the HRESs to have energy support/storage, since the ESSs can provide/store energy depending on the generated renewable energy and energy consumption [3e6]. In these HRESs, the EMS plays a key role in achieving a proper management of the energy sources. The main premise of the EMS is to make the HRES produce a certain power (to be consumed by the load in stand-alone operation or injected into the grid in grid-connected operation) using optimally the renewable energy sources, although secondary premises can be also established [7], such as controlling certain variables of the system (the charge level of the ESS devices or the DC bus voltage), operating the system at high efficiency or minimizing the generation costs. In short-term operation studies, the control objective is focused on achieving a suitable management of the energy sources and their power converters which integrate the hybrid system, taking into account their dynamic responses to face the net power variations caused by the changes in the demand or in the power generated from the renewable energy sources. In these studies, dynamic models of the energy sources and power converters are used and short-term simulations with time scale of seconds [8,9] or minutes [4,10,11] are performed. In Ref. [8], a non-linear control algorithm based on the differential flatness principle of a PV/FC/supercapacitor hybrid system was studied. The dynamic response of a gridconnected HRES with hydrogen and batteries and controlled by ANFIS-based control was analyzed in Ref. [9]. The power distribution among the energy sources of a stand-alone WT/ PV/FC hybrid system achieved by a simple control strategy was evaluated in Ref. [10]. The performance of a stand-alone WT/PV/hydrogen/battery hybrid system with EMS based on fuzzy logic during fluctuations of renewable-based power production was investigated in Refs. [4,11]. In long-term operation studies, the dynamics of the energy sources and their power converters are commonly neglected, and the control objective is focused on meeting the demand but taking into account in the energy dispatch other parameters such as the costs, the efficiency, the charge level of the ESS devices, etc. A stand-alone WT/PV/hydrogen hybrid system operating with EMS based on adaptive model predictive control was simulated with a time scale of hours in Ref. [12]. The EMS was designed for meeting the load demand and operating the hybrid system efficiently. Long-term simulations throughout one year were performed in Refs. [9,13]. The grid-connected HRES with hydrogen and batteries under study in Ref. [9] was also evaluated throughout one year. It was controlled by ANFIS-based control, taking into account the charge level of the ESS devices. In Ref. [13], three control strategies based on operating modes and costs were studied for the energy management of a stand-alone PV/hydrogen/ battery hybrid system. Long-term simulations throughout the expected lifetime of a stand-alone HRES with battery and hydrogen storage were investigated in Refs. [5,14]. An EMS based on fuzzy logic control was used in Ref. [5] to decide which ESS device should operate, considering the charge level,

the utilization costs and lifetime of the battery and hydrogen system. The EMS presented in Ref. [14] was based on a state control, which considered economic aspects and the charge level to discriminate between using the battery or hydrogen system. On the other hand, in long-term operation studies, the control objective can be formulated as an optimization problem, in which an objective function is maximized or minimized, while satisfying certain constraints, in order to decide the energy dispatch among the controllable energy sources of HRES (ESS devices). PSO is one of the most widely used methods for solving optimization problems [15e17]. Its simplicity, convergence speed and robustness are important features which justify its application to EMSs. In fact, PSO has been used in EMSs of HRESs [18e20]. In Refs. [18e20], all the energy sources were connected to a common AC bus, and the hybrid systems were simulated with a time scale of hours throughout a day. In Ref. [18], a PSO-based EMS was applied to a stand-alone WT/microturbine/battery hybrid system. In this work, the control objectives were to minimize the generation cost and maximize the operational efficiency of the microturbine. The performance of a micro-grid integrating WT, PV, microturbine, FC and battery was investigated in Refs. [19,20]. In both works, the minimization of operating cost was set out, while the minimization of the pollutants emission was also considered in Ref. [20]. As can be observed in the above mentioned papers, standalone HRESs are widely studied in the existing literature, whereas the studies on grid-connected HRES are scarce. This paper is focused on the long-term operation study of a gridconnected HRES controlled by PSO-based EMS. The HRES under study is composed of renewable energy sources (WT and PV panels), and hydrogen system (FC, electrolyzer and storage tank) and battery as ESS. All these energy sources are connected to a common DC bus, and the grid connection is performed by a three-phase inverter. Three different optimization problems are implemented in the PSO-based EMS for determining the power to be generated by/stored in the hydrogen system (FC and electrolyzer) and/or the battery: 1) Minimizing the utilization costs of the ESS devices (battery, FC and electrolyzer), which depend on the power delivered by each one; 2) Maximizing the efficiency of the whole ESS; and 3) Optimizing the lifetime of the ESS devices, reducing their life degradation. These three PSO-based EMS are evaluated by simulating the response of the HRES throughout 25 years, which is the estimated lifetime of the HRES. The three EMSs are compared in order to analyze their performance differences. This paper is organized as follows. Section 2 describes the HRES under study. The three PSO-based EMS applied to the HRES are explained in Section 3. Section 4 presents the simulation results, and finally, the conclusions are established in Section 5.

