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Coupling an adaptive protection system with an energy management system for microgrids
The Electricity Journal 32 (2019) 106675
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Coupling an adaptive protection system with an energy management system for microgrids
T
Oscar Núñez-Mataa,*, Rodrigo Palma-Behnkeb, Felipe Valenciab, Alexander Urrutia-Molinab, Patricio Mendoza-Arayab, Guillermo Jiménez-Estévezb,c a
School of Electrical Engineering, University of Costa Rica, San Pedro, Costa Rica Energy Center, Department of Electrical Engineering, Faculty of Mathematical and Physical Sciences, University of Chile, Santiago, Chile c Department of Electrical and Electronics Engineering, Universidad de los Andes, Bogotá, Colombia b
A R T I C LE I N FO
A B S T R A C T
Keywords: Microgrids Optimization Distributed power generation Uncertainty Energy management
Energy Management Systems (EMS) are control schemes in charge of defining the optimal scheduling of dispatchable units in a microgrid. Output set points from the EMS could be exploited in other control loops that may benefit from the microgrids expected behavior improving their operation. In this context, this paper proposes a novel coupled operation of an EMS with an Adaptive Protection System (APS), such that the information of the EMS serves to compute the parameters of the protective devices deployed in a microgrid. This aspect is formalized in the present proposal based on already developed APS and EMS frameworks. To evaluate the performance of the proposal, the microgrid installed in Huatacondo, north of Chile, was used as a test-bed. The obtained results indicate that considering the information provided to the APS form the EMS improves the overall performance of the microgrid in case of failure, despite the operating conditions. This also indicates that moving towards an integrated design of the EMS and APS might enhance the behavior (robustness) of a microgrid (as a whole) in case of failures or contingencies.
1. Introduction The design of adaptive protection systems (APS) for microgrids have recently gained high interest in the research community due to i) the rising use of microgrids for electrification purposes (Parhizi et al., 2015; Khodayar, 2017), ii) the particular features of microgrids that make that commonly used methodologies for the design of protection schemes in power grids might fail (Alabdulwahab and Shahidehpour, 2016; Laaksonen, 2010), and iii) the capabilities of APS to deal with the variability of the operating features of microgrids, and thus guarantee an adequate operation of the protection devices (Habib et al., 2017). In general, microgrids represent a combination of information and communication technologies together with electric power sources and loads, into a unified active power system (Panwar et al., 2012; Zamani et al., 2013; Lidula and Rajapakse, 2011). A microgrid is able to operate connected to a main grid or in isolated mode (Teimourzadeh et al., 2016; Olivares et al., 2014). The presence of the main grid and more than one distributed energy resource (DER) requires the control of the energy flow among the various sources. Then energy management systems (EMS) have been designed to control the energy flow so that: i) the continuity in the load supply is ensured; and, ii) the cost of the ⁎
Corresponding author. E-mail address: [email protected] (O. Núñez-Mata).
https://doi.org/10.1016/j.tej.2019.106675
1040-6190/ © 2019 Elsevier Inc. All rights reserved.
energy production is minimized (Olatomiwa et al., 2016). Despite the different operating conditions of microgrids, the variability introduced by distributed energy resources, loads, and the changes in the operating point defined by the operation of the EMS, an appropriate response of the protection devices is required (Xu et al., 2015). In this context, APS appear as one of the most suitable solutions for the design of protection systems for microgrids. However, it has been found that APS require high information exchange for their implementation. One way to overcome this issue is to take advantage of the information that the EMS has to reduce the communication burden/ complexity in the implementation of APS in microgrids. Roughly speaking, the EMS is a controller that defines the operating points of energy resources taking into consideration the expected generation of the distributed energy resources (e.g., solar and wind), the topology of the distribution grid, the expected energy demand, and in case of grid connected microgrids the information of the external grid (e.g., market price, and operating condition of the main grid) (Núñez-Mata et al., 2018; Alabdulwahab and Shahidehpour, 2016; Meng et al., 2016). Therefore, part of this information can be exchanged with the APS so that the implementation of an additional communication network for the APS is minimized or avoided (Coffela et al., 2014; Lu et al., 2016).
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In the specialized literature several approaches for the implementation of APS in microgrids have been already reported. For example, in (Ustun et al., 2012) an APS based on overcurrent protective devices was proposed. This protection system makes use of an extensive dedicated communication network to monitor a microgrid and thus update the settings of the protection devices. In addition to overcurrent protection devices, in (Zarei and Parniani, 2017) an APS that also included low voltage protection devices was proposed. The tuning of the settings of both overcurrent and low-voltage protection devices was done by means of an extensive search of the parameter values that maximize the performance of the microgrid. A dedicated communication network for the APS is required. Nevertheless, considering only a fixed set of parameters its complexity is reduced. This method is not scalable and therefore it application is constrained to just small microgrids nonetheless (microgrids with few lines, load centers, and energy sources). By contrast, in (Coffela et al., 2014) an APS for overcurrent protection devices that interacts with and EMS was described. In this case, the EMS sends the topology of the grid, the status of the distributed generation, and the operating mode. Based on this information the APS updated the settings of the overcurrent protection devices. Despite the interaction between an EMS and an APS, this approach did not discuss how they must operate coordinately so that unexpected behaviors are avoided. Thereby, the complexities often arising from the integration of variable generation technologies (such as wind and solar) were not included. Finally, in (Oudalov et al., 2014) also a real-time approach for the update of the parameters of overcurrent protection devices was described. In this approach, the APS interacts with an EMS to include the information about changes in the topology into the computation of the parameters of the protection devices. Specifically, the EMS communicates the status of the distributed generators, i.e., which of them are connected and injecting power to the grid. Nevertheless, as in (Coffela et al., 2014) no discussion about how the APS and the EMS must operate coordinately was included. As it can be seen from the literature review, the interaction between APS and EMS to reduce the communication complexity in the implementation of APS in microgrids was discussed yet. But, notwithstanding the efforts to define such interaction, how the APS and the EMS must be coordinated to prevent unexpected behaviors still remains as an open research question. The current paper presents a methodology for the coupled operation of APS and EMS in microgrids. The proposed methodology determines the times at which the information between APS and EMS must be exchanged, and hence guarantee a coordinated operation between both systems. The effectiveness of the proposed methodology is tested through simulations of the microgrid installed in Huatacondo, north of Chile. The remainder of this paper is organized as follows: the APS for microgrids used to formulate the proposed methodology is described in Section 2; Section 3 presents the proposed methodology; Section 4 presents the test-case; finally, the conclusions and future work are presented in Section 5.
