Amir Baharvandi
email: amir.baharvandi@gmail.com

Miadreza Shafie-Khah
email: miadreza@ubi.pt

Saber Talari
email: saber.talari@ubi.pt

João P S Catalão
email: catalao@fe.up.pt

Implementing an Integer Linear Approach to Multi-Objective Phasor Measurement Unit Placement

Keywords: Phasor Measurement Unit (PMU), Multi-objective Mathematical Programming, Integer Linear Programming, Optimal PMU Placement

In this paper, an Integer Linear Programming (ILP) problem for a model of Multi-Objective Optimal PMU Placement (MOPP) is proposed. The proposed approach concurrently deals with two objectives. The first objective is the number of phasor measurement units (PMUs) which should be minimized. The second objective function is measurement redundancy which is the number of observable buses in the case of PMU outage. In fact, whatever the amount of second objective increases, the system would be more reliable. Furthermore, some linearized formulations are defined for each nonlinear formula. In fact, the nonlinear nature of formulation related to redundancy is substituted by linear inequality and so there is no nonlinear formula such that the calculation of the problem would be simplified. Finally, a modified 9-bus test system is implemented to show how the proposed method is effective.

Introduction

The key element of the wide-area monitoring system and application of smart systems is to attain a instantaneous measurement of state variables which Phasor Measurement Units (PMUs) are able to deliver it [START_REF] Gharani Khajeh | Integrated Model Considering Effects of Zero Injection Buses and Conventional Measurements on Optimal PMU Placement[END_REF][START_REF] Manousakis | Optimal Allocation of PMUs in the Presence of Conventional Measurements Considering Contingencies[END_REF][START_REF] Korres | Optimal phasor measurement unit placement for numerical observability in the presence of conventional measurements using semi-definite programming[END_REF]. To increase the security of the system, the future state of the entire network should be estimated [START_REF] Manousakis | Taxonomy of PMU placement methodologies[END_REF]. Accordingly, devices known as PMU are used to measure the system's phasors (voltage and current) [START_REF] Abiri | An optimal PMU placement method for power system observability under various contingencies[END_REF], [START_REF] Korres | State estimation and observability analysis for phasor measurement unit measured systems[END_REF].

In this paper, two previously mentioned objective functions will be described as a Multi-Objective problem in the optimum PMU placement area. Also, to prove the usefulness of the method a case study is tested.

Relationship to Smart Systems

Since the metering of the smart systems is of paramount importance to the operator. There are several devices equipping smart cities. These devices are in the kind of electronic devices base of Internet of Things (IoT) [START_REF] Arasteh | Iot-based smart cities: A survey[END_REF]. Additionally, the internet facilitates interconnection between people and objects and it is known as the main revolution of the internet infrastructure.

One of these devices using Global Positioning System (GPS) to increase the reliability of the system is Phasor Measurement Unit. It is absolutely certain that implementation of the technological devices has increased drastically in smart cities. In this condition, Phasor Measurement Units can be very applicable in the power electrical systems of smart cities.

To make a clear relationship between the proposed research area and smart system, it should be noted that in the basis of IoT, there is a huge communication between devices using the internet. Likewise, in the smart systems, consumers can manage their consumption in order to decrease their cost of electricity [START_REF] Kianoosh | A novel multi-time-scale modeling for electric power demand forecasting: From short-term to medium-term horizon[END_REF].

First relationship between PMU and smart systems is about using the internet. PMUs implement GPS to calculate the voltage phasor of buses and in this process gathers data from buses and uses an internet interconnection with satellite subsequently sends data to a central server. This process is shown in Fig. 1. Another relationship between PMU device and smart system is about managing consumption in smart grids [START_REF] Boroojeni | Overview of the Security and Privacy Issues in Smart Grids[END_REF].

The calculation of phasors by PMU is utilized to estimate the future states of the power systems and this help to anticipate several parameters such as future consumption in order to supply future loads [START_REF] Bahrami | From demand response in smart grid toward integrated demand response in smart energy hub[END_REF]. State estimating helps the generators and consumers to manage their supply and consumption.

Problem Formulation

In the MOPP problem the best condition is making the entire network observable while the redundancy of the system is in a suitable range. It goes without saying that this observability is better to achieve using the minimum PMUs and at the same time all the system constraints would be satisfied.

In the following objective functions and constraints are introduced.

Objective Function

F1: First Objective Function

As mentioned before we want to increase the observability of the system (all buses) by minimum cost. Thus, minimum number of PMU which is equivalent the minimum cost is the first objective function:

1 min i iI F x .    (1)
where F1 is the first objective function, xi is a binary variable (which equals to 0 if the bus has not PMU and 1 otherwise) and I is a set for all buses from 1 to Nb (Nb is the number of buses).

F2: Second Objective Function The second objective is redundancy which is associated with contingency occurrence in the system. This contingency may be PMU. It means when a contingency occurs how many buses lose their observability. The desire condition is that we will have the minimum number of unobservable bus. In other word it is absolutely certain that whatever the PMUs increases the number of unobservable buses decreases as well as the redundancy increases. On the contrary, whatever the number of PMUs would be more the cost of installation related to the first objective function increases. So, we have the limitation of PMU and cost and should be aware about it.

Redundancy parameter (r) is equal to the number of observable buses while a contingency occurs. In this study we suppose that this contingency could be PMU loss. The second objective function is introduced as the number of set of buses which are observable when all the contingencies occur separately. This set is defined as R and the member of this set (r) should be maximized. Furthermore, maximizing of r is equivalent to the following formulation:

2 min . b F N r  (2)
If a PMU is placed on especial bus, that bus and buses connected to that bus are observable. For Example, in Fig. 2, assume that 3 PMUs are located at buses 1, 2, and 7 and all buses are observable. To explain the redundancy criterion, we should disregard PMUs one by one and after each contingency the observability of buses should be analyzed. Now suppose that PMU installed at bus 1 is failed. In this state, all the buses are observable too. Accordingly, the PMU located at bus 7 would be failed. In this contingency bus 7 would be unobservable. Similarly, in the case of PMU outage at bus 2, buses 3, 4 and 5 are unobservable. Thus, the number of entire unobservable buses in all PMU outages is 4 and this is the value of second objective function.

Constraints

As mentioned before, an installed PMU results in observability for he installed bus and its adjacent buses. A function (fi) is introduced as sum of ui for bus i and its neighbors.

.u  (6)

To avoid any unobservable bus, all the functions of fi should be nonzero:

1 i f , i I .    (7)
There is another issue related to zero injection buses. These buses may reduce the PMU number using KCL. In fact, when all buses associated with a zero injection bus are observable but one of them is unobservable, the KCL law leads to observability of that unobservable bus. Therefore, fi in (3) would be as follows [START_REF] Aminifar | Contingencyconstrained PMU placement in power networks[END_REF]:

, . i ij j ij j ij j I j I f g u g t q i I            (8)
, .

j ij ij iI t u q j I       (9)
where, tj is another binary variable (1 for zero injection bus and zero otherwise) and qij represents an auxiliary binary variable. Suppose a zero injection bus j with tj=1. In this state in [START_REF] Boroojeni | Overview of the Security and Privacy Issues in Smart Grids[END_REF] just one of the qij equals to 1 which means zero injection bus j results in bus i to be observable and the equality ( 9) would be correct. the impact of zero injection buses is taken into account as the second part of ( 8) that is increased if a zero injection bus causes that another bus is observable. The zero injection buses, tj and qij are explained completely in [START_REF] Aminifar | Contingencyconstrained PMU placement in power networks[END_REF]. Similarly, when a power flow is placed on one line, if one of the sending-end or receiving-end buses of that line is observable, there is no need for other end-bus to have PMU for observability and it can be observable due to other bus. Thus, constraint [START_REF] Arasteh | Iot-based smart cities: A survey[END_REF] for line ij which is a power flow line is substituted by:

1 i j ff  (10)
To formulate redundancy mathematically the following formula is presented using another binary variable i.e. βi:

3 3 1 1 . 2 i i i b b b f f N N N         (11)
In inequality [START_REF] Aminifar | Contingencyconstrained PMU placement in power networks[END_REF], when fi is higher than 2 the left hand side of [START_REF] Aminifar | Contingencyconstrained PMU placement in power networks[END_REF] will be between zero and one, and the other side would be greater than one so βi will be 1. Also, it is be equal to 0 if fi<2, then βi=0. Thus, sum of βi denotes the redundant buses with fi>2.

2 b i iI F N .      (12)
This is the constraint of redundancy which should be greater than a specific value according to necessity of system operator. where the required minimum value of redundancy is pmin.

Multi-Objective Solution Methodology

The MOPP problem is as follows:

  1 2 min ( ) , ( ) . .: ( ) 0, ( ) 0.
F x F x s t g x h x   (14)

Simulation Results

A nine-bus test system has been implemented to simulate the proposed multiobjective optimization problem. The proposed approach has been employed through a Pentium IV, 1-GHz Core i5 with 8 GB RAM using mixed integer linear programming solver CPLEX 9.0 in the GAMS [START_REF]Generalized Algebraic Modeling Systems (GAMS)[END_REF]. Fig. 3 shows the 9-bus test system. Except buses 1, 2 and 4 all the buses have load and also there are 2 generation units located at buses 1 and 2. Thus, bus 4 has no load and generator and is considered as a zero injection bus. If the first objective function is considered as the main objective, we can have an observable system using lowest number of PMUs that is 3 PMUs at buses 4, 6 and 8. Subsequently, the amount of second objective stays in the worst state and there is no redundant bus with fi>2.