Hybrid system based on renewable energy/ battery/hydrogen The configuration of the HRES under study in this paper is depicted in Fig. 1. All the energy sources and ESS devices are connected together to a common DC bus, while an AC/DC

~ o P, et al., Long-term optimization based on PSO of a grid-connected renewable Please cite this article in press as: García-Trivin energy/battery/hydrogen hybrid system, International Journal of Hydrogen Energy (2014), http://dx.doi.org/10.1016/ j.ijhydene.2014.05.064

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3

Fig. 1 e Configuration of the grid-connected HRES under study.

three-phase inverter links the HRES with the utility grid and transfers the energy drawn from the DC bus. The primary energy sources of the HRES are a small WT with a two-blade turbine and a three-phase permanent magnet synchronous generator, and several PV panels with a total rated power similar to the one of the WT. Both sources are connected to the DC bus by means of power converters with Maximum Power Point Tracking (MPPT) strategy to extract the maximum power of the sources. Although the converters structure are similar, i.e. both primary energy sources use a unidirectional DC/DC converter plus a MPPT control to extract the maximum power from the source, the WT needs a previous AC/DC converter (rectifier) to transform the three-phase voltage to continuous voltage suitable to the DC/DC converter. Two ESSs complete the HRES, supporting the primary sources. They store the excess of power generated by the renewable energy sources and supply the power deficit when necessary. One of the ESSs is a hydrogen system, combining a PEM FC, electrolyzer and hydrogen tanks. The electrolyzer uses the energy surplus from the renewable energy sources to produce hydrogen from the water that is stored in the hydrogen tanks. On the contrary, the FC uses the stored hydrogen to produce electrical energy when the renewable energy sources needs a surplus. Thus, the hydrogen system can be an attractive solution to increase the stability of the HRES, balancing the generation and demand. Nevertheless, the hydrogen system has a slow dynamic response in case of fast fluctuation in the load. Thus, an additional fast dynamic

ESS device is necessary to smooth the fluctuant energy production. In this case, a lead-acid battery covers this function, providing good performance and life characteristics [21]. The ESS devices need power converters to adapt the voltage level to the DC bus voltage and control their energy flux. The hydrogen system uses two unidirectional DC/DC converters, one transfers power from the DC bus to the electrolyzer, and the other converter exchanges power from the FC to the DC bus. The battery uses a bidirectional DC/DC converter, allowing the battery charge from/discharge to the DC bus. Finally, the energy available in the DC bus is injected into the grid through a three-phase IGBT inverter controlled by PWM technique. In summary, Table 1 shows the details of the commercially available renewable energy sources and ESS devices comprising the HRES under study [22e27].

PSO-based EMS The aim of the EMS is to determine the energy dispatch among the ESS devices for making the HRES produce a certain power using optimally the renewable energy sources. It is formulated as an optimization problem, in which an objective function is optimized (maximized or minimized), while satisfying certain constraints, in order to determine the power to be generated by/stored in the battery, the power to be generated by the FC

~ o P, et al., Long-term optimization based on PSO of a grid-connected renewable Please cite this article in press as: García-Trivin energy/battery/hydrogen hybrid system, International Journal of Hydrogen Energy (2014), http://dx.doi.org/10.1016/ j.ijhydene.2014.05.064

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Table 1 e Details of the commercially available renewable energy sources and ESS devices comprising the HRES under study. Renewable source

