4. Forecast and MPC implementation in the hybrid system management

The purpose is to report to the system operator how much power will be delivered to the grid in the next hour. Along the scenarios a priority is given, and a constraint or condition will be added to compute Ptþk∣<sup>t</sup>, changing the characteristics from Eq. (19) and recreating Pref tþk∣t .

The reported data will be the trajectory followed by the HS by means of the MPC and will have the principal characteristic of being smooth in time as to avoid any operational instability like voltage or frequency in the grid because of the influence of the wind power. Furthermore, when a trajectory is applied to the control of the HS and successfully reached, it can be demonstrated that wind power as non-dispatchable energy, when implemented in a HS, can be converted to a semi-dispatchable electric source, making the management and distribution of energy more flexible.

Initially, an algorithm to find the optimal forgetting factor (λ), by simply fitting a sequence of λ values from 0.96 to 1 was implemented, and the λ value which minimized the root mean square error (RMSE) was found for each horizon and forecast, as seen in Figure 6.

The aforementioned optimal λ is implemented to compute the forecast for the different horizons applying RLS with the forgetting factor, which results in the forecast for the different

A Proposed Energy Management System to Overcome Intermittence of Hybrid Systems Based on Wind, Solar,… http://dx.doi.org/10.5772/intechopen.76760 31

Figure 6. Optimal forgetting factor.

where M is the number of points in the average. Afterward, Pref

Ptþk∣<sup>t</sup> is computed considering that the activation vector of the FC and electrolyzer depend on the priority given by the behavior of the HS. Afterward, the forecast signal is filtered as to remove the high-frequency changes and leave a smooth signal to be used as the reference

The MPC manages the electric power of the HS and takes into account the output power, the filtered forecast, and the actual wind power, so that the control decides how much power the FC is going to produce and how much power will be directed to the electrolyzer for saving energy for future fluctuations; thus, the output power will be almost without frequency changes and minimizing in a great extent the intermittence and variability of the

4. Forecast and MPC implementation in the hybrid system management

added to compute Ptþk∣<sup>t</sup>, changing the characteristics from Eq. (19) and recreating Pref

The purpose is to report to the system operator how much power will be delivered to the grid in the next hour. Along the scenarios a priority is given, and a constraint or condition will be

The reported data will be the trajectory followed by the HS by means of the MPC and will have the principal characteristic of being smooth in time as to avoid any operational instability like voltage or frequency in the grid because of the influence of the wind power. Furthermore, when a trajectory is applied to the control of the HS and successfully reached, it can be demonstrated that wind power as non-dispatchable energy, when implemented in a HS, can be converted to a semi-dispatchable electric source, making the management and distribution

Initially, an algorithm to find the optimal forgetting factor (λ), by simply fitting a sequence of λ values from 0.96 to 1 was implemented, and the λ value which minimized the root mean

The aforementioned optimal λ is implemented to compute the forecast for the different horizons applying RLS with the forgetting factor, which results in the forecast for the different

square error (RMSE) was found for each horizon and forecast, as seen in Figure 6.

k ¼ 1, …, N is used in the MPC.

Figure 5. MPC structure.

trajectory.

30 Smart Microgrids

wind power.

of energy more flexible.

<sup>t</sup>þ6∣<sup>t</sup> is reported, and <sup>P</sup>ref

<sup>t</sup>þk∣<sup>t</sup> for

tþk∣t . horizons k as shown in Figure 7. The algorithm implemented to forecast 1 h ahead, with a resolution of 10 min, is called a six-steps-ahead forecast.