Fig. 1. Single line diagram of a generic microgrid.
energy from DER, coordinating the energy flow to and from the main grid in grid-connected mode, and ensuring the continuity of the load supply in isolated mode (Xu et al., 2014; Olatomiwa et al., 2016). Several authors consider that this ability will contribute to improve the resiliency and reliability of electrical systems, by avoiding disruptions and serve as resources for fast recovery during main grid disturbances (Hooshyar and Iravani, 2017),(Alabdulwahab and Shahidehpour, 2016). However, the offered improved features will be seriously affected if the microgrid is not properly protected during fault events that occur within its own boundaries (Hooshyar and Iravani, 2017). 2.2. Abnormalities in microgrids In (Hare et al., 2016) a complete description of fault and failure modes, causes and effects in microgrids is presented. In this work the distribution grid and the DER are selected to define the protection zones. The protection of the PCC will not be considered in this work since different proposals can be found in the literature for this purpose. Firstly, at the distribution grid level, short-circuit faults are characterized by (Mirsaeidi et al., 2014; Brahma et al., 2014): 1 Possible bi-directional flows. 2 Lower fault current level in isolated mode. 3 Similar fault current levels at different locations along the lines. A variety of methods have been published in the last years to address microgrid protection issues presented above (Brearley and Prabu, 2017). In this proposal, an adaptive protection approach is employed based on a new protection scheme. Secondly, in the DER, the different faults will be characterized depending on the type of unit. In this work, we focus in protecting PV plants, because the possible failure modes in PV plants might be difficult to detect by conventional protection devices. A similar approach can be extended to other generation units or storage devices. In PV plants, the fault currents are highly dependent on:
2. Formulation of protection system framework This section describes the APS framework considered in this paper. Without loss of generality, the approach proposed by the authors in (Núñez-Mata et al., 2018) was selected. Despite of this particular selection, the methodology proposed in this paper can be easily adapted to any other APS proposed in the literature.
1 2 3 4 5 6
2.1. Overview Fig. 1 shows the single-line diagram of a generic microgrid. DER are divided into converter and rotating-machine coupled units. Protection devices are deployed through the microgrid among grid, distribution lines, generation units, and loads. Microgrids provide any possible means of generation and utilizing 2
The The The The The The
location of the fault within the PV array. fault impedance. type of fault. irradiance level at the moment of the fault. use of blocking diodes. influence of the Maximum Power-Point Tracker (MPPT).
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2.4. Protection of the DC zone of PV plants
With the increasing interest in monitoring PV plants, different works related to the diagnostic of these systems are emerging (Triki-lahiani et al., 2018). In this proposal, a new method for the diagnostic and fault detection in PV plants in microgrids is used. Finally, in this work it is assumed that other DER present into the microgrid include self-protection against faults such as short circuit, overload, overcharge, deep discharge, and overheating. The protective method applied in each protection zone of the microgrid is established considering the aforementioned features. These methods are described below.
The protection method for the DC zone of PV plants was presented by the authors in (Nuñez-Mata et al., 2017). The proposed method works together with conventional protection devices. The proposed method works together with conventional protection devices. This model-based method estimates the PV plant condition based on real measurements and model generated data and compares the measurements with the theoretical estimates. Together with the electrical analysis of the PV plant, the diagnosis is realized by means of thermal considerations using the energy balance equation (Hu et al., 2013). The purpose of the energy balance equation is to connect the electrical and thermal characteristics of a PV panel. Using the proposed diagnostic parameters, a PV plant can be diagnosed with a per-module resolution, classifying the operation condition as:
2.3. Protection of the AC distribution grid A robust optimization strategy is used to adjust and coordinate the protective devices. These devices are based on a protection scheme, in which two directional elements are operating in an interleaved manner. These are: i) directional overcurrent element; and, ii) undervoltage element. The protective device requires the internal coordination of both elements, so that the former detects any overcurrent in the microgrid being protected, and the later serves as a backup element in case of low fault-current contingencies. Low-voltage devices are selected as backup devices since (often) low fault-current contingencies might cause a decrease in the voltages in the grid. We establish an optimization problem with the objective of minimizing a certain cost function defined as ‖Ax − b‖, considering some uncertainty in the data of A and b . It is assumed that A and/or b are random variables belonging to mxn and mx1 respectively, and xε nx1. The scenario approach is used to structure uncertain input data. A scenario represents a possible realization of the uncertain input data. A specific case of the above is to model the variation in the matrix A by means of a worst-case approach (Boyd and Vandenberghe, 2004). Thus, we have a finite set of k fault scenarios defined as: A = {A1 , …, Ak } . The optimization problem is defined as:
min [ max ‖Ai x − b‖] i = 1,..., k