Rated value

Number of elements

Manufacturer

Wind turbine Photovoltaic panel PEM fuel cell

1.5 kW

1

Bornay 1500

1.2 kWp

9

Eoplly EP125M/72-180W

1.2 kW

1

Heliocentrics Nexa 1200

Energy storage system

Rated value

Number of elements

Manufacturer

Lead-acid battery

14.18 kWh

12

PEM hydrogen electrolyzer Hydrogen tank

0.48 kW

1

2280 l

3

BAE Secura 6 PVS 660 Heliocentrics HG-60 Metal hydride canister HS 760

or the power to be consumed by the electrolyzer for hydrogen production (hydrogen energy storage). Different types of methods are used to solve a great variety of optimization problems. In linear optimization problems, the optimal solution can be guaranteed. Nevertheless, nonlinear problems turn out to be more difficult to solve than the linear ones, specially, when there are many local optimum solutions. In general, methods used to solve non-linear problems like enumeration, gradient and direct search methods only guarantee a sub-optimal solution. Another group of methods are those that make use of statistic strategies including meta-heuristic techniques. The statistic strategies let the algorithm escape from local optimum solutions, whereas meta-heuristic techniques are used to guide the search. PSO algorithms are grouped within this category. PSO algorithm, originally intended for simulating social behavior, is a meta-heuristic optimization method based on population developed by Kennedy and Eberhart [28], where a random initial population is guided using diversification and intensification. Unlike other evolutionary techniques, PSO has the advantage that uses neither crossover nor mutation. PSO consists of a population formed by individuals or agents called particles, where each one represents a possible solution of the problem. The position of each particle is updated at each iteration of the algorithm, by moving with a certain velocity. Each particle has its own position and the equation that governs its behavior is given by Eq. (1). xðk þ 1Þ ¼ xðkÞ þ vðk þ 1Þ

the stochastic characteristic of the algorithm by r1 and r2 , which are random numbers with uniform distribution between 0 and 1. On the other hand, c1 and c2 are coefficients of particle acceleration, usually chosen from the range [0, 2]. The information used by particles for moving in the correct direction is supplied by the difference between the position of the particle with best local and global fitness with respect to the actual position. Thus, xpbest corresponds to the best position of a given particle and xgbest to the best position of the entire set of particles. Many versions of original PSO algorithm have been proposed in the literature in order to improve the performance of the original algorithm. This paper uses the approach introduced by Shi and Eberhart [29]. They proposed the concept of inertia weight for balancing the local and global search during the optimization process. At the beginning, diversification is heavily weighted, whereas intensification is heavily weighted at the end of the search procedure. Therefore, Eq. (2) is changed by Eq. (4), where wðkÞ is calculated as follows: wðkÞ ¼ wmax 

wmax  wmin niter

(3)

 

vðk þ 1Þ ¼ wðkÞ$vðkÞ þ r1 $c1 xpbest ðkÞ  xðkÞ þ r2 $c2 xgbest ðkÞ  xðkÞ (4) According to Shi and Eberhart [30], the following parameters are appropriate and the values do not depend on problems: c1 ¼ c2 ¼ 2:0, wmax ¼ 0:9 and wmin ¼ 0:4. Fig. 2 illustrates the principle of particles movement in PSO, and Fig. 3 shows the flowchart of the PSO algorithm applied at each simulation step (one hour) in order to solve the optimization problem formulated in each EMS. Three EMSs, each one implementing a different objective function but with the same constraints, are presented in this work. In the three EMSs, the output variables obtained from solving the optimization problem are the powers of the ESS devices, i.e., the power to be generated by/stored in the battery (Pbat), the power to be generated by the FC (Pfc) and the power to be

(1)

The velocity is calculated using diversification and intensification. Diversification lets to explore other regions and helps to escape from local optima, whereas intensification helps to improve the quality of the solution. Eq. (2) represents the traditional PSO equation for the velocity.  

vðk þ 1Þ ¼ vðkÞ þ r1 $c1 xpbest ðkÞ  xðkÞ þ r2 $c2 xgbest ðkÞ  xðkÞ (2) The first term of the right hand side of Eq. (2) corresponds to the diversification process and the second and third terms to the intensification one. The intensification terms introduce

Fig. 2 e Principle of particles movement in PSO.