In the flip side, if we solve the single objective problem so that the second objective function is the main objective, there would be 6 PMU at buses 4-9. In this condition the second objective is equal to 3. The amount of F2 cannot be less than 3, because there are 3 buses in the system which cannot have fi>2.

The Pareto points of the proposed Multi-objective method are shown in Fig. 4 and Table I. As we can see from these Figure andTable, two objective functions have conflict manner and they cannot be optimum simultaneously. Whatever the first objective function would be minimized the second one would be worse and conversely. Also, constraint [START_REF] Chen | Placement of PMUs to enable bad data detection in state estimation[END_REF] can decrease the number of points. For example in the case of pmin = 1 (the system needs 1 redundant bus) the first row of Pareto set in Table I should be disregarded. It is noteworthy that, the proposed approach can be implemented on larger case studies such as 118-bus or more because the formulation is linear and not complex.

Conclusion

This paper proposed a multi-objective PMU placement taking into account the redundancy as an extra objective. The redundancy criterion was introduced when a PMU was not in a suitable operation mode. Moreover, the formulation is linear for redundancy which decreases the complexity of the calculation. Two conflict objective functions are minimized simultaneously and the system operator can make a trade-off between points in the Pareto set and choose the best solution point according to the system requirements.

Fig. 1 .

 1 Fig. 1. PMU process

Fig. 2 .

 2 Fig. 2. Example for illustration

Fig. 3 .

 3 Fig. 3. Modified 9-bus test system

Fig. 4 .

 4 Fig. 4. Pareto set related to test system

Table . 1

 . Pareto points for 9-Bus Test System According to MOPP

	Pareto #	F1 F2	Number of PMU buses	Number of Redundancy buses
	1	3	9	4,6,8	-
	2	4	7	6,7,8,9	7,8
	3	5	6	2,5,6,7,8	6,7,8
	4	6	3	4,5,6,7,8,9	4,5,6,7,8,9

Acknowledgment. This work was supported by FEDER funds through COMPETE 2020 and by Portuguese funds through FCT, under Projects SAICT-PAC/0004/2015 -POCI-01-0145-FEDER-016434, POCI-01-0145-FEDER-006961, UID/EEA/50014/2013, UID/CEC/50021/2013, and UID/EMS/00151/2013. Also, the research leading to these results has received funding from the EU Seventh Framework Programme FP7/2007-2013 under grant agreement no. 309048.


Ruben S Anjos

Rui Melício
email: ruimelicio@gmail.com

Victor M F Mendes

Hugo M I Pousinho

Crystalline Silicon PV Module under Effect of Shading Simulation of the Hot-Spot Condition

Keywords: Hot-spot, PV systems, shading effect, breakdown voltage, modelling

This paper centers on the silicon crystalline PV module technology subjected to operation conditions with some cells partially or fully shaded. A shaded cell under hot-spot condition operating at reverse bias are dissipating power instead of delivering power. A thermal model allows analyzing the temperature increase of the shaded cells of the module under hot-spot condition with or without protection by a bypass diode. A comparison of the simulation results for a crystalline PV module without shading and with partial or full shading is presented.

Introduction

The demand for energy, the scarcity of nonrenewable fuels and the need for CO2 emissions decrease have resulted in a consciousness of the significance of energy efficiency and energy savings [START_REF] Fialho | PV systems linked to the grid: parameter identification with a heuristic procedure[END_REF][START_REF] Saraiva | Simulation and experimental results for a photovoltaic system formed by monocrystalline solar modules[END_REF] and DSM software and Smart Grid architecture have been developed in order to endure consumers on energy use. Also, renewable energy sources coming from solar and wind energy sources are attractive to go into exploitation, considering large scale and mini or micro scale conversion systems [START_REF] Fialho | PV systems linked to the grid: parameter identification with a heuristic procedure[END_REF][START_REF] Fialho | Simulation of a-Si PV system grid connected by boost and inverter[END_REF]. A photovoltaic system (PVS) converts solar energy into electric energy. The main device of a PVS is a cell. Cells may be assembled to form modules or arrays. A module is composed by cluster of cells linked in series. An array can be a module or a set of modules linked in parallel, series, total cross tied or bridge-linked to form large PVS. These PVSs may have or not a tracking system in order to achieve higher energy conversion values during sunny days due to the diverse positions to accumulate the sun´s irradiation [START_REF] Fialho | A simulation of integrated photovoltaic conversion into electric grid[END_REF]. The array performance depends on the solar irradiation, operating conditions, array configuration, environmental parameters as temperature and wind speed, and shading. The shading on arrays occurs due to the dust, bird droppings, leaves, snow or shadowing caused by near buildings, near trees or a passing cloud. The shading can be partial or full with respect to a PV module [START_REF] Anjos | Simulation of the effect of shading on monocrystalline solar module technology under hot spot condition[END_REF]. The existence of shaded cells in a module causes a mismatch on the equilibrium of photo-generation of electron-hole pairs of the cells. Under this mismatch, a nonshaded cell of the module operates at a generation of carriers higher than those of the shaded ones [START_REF] Anjos | Simulation of the effect of shading on monocrystalline solar module technology under hot spot condition[END_REF]. Consequently, the shaded cells are into reverse bias [START_REF] Anjos | Simulation of the effect of shading on monocrystalline solar module technology under hot spot condition[END_REF]. The hot-spot occurs after a cell, or cluster of cells in a PV module, operates at reverse bias, dissipating power and operating at abnormally high temperatures. Cells exposed to higher temperatures will degrade at a higher rate than others. If a cell is shaded and operation at high temperatures occurs for a long time, then irreparable damage is due to happen to the solar cell. If a bypass diode is used, a cell in reverse bias renders useless the rest of the cells under the bypass diode [START_REF] Giaffreda | Model for thermal behavior of shaded photovoltaic cells under hot-spot condition[END_REF]. The hot-spot occurrence is particularly frequent and always happens when a shadow covers the module, thus reducing the photo-generated current [START_REF] Daliento | A modified bypass circuit for improved hot spot reliability of solar panels subject to partial shading[END_REF][START_REF]IEC Standard 61215: Crystalline silicon terrestrial photovoltaic (PV) modules-design qualification and type approval[END_REF]. In order to protect shaded cells from breakdown voltage (BV), bypass diodes linked in antiparallel with the cell or module is applied. In the 1990s authors contributed to improve the PVS module design and to determine the maximum solar cells number per bypass diode in order to avoid the hotspots condition [START_REF] Arnett | Photovoltaic module hot-spot durability design and test methods[END_REF][START_REF] Shepard | The integration of bypass diodes with terrestrial photovoltaic modules and arrays[END_REF]. Based on these knowledges, according to IEC 61215 a hot-spot endurance test became part of the type approval for crystalline silicon modules [START_REF]IEC Standard 61215: Crystalline silicon terrestrial photovoltaic (PV) modules-design qualification and type approval[END_REF].

The diodes bypass limit the reverse voltage that can be applied to a cell, thus avoiding it from reaching the BV when the cell is shaded [START_REF] Giaffreda | Model for thermal behavior of shaded photovoltaic cells under hot-spot condition[END_REF]. Due to impurities and defects inside the silicon, some cells may display a great reverse current even before reaching the BV. This impurities and defects inside the silicon are commonly modeled by inserting a shunt resistance, whose value depends on the distribution of defects/impurities and concentration inside the cell [START_REF] Giaffreda | Model for thermal behavior of shaded photovoltaic cells under hot-spot condition[END_REF]. If the shunt resistance is low enough the hot-spot condition can occur even before that the PV cell enters the BV even if bypass diodes are used [START_REF] Giaffreda | Model for thermal behavior of shaded photovoltaic cells under hot-spot condition[END_REF]. Devido ao aquecimento do ponto quente, a célula fotovoltaica pode atingir uma temperatura suficientemente alta para causar danos permanentes, embora isso demore mais para ocorrer do que quando as células operam em quebra de tensão [START_REF] Giaffreda | Model for thermal behavior of shaded photovoltaic cells under hot-spot condition[END_REF].

This paper is considered with the exposition to shading of a silicon crystalline PV module and as a consequence subjected to condition of hot-spot. A thermal model is used in order to allow estimating the temperature of the area subjected to the condition of hot-spot. The paper is systematized as follows. Section II presents the relationship to smart systems. Section III presents the modeling of the PVS module forward biased, using the five parameters model, i.e., the single-diode, shunt and series resistances circuit; the model of the PV module reverse biased; and the thermal model which allows estimating the hot-spot temperature. Section IV presents the case studies. In the first case, the I-V characteristic curves are simulated considering the reverse bias of the PV module: simulation results for a silicon crystalline PV module technology without shading are compared with the module partially shaded or fully shaded; without or with bypass diode. In the second case, a thermal model allows analyzing the temperature increase of the shaded cells of the module subjected to condition of hot-spot without or with a bypass diode. Finally, concluding remarks are given in Section V.