The time series models are updated in each iteration of the process as described later in this section, taking into account the new data measured every 10 min. The expressions obtained for each horizon of wind forecast (k ¼ 1, …, 6) are

$$Y\_t = 161.2 + 0.8324Y\_{t-1} + 0.0461Y\_{t-2} \tag{21}$$

$$Y\_{t+1} = 248.3 + 0.8275Y\_t - 0.00922Y\_{t-1} \tag{22}$$

$$Y\_{t+2} = 342.65 + 0.8228Y\_{t+1} - 0.0698Y\_t \tag{23}$$

$$Y\_{t+3} = 439.93 + 0.7703Y\_{t+2} - 0.0835Y\_{t+1} \tag{24}$$

$$Y\_{t+4} = 525.09 + 0.6905Y\_{t+3} - 0.0641Y\_{t+2} \tag{25}$$

$$Y\_{t+5} = 634.7 + 0.7555Y\_{t+4} - 0.1978Y\_{t+3}.\tag{26}$$

The autocorrelation function (ACF) analysis result of the short-term forecast is shown in Figure 8, indicating that the used model is suitable for this study purpose.

Figure 9 shows the RMSE as a function of the horizon k (10-min steps). The black curve is the RMSE for persistence, and the red curve is for RLS. Some improvement of the RLS over the persistence is observed, but it is beyond the scope of the study to investigate the impact of using the forecasts compared to persistence.

Figure 7. Actual and k-step ahead forecast.

The forecast is done using the following steps which are repeated every 10 min:

the last one, as to always have a 1-h-ahead forecast generation. • Report the new 1-h-ahead updated trajectory to the system operator.

measurements and managing the round-the-clock HS energy.

min Pθ, <sup>k</sup>

flow of the HS, where kk is the vector norm or <sup>ℓ</sup>

elements connected to the storage calculated by the MPC; Pref

• Update the "last trajectory," adding the last 10 min of the "new trajectory" at the end of

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33

Different EMS scenarios were tested to be applied in the interaction of the inner elements of the hybrid system, with a variety of priorities for the output power, using the proposed developed

This section presents and explains the different scenarios of control with the more significant outputs within the ones implemented, and based on these scenarios, a decision about which one is more convenient to implement can be taken, depending on the purpose of the HS, the

The computational process for all scenarios is shown in Figure 10 in a generalized way, in which constraints change in their decision-making depending on the priority for the HS of the case. The flowchart presents a never-ending iterative process, updating the historical wind

With the computed forecast and the application of the models for the different elements that integrate the HS, the objective function can be deduced and implemented to be minimized in

Pw, <sup>k</sup> <sup>þ</sup> Ppv, <sup>k</sup> � Pref

�Pnom

The MPC minimizes an objective function subjected to constraints and manages the power

on the forecasted wind power computed with Eq. (20); Pfc is the power to be generated by the FC; Pel is the power destined to the electrolyzer; ϕ represents the level of storage; ϕ<sup>0</sup> represents

� � �

N

k¼0

subject to <sup>ϕ</sup> <sup>¼</sup> <sup>X</sup>

<sup>t</sup>þk∣t, <sup>k</sup> <sup>þ</sup> <sup>P</sup>ϕ, <sup>k</sup>

� lfcPfc, <sup>k</sup> þ lelPel, <sup>k</sup> þ lst, <sup>k</sup>ϕ<sup>0</sup>

fc

ϕmin ≤ϕ ≤ϕmax

Pϕ, <sup>k</sup> ¼ Pfc, <sup>k</sup> � Pel, <sup>k</sup> 0 ≤ Pel, <sup>k</sup> 0 ≤ Pfc, <sup>k</sup>

2

ele ≤ Pϕ, <sup>k</sup> ≤ Pnom

� � �


<sup>t</sup>þk∣<sup>t</sup> is the reference trajectory based

(27)

• Update historic data.

MPC method.

the MPC:

• Run 1-h-ahead forecast.

• Compute the "new trajectory."

priorities, and output power needed.

Figure 8. ACF of the residuals.

Figure 9. RMSE as function of the horizon.

The forecast is done using the following steps which are repeated every 10 min:

• Update historic data.

Figure 8. ACF of the residuals.

32 Smart Microgrids

Figure 9. RMSE as function of the horizon.


Different EMS scenarios were tested to be applied in the interaction of the inner elements of the hybrid system, with a variety of priorities for the output power, using the proposed developed MPC method.