1 Normal: in this condition the measured and estimated values are within allowable limits. 2 Abnormal-not damaged: this condition is considered a recoverable fault. 3 Abnormal-damaged: this condition is considered an unrecoverable fault. The thresholds for the diagnostic parameters can be experimentally obtained as was shown in (Nuñez-Mata et al., 2017). Following this approach, it is possible to detect the following fault conditions in PV modules: i) open circuit; ii) line-to-line or line-to-ground short-circuit; iii) shading or excessive dust condition; iv) internal failure in the module; v) failure in bypass diode; and, vi) advanced degradation. 3. Methodological proposal for a coupled EMS-APS The current section presents the proposed methodology for the coupled operation between an APS and an EMS in microgrids. The EMS presented by the authors in (Palma et al., 2013) is selected to illustrate the proposed methodological approach. It involves the following main elements that must be included in the design of this type of systems: a prediction of the generation capacity of the variable generation technologies, a prediction of the energy demand, an estimation of the operating conditions of the energy storage system available, a measurement of the current operation state of the conventional generation technologies, an strategy to deal with the uncertainty in the expected operating conditions of the microgrid, and an optimization method that combines all the aforementioned information to determine the connection/disconnection of generation units and the power to be provided by the connected units. Fig. 2 displays a block diagram of the EMS approach described in (Palma et al., 2013). In this figure PSmax and PSmin are the expected maximum and minimum values of the solar power; PEmax and PEmin are the expected maximum and minimum values of the wind power; PLmax and PLmin are the expected maximum and minimum values of the energy load; ESOC , Vi , and Ii are the battery charge, the output voltage, and the output current of the battery energy storage system; Bgi is the status of the diesel generator (on/off). The EMS provides the power references for PS and PE (solar and wind production); PD for the diesel generator; and PI for the battery storage system (positive or negative). PUS is the expected non-supplied power and SL is the signal used for the demand response. Note that in Fig. 2 the EMS already has information that is required by the APS described in section 2. Therefore, there is an opportunity to take advantage of this fact to reduce the complexity in the communication network of a microgrid with these two systems. Indeed, the implementation of dedicated communication networks for each of these systems could be partially avoided. In this regard, in this paper it is proposed to exchange specifically the forecasts, the weather parameters, and the commitment and dispatch of the generation units used
(1)
We can cast the problem in epigraph form as:
mint x,t
s. t . ‖Ai x − b‖ ≤ t , i = 1, …, k
(2)
which can be solved in a variety of ways, with t as an auxiliary variable. If z (s, pk ) denotes the cost function related to the protection coordination problem in microgrids, where s represents the protection device settings; s ∈ S , S represents the set of permissible settings; and pk represents the fault conditions, pk ∈ P , k = 1, …, K , with K being the maximum number of fault events considered, and P the finite set of scenarios. The coordination problem can be established as:
mint s, t
s.t.
z (s, pk ) ≤ t , ∀ k simin ≤ si ≤ simax (bounds of the setting of the i protective element) Timin ≤ Ti ≤ Timax (bounds of the operating time of the i protective device) Ti = f (si ) (characteristic function of the i protective element)
h (T ) ≥ 0 (coordination criteria)
(3)
Eq. (3) can be particularized to the problem of selecting the settings for a coordinated operation of directional overcurrent and undervoltage protection elements. The way in which the optimization problem is solved is explained in (Núñez-Mata et al., 2018). 3
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Fig. 2. Configuration of an EMS for microgrids based on a rolling horizon strategy.
Fig. 3. Proposed interaction between both APS and EMS in microgrids.
operation of the microgrid.. Then, at time t1 the APS executes a diagnosis of the operating conditions of the DER in the microgrid. For such diagnosis, the EMS sends the forecasts, the weather parameters, and the commitment and dispatch of the generation units to the APS. After that, at time t2 the APS made a decision about the tripping or not of the DER currently in operation in the microgrid At time t3 the APS informs to the EMS about the operating status of the DER in the microgrid, so that this information is available in the next computations of the EMS. At this time, there is also a transition in the coupled operation of the APS and the EMS. This transition is defined by the decision about tripping or not any DER committed and dispatched by the EMS during the black-start procedure. If a tripping decision is made, the pre-defined settings charged in the APS are sending to the protection devices. Otherwise, the APS waits until time t6 to compute the new set of settings. At times t4 and t5 the EMS computes the unit commitment and the economic dispatch of the generation units. Here, there is another transition in which the EMS informs the updated commitment and dispatch of the units to the APS and goes to time t8 . At time t6 the APS analyze again the operating conditions of the microgrid. This analysis also generates a transition: if a change in the operating conditions is detected, then the APS goes to time t7 ; otherwise, the APS goes to time t8 . At time t7 the APS updates the settings of the protection devices following the procedure introduced in Section 2, and inform to the EMS about the changes in the topology of the microgrid. Finally, at time t8 both the APS and the EMS re-start their corresponding procedures and go to time t1. It is important to notice that at time t8 the initial states for the APS and the EMS are also updated and are equal to those obtained during the execution of all procedures shown in Fig. 4. The reason for this selection is that whether a communication failure is detected, both
in (or generated by) the EMS. Furthermore, it is also proposed to exchange the microgrid topology and the status of the DER used by the APS to compute the settings of the protection devices. The remaining information required by the EMS and by the APS is taken directly from the microgrid through the devises/sensors implemented in the microgrid. Fig. 3 shows the proposed interaction between the APS described in Section 2 and the EMS presented in (Palma et al., 2013). The flows of information from/to the APS and the EMS shown in Fig. 3 are proposed because of the following factors:
• The APS require the forecasts and weather parameters to generate •
the uncertainty set associated with the current operation of the microgrid. Furthermore, the APS also require information about the committed units and their power injections to compute. The EMS requires the topology of the microgrid and the status of the DER to compute the power to be injected by the generation units so that the operating costs are reduced.