Relationship to Smart Systems

Technological Innovation has entered into the age of smart systems and smart grids, when machines implement human functions of analyzing real data and making decisions [START_REF] Arsan | Smart systems: from design to implementation of embedded smart systems[END_REF]. A Smart system is an embedded systems architecture which embodies advanced automation systems to provide control and monitoring over the something's functions, for example a PVS. Hence, the advances in science and technology allow the opportunity to bridge physical components and cyber space, leading to Cyber-Physical Systems [START_REF] Viveiros | Control and supervision of wind energy conversion systems[END_REF][START_REF] Fialho | Simulation of a-Si PV system linked to the grid by DC boost and three-level inverter under cloud scope[END_REF]. The coordination and interaction of smart systems of PVSs require handling number of challenges to increase the reliability and the service life time of the PV modules. The performance of a PV modules depends on the operating conditions, i.e., depends not only on solar irradiation and temperature, but also on the array configuration and shading effects. The hot-spot on a PV cell produces degradation or a permanent damage in the shaded cells, with a consequent reduction of the provided power [START_REF] Rossi | Modeling and detection of hotspot in shaded photovoltaic cells[END_REF].

Statistics show degradation rates of the nominal power for thin-film and silicon PVS modules of 0.8%/year [START_REF] Jordan | Photovoltaic degradation rates-an analytical review[END_REF]. Hence, to raise the service life and the reliability of PVS modules one has to recognize the challenges involved. Successfully integrating PV power into the existing electric grid is a complex challenge that relies on distributed, interconnected smart systems, which are proliferating within the engineering industry [START_REF] Viveiros | Control and supervision of wind energy conversion systems[END_REF].

Modeling

The circuit for the PVS entails in a current controlled generator, a single-diode, a shunt and series resistances. Thus for the forward bias module the I-V characteristic is formulated [START_REF] Anjos | Simulation of the effect of shading on monocrystalline solar module technology under hot spot condition[END_REF] given by:

p S mV IR V S R IR V e I I I T S       ) 1 ( 0 (1)
where the output module current is I, the photo-generated current is IS, the reverse bias saturation diode current is I0, the output voltage is V, the shunt and the series resistances are respectively Rp and RS, the ideality factor is m, the thermal voltage of the p-n junction is VT.

This circuit for the reverse bias module is shown in Fig. 1. Where in Fig. 1 the current through diode D is ID, described by the Shockley diode equation, the reverse breakdown current which is a portion of Ip dissipated in Rp when the module is reverse biased is Ibd [START_REF] Anjos | Simulation of the effect of shading on monocrystalline solar module technology under hot spot condition[END_REF][START_REF] Giaffreda | Model for thermal behavior of shaded photovoltaic cells under hot-spot condition[END_REF]. For the reverse bias module, the Ibd [START_REF] Arsan | Smart systems: from design to implementation of embedded smart systems[END_REF] is given by:

b bd S p S bd V IR V a R IR V I      ) 1 ( (2)
where the portion of the linear current involved in avalanche breakdown is a . For the reverse bias module the I-V characteristic is formulated by an implicitly function [START_REF] Ramaprabha | Matlab based modelling of SPVA characterization under reverse bias condition[END_REF] given by:

] ) 1 ( [1 ) 1 ( 0 b bd S p S mV IR V S V IR V a R IR V e I I I T S           (3)
where the BV is bd V , the avalanche breakdown factor is b. The thermal model allows estimating the temperature THS of the module's area AHS under hot-spot condition [START_REF] Anjos | Simulation of the effect of shading on monocrystalline solar module technology under hot spot condition[END_REF][START_REF] Giaffreda | Model for thermal behavior of shaded photovoltaic cells under hot-spot condition[END_REF]. The time instant t at which the PV module goes into shading condition and reverse biased is denoted by tHS. Considering the PV module staying into shading condition from tHS until at time t ≥ tHS, the temperature THS (t) is incremented relatively to the ambient one, Tamb. The expression for computing THS (t) [START_REF] Giaffreda | Model for thermal behavior of shaded photovoltaic cells under hot-spot condition[END_REF] is given by:

Rs Is

ii i T t T amb HS    ) ( (4)
where i and ii [START_REF] Giaffreda | Model for thermal behavior of shaded photovoltaic cells under hot-spot condition[END_REF] are respectively given by:

] ) 1 ( [ ) ( TH TH HS C R t t TH e G A R i        (5) ] 1 [ ) ( HS TH HS TH HS C R t t HS TH diss e R P ii        (6)
where the whole PV module area is A, the solar irradiance is G, the relative gap in the irradiation between the non-shaded modules and the shaded module linked in series is γ, the dissipated power on the Rp resistance of the shaded module is Pdiss, the thermal capacitance and thermal resistance of the module with area AHS under hotspot condition are CTH-HS and RTH-HS, respectively, the thermal capacitance and thermal resistance of the remaining module area are CTH and RTH. In the thermal model, the parameters RTH-HS, CTH-HS are constants and depend of the materials which composing the upper layers of the module cells [START_REF] Giaffreda | Model for thermal behavior of shaded photovoltaic cells under hot-spot condition[END_REF]. Considered hot-spot condition on the two cells of the PV module with 36 cells is shown in Fig. 2.

In Fig. 2, Acell is the area of each cell of the module and AHS is the area of the module under hot-spot condition. The range of values of AHS for the simulation is taken from [START_REF] Giaffreda | Model for thermal behavior of shaded photovoltaic cells under hot-spot condition[END_REF], where is said to be set experimentally in the range between 5% and 10% of Acell.

Simulation Results

The models for the PV module forward biased, for the PV module reverse biased and for the thermal model that allows assessing the temperature of the area under hot-spot condition are implemented in Matlab/Simulink.

The module has 36 cells linked in series, the shaded area AHS is equal to the area of two cells, i.e., taking approximately 5.6% of the area A associated with the capture of energy. The data of the manufacture for the crystalline silicon PV module Isofotón I-53 at STC [START_REF] Fialho | PV system modeling by five parameters and in situ test[END_REF] is displayed in Table 1. The forward breakover voltage considered for silicon diode is 0.7 V. The equivalent circuit for the reverse bias module shown in Fig. 1 has the values for the RP and RS given in [START_REF] Ramaprabha | Matlab based modelling of SPVA characterization under reverse bias condition[END_REF].

Table 1. Isofotón I-53 solar module data

First Case Study. The simulation results for a silicon crystalline PV module technology without shading are compared with the module partially shaded (MPS) or fully shaded (MFS) in order to study the current and voltage when the module is reverse biased. Considering the MPS or MFS linked in series with one non-shaded module, if the shaded module is a MPS his irradiance is 500 W/m 2 and if shaded module is MFS his irradiance is 0 W/m 2 . When the shaded PV module is reverse biased we assume Vbd = -10 V, a = 1.93 and b = 1.10 in [START_REF] Fialho | Simulation of a-Si PV system grid connected by boost and inverter[END_REF]. Considered that the module is without bypass diode: the I-V reference curve of the non-shaded module (black) and respective symmetric curve (also in black but in the second quadrant), the I-V curves of the MPS (orange) or MFS (blue) are shown in Fig. 3. 4 allows observing the effect of the bypass protection. The maximum power dissipated in the shaded module without bypass diode is 36.20 W at MPDP while for the shaded module with bypass diode is 2.28 W at MPDP. For the two case studies the maximum power point (MPP) when the module is partially shaded is at a voltage value of 15.58 V and a current value of 1.45 A, while when the module is fully shaded the voltage value is 0.00 V and a current value is 0.00 A. Hence, the bypass diode does not affect the module at MPP. in [START_REF] Fialho | PV system modeling by five parameters and in situ test[END_REF]. The PV module has a THS of approximately 60ºC for a lower than tHS = 5 s. Fig. 5 reveals that the MPS without bypass diode reaches the critical temperature of 150ºC [START_REF] Giaffreda | Model for thermal behavior of shaded photovoltaic cells under hot-spot condition[END_REF] at time of 11.63 s, i.e., 6.63 s after getting shaded and reverse biased, while with bypass diode decreases temperature 57.92ºC in same time. The curves of the temperature for the MFS in function of the time reveals that the MFS without bypass diode reaches the critical temperature of 150ºC [START_REF] Giaffreda | Model for thermal behavior of shaded photovoltaic cells under hot-spot condition[END_REF] at time of 11.71 s, i.e., 6.71 s after getting shaded and reverse biased, while with bypass diode reaches the temperature of 56.79ºC in same time. Thus the MPS reaches the temperature of 150ºC faster than MFS not depending on the usage of bypass diode.

Conclusions

An addressing of a model for PV module under hot-spot condition is study in what regards the model simulation. The addressing allows concluding that when a module operates at reverse bias due to the non-uniform cells illumination, the module is exposed to a quantified temperature increase and substantial power loss. Also, allows quantifying the advantage of using the diode for bypass protection in what regards performance of the module.

Fig. 1 .

 1 Fig. 1. Circuit for the reverse bias module.

Fig. 2 .

 2 Fig. 2. Two cells shaded PV module under hot-spot condition [5].

Fig. 3 .

 3 Fig. 3. No bypass protection. I-V curves (black), MPS (orange), MFS (blue).

Fig. 3

 3 Fig. 3 reveals that at the maximum power dissipation point (MPDP) the MPS and the MFS is reverse biased with a same voltage value of -11.24 V and a current value of 3.22 A. The maximum power point (MPP) when the module is partially shaded is at a voltage value of 15.58 V and a current value of 1.45 A. Considered that the module is with bypass diode: the I-V reference curve of the non-shaded module (black) and respective symmetric curve (also in black but in the second quadrant), the I-V curves of the MPS (orange) or MFS (blue) are shown in Fig. 4.