This section presents and explains the different scenarios of control with the more significant outputs within the ones implemented, and based on these scenarios, a decision about which one is more convenient to implement can be taken, depending on the purpose of the HS, the priorities, and output power needed.

The computational process for all scenarios is shown in Figure 10 in a generalized way, in which constraints change in their decision-making depending on the priority for the HS of the case. The flowchart presents a never-ending iterative process, updating the historical wind measurements and managing the round-the-clock HS energy.

With the computed forecast and the application of the models for the different elements that integrate the HS, the objective function can be deduced and implemented to be minimized in the MPC:

$$\begin{array}{ll}\min\_{P\_{\theta,k}} & \left\| P\_{w,k} + P\_{pv,k} - P\_{t+kl;k}^{ref} + P\_{\phi,k} \right\| \\ \text{subject to} & \phi = \sum\_{k=0}^{N} -l\_{fc} P\_{fc,k} + l\_{cl} P\_{cl,k} + l\_{st,k} \phi\_0 \\ & & \phi\_{min} \le \phi \le \phi\_{max} \\ & & -P\_{clc}^{nom} \le P\_{\phi,k} \le P\_{fc}^{nom} \\ & & P\_{\phi,k} = P\_{fc,k} - P\_{cl,k} \\ & & 0 \le P\_{cl,k} \\ & & 0 \le P\_{fc,k} \end{array} \tag{27}$$

The MPC minimizes an objective function subjected to constraints and manages the power flow of the HS, where kk is the vector norm or <sup>ℓ</sup> 2 -norm; P<sup>ϕ</sup> is the power flow from/to the elements connected to the storage calculated by the MPC; Pref <sup>t</sup>þk∣<sup>t</sup> is the reference trajectory based on the forecasted wind power computed with Eq. (20); Pfc is the power to be generated by the FC; Pel is the power destined to the electrolyzer; ϕ represents the level of storage; ϕ<sup>0</sup> represents

5. Energy management system scenarios

5.1. Smoothing the wind power

Figure 12. Actual wind and PV power.

Figure 11. Pw forecast at t = 27h30min.

Two scenarios are implemented to validate the algorithm proposed in this work. The scenarios establish priorities and have the main purpose of zero network frequency disturbance and as a

A Proposed Energy Management System to Overcome Intermittence of Hybrid Systems Based on Wind, Solar,…

The purpose of the first scenario MPC as an EMS is smoothing the wind power, employing the power generated by the FC in a prioritized way as an electric generation source. The second scenario focuses on having an as much as possible constant output power, from the HS and the

Figure 12 displays the actual active wind and PV power available, in which the intermittence with abrupt ramps is noticeable, i.e., sudden changes from 20 to 3200 W in the wind power.

In this scenario, the priority is to smooth the wind power generated by the turbine, managing

To calculate the vector Pelmax, both physical and characteristic constraints for the scenario are considered. In this scenario, the hydrogen level in the tank will be at least lower than an upper threshold level (Γelup) when charging or until the maximum of the tank is reached because of surplus of wind power, and above a lower threshold (Γeldown) of the tank, described mathematically as

must calculate Pelmax and Pfcmax considering the restrictions of the electrolyzer and the FC.

<sup>t</sup>þk∣<sup>t</sup> from Eq. (19), first one

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35

second purpose possession of a more stable and semi-dispatchable power generation.

probable "sacrifices" to accomplish it, regarding the wind power surplus.

the output power of the whole HS at every moment. To compute Pref

Figure 10. HS operation flowchart.

the last measure of H<sup>2</sup> stored; ϕmax and ϕmin are the physical limits of the storage; lel, lfc, and lst represent the impulse response of the electrolyzer; and FC and storage, respectively, based on the aforementioned models.