However, it is important to remark that the interaction presented in Fig. 3 not only applies for the specific APS and EMS introduced in this paper. Such interaction is general enough to be extended to any APS and EMS implemented in a microgrid. In addition to the interaction between the APS and the EMS, an adequate synchronization with both systems is required to guarantee a proper operation of the microgrid as a whole. With this aim, the timetable shown in Fig. 4 is proposed to carry out the individual calculations in the APS and the EMS. At the beginning, i.e., at time t0 both systems (the APS and the EMS) are in a safe mode. That is, a set of predefined settings for the protection devices are charged in the APS, and the EMS is ready to execute the black-start procedure to initiate the 4
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Fig. 4. Coordinated operation of a coupled EMS-APS for microgrids.
setting are installed in the AC distribution grid. Besides, the PV plants include fuses to protect the DC zone. This protection system has problems clearing short-circuit faults, mainly, due to the significantly low fault current level during pure inverter operation, as was demonstrated in (Hare et al., 2016).
systems go to their corresponding safe-mode. That is, they keep the protection settings and the commitment and economic dispatch unchanged until the communication is restored. Furthermore, an alarm is displayed to make aware the operator of the microgrid about the anomalies taking place in the communications. This enhances the robustness of the microgrid to failures in the communication networks. In addition, going besides the interaction between the APS and the EMS, and proposing a time-scheduling for the implementation of this coupled operation significantly differentiates the proposed approach from those reported in (Ustun et al., 2012; Ates et al., 2016; Zarei and Parniani, 2017; Oudalov et al., 2014). In fact, until the literature review allowed knowing, this is the first attempt to propose a timetable to synchronize the operation of both an APS and an EMS. Moreover, such a coordinated operation takes advantage of the synergies of both systems to i) reduce the complexity of the communication network, ii) improve the performance of the EMS by having updated information about the status of the DER, and iii) enhance the adjustment of the settings performed by the APS through the exchange of information about the expected operation of the microgrid and its associated uncertainty. Next, the proposed methodology for the coupled operation between an APS and an EMS is assessed in the Huatacondo microgrid.
4.2. Criteria for the design of the EMS-APS for the ESUSCON microgrid Statistics for fault occurrence are not currently available for the ESUSCON microgrid. There are only occasional fault reports, which do not allow for the establishment of a model of likelihood of a fault happening, according to the different conditions. Given the uncertainty of the data, the reliability of the current protection system cannot be studied and ensured. For coordinating the EMS and APS, the following steps were considered:
• The APS executes the diagnostic procedure of each DER, to assess the operating condition and detect faults. • The EMS optimizes the system operation using forecast data and the related operating constraints • The EMS sends the data and information about the operation of the microgrid to the APS. • The APS decides if an update of the settings of the Protective Devices
4. Study case and discussion In this section, the fault simulation results obtained with a coupled operation of an APS and the EMS described in Section 3 are presented.
(PD) is required.
Thus, the protection system can handle the possible variability inside the 15 min’ period.
4.1. Details of the test-bench The application test was done using real data of an existing off-grid PV microgrid installed in Huatacondo, a settlement located at the Atacama Desert, Chile, also called the ESUSCON microgrid (by the acronym in Spanish of Electricidad Sustentable Cóndor) (Palma et al., 2011). The low voltage distribution grid comprises overhead power lines, with circuits in radial and meshed configuration, with a line-to-line voltage of 380[V]/50[Hz]. The microgrid considers the following DER: a 120[kW] Diesel generator; a 40[kW] lead-acid BESS with the corresponding bi-directional inverter; and two PV plants (50[kWp] and 10[kWp]) each with its own inverter. Fig. 5 shows the single line diagram. The energy sources are coordinated by means of an EMS based on approach presented in (Palma et al., 2013). The EMS oversees the energy sources defining the power setpoints for the Diesel generator and the BESS. Regarding the current protection system, circuit breakers with fixed
4.3. Obtained results and discussion To evaluate the coupled operation of the EMS-APS, two different operating conditions were selected as follows: 1 Operating condition at 2 PM: it is characterized by a low demand, which is supplied exclusively by the PV plants while the BESS is being charged. 2 Operating condition at 11 PM: it is characterized by a high demand. The Diesel generator mainly supplies the demand, and the BESS is being charged. Consequently, distinct protection settings are expected for both cases. For comparison purposes, a commonly used methodology (A) was applied to solve the coordination problem in the operating condition at 2 PM (Raj et al., 2014). In the proposed alternative methodology (B), 5
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Fig. 5. Single line diagram of the ESUSCON microgrid.
both methodologies (A and B) are shown in Tables 1 and 2. The time dial settings were defined as integer variables as part of the optimization problem, considering that the PD settings can take discrete values. In this study case, time dial settings of the overcurrent and undervoltage protection elements are adjusted in discrete steps of 0,01 [s], and the minimum values are: 0,05 [s] for overcurrent element and 0,00 [s] for undervoltage element. As shown in Table 1, the time dial settings for the overcurrent devices are lower at the 2 PM condition, compared to the 11 PM condition, which means that the methodology A selected a lower curve given the low fault currents. For undervoltage devices, the settings are higher at the 11 PM condition, since at this condition, the high fault currents allow them to be cleared by the overcurrent element. Besides, the settings of the devices obtained with the alternative methodology A were slightly lower than or the same as those of the proposed methodology B. However, the coordination margins are narrower in the methodology A, compared to the methodology B (proposed methodology). RMS fault simulations were performed to compare the effectiveness of both methodologies. The following short-circuit faults were made: three-phase (3 PH), and phase-to-phase (L-L), with zero and maximum
the protection coordination problem is formulated as a linear programming method, related with the operating time T of the PD, as follows: N
MinT =
M
L
X
∑ ∑ ∑ ⎛⎜Tijlp + ∑ Tijlxijl⎞⎟ i=1 j=1 l=1
⎝
r
x=1
(19)
⎠
where N is the total number of PD, with i as the identifier of each one of them; M is the total number of faults considered, with j as the identifier of each one of them; and, L is the total number of fault locations, with l as the identifier of each one of them. The superscript p corresponds to the primary PD, while rx represents the backup PD, with X denoting the r total number of backup PD. Tijlp and Tijlxijl are the operating times of primary PD p and backup PD rx , respectively, for the near-end fault. The constraints used in the alternative methodology were the same ones that were raised for the proposed methodology (B). Besides, the alternative methodology was applied considering a single operation scenario (base). The aim was to compare the performance of both methodologies clearing different faults using RMS simulations. The results obtained of Table 1 Obtained results for PD settings in two operating conditions. Bus No.