Fig. 4 .

 4 Fig. 4. Bypass protection. I-V curves (black), MPS (orange), MFS (blue).

Fig. 4

 4 Fig.4reveals that at MPDP the shaded module is reverse biased with the value of voltage of -0.70 V and the value of the current of about 3.38 A. A comparison of Fig.3and Fig.4allows observing the effect of the bypass protection. The maximum power dissipated in the shaded module without bypass diode is 36.20 W at MPDP while for the shaded module with bypass diode is 2.28 W at MPDP. For the two case studies the maximum power point (MPP) when the module is partially shaded is at a voltage value of 15.58 V and a current value of 1.45 A, while when the module is fully shaded the voltage value is 0.00 V and a current value is 0.00 A. Hence, the bypass diode does not affect the module at MPP. in[START_REF] Fialho | PV system modeling by five parameters and in situ test[END_REF].

  . The simulation results for a silicon crystalline PV module technology without shading are compared with the module partially shaded or fully shaded in order to evaluate the evolution of the module temperature under hot-spot condition. The value of Tamb considered is 25ºC and the time instant tHS considered is 5 s. Consider the MPS operating at MPDP the curves of the temperature THS for the area under hot-spot condition in function of the time: without bypass diode (blue) and with bypass diode (green) are shown in Fig.5.

Fig. 5 .

 5 Fig. 5. MPS, temperature on the area of hot-spot condition.

Acknowledgment. This paper was in part supported by Portuguese Funds, Foundation for Science and Technology, UID-EMS-50022-2013, LAETA project 2015/2020.

Without Bypass Diode

With Bypass Diode


Roman Kosenko
email: roman.kosenko@ttu.ee

Dmitri Vinnikov

Soft-Switching Current-FED Flyback Converter with Natural Clamping for Low Voltage Battery Energy Storage Applications

Keywords: current-fed converter, soft-switching, flyback converter, ZVS, ZCS, switching sequence, clampless, energy storage

This paper introduces a new galvanically isolated current-fed step-up dc-dc converter intended for high voltage gain applications. The converter have fully-controllable voltage doubler rectifier, with control signals synchronous to that of the inverter switches. The proposed converter can regulate output voltage within wide range of the power and input voltage variations. Proposed converter does not require snubbers or resonant switches and with proposed control sequence ensures switches operation under soft-switching conditions in all transient states. Soft-switching in semiconductors allows achieving high efficiency. Moreover, the input side current is continuous. The operating principle for the energy transfer from the current-fed to voltage-fed side is described, design guidelines along with experimental verification of the proposed converter are shown in this paper. The converter proposed can be used as a front-end converter for grid connected battery storage.

Introduction

New trends in European Union green energy policy encourage use of distributed power generation sources [START_REF] Blaabjerg | Renewable Energy Devices and Systems -State-of-the-Art Technology, Research and Development, Challenges and Future Trends[END_REF]. For more efficient use of alternative energy sources in residential applications it is preferable to include battery energy storage (BES) to those systems [START_REF] Heymans | Economic analysis of second use electric vehicle batteries for residential energy storage and load-levelling[END_REF]- [START_REF] Chen | Sizing of Energy Storage for Microgrids[END_REF]. Battery energy storage systems (BESS) are used as buffer to store exceeded energy so that later it can help to suppress grid shortages or even inject power to the grid in case of high electric energy demand. This helps to minimize the impact of renewable power generation sources on electric grid stability [START_REF] Wang | Active Demand Response Using Shared Energy Storage for Household Energy Management[END_REF].

BESSs in residential installations have power rating up to 8 kW. Battery chemistry for battery energy storage also varies significantly. In recent years lithium iron phosphate (LiFePO4) batteries are preferable for residential usage as they have one of the highest safety and reliability characteristics among lithium batteries. Another important feature is that they can preserve nominal capacity even after few thousands of discharge-charge cycles [START_REF] Dunn | Electrical energy storage for the grid: a battery of choices[END_REF]- [START_REF] Whittingham | History, Evolution, and Future Status of Energy Storage[END_REF].

For connecting the BESS to the AC-grid the additional inverter stage is used. The intermediate DC-link of the system is traditionally formed by different voltage-fed (VF) DC-DC converters [START_REF] Fernão Pires | Power converter interfaces for electrochemical energy storage systems -A review[END_REF], [START_REF] Kong | Analysis and control of isolated current-fed full bridge converter in fuel cell system[END_REF]. One of the most reliable and widely used solution is dual active bridge (DAB) converter as they have soft switching capability and thus high operational performance [START_REF] Tan | Design and Performance of a Bidirectional Isolated DC-DC Converter for a Battery Energy Storage System[END_REF]. Soft-switched current-fed (CF) converters with soft-switching capability is a topic of interest in recent years. Among them there are converters with additional clamping circuits [START_REF] Prasana | Extended Range ZVS Active-Clamped Current-Fed Full-Bridge Isolated DC/DC Converter for Fuel Cell Applications: Analysis, Design, and Experimental Results[END_REF], [START_REF] Andersen | A ZVS-PWM Three-Phase Current-Fed Push-Pull DC-DC Converter[END_REF], with resonant-assisted transition [START_REF] Chen | Study and Implementation of a Current-Fed Full-Bridge Boost DC-DC Converter With Zero-Current Switching for High-Voltage Applications[END_REF], and others additional circuits [START_REF] Wang | A ZCS Current-Fed Full-Bridge PWM Converter With Self-Adaptable Soft-Switching Snubber Energy[END_REF], [START_REF] Chakraborty | Parasitics assisted soft-switching and naturally commutated current-fed bidirectional push-pull voltage doubler[END_REF]. Additional advantage of current-source converters is continuous input current. This places them as preferable choice for battery powered applications [START_REF] Fernão Pires | Power converter interfaces for electrochemical energy storage systems -A review[END_REF], [START_REF] Kong | Analysis and control of isolated current-fed full bridge converter in fuel cell system[END_REF].

Fully-controllable switches at the rectifier side of isolated CF converters brings new challenges and benefits [START_REF] Xuewei | Naturally Clamped Zero-Current Commutated Soft-Switching Current-Fed Push-Pull DC/DC Converter: Analysis, Design, and Experimental Results[END_REF]- [START_REF] Kosenko | Full-Soft-Switching High Step-Up Bidirectional Isolated Current-Fed Push-Pull DC DC Converter for Battery Energy Storage Applications[END_REF]. This paper introduces CF flyback DC-DC converter with novel regulation1 algorithm. This algorithm has duration of transient switching intervals that is constant in a whole operation range and so ensures soft switching across that range. High efficiency can be achieved due to minimizer dynamic losses even at relatively high operational frequencies.

Relationship to Smart Systems

Nowadays smart systems are used in all major sectors of human life such as healthcare, safety, automotive and environment. Typical smart system consist of diverse components that are usually physically organized in different physical nodes. And with the rapidly increasing interest in the concept of the Internet of things all those nodes and even entire systems have to be operating and connected to each-other all the time without any interruptions. To ensure this the most critical smart-systems accommodates backup battery-based power supplies. Battery energy storage in household is used to accumulate energy from renewable energy sources when it is not used or from the grid when it has a lowest price. After battery is charged it can be used when it is required by user or when grid is under heavy load and requires additional power. This means that battery energy storage together with smart learning system with energy demand tracking as a part of smart house can increase stability of local power supply system and decrease yearly average grid power usage. So a major research direction of power electronics in smart systems is the development of highefficiency robust power converters for battery-storage.

General Description

Topology of the discussed converter is shown in Fig. 1. It can be considered as derivation of the CF dual-inductor push-pull topology [START_REF] Kosenko | Full Soft-Switching Bidirectional Isolated Current-Fed Dual Inductor Push-Pull DC-DC Converter for Battery Energy Storage Applications[END_REF] proposed earlier. It consists of the CF inverter with four-quadrant (4Q) switches at the input side and the controllable rectifier at the output VF side. Isolating transformer introduces additional step-up needed to interface low-voltage sources to the high voltage DC-link. Converter provides low level of ripple CF terminal that is faced to battery. By controlling the duration of the reactive energy circulation interval regulation of output voltage is achieved.

Full soft-switching CF flyback converter topology can be used as interface power converter for connecting battery to the DC-grid or as the first conversion stage in ACgrid connected battery storage. In such applications the proposed topology and proposed control algorithm have the following distinguish features:  High voltage gain that allows to use transformers with lower turns ratio to connect low voltage 12V and 24V batteries to a standard 400V DC-grid.  Current-fed terminal that ensures that the battery is operating with continuous input current. This allows increasing battery elements lifetime.  Natural clamping of input inductor is achieved with the special control algorithm without any additional active or passive elements.  Soft-switching is achieved in all switching elements through the wide operation range in both energy transfer directions.