The constraints for the FC and electrolyzer are vectors with the physical nominal values of the elements represented by Pnom fc and Pnom ele , respectively, and implemented in the MPC. Pw, <sup>k</sup> represents the vector with the actual power (k ¼ 0) and forecasted wind power (k ¼ 1, …, N) meaning Pw <sup>¼</sup> <sup>P</sup><sup>w</sup> <sup>k</sup>¼<sup>0</sup>; <sup>P</sup>b<sup>w</sup> tþk∣t h i. Ppv, <sup>k</sup> is the vector of actual and forecasted solar radiation, being a vector of the same form as Pw. Figure 11 is explained for a better understanding of Pw, where t ¼ 27h30min represents the actual instant, being the k ¼ 0 point. The continuous line before the actual point is the historic data incorporated in the time series analysis, and the dashed line after it is the 1-h-ahead forecast representing the six-steps-ahead, mentioned in Sections 2 and 3.

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Figure 11. Pw forecast at t = 27h30min.

#### 5. Energy management system scenarios

Two scenarios are implemented to validate the algorithm proposed in this work. The scenarios establish priorities and have the main purpose of zero network frequency disturbance and as a second purpose possession of a more stable and semi-dispatchable power generation.

The purpose of the first scenario MPC as an EMS is smoothing the wind power, employing the power generated by the FC in a prioritized way as an electric generation source. The second scenario focuses on having an as much as possible constant output power, from the HS and the probable "sacrifices" to accomplish it, regarding the wind power surplus.

Figure 12 displays the actual active wind and PV power available, in which the intermittence with abrupt ramps is noticeable, i.e., sudden changes from 20 to 3200 W in the wind power.

#### 5.1. Smoothing the wind power

the last measure of H<sup>2</sup> stored; ϕmax and ϕmin are the physical limits of the storage; lel, lfc, and lst represent the impulse response of the electrolyzer; and FC and storage, respectively, based on

The constraints for the FC and electrolyzer are vectors with the physical nominal values of the

represents the vector with the actual power (k ¼ 0) and forecasted wind power (k ¼ 1, …, N)

a vector of the same form as Pw. Figure 11 is explained for a better understanding of Pw, where t ¼ 27h30min represents the actual instant, being the k ¼ 0 point. The continuous line before the actual point is the historic data incorporated in the time series analysis, and the dashed line after it is the 1-h-ahead forecast representing the six-steps-ahead,

ele , respectively, and implemented in the MPC. Pw, <sup>k</sup>

. Ppv, <sup>k</sup> is the vector of actual and forecasted solar radiation, being

fc and Pnom

the aforementioned models.

Figure 10. HS operation flowchart.

34 Smart Microgrids

elements represented by Pnom

mentioned in Sections 2 and 3.

<sup>k</sup>¼<sup>0</sup>; <sup>P</sup>b<sup>w</sup> tþk∣t h i

meaning Pw <sup>¼</sup> <sup>P</sup><sup>w</sup>

In this scenario, the priority is to smooth the wind power generated by the turbine, managing the output power of the whole HS at every moment. To compute Pref <sup>t</sup>þk∣<sup>t</sup> from Eq. (19), first one must calculate Pelmax and Pfcmax considering the restrictions of the electrolyzer and the FC.

To calculate the vector Pelmax, both physical and characteristic constraints for the scenario are considered. In this scenario, the hydrogen level in the tank will be at least lower than an upper threshold level (Γelup) when charging or until the maximum of the tank is reached because of surplus of wind power, and above a lower threshold (Γeldown) of the tank, described mathematically as

Figure 12. Actual wind and PV power.

$$P\_{el\text{max}} = \begin{cases} \widehat{P}\_{t+k\text{|t}}^w + \widehat{P}\_{t+k\text{|t}}^{pv} & \phi \le \phi\_{\text{max}} \Gamma\_{elup} \\ 0 & \phi > \phi\_{\text{max}} \Gamma\_{elup} \end{cases} \quad |\phi \le \phi\_{\text{max}} \Gamma\_{eldown} \,\text{s}.\tag{28}$$

Pfcmax <sup>¼</sup> <sup>P</sup>nom

(

where the nominal power of the FC is represented by Pnom

the FC and to help linearize the output power of the HS.

power and the power from the FC given by Pfc, <sup>k</sup> <sup>¼</sup> <sup>P</sup>nom

simultaneously, this reference will be the output power of the HS.