PD No.
Settings for 2 PM with (B) p
T1 T2 T3
T5
T8
T9
PD1.3 PD2.1 PD2.2 PD3.1 PD3.2 PD3.3 PD5.1 PD5.2 PD5.3 PD5.4 PD8.1 PD8.2 PD8.3 PD9.1 PD9.2 PD9.3
Settings for 2 PM with (A) p
Settings for 11 PM with (B)
Ii [A]
TDSi [s]
DJ [s]
Ii [A]
TDSi [s]
DJ [s]
Iip [A]
TDSi [s]
DJ [s]
60 40 40 40 40 40 40 25 10 10 15 10 40 15 40 10
0,08 0,05 0,16 0,05 0,05 0,07 0,05 0,06 0,05 0,05 0,05 0,05 0,13 0,09 0,10 0,05
0,14 0,07 0,14 0,05 0,02 0,05 0,12 0,04 0,03 0,03 0,07 0,03 0,13 0,05 0,14 0,03
60 40 40 40 40 40 40 25 10 10 15 10 40 15 40 10
0,07 0,05 0,10 0,05 0,05 0,07 0,06 0,06 0,05 0,05 0,05 0,05 0,08 0,09 0,05 0,05
0,06 0,05 0,16 0,03 0,03 0,05 0,09 0,04 0,03 0,03 0,08 0,03 0,06 0,05 0,11 0,03
125 70 70 70 70 70 70 70 10 30 70 10 70 70 70 10
0,08 0,07 0,17 0,05 0,05 0,07 0,08 0,05 0,05 0,05 0,05 0,05 0,14 0,08 0,11 0,05
0,19 0,17 0,12 0,10 0,02 0,17 0,30 0,12 0,08 0,09 0,12 0,08 0,10 0,26 0,35 0,08
6
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Table 2 Obtained results of RMS simulations in Line L3-5 with settings of Table 3 at 2 PM. Type
Fault resistance
PD No.
Location on the line based on the total distance 1%
3 PH
Rf = 0 Rf = 1,5[Ω]
L-L
Rf = 0 Rf = 1,5[Ω]
PD3.3 PD5.1 PD3.3 PD5.1 PD3.3 PD5.1 PD3.3 PD5.1
50%
99%
Clearing time with (B) [s]
Clearing time with (A) [s]
Clearing time with (B) [s]
Clearing time with (A) [s]
Clearing time with (B) [s]
Clearing time with (A) [s]
0,318 0,621 0,433 0,805 0,736 1,344 0,054 0,085
0,318 0,593 0,433 0,672 0,736 1,284 0,055 0,055
0,329 0,642 0,445 0,816 0,771 1,375 0,054 0,085
0,329 0,594 0,445 0,673 0,772 1,316 0,050 0,050
0,342 0,665 0,459 0,828 0,810 1,409 0,054 0,084
0,341 0,594 0,459 0,664 0,810 1,353 0,054 0,055
(OC) (OC) (OC) (OC) (OC) (OC) (UV) (UV)
(OC) (OC) (OC) (UV) (OC) (OC) (UV) (UV)
(OC) (OC) (OC) (OC) (OC) (OC) (UV) (UV)
(OC) (OC) (OC) (UV) (OC) (OC) (UV) (UV)
(OC) (OC) (OC) (OC) (OC) (OC) (UV) (UV)
(OC) (OC) (UV) (UV) (OC) (OC) (UV) (UV)
Fig. 6 shows the diagram of the microgrid in order to support the coupled EMS-APS proposed for the ESUSCON microgrid.
fault resistance (Rf ). Besides, three fault locations were considered, defined at 1 %, 50 %, and 99% of the line length. Table 2 shows the results obtained in a fault in line L3-5 for comparison purposes. The results showed a dissimilar performance in both methodologies. The proposed methodology correctly cleared 100 % of the evaluated fault conditions. However, the alternative methodology only cleared 25 % of the evaluated fault conditions (see bolded and underlined results in Table 2), since it operated in an uncoordinated manner. The time indicated on Table 2 on bold and underline corresponds to the clearing time of the backup device. This implied that the area of the microgrid was disconnected due to these fault conditions was larger than the optimal. A special case was presented in the L-L fault with maximum fault resistance at 1 % of the location, which caused the disconnection of the PV plant No 1, and the BESS had to assume the required generation difference.
5. Conclusions Results described in previous works reported the need and advantages of APS in microgrids. However, these studies have either been focused on the mere use of APS systems or have been just dedicated to the design of a new protection function. In this paper, the coupling between existent controllers (like the Energy Management System) and protection devices was taken into consideration to formulate a novel APS. In the proposed protection system, particular attention was paid to the information exchange between the EMS and the APS, so that the information required by the APS to adequately operate is always available and provided by the EMS. As the results show, the proposed APS performed better than similar protection systems already reported in the literature in all cases (Raj et al., 2014). This finding extends those in (Núñez-Mata et al., 2018), confirming that the information available in the EMS to compute the optimal scheduling of the dispatchable generation units support the operation of an APS. In addition, the results obtained in this paper can be easily extended to EMS and APS with capabilities that differ from the ones considered in the study case. Likewise, indicate that a co-design of the EMS and APS might be performed for a wide range of microgrids. Most notably is the fact that, notwithstanding the vast the literature on this topic, this is one of the first attempts to propose the EMS as a supporting entity of an APS. The results provided in this paper represent compelling evidence that the coupling between the EMS and the APS could be exploited to enhance the behavior of a microgrid in case of failure/contingency. However, some limitations are worth noting.