Reduced number of semiconductor devices as compared to the full-bridge boost converter (FSS-IFBBC) [START_REF] Chub | Full soft-switching bidirectional current-fed DC-DC converter[END_REF], and the same number of switches that in the dual inductor push-pull converter [START_REF] Kosenko | Full Soft-Switching Bidirectional Isolated Current-Fed Dual Inductor Push-Pull DC-DC Converter for Battery Energy Storage Applications[END_REF] and CF push-pull converter [START_REF] Kosenko | Full-Soft-Switching High Step-Up Bidirectional Isolated Current-Fed Push-Pull DC DC Converter for Battery Energy Storage Applications[END_REF] that are using same control algorithm.



High transformer utilization factor due to the double-ended operation with one part of the switching period (t0-t7) proposed topology operates similar to the flyback converter and during the other part (t7-t12) it operates similar to the forward converter.



Transformer flux runaway protection capability without use of RCD snubbers or additional transformer windings. During operation the transistors S1.2 and S2.2 are controlled synchronously to S1.1 and S2.1 and are turned off (acting like diodes) for transient intervals.

Converter Operation

This section describes converter operation with power flow from VCF to VVF terminal. Generalized control algorithm and simulated waveforms presented in Fig. 2. Switching period with the proposed control algorithm consists of twelve time intervals that are described in details further. Interval 1 (t0 < t < t1). Interval starts with turn off of switch S2.2. Current is been redirected to the body diode (BD) of S2.2. S1 with assistance of the transformer leakage inductance Lleak is turned on under soft-switching conditions. Leakage inductance acts like a snubber by slowing down current rise in S1 to separate voltage fall and current rise and thereby to minimize switching losses. The inductor LCF is storing energy. Switch S3 is kept on forcing the inverter side transformer current to transfer from S2 to S1.

I S1 V S1 I CF t V S1 I S3 I S3 I S2 V S2 V S2 t V S2 V S2 I S4 I S4 t I S4 V S4 V S4 V S4
Interval 2 (t1 < t < t2). Interval starts after S2 current reaches zero and BD of S2.2 is turned off naturally. Inductor LCF is storing energy from CF terminal. Transformermagnetizing inductance starts accumulating energy from C3 through S3. The duration of this interval is calculated so that the current through S1 will have sufficient time to drop to zero at minimal input voltage at nominal power.

Interval 3 (t2 < t < t3). Interval starts with turning off switch S2.1 under ZCS conditions. Interval is needed to separate switching transitions in CF and VF sides and in most cases it can be omitted.

Interval 4 (t3 < t < t4). Switch S3 is turned off with soft-switching assisted by snubber capacitor C1. Capacitor C1 is charging from zero to VVF and C2 is discharged from VVF to zero. The transformer output The voltage polarity at the transformer output is changed.

Interval 5 (t4 < t < t5). Interval begins when the transformer current starts flowing through BD of S4. This interval is a part of power delivery mode from CF to VF terminal. Energy accumulated in the transformer magnetizing inductance LM is delivered to the output terminal through the BD of S4. Interval duration is Duration of this interval is calculated so that it is equal to the snubber capacitor recharge time in point of the minimal operational power. In this case soft-switching will be achieved in the whole operation range while the conduction losses across BD will be minimized.

Interval 6 (t5 < t < t6). During this interval S4 turned on at ZVS conditions. Energy accumulated in LM is delivered to the VF terminal through switch S4.

Interval 7 (t6 < t < t7). Switch transients on this interval are analogical to those on interval 1. 4Q switch S2 is turned on with soft-switching assisted by Lleak.

Interval 8 (t7 < t < t8). Interval is analogical to interval 2. At the beginning of interval current through S2 reaches zero by those forcing BD of S2.1 to turn off naturally. Reverse energy transfer interval is started. LCF accumulating energy from VF (C4 through S3 and Tr) and CF terminals.

Interval 9 (t8 < t < t9). During this interval S2.1 turned off under ZCS conditions. Processes in converter are analogical to interval 8. Duration of intervals 8 and 9 defines the converter gain factor.

Simplified converter voltage gain factor can be derived analogically to [START_REF] Kosenko | Full Soft-Switching Bidirectional Isolated Current-Fed Dual Inductor Push-Pull DC-DC Converter for Battery Energy Storage Applications[END_REF] and is expressed as follows:

REV CF VF FRW D nV V F     4 1 2 2 , ( 1
)
where n is the transformer turns ratio. Interval 10 (t9 < t < t10) and Interval 11 (t10 < t < t11). Are analogical to intervals 4 and 5 accordingly. Starting from instant t10 energy is delivered to transferred from the input to the output terminal through the transformer and body diode of S4 like in classical forward DC-DC converter.

Interval 12 (t11 < t < t12). At the instant t11 switch S4 is turned on under ZVS conditions. Current is transferred from BD to switch thereby decreasing conduction losses.

Experimental Results

For the experimental study converter was designed to operate with a 4-cell LiFePO4 battery was selected. Experimental prototype design parameters are shown in Table 1. Semiconductor devices parameters used are shown in Table 2.

Experimental data is shown in Table 3 The difference in theoretical and experimental voltage gains can be explained by the assumptions and simplifications used in theoretical equation.

Conclusions

A novel current-fed flyback converter was proposed and analyzed. One of the main features of proposed converter is that all semiconductors are operating under softswitching conditions at all switching transients. Also proposed converter due to utilization of the circulating energy features higher DC voltage gain than in traditional current-fed converters. Moreover, due to the special control algorithm it futures natural clamping of input inductor convertor does not require any additional clamping circuits. Peak power stage efficiency during experimental verification reached 96.34%. Сconverter switching frequency can be increased without significant efficiency drop thanks to the soft-switching operation. This allowing to decrease size and price of magnetic components and so the price of BESS in general.

Fig. 1 .

 1 Fig. 1. Proposed converter topology.

Fig. 2 .

 2 Fig. 2. Simulated operation waveforms.

Table 1 .

 1 Converter Parameters

	Parameter	Decsignator Value
	Nominal power, W	P	100
	CF voltage, V	VCF	10-15
	VF voltage, V	VVF	400
	Operation frequency, kHz	fSW	100
	Transformer turns ratio (Np/Ns)	N	1:6
	Magnetizing inductance, mH	LTX_m	3.4
	Transformer secondary leakage ind., uH	LTX_leak	8
	Inductance of input inductors, uH	LA, LB	44
	Capacitance of output filter, uF	C3,C4	2.2
	Capacitance of snubber, pF	C1,C2	265

Table 2 .

 2 P Semiconductor Devices Parameters

	Component	Device	Parameters
	CF MOSFETs	Infineon BSC035N10NS5	VDS=100V; RDS(on)=3.5mΩ ID=100A, trr= 62 ns, COSS=770 pF
	VF MOSFETs	Infineon IPB60R190C6	VDS=650V; DS(on)=190mΩ ID=59 A, trr=430 ns, COSS=85pF

Table 3 .

 3 Experimental Results Forward Operation

	VCF, V	DREV	Theoretical gain	Experim. gain	Losses, W	η,%
	10	0.104	3.42	3.33	7.89	92.11
	11	0.086	3.05	3.03	6.29	93.71
	12	0.069	2.77	2.78	5.39	94.61
	13	0.051	2.51	2.56	4.53	95.47
	14	0.033	2.31	2.38	3.81	96.19
	15	0.017	2.15	2.22	3.66	96.34

Acknowledgments. This work has been supported by the Estonian Research Council grant PUT744 and by the Estonian Centre of Excellence in Zero Energy and Resource Efficient Smart Buildings and Districts, ZEBE, grant 2014-2020.4.01.15-0016 funded by the European Regional Development Fund.


Beatriz Andres
email: bandres@cigip.upv.es

Raul Poler
email: rpoler@cigip.upv.es

Supporting the Strategies Alignment Process in Collaborative Networks

Keywords: alignment, strategy, objective collaborative network, performance indicator, system dynamics, simulation

The establishment of collaborative relationships with the network partners provides them important advantages, such as competitiveness and agility, when responding to the current rapid market evolutions. Nonetheless, the participation in collaborative networks becomes a complex process that starts with the alignment of all the enterprises' objectives and strategies. Smart systems and approaches are needed in order support collaborative partners to deal with the strategies alignment challenge. The lack of alignment emerges because each enterprise defines its own objectives and strategies, to perform their business, and it could happen that non-compatible strategies are activated, involving the appearance of conflicts between strategies of different enterprises. To this regard, a decision support system is proposed, consisting of a mathematical model, a system dynamics method, a simulation tool and a guideline, with the main aim of supporting the process of identifying aligned strategies, among the enterprises of the collaborative network.

Introduction

The concept of collaborative networks (CN) has been widely studied over the last years due to the positive effects undergone by the enterprises that collaborate [START_REF] Poler | Intelligent Nonhierarchical Manufacturing Networks[END_REF]. In the work of consolidating a new discipline in CN, Camarinha-Matos and Afsarmanesh [START_REF] Camarinha-Matos | Collaborative Networks: Reference Modelling[END_REF] define CN as a network consisting of a variety of autonomous and heterogeneous entities that collaborate to better achieve common or compatible goals, to jointly generate value, and whose interactions are computer network supported. Collaborative processes have been widely studied over the last years due to their decisive contribution in the proper operation of the CN. With the aim of consolidating the wealth of knowledge in the research area of collaborative processes Andres and Poler [START_REF] Andres | Models , guidelines and tools for the integration of collaborative processes in non-hierarchical manufacturing networks: a review[END_REF] perform a deep analysis that has allowed to (i) classify the most relevant collaborative processes according to the decision making level: strategic, tactical, and operational, and (ii) analyse for each process the models, guidelines and tools proposed in the literature to address them. The authors conclude that amongst all the collaborative processes studied, the ones that need to be addressed from the collaborative perspective, through proposing new contributions to fill the decentralized and collaborative features, are (i) at the strategic decision-making level: the strategies alignment process; (ii) at the tactical level: share costs and profits, and uncertainty management; and (iii) at the operational level: collaborative lotsizing.