5.2. Prioritizing the constant output power level of the hybrid system

Pref tþk∣t

where a will take values between 0 and 1.

6. Results and discussions

fc � <sup>P</sup>b<sup>w</sup>

<sup>t</sup>þk∣<sup>t</sup> <sup>þ</sup> <sup>P</sup>bpv

tþk∣t � � Pfcmax, <sup>k</sup> <sup>&</sup>gt; <sup>0</sup>

percent of hydrogen stored for the FC to generate power; in case that the threshold cannot be met, the value of Pfcmax will be zero. Furthermore, Pfcmax is computed taking into account Pnom

to ensure that the max value asked from the FC will not be greater than the nominal power of

The maximum power of the HS in this scenario will be determined as the sum of the wind

The sum of the power generated by the FC and forecast for the wind turbine will be filtered using Eq. (20) which will smooth the flickers, resulting in the reference for the MPC (Eq. (27));

When the priority is to have an as much as possible constant output power from the HS, the resulting analysis of the previous scenario is of great importance. Because of the intermittence of wind speed, the appearance of many sags in the power forecast and actual power is common. To maintain a linear output power means the need to "sacrifice" other characteristics as, in this case,

In this scenario, the filter at the control part of the model was modified from Eq. (20) as to have more energy stored and to decrease the output magnitude to 60% (a ¼ 0:6) of the original power value. Thus, the residual 40% will be supplied to the electrolyzer and consequently will generate hydrogen

Each subsection from Section 5 focuses on the required characteristics needed to smooth and flatten the output power of the HS in order to overcome the intermittence of wind power.

When the HS is connected to the network, it will not destabilize the frequency as demonstrated in this section. The obtained results show the first week behavior of the HS, for the reader to notice in a clear way the dispatchability and reduction of intermittence in comparison to a

the magnitude of power supplied from the HS to the distribution network given by

½ �¼ <sup>k</sup> <sup>a</sup> M M X�1 j¼0

to store. The updated filtered signal will be the output power of the HS for the next hour.

system without the application of the proposed model as the actual wind power.

fc � <sup>P</sup>b<sup>w</sup>

tþk∣t .

0 Pfcmax, <sup>k</sup> ≤ 0

A Proposed Energy Management System to Overcome Intermittence of Hybrid Systems Based on Wind, Solar,…

jϕmaxΓfc ≤ ϕ

fc and Γfc is the threshold of minimum

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Ptþk∣<sup>t</sup>½ � k þ j (30)

(29)

37

fc

The hydrogen storage will be charged when the tank presents a specified level of hydrogen, represented by the lower threshold (Γeldown), bearing in mind a base amount of hydrogen in case of contingency because of the uncertainty of the wind speed. The base amount will be assumed as the hydrogen required to generate maximum power from the FC in the next 2 h, ensuring a smooth change in the output power without affecting the frequency of the network. In the charging period, the electrolyzer will use the wind power to produce hydrogen until the storage level reaches a specified upper threshold (Γelup) in which it stops charging. In this work, Γelup represents the hydrogen required for the FC to work 8 h ahead at maximum power.

Storage plays a great role when it comes to maintaining a stable output power of the HS for long periods of time. The activation and deactivation of the electrolyzer are the principal factors for maintaining a desired storage level. As seen in Figure 13, a global flowchart explains how it was applied in the model, giving an idea of how it can be modified depending on the priority of the storage required from the HS.

To ensure constant smooth output power from the HS, constraints were applied on the activation of the FC. As a result the FC generates more frequently and compensates the variability in the wind power, according to the amount of hydrogen stored. When there is no wind, the output power is determined by the nominal FC value. Given the uncertainty in the wind speed, this constraint was necessary to attain the designated power value without having to ask for backup from the grid. Therefore, the nominal value of the FC must be implemented in the MPC and in the activation of the FC. To compute Pref <sup>t</sup>þk∣<sup>t</sup> from Eq. (20), the activation vector of the FC is then calculated by

Figure 13. Activation of electrolyzer.