4.4. Implementation considerations For the practical implementation of the coupling between an EMS with an APS proposed in this paper in the ESUSCON microgrid, three levels, defined in accordance with the functions to be performed, should be considered: 1) The first level comprises field devices such as controllers, actuators, protective devices, and sensors. 2) The second level is the data acquisition layer, in charge of the communications within the microgrid. 3) The third level corresponds to the protection and monitoring system, where the procedures for diagnostics and adjustment of protective devices are executed.
Fig. 6. Communication diagram of the coupled EMS-APS proposed for ESUSCON microgrid. 7
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Future works should include a detailed model of the communication channels to evaluate the impact of delays and other issues associated with the information exchange in the performance of the coupled EMSAPS. Finally, a methodology to define the settings of the protective devices for the safe operation mode will be proposed.
and Energy Society General Meeting. IEEE, Detroit, MI, pp. 1–7. https://doi.org/10. 1109/PES.2011.6039749. Panwar, M., Duggan, G., Griffin, R., Suryanarayanan, S., Zimmerle, D., Pool, M., Brunner, S., 2012. Dispatch in microgrids: lessons from the fort collins renewable and distributed systems integration demostration project. Electr. J. 25, 71–83. https://doi. org/10.1016/j.tej.2012.09.011. Parhizi, S., Lotfi, H., Khodaei, A., Bahramirad, S., 2015. State of the art in research on microgrids: a review. IEEE Access 3, 890–925. https://doi.org/10.1109/ACCESS. 2015.2443119. Raj, T., Thangaraj, R., Allamsetty, S., Pant, M., 2014. Coordination of directional overcurrent relays using opposition based chaotic differential evolution algorithm. Int. J. Electr. Power Energy Syst. 55, 341–350. https://doi.org/10.1016/j.ijepes.2013.09. 032. Teimourzadeh, S., Aminifar, F., Davarpanah, M., Guerrero, J., 2016. Macroprotections for microgrids. IEEE Ind. Electron. Mag. 10, 6–18. https://doi.org/10.1109/MIE.2016. 2569620. Triki-lahiani, A., Abdelghani, A., Slama-belkhodja, I., 2018. Fault detection and monitoring systems for photovoltaic installations: a review. Renew. Sustain. Energy Rev. 82, 2680–2692. https://doi.org/10.1016/j.rser.2017.09.101. Ustun, T., Ozansoy, C., Zayegh, A., 2012. Modeling of a centralized microgrid protection system and distributed energy resources according to IEC 61850-7-420. IEEE Trans. Power Syst. 27, 1560–1567. https://doi.org/10.1109/TPWRS.2012.2185072. Xu, X., Mitra, J., Cai, N., Mou, L., 2014. Planning of reliable microgrids in the presence of random and catastrophic events. Int. Trans. Electr. Energy Syst. 24, 1151–1167. https://doi.org/10.1002/etep.1768 Planning. Xu, X., Mitra, J., Wang, T., Mu, L., 2015. An evaluation strategy for microgrid reliability considering the effects of protection system. IEEE Trans. Power Deliv 1–9. https:// doi.org/10.1109/TPWRD.2015.2440664. Zamani, A., Sidhu, T., Yazdani, A., 2013. A communication-based strategy for protection of microgrids with looped configuration. Electr. Power Syst. Res. 104, 52–61. https:// doi.org/10.1016/j.epsr.2013.06.006. Zarei, S., Parniani, M., 2017. A comprehensive digital protection scheme for low voltage microgrids with inverter-based and conventional distributed generations. IEEE Trans. Power Deliv. 32, 441–452. https://doi.org/10.1109/TPWRD.2016.2566264.
Funding This work was supported by the FONDAP/CONICYT [grant number 15110019]. Acknowledgments This research was supported by the University of Costa Rica. References Alabdulwahab, A., Shahidehpour, M., 2016. Microgrid networking for the monitoring, control and protection of modern power systems. Electr. J. 29, 1–7. https://doi.org/ 10.1016/j.tej.2016.11.012. Ates, Y., Uzunoglu, M., Karakas, A., Rifat, A., Nadar, A., Dag, B., 2016. Implementation of adaptive relay coordination in distribution systems including distributed generation. J. Clean. Prod. 112, 2697–2705. https://doi.org/10.1016/j.jclepro.2015.10.066. Boyd, S., Vandenberghe, L., 2004. Convex optimization. Cambridge, UK. . Brahma, S., Trejo, J., Stamp, J., 2014. Insight into microgrid protection. In: IEEE (Ed.), IEEE PES Innovative Smart Grid Technologies Conference Europe. IEEE, Istanbul. pp. 1–6. Brearley, B., Prabu, R., 2017. A review on issues and approaches for microgrid protection. Renew. Sustain. Energy Rev. 67, 988–997. https://doi.org/10.1016/j.rser.2016.09. 047. Coffela, F., Booth, C., Dysko, A., 2014. An adaptive overcurrent protection scheme for distribution networks. IEEE Trans. Power Deliv. 30, 561–568. https://doi.org/10. 1109/TPWRD.2013.2294879. Habib, H., Lashway, C., Mohammed, O., 2017. A review of communication failure impacts on adaptive microgrid protection schemes and the use of energy storage as a contingency. Trans. Ind. Appl. 99, 1–8. https://doi.org/10.1109/TIA.2017.2776858. Hare, J., Shi, X., Gupta, S., Bazzi, A., 2016. Fault diagnostics in smart micro-grids: a survey. Renew. Sustain. Energy Rev. 60, 1114–1124. https://doi.org/10.1016/j.rser. 2016.01.122. Hooshyar, A., Iravani, R., 2017. Microgrid protection. Proc. IEEE 105, 1332–1353. https://doi.org/10.1109/JPROC.2017.2669342. Hu, Y., Gao, B., Song, X., Tian, G.Y., Li, K., He, X., 2013. Photovoltaic fault detection using a parameter based model. Sol. Energy 96, 96–102. https://doi.org/10.1016/j.solener. 2013.07.004. Khodayar, M., 2017. Rural electrification and expansion planning of off -grid microgrids. Electr. J. 30, 68–74. https://doi.org/10.1016/j.tej.2017.04.004. Laaksonen, H.J., 2010. Protection principles for future microgrids. IEEE Trans. Power Electron. 