~ o P, et al., Long-term optimization based on PSO of a grid-connected renewable Please cite this article in press as: García-Trivin energy/battery/hydrogen hybrid system, International Journal of Hydrogen Energy (2014), http://dx.doi.org/10.1016/ j.ijhydene.2014.05.064

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 min OF ¼ f Pbat ; Pfc ; Plz

5

(5)

Subject to: 9 0 < Pbat < Pmax bat;dis > > max = 0 < Pfc < Pfc with Pnet  0 Plz ¼ 0 > > ; Pbat þ Pfc ¼ Pnet

(6)

9 0 < Pbat < Pmax bat;char > > = Pfc ¼ 0 with Pnet < 0 max 0 < Plz < Plz > > ; Pbat  Plz ¼ Pnet

(7)

max max max where Pmax fc , Plz , Pbat;char and Pbat;dis , are the maximum powers that the FC, electrolyzer and battery can absorb or generate in the next simulation step, depending on the current hydrogen tank level, LH2, and battery state-of-charge (SOC), SOC. In the case of the FC, its maximum power in the next simulation step is limited according to Eq. (8). Moreover, the maximum power that the electrolyzer can absorb in the next simulation step is calculated from Eq. (9).

  Enom LH H2 $ 2 ¼ min Pnom Pmax fc fc ; htherm $Uf $hstack $ Dt 100

(8)

   Mnom 100  LH2 H nom Pmax ¼ min Pnom þ A$ 2 $ lz lz ; B$QH2 Dt 100

(9)

where Pnom and Pnom are the nominal powers of the FC and lz fc electrolyzer, Enom H2 is the nominal capacity of the hydrogen tank is the hydrogen tank capacity and LH2 is in energy terms, Mnom H2 the current level of the hydrogen tank. The other parameters depend on the selected FC and electrolyzer. Thus, htherm is the FC thermodynamic efficiency, Uf is the FC utilization factor, hstack is the FC stack efficiency, A and B are constants of the is the nominal hydrogen flow of electrolyzer model, and QHnom 2 the electrolyzer. The maximum discharge and charge powers of the battery during the next simulation step are calculated as follows.    nom SOC  SOCmin nom Ebat $ Pmax bat;dis ¼ min Pbat ; Dt 100

(10)

   nom 100  SOC nom Ebat $ Pmax ¼ min P ; bat;dis bat 100 Dt

(11)

where Pnom bat is the nominal battery power defined by its datasheet, SOCmin is the minimum SOC allowed by the battery, which is recommended by the manufacturer, and Enom bat is the nominal battery energy. Finally, making an energy balance between the incoming and outgoing energy in the hydrogen tank and in the battery, their current levels can be calculated as Fig. 3 e Classical flowchart of the PSO algorithm.

consumed by the electrolyzer for hydrogen production (Plz). Thus, in these objective functions, the x parameter presented in Eq. (1) is defined as x ¼ [Pbat, Pfc, Plz]T. In general, for each EMS, the objective function OF has the following expression and constraints.

 LH2 ð%Þ ¼ 100$ 1 

i 1 Xh in QH2  QHout $t 2 CAPH2

 1 X Pbat $t SOCð%Þ ¼ 100$ 1  nom Ebat

(12)

(13)

where QHin2 and QHout are the incoming and outgoing hydrogen 2 flow in the tank, and CAPH2 is the nominal capacity of the hydrogen tank in kg.

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Fig. 4 depicts the general scheme of the PSO-based EMS. In each EMS, the scheme and expressions presented above are the same. The difference between the EMSs lies in the objective function OF. As can be seen in Fig. 4, the power demanded by the grid (Pdem) is defined by the system operator taking into account the maximum available power (Pav). Thus, the HRES can inject into grid a power in the interval [0, Pav]. The maximum available power Pav is calculated as Pav ¼ Pwt þ Ppv þ

Emax Emax bat;des fc þ 24 24

(14)

where Pwt is the WT power, Ppv is the PV panels power, Emax is fc the maximum available energy in the hydrogen tank, and Emax bat;des is the maximum available energy in the battery. Furthermore, Emax fc /24 represents the power that the FC could generate to drain the hydrogen tank in a day, and similarly, Emax bat;des /24 is the power that the battery could deliver to be discharged in a day. Finally, the net power (Pnet) to be provided by/stored in the ESS is determined as the difference between the power demanded by the grid (Pdem) and the power generated by the renewable energy sources (Prw ¼ Pwt þ Ppv).