Enterprises willing to collaborate must overcome a set of barriers not only associated with the establishment of collaborative processes identified by [START_REF] Andres | Models , guidelines and tools for the integration of collaborative processes in non-hierarchical manufacturing networks: a review[END_REF] [4] (e.g. products design, demand forecasting, operations planning, replenishment, uncertainty management, share costs and profits, scheduling, information exchange, interoperability, etc.), but also when defining compatible goals, activating complementary strategies [START_REF] Andres | Computing the Strategies Alignment in Collaborative Networks[END_REF] or aligning their core values [START_REF] Bititci | Managing synergy in collaborative enterprises[END_REF] [START_REF] Macedo | A method to analyse the alignment of core values in collaborative networked organisations[END_REF]. Focusing on the strategies alignment process, the mere consideration of all the enterprises' objectives when deciding which strategies are the best ones to carry out will allow achieving higher levels of adaptability, agility, and competitiveness [START_REF] Poler | Intelligent Nonhierarchical Manufacturing Networks[END_REF], strengths that are specially valued in current turbulent contexts and dynamic markets. Considering this, the strategies alignment process is hereafter addressed; with the main aim of dealing with the conflicts appearing when strategies misalignments emerge, in the CN context. Intuitively, as the activation of strategies has a direct influence on the objectives achievement, it can be understood that the strategies will be characterised by being aligned when each activated strategy not only promotes the achievement of the objectives defined by the enterprise that formulates such strategy, but also when positively influences the accomplishment of the objectives defined by the rest of the network partners.

Considering the importance of aligning strategies, among the enterprises of the same network, in terms of improving the CN relationships, there is a lack of an integrated approach to support enterprises on the modelling, assessment and solution of the strategies alignment process from a collaborative and integrated perspective. In the light of this, the following research questions are raised to support the strategies alignment process, in order to solve them as the objective of this research.

RQ1. How to model the impact that each strategy, formulated by one enterprise, has on the objectives defined by the other network enterprises? That is, how to model the impact of the strategies at the inter-enterprise level?

RQ2. What would be an adequate model to support the process of identification of aligned strategies, through modelling the strategies impact in the objectives, in CN context?

RQ3. What would be an adequate method to support the process of identification of aligned strategies, and to represent causal relationships (impacts) between the strategies and the objectives, in CN context?

RQ4. What would be an adequate tool to support the process of identification and assessment of aligned strategies, and to compute the strategies impact on the objectives performance at enterprise and network level, in CN context?

RQ5. What would be an adequate guideline to support the process of identification and assessment of aligned strategies, and to analyse the strategies impact on the objectives and identify misalignments, in CN context?

In order to give response to the raised research questions, the paper is organized as follows: Section 2 discusses the relationship between the approach proposed in this paper, to deal with the strategies alignment process, and the Smart Systems; Section 3 summarizes literature review performed in the research area of strategies alignment; Section 4 introduces the decision support system, consisting of a mathematical model, a system dynamics method, a simulation tool and a guideline, with the main aim of supporting the process of identifying aligned strategies among the enterprises of the CN; Section 5 the proposed approach is validated in a real use case from food industry; finally, Section 6 provides the conclusions and the paper discussion.

Relationship with Smart Systems

Smart Systems (SS) have multi-disciplinary applications in different areas of research, such as the social, economic, healthcare, energy, safety and security, logistics, ICT, and manufacturing. The area under study of this paper is focused on the CN operation, which includes service and manufacturing sectors. The novelty in SS is the integration of different components, regardless the technologies and materials in which are created [START_REF] Akhras | Smart materials and smart systems for the future[END_REF]. The proposed strategies alignment approach is applied in different industries and sectors that have in common the CN to which they belong; in this regard, SS can support the diversity associated to this approach. The decision support system proposed could be used as part of a SS that allows, through using real time information of other interoperable components, identifying in each enterprise the most appropriate aligned business strategies. The negotiation process, for identifying the most appropriate strategies, could be also included in a SS in order to allow enterprises make smart decisions with regards the strategies to activate during their participation in the CN. In the light of this, the strategies alignment approach proposed could benefit from the real-time information, response capability, tracking and monitoring features that provide the SS. The integration of different systems, for the implementation of the strategies alignment approach in SS, is a key question to answer, bringing together interdisciplinary technological approaches and solutions for overcoming potential limitations in the establishment of collaborative process and the stable and sustainable operation of the CN.

Problem Definition and Conceptualization

The literature review carried out in [START_REF] Andres | Models , guidelines and tools for the integration of collaborative processes in non-hierarchical manufacturing networks: a review[END_REF] has allowed identifying, firstly, the most important processes to perform in a CN, and secondly, amongst all these processes, those that have a lack of contributions from the CN context. As stated in Section 1, the strategies alignment process is included in the group of potential processes to propose solutions in collaborative decentralised scenarios. According to the analysis carried out in [START_REF] Andres | Models , guidelines and tools for the integration of collaborative processes in non-hierarchical manufacturing networks: a review[END_REF] it can be concluded that, to the best of our knowledge, the strategies alignment process is a collaborative process that requires to be studied, so that models, guidelines and tools for its analysis, assessment and resolution, in the CN context, have to be proposed.

In a network of enterprises, the alignment can be defined as a proper or desirable coordination or relationship of the components of this network. More concretely, in the management field, the concept of alignment can be considered as a situation in which the strategies, formulated by the entities belonging to the network, are strictly combined under a set of functions to achieve the objectives [START_REF] Piedade | Managing Performance to Align the Participants of Collaborative Networks : Case Studies Results[END_REF]. CNs consist of autonomous and heterogeneous enterprises [START_REF] Camarinha-Matos | Collaborative networks : a new scientific discipline[END_REF] each one defining its own objectives. The formulation of the strategies answers the question: How to reach the objectives? Once the planner decides the scope, situation or problem that aims to modify, a goal is drawn to guide the processes of change and then to trace the trajectory of necessary events over time to achieve that purpose. Strategy is the way forward to achieve the objectives. The business strategies are the set of actions raised to achieve the defined objectives; therefore, each enterprise of the CN formulates its own strategies with the main aim of achieving the defined objectives. There will be times in which all the strategies formulated are activated. Nevertheless, sometimes only a few of the formulated strategies will be activated, due to, for example, a restriction associated with the budged. Lets consider two enterprises (E) in a CN, each one defines two objectives (O) and formulates two strategies (str). Each objective has associated a KPI to measure its achievement. In this regard, E1 acquires the role of the distributor in the CN and defines O11: Increase the product sales by a 10%, and O12: Reduce the product costs by a 30%; and formulates str11: Invest 0,5 m.u on marketing activities, and str12: Conduct negotiations with other manufacturers to reduce the purchasing costs. E2 acquires the role of the manufacturer in the CN and defines O21: Increase the profit by a 15%, and O22: Reduce the quantity of product that cannot be sold by 100 %; and formulates str21: Use different distribution channels to sell the product in other markets, and str22: Buy one machine to make derivative products, reprocessing the product that cannot be sold (i.e. low cost product). With this example it can be observed that the str12 is not compatible with the str21, because str12 is devoted to establish new relations with other manufacturers, which will involve the reduction of the profit defined in O21. Moreover, if E1 conducts negotiations with other manufacturers (str12), the O22 will be negatively influenced. Focusing on E2, the str21 focuses on the generation of alliances with distribution channels different from the one provided by E1. The activation of str21 negatively influences O11, defined for reducing the product sales; and consequently the O12 that leads to reduce the product costs. Considering the aforementioned, the str12 and str21 are considered misaligned, if activated at the same time. On the other hand, str11 and str22 are considered to be aligned because the two formulated strategies positively influence the achievement of the objectives defined. Assuming that, the strategies alignment concept is defined next as: "the set of strategies, formulated by the enterprises belonging to the CN, whose activation positively influence, on the whole, the objectives achievement of the majority of the enterprises participating in the CN; obtaining the best performance at the network level, although small number of the strategies negatively influence any of the defined objectives" [START_REF] Andres | A decision support system for the collaborative selection of strategies in enterprise networks[END_REF]. It must be considered that (i) individual enterprises take part in several networks, so that it is likely that some of the enterprises taking part in these networks have contradictory objectives and consequently contradictory strategies; and (ii) the enterprises belonging to one specific CN are heterogeneous and contradictory objectives and strategies might arise. Therefore, for enterprises belonging to a CN, the defined objectives and the strategies formulated by one enterprise could favour, or not, the objectives defined by other enterprises. In order to achieve the ideal situation, enterprises belonging to a CN should be able to identify those aligned strategies, whose activation promotes the improvement of the objectives defined by the majority of the networked enterprises, or at least the activated strategies do not negatively influence on the objectives attainment [START_REF] Andres | Computing the Strategies Alignment in Collaborative Networks[END_REF].