25, 2910–2918. https://doi.org/10.1109/TPEL.2010.2066990. Lidula, N., Rajapakse, A., 2011. Microgrids research: a review of experimental microgrids and test systems. Renew. Sustain. Energy Rev. 15, 186–202. https://doi.org/10. 1016/j.rser.2010.09.041. Lu, X., Wang, J., Guo, L., 2016. Using microgrids to enhance energy security and resilience. Electr. J. 29, 8–15. https://doi.org/10.1016/j.tej.2016.11.013. Meng, L., Riva, E., Luna, A., Dragicevic, T., Vasquez, J., Guerrero, J., 2016. Microgrid supervisory controllers and energy management systems: a literature review. Renew. Sustain. Energy Rev. 60, 1263–1273. https://doi.org/10.1016/j.rser.2016.03.003. Mirsaeidi, S., Mat Said, D., Wazir Mustafa, M., Hafiz Habibuddin, M., Ghaffari, K., 2014. An analytical literature review of the available techniques for the protection of microgrids. Int. J. Electr. Power Energy Syst. 58, 300–306. https://doi.org/10.1016/j. ijepes.2014.01.032. Nuñez-Mata, O., Palma, R., Valencia, F., Mendoza, P., Cotos, J., 2017. Integrated protection and monitoring system for safe operation of photovoltaic-based isolated microgrids. In: IEEE (Ed.), IEEE PES General Meeting. IEEE. Chicago, IL, USA, USA. pp. 1–5. Núñez-Mata, O., Palma, R., Valencia, F., Mendoza, P., Jiménez, G., 2018. Adaptive protection system for microgrids based on a robust optimization strategy. Energies 11, 308. https://doi.org/10.3390/en11020308. Olatomiwa, L., Mekhilef, S., Ismail, M., Moghavvemi, M., 2016. Energy management strategies in hybrid renewable energy systems : a review. Renew. Sustain. Energy Rev. 62, 821–835. https://doi.org/10.1016/j.rser.2016.05.040. Olivares, D., Cañizares, C., Kazerani, M., 2014. A centralized energy management system for isolated microgrids. IEEE Trans. Smart Grid 5, 1864–1875. https://doi.org/10. 1109/TSG.2013.2294187. Oudalov, A., Degner, T., Overbeeke, Fvan, Yarza, J.M., 2014. Microgrid protection. In: IEEE (Ed.), Microgrids Architectures and Control. Wiley, pp. 117–164. Palma, R., Benavides, C., Lanas, F., Severino, B., Reyes, L., Llanos, J., Sáez, D., 2013. A microgrid energy management system based on the rolling horizon strategy. IEEE Trans. Smart Grid 4, 996–1006. https://doi.org/10.1109/TSG.2012.2231440. Palma, R., Ortiz, D., Reyes, L., Jiménez, G., Garrido, N., 2011. A social SCADA approach for a renewable based microgrid –The Huatacondo Project–. In: IEEE (Ed.), Power
Oscar Núñez-Mata. He received his B.Sc. on Electrical Engineering from the Universidad de Costa Rica, San José, and the M.Sc. and Ph.D. degrees from the Universidad de Chile, Santiago, in 2014 and 2018 respectively. He is visiting professor at the School of Electrical Engineering, University of Costa Rica. He is with the Electric Power and Energy Research Laboratory (EPERLab), University of Costa Rica. Co-founder of the initiative Comunidad Solar R9 - IEEE. His research field is the operation of electrical systems, renewable energy, education and micro grids. Rodrigo Palma-Behnke. He received his B.Sc. and M.Sc. on Electrical Engineering from the Pontificia Universidad Católica de Chile and a Dr.-Ing. from the University of Dortmund, Germany. He is associate professor at the Electrical Engineering Department, University of Chile. He is with the Energy Center, University of Chile, and Director of the Solar Energy Research Center. Secretary in the IEEE Task Force Micro Grids Stability Analysis and Modeling. Co-founder of the initiative Comunidad Solar R9 - IEEE. Director of the Ayllu Solar. His research field is the planning and operation of electrical systems, solar energy, smart grids and microgrids. Felipe Valencia. He received the Master’s and Ph.D. (magna cum laude) degrees in Control Engineering from the Universidad Nacional de Colombia. He is a Control Engineer with the Universidad Nacional de Colombia. He is currently a Full Time Researcher with the Solar Energy Research Center Solar Energy Research Center, Department of Electrical Engineering, University of Chile. His current research interests include design of distributed and hierarchical strategies for controlling large-scale systems, researching on different fields such as power energy generation, transmission, and distribution systems, transportation, and smart grids. Alex Urrutia-Molina. He received the B.Sc. degree in Electronic Engineering from the Universidad de la Frontera, Temuco, in 2013. He is currently a PhD student in Electrical Engineering at Department of Electrical Engineering, University of Chile. His main research interests are renewable energy, distributed generation, smart grids and micro grids. Patricio Mendoza-Araya. He received the B.Sc. degree in Electrical Engineering from the Universidad de Chile, Santiago, in 2007 and the Ph.D. degree from the University of Wisconsin-Madison. He is currently an assistant professor at Universidad de Chile, Santiago. He is a researcher with the Solar Energy Research Center Solar Energy Research Center, Department of Electrical Engineering, University of Chile. His main research interests are power electronics, renewable energy, distributed generation, smart grids and micro grids. Guillermo Jiménez-Estévez. He received the B.Sc. degree in electrical engineering from the Escuela Colombiana de Ingeniería, Bogotá, in 1998 and the M.Sc. and Ph.D. degrees from the Universidad de Chile, Santiago, in 2003 and 2010 respectively. He was Director of the Energy Center of Physical and Mathematical Sciences Faculty of the Universidad de Chile and currently is visiting professor at Universidad de los Andes, Colombia. His main research interests are power systems planning and operation, renewable energy, distributed generation, smart grids, micro grids and regulation.