A Proposed Energy Management System to Overcome Intermittence of Hybrid Systems Based on Wind, Solar,… http://dx.doi.org/10.5772/intechopen.76760 37

$$P\_{f\text{max}} = \begin{cases} P\_{fc}^{\text{nom}} - \left(\widehat{P}\_{t+k|t}^{\text{pv}} + \widehat{P}\_{t+k|t}^{\text{pv}}\right) & P\_{f\text{max},k} > 0\\ 0 & P\_{f\text{max},k} \le 0 \end{cases} \quad |\phi\_{\text{max}}\Gamma\_{f\in} \le \phi\tag{29}$$

where the nominal power of the FC is represented by Pnom fc and Γfc is the threshold of minimum percent of hydrogen stored for the FC to generate power; in case that the threshold cannot be met, the value of Pfcmax will be zero. Furthermore, Pfcmax is computed taking into account Pnom fc to ensure that the max value asked from the FC will not be greater than the nominal power of the FC and to help linearize the output power of the HS.

The maximum power of the HS in this scenario will be determined as the sum of the wind power and the power from the FC given by Pfc, <sup>k</sup> <sup>¼</sup> <sup>P</sup>nom fc � <sup>P</sup>b<sup>w</sup> tþk∣t .

The sum of the power generated by the FC and forecast for the wind turbine will be filtered using Eq. (20) which will smooth the flickers, resulting in the reference for the MPC (Eq. (27)); simultaneously, this reference will be the output power of the HS.

#### 5.2. Prioritizing the constant output power level of the hybrid system

When the priority is to have an as much as possible constant output power from the HS, the resulting analysis of the previous scenario is of great importance. Because of the intermittence of wind speed, the appearance of many sags in the power forecast and actual power is common. To maintain a linear output power means the need to "sacrifice" other characteristics as, in this case, the magnitude of power supplied from the HS to the distribution network given by

$$P\_{t+k|t}^{ref}[k] = \frac{a}{M} \sum\_{j=0}^{M-1} P\_{t+k|t}[k+j] \tag{30}$$

where a will take values between 0 and 1.

Pelmax <sup>¼</sup> <sup>P</sup>b<sup>w</sup>

36 Smart Microgrids

on the priority of the storage required from the HS.

the MPC and in the activation of the FC. To compute Pref

of the FC is then calculated by

Figure 13. Activation of electrolyzer.

(

<sup>t</sup>þk∣<sup>t</sup> <sup>þ</sup> <sup>P</sup>bpv

<sup>t</sup>þk∣<sup>t</sup> <sup>ϕ</sup> <sup>≤</sup>ϕmaxΓelup 0 ϕ > ϕmaxΓelup

The hydrogen storage will be charged when the tank presents a specified level of hydrogen, represented by the lower threshold (Γeldown), bearing in mind a base amount of hydrogen in case of contingency because of the uncertainty of the wind speed. The base amount will be assumed as the hydrogen required to generate maximum power from the FC in the next 2 h, ensuring a smooth change in the output power without affecting the frequency of the network. In the charging period, the electrolyzer will use the wind power to produce hydrogen until the storage level reaches a specified upper threshold (Γelup) in which it stops charging. In this work, Γelup represents the hydrogen required for the FC to work 8 h ahead at maximum power.

Storage plays a great role when it comes to maintaining a stable output power of the HS for long periods of time. The activation and deactivation of the electrolyzer are the principal factors for maintaining a desired storage level. As seen in Figure 13, a global flowchart explains how it was applied in the model, giving an idea of how it can be modified depending

To ensure constant smooth output power from the HS, constraints were applied on the activation of the FC. As a result the FC generates more frequently and compensates the variability in the wind power, according to the amount of hydrogen stored. When there is no wind, the output power is determined by the nominal FC value. Given the uncertainty in the wind speed, this constraint was necessary to attain the designated power value without having to ask for backup from the grid. Therefore, the nominal value of the FC must be implemented in

jϕ ≤ϕmaxΓeldown:

<sup>t</sup>þk∣<sup>t</sup> from Eq. (20), the activation vector

(28)

In this scenario, the filter at the control part of the model was modified from Eq. (20) as to have more energy stored and to decrease the output magnitude to 60% (a ¼ 0:6) of the original power value. Thus, the residual 40% will be supplied to the electrolyzer and consequently will generate hydrogen to store. The updated filtered signal will be the output power of the HS for the next hour.