Literature Review

A summary of the review performed to analyse how the strategies alignment process has been treated in the literature is presented. In the light of this, some models guidelines and tools are identified and briefly described. The gaps and trends related to the strategies alignment process from a collaborative perspective are identified, as a result of the analysis performed. The initial round of search was based on a broad meaning of keywords and contexts (enterprise and network level), to ensure that papers adopting an alternative nomenclature, were identified. Alignment of strategies, alignment of actions, alignment of decisions, collaborative decisions design, collective decisions and alignment in supply chain where the keywords used. The found works proposed models, guidelines and tools to deal with the alignment of decisions from different decision making levels and different perspectives of application (i) one in which the decisions are collaboratively made and from the beginning of the decision making the decisions are aligned, and (ii) another one in which the each partner defines its own decisions and then these are pooled in order to identify those that are more aligned with the decisions of other network partners. Considering the reviewed works, Table 1 is generated, listing and briefly describing the works. Due to space restrictions the table presents works from 2012. The selected contributions are analysed considering if the proposed approaches are designed when (i) the decisions are collaboratively and centralised (C) made or, unlike, (ii) the decisions are decentralised (D) made by each CN partner and after that the decisions are aligned. A set of models, guidelines and tools are proposed in the literature with the main aim of aligning decisions among the enterprises of the network. Some of them can be highlighted: classified as models it can be found the multi-criteria methods such as FMCDS or MCDM; fuzzy approaches deal with uncertain information. As regards the guidelines, collaborative strategies or negotiation-based schemes such as S-DSP are found. Considering the methods, TOPSIS, MCOGA, GA, ANP, causal maps can be emphasised. Concerning tools MECDSS is found. Despite of the importance of aligning strategies, in terms of avoiding partnership conflicts, to the best of our knowledge, there are some gaps in the literature as regards contributions that provide a holistic approach that allows considering all the strategies formulated by all the partners in the CN context. The performed review has allowed identifying possible trends, gaps and actions in the topic under study. This actions are summarised as follows: (i) propose a complete approach to deal with the strategies alignment process by considering all the strategies formulated by all the enterprises of the CN; (ii) identify the aligned strategies from an holistic perspective regardless of their nature and type, taking into account the CN context; (iii) model the strategies alignment process considering the intra-enterprise strategies alignment (alignment of the strategies defined in the same enterprise), and inter-enterprise strategies alignment (alignment among the strategies defined by different enterprises of the network); (iv) consider the performance approach to measure the strategies influence, that when activated, it will be measured considering the increase and decrease of the KPIs defined in each enterprise. Definitely, from the performed literature review, there is a need to propose a framework consisting of a model method, tools and guidelines to address the strategies alignment process from a holistic perspective by equally considering all the network partners.

Approach to Support the Strategies Alignment Process

An approach that consists of a model, method, tool and guideline is proposed, to deal with the strategies alignment process, in the CN context.

Mathematical Model

The proposed model allows to formally represent, in a mathematical notation, the influences that the strategies activated in one enterprise have on the performance indicators (KPI) defined to measure the achievement of the objectives, both in the same enterprise and in other CN enterprises [START_REF] Andres | A decision support system for the collaborative selection of strategies in enterprise networks[END_REF]. In order to represent the influences and relations between the KPIs and the strategies a mathematical notation model is proposed: the Strategies Alignment Model (SAM). First of all, the set of parameters and decision variables, used to model the SAM, are defined in Table 2.

The SAM is hereafter developed, consisting of an objective function and the associated restrictions, representing the relations amongst all the defined variables and parameters. The main aim is to identify, amongst all the strategies defined, those strategies that have higher level of alignment. The activation of the aligned strategies positively influences the majority of the objectives defined by the networked partners, maximising the performance at network level. The SAM computes the KPIs improvement or worsening when a strategy is activated. Thus, the developed model supports enterprises on the decision making as regards the number of units of strategy (u_stris) to be activated and the time in which the strategies have to be activated (ti_stris) with the objective of maximising the network performance, given by as the homogenised version of the . Therefore, the objective function of the SAM is mathematically represented by the following equation ( 1):

(1)
The homogenised version of is obtained through the homogenisation of parameters related to KPIs ( ) (2); and the normalisation of the parameters related to durations and time (3), based on the horizon (H) of time in which the strategies alignment process is modelled.

Two decision variables, u_stris and t_stris, are defined in order to maximise the parameter . The decision variable u_stris decomposes the strategy (stris) in units of strategy, allowing representing the "intensity" in which each strategy stris is activated. One unit of strategy has an associated a cost (c_stris). Therefore, depending on the parameter c_stris, the enterprise' budget (bi) will be reduced in a lesser or larger extent (4). The budget, bi, owned by each company defines the monetary capacity constraint [START_REF] Andres | Computing the Strategies Alignment in Collaborative Networks[END_REF]. In order to identify the influence that one unit of strategy (u_stris = 1) has over the , parameter is used (6).

(

) (5) 4
The influence that one strategy stris has on a particular is modelled through the function . This function, [START_REF] Piedade | Managing Performance to Align the Participants of Collaborative Networks : Case Studies Results[END_REF], is a piecewise function that depends on the time [f1(t)], that is, the duration parameters (d1_stris, d2_stris, d3_stris and d4_stris) and the decision variable ti_stris. Besides, is modelled according to a ramp shape ( ) [START_REF] Macedo | A method to analyse the alignment of core values in collaborative networked organisations[END_REF]. The representation of the ramp allows modelling that, after the delay time (d1_stris), the stris progressively influences the . ( 7) [START_REF] Piedade | Managing Performance to Align the Participants of Collaborative Networks : Case Studies Results[END_REF] The influence received by the KPIs defined in one enterprise i (11) is caused by both intra-enterprise influence, (9) and inter-enterprise influences, , [START_REF] Andres | A decision support system for the collaborative selection of strategies in enterprise networks[END_REF]. ) must be considered [START_REF] Lin | Design of a Global Decision Support System for a manufacturing SME: Towards participating in Collaborative Manufacturing[END_REF]. At enterprise and network level the parameters and are defined as ( 13). ( 12) ;

(13)

System Dynamics Method

The method used is based on system dynamics (SD), and will allow to graphically represent and solve the proposed mathematical model, from a CN perspective. SD will enable to characterise the causal relationships between the strategies and the objectives; modelling the influences that the objectives experience when certain set of strategies are activated. Moreover, SD will favour to understand the structure and dynamics of complex systems, such as the CN 3. The flow diagram of the SD SAM is represented in Figure 1, and will be extended according to the number of enterprises, the number of KPIs defined and the strategies formulated.

KPIixk = curve_KPIixk[dimension_KPIixk] Curve_KPIixk_T = IF ((curve_KPIixk[dimension_KPIixk] >= Threshold_KPIixk[dimension_KPIixk]) THEN (curve_KPIixk[dimension_KPIixk] -Threshold_KPIixk[dimension_KPIixk]) ELSE (IF (curve_KPIixk[dimension_KPIixk]<0) THEN curve_KPIixk[dimension_KPIixk] ELSE 0)) KPIixk_T = curve_KPIixk_T[ dimension_KPIixk ] fulfill_KPIixk_min = IF ((KPIixk_T[ dimension_KPIixk ] >= KPIixk_min[ dimension_KPIixk ]) THEN 1 ELSE 0) KPI_i = Σ KPIixk_T.get(index_KPixk) • Wixk[dimension_KPIixk] KPI_GLOBAL = Σ KPI_i / n

Simulation Tool

The proposed simulation software tool is used to solve and represent the strategies alignment model, based on SD rigorous method. The use of computational tools allows automatically solving the strategies alignment process. System-dynamic's simulation based models supports on the process of computing the strategies to activate and the time slot in which activate them, optimising the global performance of the CN. Considering the SAM developed and the SD resolution method described, three tools used to address the strategies alignment process, from a CN context, are described: (i) AnyLogic simulation software is selected to support the system dynamics (SD) method, in which the SAM is solved; (ii) a Database Management System (DMS) is proposed to store all the information required in the SAM. The parameters required to feed the SAM are gathered in a Microsoft Access Database specifically designed; and (iii) the Strategies Alignment GENerator (SAGEN) is designed as an application to automatically generate the SAM in SD simulation software. In this regard, SAGEN contains the set of procedures that allow generating the required structure, in XML language, to create the strategies alignment simulation model in the SD simulation software selected (AnyLogic). The procedures are created according to the requirements of the XML schema, for its reading in AnyLogic. The programing language used to build SAGEN is Pascal. Lazarus [START_REF] Free | [END_REF] is used as an Integrated Development Environment (IDE) that uses Free Pascal compiler. In order to have a deeper insight of SAGEN programming and the procedures creation, we refer readers to [START_REF] Andres | Technological Innovation for Cyber-Physical Systems[END_REF]. To automatically generate the SAM in SD simulation software, the user firstly introduces the information required to solve the SAM in the DMS, through SAGEN user interface (SAGEN UI) (see Figure 2). SAGEN UI is connected with Microsoft Access Database 2010 through an OCDBConnection. The information stored in Microsoft Access Database 2010 contains all the tables and fields necessary to create the XML file that contains the SAM to be simulated in AnyLogic simulation software. In a second step, the user creates the XML file, which results from the execution of the procedures programmed in SAGEN. The XML file automatically created in SAGEN contains the strategies alignment simulation model, which can be loaded in AnyLogic simulation software. The SAM is automatically created containing the flow diagram, as well as the simulation and the optimisation experiments. AnyLogic simulation software is selected due to brings together the most common modelling methods: System Dynamics (SD), Discrete Events (DE), and Agent Based (AB). AnyLogic integrates both simulation and optimisation experiments. Accordingly, in the optimization experiments, AnyLogic searches the values of the model parameters that lead to obtain greater performance levels of the model, given an objective function and the set constraints and requirements. OptQuest is the engine used by AnyLogic to carry out the optimisation of the represented simulation model [START_REF] Kleijnen | Optimization of simulated systems: OptQuest and alternatives[END_REF].