Cbat ¼

Cac fc nom Pfc $Hmax fc

V Wh

(17)

Clz ¼

 Cac V lz max Wh Pnom lz $Hlz

(18)

EMS based on ESS efficiency maximization This EMS maximizes the ESS efficiency at every simulation step. In this case, the objective function is defined by Eq. (19), where hESS represents the ESS efficiency, calculated by Eq. (20). minf1  hESS g Pbat þ Pfc þ Plz in in Pin bat þ Pfc þ Plz

(19)

(20)

in in where Pin bat , Pfc and Plz are the input powers of the battery, FC and electrolyzer, which are obtained dividing the output powers of battery, FC and electrolyzer by their efficiencies. In the case of the battery, this efficiency can be calculated by the Rint model [31].

(15)

where Cfc,Pfc, Cfc,Pfc and Clz,Plz are the battery, FC and electrolyzer utilization costs in a certain hour (V/h). Cbat, Cfc and Clz are calculated as follows



ac ac where Cac bat , Cfc and Clz are the acquisition costs of the battery, FC and electrolyzer, updated with the annual interest rate in every replacement. Furthermore, Nmax cycle is the battery life in max and H are the FC and electrolyzer lifes in cycles, and Hmax lz fc working hours.

EMS based on utilization cost minimization

  min C ¼ Cbat $Pbat þ Cfc $Pfc þ Clz $Plz

(16)

Cfc ¼

hESS ¼

This EMS attempts to minimize, at every working hour, the utilization cost of the ESS devices (V/h), calculated as the sum of the battery, FC and electrolyzer utilization costs, which depend on the power delivered by each device. The objective function formulated in this EMS is defined as

V Wh



Cac bat nom Ebat $Nmax cycle

hbat

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi! 8 > 1  4Rbat Pbat > > 0:5$ 1 þ Pbat > 0 > > < U2bat ¼ , sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi! > > 1  4Rbat Pbat > > >2 1þ Pbat  0 : U2bat

(21)

Fig. 4 e General scheme of the PSO-based EMS. ~ o P, et al., Long-term optimization based on PSO of a grid-connected renewable Please cite this article in press as: García-Trivin energy/battery/hydrogen hybrid system, International Journal of Hydrogen Energy (2014), http://dx.doi.org/10.1016/ j.ijhydene.2014.05.064

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where Rbat is the internal resistance of the battery, and Ubat is the battery open circuit voltage. With respect to the FC, the relationship between Pfc and its efficiency is approximated by a third degree polynomial, obtained from the method of least squares. hfc ¼ p1 $P3fc þ p2 $P2fc þ p3 $Pfc þ p4

(22)

Fig. 5 (a) depicts the real curve which relates the FC efficiency [26] with its power and the fitting curve. Finally, the electrolyzer efficiency does not depend on the power. This value decreases with the working hours of the electrolyzer, and it is expressed by hlz ¼

h0lz 1 þ Htlz $Rlz

(23)

where h0lz is the initial electrolyzer efficiency, Htlz are the current working hours of the electrolyzer, and Rlz is the electrolyzer power degradation in one working hour.

EMS based on ESS lifetime optimization

The energy delivered or absorbed by the battery during a simulation step is proportional to the total exchange energy by the battery during its life. The unit life degradation of the battery during a simulation step is calculated as Dbat ¼

0:5$Pbat $Dt max Enom bat $Ncycle $B

(25)

wherePbat $Dt represents the energy delivered or absorbed by max the battery at a simulation step, and Enom bat $Ncycle is the maximum energy that the battery can exchange along its life. B is a parameter denoted as discharge factor, whose value depends on the battery power. If the battery is absorbing power its value is one. On the contrary, if the battery is delivering power its value depends on the depth of discharge in pu (DODpu), calculated as the ratio between the DOD during a simulation step and the nominal DOD assigned to the battery (DODnom). Fig. 5(b) relates the discharge factor with DODpu e Eq. (27) e, approximated by a third degree polynomial. Note that B factor attempts to penalize the discharges with a DOD higher than the DODnom by obtaining a value smaller than one. 

q1 $DOD3pu þ q2 $DOD2pu þ q3 $DODpu þ q4 1

discharge charge

The aim of this EMS is to reduce the life degradation of the ESS at every simulation step. Thereby, the objective function aims to minimize the sum of the life degradation of the battery, FC and electrolyzer, and therefore, optimize the lifetime of the ESS devices.