#### 6. Results and discussions

Each subsection from Section 5 focuses on the required characteristics needed to smooth and flatten the output power of the HS in order to overcome the intermittence of wind power.

When the HS is connected to the network, it will not destabilize the frequency as demonstrated in this section. The obtained results show the first week behavior of the HS, for the reader to notice in a clear way the dispatchability and reduction of intermittence in comparison to a system without the application of the proposed model as the actual wind power.

#### 6.1. Active power delivered from the HS

The designed HS system is to be connected to the distribution network as mentioned before. From results, it can be seen how the unpredictability from the wind power (Figure 12) is solved as shown in the HS active output power obtained with the proposed AEMS model (Figure 14). The network frequency will not be affected considering that the HS output power can be dispatched according to the final user needs and the information can be sent to the system operator with 1 hour prior.

Figure 14 presents the HS output power for both scenarios. Results from the first scenario show that because of the periods of charge, the HS output power has periods of no power delivered to the network and the implementation of the constraints required to activate the electrolyzer is also the moment of no power generated by the HS resulting from the electrolyzer consuming all the power generated by the wind turbine with the purpose of storing energy as hydrogen. Note in Figure 14 that the response obtained from the second scenario, when compared to the first scenario, is flatter, more stable, and more constant as it was expected.

The storage constantly maintains a minimum level of hydrogen as shown in Figure 15; thanks to this, the FC can work longer permitting a smooth or flat output from the HS. Comparing the production and consumption of hydrogen from both scenarios, results show that there is 16% less production of H<sup>2</sup> and 5.6% less consumption of it in the second scenario at the end of the week. However, the storage of the second scenario presents 45% more hydrogen in comparison with the first scenario where it is almost empty as seen in Figure 15. These results are a consequence of the level of power required in each scenario. The second delivers a lower level of active power to possess a more constant output at all time, in contrast to the first scenario, which due to the level of power delivered needs periods of zero output

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The storage from the first scenario always has a good level of hydrogen ready to be used by the FC in a more efficient way as in Figure 15, but clearly because of the charging periods of

The hydrogen consumption is constant, and, therefore, the tank will not reach a high level of storage, tending instead to maintain a lower mean of hydrogen, as shown in Figure 15.

Taking into account the importance of charging periods for the storage of hydrogen and of keeping an output power level as linear as possible, decreasing the output power level to generate hydrogen was required for longer periods of time and for the FC to work as

The FC can act as a means of contingency to smooth the output power thanks to the hydrogen stored in the tank, and because of the decision-making based on the forecast that gives information about future possible fast changes in wind power, it is possible the use the FC as

The surplus of energy from the high peaks of wind power is saved as hydrogen for future implementation, making smooth wind power and semi-dispatchable HS-generated power

backup power to have an almost linearized power level at the output of the HS.

a measure for preventing disturbance of the frequency of the network.

hydrogen, the output power drops to zero at the output of the HS.

power as to generate H<sup>2</sup> to storage.

Figure 15. Behavior of hydrogen storage in the different scenarios.

6.3. Findings of the method

possible.

Observing the results of the different scenarios, the HS output power can be adjusted to meet a specific load which modifies the trajectory followed by the MPC and the constraints applied. In addition, because of the flexibility of the MPC, the constraints that rule the charge and discharge of the hydrogen storage can be modified considering the purpose of the HS.

The reduction of volatility of the power delivered to the distribution network is noticeable, comparing the wind power from Figure 12 and the output power of the HS from Figure 14.