Fig. 2. SAGEN User Interface

Supporting the Strategies Alignment Process 15

Guideline

A guideline is proposed as a complementary mechanism to the model, method and tool, with the main aim of supporting the enterprises, which belong to a CN, on addressing, assessing and solving the strategies alignment process [START_REF] Andres | Towards a Methodology to support the Strategies Alignment Process in Collaborative Networks[END_REF]. The guideline consists of twelve phases, hereafter briefly described. Phase 1 starts with the identification of the CN partners, willing to align their strategies. Phase 2 focuses on the enterprises' roles definition. Phase 3 continues with the collection of the data required as an input of the SAM related with the KPIs and the parameters associated (kpiixk, Δkpiixk, Δkpiixk max , Threshold_kpiixk, wixk). Phase 4 is devoted to the collection of data, from the CN enterprises, related with the strategies and the parameters associated (stris, c_stris, d1_stris, d2_stris, d4_stris). In Phase 5 the collaborative partners agree the type of collaboration to carry out in the CN. Three collaboration levels (CL) are defined depending on the data exchanged: (i) CL1, enterprises only exchange information as regards the KPIs defined and enumerated kpiik; (ii) CL2, enterprises exchange information about the KPIs and the parameters that characterize them, and the number of strategies (only the IDs of the strategies, not the definition) and the parameters that characterize them; and (iii) CL3, enterprises exchange information as regards the KPIs defined and the parameters that characterize them, and the definition of the strategies formulated and the parameters that characterize them. In Phase 6, the values of influence are estimated by each enterprise, val_stris_kpiixk. The data retrieved in Phase 4, 5 and 6 is gathered in Phase 7, by using a template. In Phase 8 the gathered data is introduced in the DMS. SAGEM allows automatically creating, in Phase 9, the SAM in the simulation software selected, AnyLogic. The resolution of the model is performed in Phase 10, and the SAM solutions are generated. The negotiation of the SAM results is performed in Phase 11, which depends on the collaboration type previously agreed. When negotiating, each enterprise selects the alternative of solution, that best fits to its requirements. The alternative of solution is exchanged with the other partners of the CN, and a negotiation process is started until the CN partners agree on the alternative of solution selected, which generates the closest performance to the optimum for each partner. In order to give the reader a better insight of the negotiation process, a scheme of the Negotiation Process for the Level 1 of Collaboration is described in [START_REF] Andres | Towards a Methodology to support the Strategies Alignment Process in Collaborative Networks[END_REF]. Finally, Phase 12 allows, after carrying out the negotiation, identifying potential appearing conflicts when activating certain strategies. In Phase 12, possible misalignments and negative-influences appearing in the alternative of solution selected are to be identified, analysed and solved.

Validation of the Proposal

The stage of verification and validation aim to assess, give credibility and accredit the proposed original work [START_REF] Kleijnen | Verification and validation of simulation models[END_REF]. In order to show the relevance of the model, method, tool and guideline proposed to deal with the strategies alignment problem a three validation elements are considered: (i) validation of the research by peer reviewed publications; (ii) development of empirical experiments; and (iii) real application of the complete approach in two networks belonging to the food (Pilot 1) and automotive industry (Pilot 2). The implementation of the proposed contribution allows identifying critical points of application; and the pilots allow showing the use that the enterprises give to the proposed contribution, as well as determining the practical relevance when applying the strategies alignment model in the CN. For the validation of the proposed approach, a real simplified use case from food industry is presented. The simulated CN consists of two enterprises, the distributor (E1) and the manufacturer (E2), each one defining two objectives (oixk) whose achievement is measured through the KPIs (kpiixk): E1 defines kpi111 and kpi121; E2 defines kpi211 and kpi221. In order to achieve the objectives defined, each enterprise formulates two strategies (stris): E1 formulates str11 and str12; E2 formulates str21 and str22. Each enterprise also defines the data related to the strategies (durations and costs) and the associated to the corresponding KPIs (minimum values, threshold and weights). The objectives and the strategies are described in Section 3, in order not to repeat we refer the reader to that section. Moreover, the enterprises have a certain budget (bi) to carry on the formulated strategies. The values of influence that each strategy has on the defined KPIs are given by the parameter val_stris_kpiixk. All the data related with the objectives and strategies defined in the food industry use case are shown in Table 4. The data depicted on the cells in dark grey correspond to the values of influence that the strategies defined in one enterprise have on the KPIs defined in the same enterprise (intra-enterprise values of influence). While the white coloured cells represent the values related to the inter-enterprise influences. In the non-collaborative scenario only the inter-enterprise values of influence will be used. Whilst in the collaborative scenario will take into consideration both intra and inter-enterprise values of influence.

Table 4. Real simplified from food industry use case: Data

In the collaborative scenario the enterprises participating take into account the influences of all the strategies formulated by the enterprises. The optimisation experiment carried out in the simulation software used (AnyLogic) generates a set of solutions, as regards the units of strategies to activate and the time in which to activate them. The values concerning the enterprise performance indicators (kpi'i) and the network performance indicator (kpi'net) are computed in the simulation experiment. The experiments have been also performed in the non-collaborative scenario, in which the decision-making is made from an isolate perspective without considering how the strategies formulated by other network enterprises affect the achievement of its objectives (performance). In Table 5 the results of both scenarios, non-collaborative (NC) and collaborative (C) are compared. The optimised solution of the collaborative scenario (using the SAM) generates, at network level, a performance significantly higher than the performance resulting from the solution obtained in the non-collaborative scenario. Moreover, the solution obtained in the non-collaborative scenario breaches the restriction of non-negativity of all the KPIs of the network (fulfilment_kpi'ixk > 0). Whereas that the solution of the collaborative scenario complies with the restriction of non-negativity being the fulfilment of all the KPIs 1.

Conclusions

The developed research aims to provide a better understanding on the ways of establishing sustainable collaborative relationships within the partners of a CN. In this regard, a complete approach consisting of a model, a method a tool and a guideline is proposed, to support the strategies alignment process, in the CN context. The complete approach allows to automatically identifying the set of strategies to be activated, and the time in which to activate them, in order to obtain maximum levels of network performance. SD method is proposed to solve the SAM, and three tools support the computation of the SAM: (i) simulation software; (ii) DMS; and (iii) SAGEN tool, that automatically builds the SAM in the simulation software. Finally, a guideline is proposed, to give the CN partners a vision of how to perform the strategies alignment process. Despite the advantages of the application of the strategies alignment approach, there is a main drawback related with the information gathering as regards the value val_stris_kpi'ixk, especially if the strategy stris has never been activated before, this parameter it is very difficult to estimate. In the light of this, network enterprises can opt for (i) estimating the parameter val_stris_kpi'ixk or (ii) waiting until the strategy (stris) is activated and measure the real value of val_stris_kpi'ixk. If the enterprise has stored the increase of the KPIs when a strategy specific strategy was activated in the past (Δkpi'ixk | stris), the enterprise can objectively compute val_strjs_kpi'ixk, for strategies activated in the same enterprise; and val_strjs_kpi'ixk for strategies active in different network enterprises.

Future research work leads to deal with the collection of the data required in an accurate way. For doing this, complementary sensitivity analysis is to be proposed in order to identify the robustness of the optimised solution obtained, resulting from the implementation of the SAM in the simulation software AnyLogic. A second future line of research leads to design a distributed multi-agent system model so that each network node is represented as an agent and simulates in its own hardware and software one part of the strategies alignment model (its own part). Moreover, other applications can be identified to the proposed work, such as supporting the partners' selection process from a collaborative perspective.

  [START_REF] Andres | A decision support system for the collaborative selection of strategies in enterprise networks[END_REF] [START_REF] Andres | Collaborative Strategies Alignment to enhance the Collaborative Network agility and resilience[END_REF]. The causal loop diagram is the graphical description that represents the system in SD. It includes all the system elements and represents the relationships among them. The causal diagram allows to qualitatively represent the behaviour of the modelled system. In order to carry out a quantitative analysis the flow diagram is constructed. The flow diagram interprets the causal loop diagram (the information and the casual relationships depicted) into a terminology that allows transcribing the equations within a simulation software. The parameters modelled in the SAM are translated for its use in the SD simulation software. Moreover the equations remain as shown in Table

Fig. 1 .Table 3 .

 13 Fig. 1. Flow diagram in SD of the Strategies Alignment Model.Table 3. Equations of the flow diagram

Table 1 .

 1 Contributions dealing with the research topic decisions alignment

Table 5 .

 5 Collaborative scenario vs. Non-Collaborative scenarios: Optimization Results

Acknowledgments. This work has been funded in part by Programa Val i+d para investigadores en formación (ACIF 2012).