B¼

  min D ¼ Dbat þ Dfc þ Dlz

Furthermore, the unit life degradation of the FC is calculated through the next equation.

(24)

where Dbat, Dfc and Dlz are the unit life degradations of the battery, FC and electrolyzer.

DODpu ¼

Dfc ¼

Pbat $Dt DODnom $Enom bat



sto Hsto fc þ Cfc 3  Hfc Hmax fc

(26)

(27)

(28)

represents the degradation suffered by the FC where Hsto fc when stored, and Cfc is the FC load index, defined as the relationship between the actual FC current and the nominal FC current (Ifc/Inom ). Again, a polynomial is used to obtain the fc relationship between the FC load index and FC power from the polarization curve of the FC. Fig. 5c shows the real curve of the FC load index [26] with its power and the fitting curve. Cfc ¼ q1 $P2fc þ q2 $Pfc þ q3

(29)

Regarding the electrolyzer, as in the case of the FC, its unit life degradation is obtained by the following expression. Dlz ¼


 sto Hsto lz þ Clz 3  Hlz max Hlz

(30)

where Hsto lz is the electrolyzer degradation when stored, and Clz is the electrolyzer load index, defined as the relationship between the actual electrolyzer power and its nominal power (Plz/Pnom ). lz

Comparative study: simulation results

Fig. 5 e a) FC efficiency, b) battery discharge factor, and c) FC load index.

The three EMSs have been compared and tested with the HRES described in Section 2, and the wind speed and sun irradiance data collected from a weather station located in Algeciras

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i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 4 ) 1 e1 2

 diz), Spain. Throughout this section, the three PSO-based (Ca EMSs have been denoted as follows: EMScost for the EMS that minimizes the ESS utilization costs, EMSeff for the EMS that optimizes the ESS efficiency, and EMSlife for the EMS that optimizes the lifetime of the ESS devices. The available meteorological data were collected during a year, and then used for each of the 25 years simulated (expected lifetime of the HRES). Fig. 6 illustrates the wind speed and sun irradiance collected during one year (Fig. 6(a and b)), the power generated by the WT and PV panels during one year (Fig. 6(c and d)), and a detail of one week of these power (Fig. 6(e and f)). These devices incorporate MPPT controllers so that they are delivering the maximum available power from the wind and sun every hour. The maximum power available in the HRES for the EMScost ðPmax ava Þ and the power injected into grid (Pinj) by it during a week are shown in Fig. 7 (a). Fig. 7(b) illustrates the accumulative difference of powers between the EMSeff and EMScost during the 25 years and Fig. 7(c) those between the EMSlife and EMScost. In both cases, the differences were calculated as EMSeff-EMScost and EMSlife-EMScost, and as can be observed the accumulative differences are positive at the end of the life of the HRES. It can be concluded from these figures and Table 2 that the worst result is obtained with the EMScost, and as expected, the EMSeff is able to inject more energy into the grid. The powers delivered by the hydrogen system and the battery during one year are represented in Fig. 8. In this figure, the power stored in the battery and the power consumed by the electrolyzer are considered negative. As expected, the EMSeff tries to avoid the use of the hydrogen system to

increase the ESS efficiency, so that most of the net power is generated or absorbed by battery. In the EMScost, the use of the hydrogen system prevails over the use of the battery. Furthermore, it can be observed the right control of the electrolyzer performed by the three EMSs, since the negative values of the hydrogen power never exceed the nominal power of the electrolyzer. On the other hand, Fig. 9 represents the hydrogen tank level and battery SOC along the lifetime of the HRES. It reflects the right performance of the EMSs, since the battery SOC is never below 20%, as recommended by the manufacturer for avoiding deep discharges. For the hydrogen tank level, the lowest limit was set to 0. As observed, the main differences among the EMSs appear in the hydrogen tank level. In fact, the hydrogen tank is never completely filled with the EMScost and the EMSlife, whereas, with the EMSeff, it is completely filled during some periods of time. This is due to that, in the EMSeff, the use of the battery prevails over the use of the hydrogen system. Fig. 10 shows the evolution of the unit life level of the FC, electrolyzer and battery used throughout the lifetime of the HRES. It can be seen again that the battery use is lower with the EMScost. Thus, the EMScost only need one replacement of the battery, whereas the EMSeff needs four replacements and the EMSlife three replacements. On the contrary, the overuse of the hydrogen system with the EMScost makes that this EMS needs more electrolyzers and FCs. In fact, with the EMScost, the electrolyzer needs to be replaced three times and the FC two times. These values are improved by the other EMSs. In the case of the FC, the EMSlife and EMSeff need only one