#### 6.2. Hydrogen storage behavior

The behavior of the hydrogen generated by the electrolyzer and consumed by the FC in the different scenarios can be seen in Figure 15, where it can be noticed that when the electrolyzer works the hydrogen tank is charged and when the FC works the storage level of hydrogen will decrease.

Figure 14. Total power generated by the HS in both scenarios.

A Proposed Energy Management System to Overcome Intermittence of Hybrid Systems Based on Wind, Solar,… http://dx.doi.org/10.5772/intechopen.76760 39

Figure 15. Behavior of hydrogen storage in the different scenarios.

6.1. Active power delivered from the HS

38 Smart Microgrids

system operator with 1 hour prior.

6.2. Hydrogen storage behavior

Figure 14. Total power generated by the HS in both scenarios.

decrease.

The designed HS system is to be connected to the distribution network as mentioned before. From results, it can be seen how the unpredictability from the wind power (Figure 12) is solved as shown in the HS active output power obtained with the proposed AEMS model (Figure 14). The network frequency will not be affected considering that the HS output power can be dispatched according to the final user needs and the information can be sent to the

Figure 14 presents the HS output power for both scenarios. Results from the first scenario show that because of the periods of charge, the HS output power has periods of no power delivered to the network and the implementation of the constraints required to activate the electrolyzer is also the moment of no power generated by the HS resulting from the electrolyzer consuming all the power generated by the wind turbine with the purpose of storing energy as hydrogen. Note in Figure 14 that the response obtained from the second scenario, when compared to the first

Observing the results of the different scenarios, the HS output power can be adjusted to meet a specific load which modifies the trajectory followed by the MPC and the constraints applied. In addition, because of the flexibility of the MPC, the constraints that rule the charge and

The reduction of volatility of the power delivered to the distribution network is noticeable, comparing the wind power from Figure 12 and the output power of the HS from Figure 14.

The behavior of the hydrogen generated by the electrolyzer and consumed by the FC in the different scenarios can be seen in Figure 15, where it can be noticed that when the electrolyzer works the hydrogen tank is charged and when the FC works the storage level of hydrogen will

discharge of the hydrogen storage can be modified considering the purpose of the HS.

scenario, is flatter, more stable, and more constant as it was expected.

The storage constantly maintains a minimum level of hydrogen as shown in Figure 15; thanks to this, the FC can work longer permitting a smooth or flat output from the HS.

Comparing the production and consumption of hydrogen from both scenarios, results show that there is 16% less production of H<sup>2</sup> and 5.6% less consumption of it in the second scenario at the end of the week. However, the storage of the second scenario presents 45% more hydrogen in comparison with the first scenario where it is almost empty as seen in Figure 15. These results are a consequence of the level of power required in each scenario. The second delivers a lower level of active power to possess a more constant output at all time, in contrast to the first scenario, which due to the level of power delivered needs periods of zero output power as to generate H<sup>2</sup> to storage.

The storage from the first scenario always has a good level of hydrogen ready to be used by the FC in a more efficient way as in Figure 15, but clearly because of the charging periods of hydrogen, the output power drops to zero at the output of the HS.

The hydrogen consumption is constant, and, therefore, the tank will not reach a high level of storage, tending instead to maintain a lower mean of hydrogen, as shown in Figure 15.

#### 6.3. Findings of the method

Taking into account the importance of charging periods for the storage of hydrogen and of keeping an output power level as linear as possible, decreasing the output power level to generate hydrogen was required for longer periods of time and for the FC to work as backup power to have an almost linearized power level at the output of the HS.

The FC can act as a means of contingency to smooth the output power thanks to the hydrogen stored in the tank, and because of the decision-making based on the forecast that gives information about future possible fast changes in wind power, it is possible the use the FC as a measure for preventing disturbance of the frequency of the network.

The surplus of energy from the high peaks of wind power is saved as hydrogen for future implementation, making smooth wind power and semi-dispatchable HS-generated power possible.