Fig. 6 e a) Wind speed during one year, b) sun irradiance during one year, c) WT power during one year, d) PV power during one year, e) detail of one week of WT power, and f) detail of one week of PV power. ~ o P, et al., Long-term optimization based on PSO of a grid-connected renewable Please cite this article in press as: García-Trivin energy/battery/hydrogen hybrid system, International Journal of Hydrogen Energy (2014), http://dx.doi.org/10.1016/ j.ijhydene.2014.05.064

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i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 4 ) 1 e1 2

calculated from the simulations of HRES throughout 25 years. These parameters are the following ones: Total acquisition cost (Ct,ad), defined as the sum of the acquisition costs of FC, electrolyzer and battery, updated with the annual interest rate in every replacement; average utilization cost (C) calculated inj from Eq. (15); energy injected into the grid ðEinv Þ; energy used by the ESS devices (Eused); average ESS efficiency (hESS) calculated from Eq. (20); ESS efficiency (hE,ESS) and HRES energy efficiency (hHS), whose expressions will be developed below; total needed elements (Nt); addition of the life degradations of ESS devices (D); average battery SOC (SOCavg); and average hydrogen tank level (LH2,avg). The efficiencies of the ESS (hE,ESS) and HRES (hHS) are calculated by Z

T

hE;ESS ¼ Z 0 T 0

Z Pbat $dt þ Pin bat $dt þ

T

Z0T 0

Z Pfc $dt þ Pin fc $dt þ Z

Fig. 7 e Maximum power available in the HRES and power injected into the grid: a) EMScost, b) accumulative difference of powers between EMSeff and EMScost, and c) accumulative difference of powers between EMSlife and EMScost.

replacement almost simultaneously, and they also require one replacement of the electrolyzer, but in this case it is carried out before in the EMSlife. Nevertheless, the addition of all the unit life levels is lower using the EMSlife (see Table 2), justifying the correct performance of this EMS for optimizing the lifetime of the ESS devices. Finally, in order to compare the performance of the three PSO-based EMSs, the parameters shown in Table 2 have been

Table 2 e Final results obtained by each EMS after 25 years. Parameter Total acquisition cost, Ct,ad (V) Average utilization cost, C (V/h) Energy injected into the grid, Einj inv (kWh) Energy used by ESS (bat þ H2), EESS (kWh) Average ESS efficiency, hESS (%) ESS efficiency, hE,ESS (%) HRES efficiency, hHS (%) Total needed elements, Nt () Summation of life degradations of ESS (bat þ H2), D () Average battery SOC, SOCavg (%) Average H2 tank level, LH2,avg (%)

EMScost

EMSeff

EMSlife

55777

59309

54007

0.2626

0.8212

0.3268

1.11  105

1.24  105

1.22  105

6.62  107

7.06  107

7.02  107

71.37

96.23

92.77

51.93 58.95 9

92.19 66.98 9

82.07 66.15 8

7.86

7.48

7.21

37.79

53.42

63.08

7.17

70.86

5.38

hE;ESS

T

T

Z0 T 0

Plz $dt (31) Pin lz $dt

inj

Pin $dt ¼Z T Z Z T
 QH2 $dt þ Pwt þ Ppv $dt þ Elow;H2 0

0

0

T

Pbat;des $dt

0

(32) inj Pin

where is the power injected into the grid, QH2 is the hydrogen mass flow, and Elow,H2 is the lower heating value of hydrogen. According to Table 2, the following conclusions can be established. The lowest utilization costs (C) are achieved by the EMScost, the best efficiency results (hESS, hE,ESS and hHS) are obtained by the EMSeff, and the lowest values for the total acquisition cost, the needed elements (Nt) and the addition of life degradations of the ESS devices (D) are reached by EMSlife. The highest energy contribution into the grid and energy used by the ESS devices are achieved by the EMSeff, although with a cost three times higher respect to the EMScost. In the EMSlife, the utilization cost, efficiencies, energy contribution into the grid and energy used by the ESS devices are similar to the best values obtained by the other EMSs. On the other hand, the EMSeff presents the highest average level in the hydrogen tank, whereas the EMScost and EMSlife reach quite low levels. The highest average battery SOC is achieved by the EMSlife, and the lowest by the EMScost.

Conclusions In this paper, three PSO-based EMSs for a grid-connected HRES, integrating renewable energy sources (WT and PV), hydrogen system and battery, were evaluated through longterm simulations of 25 years. In the HRES under study, the renewable energy sources were made to work at the maximum efficiency point, and the EMS was responsible for controlling the ESS devices. The renewable power, the battery SOC, the hydrogen tank level and the power demanded by the grid were used as input variables to the EMS, whereas the output variables were the power to be generated by/stored in the battery, the power to be generated by the FC and the power to be consumed by the

~ o P, et al., Long-term optimization based on PSO of a grid-connected renewable Please cite this article in press as: García-Trivin energy/battery/hydrogen hybrid system, International Journal of Hydrogen Energy (2014), http://dx.doi.org/10.1016/ j.ijhydene.2014.05.064

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i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 4 ) 1 e1 2

Fig. 8 e Hydrogen and battery powers during one year: a) Hydrogen power (EMScost); b) battery power (EMScost); c) hydrogen power (EMSeff); d) battery power (EMSeff); e) hydrogen power (EMSlife); and f) battery power (EMSlife).

Fig. 9 e Hydrogen level and battery SOC throughout 25 years: a) Hydrogen level (EMScost); b) battery SOC (EMScost); c) hydrogen level (EMSeff); d) battery SOC (EMSeff); e) hydrogen level (EMSlife); and f) battery SOC (EMSlife). ~ o P, et al., Long-term optimization based on PSO of a grid-connected renewable Please cite this article in press as: García-Trivin energy/battery/hydrogen hybrid system, International Journal of Hydrogen Energy (2014), http://dx.doi.org/10.1016/ j.ijhydene.2014.05.064

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 4 ) 1 e1 2

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application of the presented EMSs to similar systems with other power range and/or different power/capacity ratio between individual units is not complex, since only small changes would be necessary, that is, solely the characteristic parameters of the ESS devices should be updated according to the equipment used in the HRES.

Acknowledgments This work was supported by the Spanish Ministry of Science and Innovation under Grant ENE2010-19744/ALT.

references

Fig. 10 e Unit life level: a) FC; b) electrolyzer; and c) battery.

electrolyzer for hydrogen production. Each EMS implemented a different objective function but with the same constraints, solved by applying a PSO algorithm. The control objectives formulated in each EMS were: 1) minimization of the ESS utilization costs (in the EMS denoted as EMScost), 2) maximization of the ESS efficiency (EMSeff), and 3) optimization of the lifetime of the ESS devices (EMSlife). The simulations demonstrated the right performance of the three PSO-based EMSs and their differences. Hence, the following conclusions were drawn from the analysis of results. The EMScost achieved the lowest utilization costs of the ESS devices. The EMSeff obtained the highest efficiencies, energy contribution into the grid and energy used by the ESS devices. And finally, the EMSlife showed the best results, reaching the lowest values for the life degradation of the ESS devices, the needed devices, and thus, the total acquisition cost of HRES, while the rest of parameters (utilization cost, efficiencies, energy contribution into the grid and energy used by the ESS devices) were similar to the best values obtained by the other EMSs. On the other hand, it is worth mentioning that the DC/DC power converters associated to the ESS devices play an important role, given that controlling their duty cycle allows the EMSs to manage the whole energy dispatch of the HRES according to the proposed strategies. Moreover, in these EMSs, it is also essential a precise setting of the characteristic parameters of the devices (those used in the controls, such as powers, costs, cycles, lifes, etc.), which would enable the EMS to achieve a proper and efficient energy management of the ESS devices, avoiding their misuse, and therefore, making the HRES more profitable. Finally, it is also noteworthy that the

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~ o P, et al., Long-term optimization based on PSO of a grid-connected renewable Please cite this article in press as: García-Trivin energy/battery/hydrogen hybrid system, International Journal of Hydrogen Energy (2014), http://dx.doi.org/10.1016/ j.ijhydene.2014.05.064