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Coupling an adaptive protection system with an energy management system for microgrids
T
Oscar Núñez-Mataa,*, Rodrigo Palma-Behnkeb, Felipe Valenciab, Alexander Urrutia-Molinab, Patricio Mendoza-Arayab, Guillermo Jiménez-Estévezb,c a
School of Electrical Engineering, University of Costa Rica, San Pedro, Costa Rica Energy Center, Department of Electrical Engineering, Faculty of Mathematical and Physical Sciences, University of Chile, Santiago, Chile c Department of Electrical and Electronics Engineering, Universidad de los Andes, Bogotá, Colombia b
A R T I C LE I N FO
A B S T R A C T
Keywords: Microgrids Optimization Distributed power generation Uncertainty Energy management
Energy Management Systems (EMS) are control schemes in charge of defining the optimal scheduling of dispatchable units in a microgrid. Output set points from the EMS could be exploited in other control loops that may benefit from the microgrids expected behavior improving their operation. In this context, this paper proposes a novel coupled operation of an EMS with an Adaptive Protection System (APS), such that the information of the EMS serves to compute the parameters of the protective devices deployed in a microgrid. This aspect is formalized in the present proposal based on already developed APS and EMS frameworks. To evaluate the performance of the proposal, the microgrid installed in Huatacondo, north of Chile, was used as a test-bed. The obtained results indicate that considering the information provided to the APS form the EMS improves the overall performance of the microgrid in case of failure, despite the operating conditions. This also indicates that moving towards an integrated design of the EMS and APS might enhance the behavior (robustness) of a microgrid (as a whole) in case of failures or contingencies.
1. Introduction The design of adaptive protection systems (APS) for microgrids have recently gained high interest in the research community due to i) the rising use of microgrids for electrification purposes (Parhizi et al., 2015; Khodayar, 2017), ii) the particular features of microgrids that make that commonly used methodologies for the design of protection schemes in power grids might fail (Alabdulwahab and Shahidehpour, 2016; Laaksonen, 2010), and iii) the capabilities of APS to deal with the variability of the operating features of microgrids, and thus guarantee an adequate operation of the protection devices (Habib et al., 2017). In general, microgrids represent a combination of information and communication technologies together with electric power sources and loads, into a unified active power system (Panwar et al., 2012; Zamani et al., 2013; Lidula and Rajapakse, 2011). A microgrid is able to operate connected to a main grid or in isolated mode (Teimourzadeh et al., 2016; Olivares et al., 2014). The presence of the main grid and more than one distributed energy resource (DER) requires the control of the energy flow among the various sources. Then energy management systems (EMS) have been designed to control the energy flow so that: i) the continuity in the load supply is ensured; and, ii) the cost of the ⁎
Corresponding author. E-mail address: [email protected] (O. Núñez-Mata).
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1040-6190/ © 2019 Elsevier Inc. All rights reserved.
energy production is minimized (Olatomiwa et al., 2016). Despite the different operating conditions of microgrids, the variability introduced by distributed energy resources, loads, and the changes in the operating point defined by the operation of the EMS, an appropriate response of the protection devices is required (Xu et al., 2015). In this context, APS appear as one of the most suitable solutions for the design of protection systems for microgrids. However, it has been found that APS require high information exchange for their implementation. One way to overcome this issue is to take advantage of the information that the EMS has to reduce the communication burden/ complexity in the implementation of APS in microgrids. Roughly speaking, the EMS is a controller that defines the operating points of energy resources taking into consideration the expected generation of the distributed energy resources (e.g., solar and wind), the topology of the distribution grid, the expected energy demand, and in case of grid connected microgrids the information of the external grid (e.g., market price, and operating condition of the main grid) (Núñez-Mata et al., 2018; Alabdulwahab and Shahidehpour, 2016; Meng et al., 2016). Therefore, part of this information can be exchanged with the APS so that the implementation of an additional communication network for the APS is minimized or avoided (Coffela et al., 2014; Lu et al., 2016).
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In the specialized literature several approaches for the implementation of APS in microgrids have been already reported. For example, in (Ustun et al., 2012) an APS based on overcurrent protective devices was proposed. This protection system makes use of an extensive dedicated communication network to monitor a microgrid and thus update the settings of the protection devices. In addition to overcurrent protection devices, in (Zarei and Parniani, 2017) an APS that also included low voltage protection devices was proposed. The tuning of the settings of both overcurrent and low-voltage protection devices was done by means of an extensive search of the parameter values that maximize the performance of the microgrid. A dedicated communication network for the APS is required. Nevertheless, considering only a fixed set of parameters its complexity is reduced. This method is not scalable and therefore it application is constrained to just small microgrids nonetheless (microgrids with few lines, load centers, and energy sources). By contrast, in (Coffela et al., 2014) an APS for overcurrent protection devices that interacts with and EMS was described. In this case, the EMS sends the topology of the grid, the status of the distributed generation, and the operating mode. Based on this information the APS updated the settings of the overcurrent protection devices. Despite the interaction between an EMS and an APS, this approach did not discuss how they must operate coordinately so that unexpected behaviors are avoided. Thereby, the complexities often arising from the integration of variable generation technologies (such as wind and solar) were not included. Finally, in (Oudalov et al., 2014) also a real-time approach for the update of the parameters of overcurrent protection devices was described. In this approach, the APS interacts with an EMS to include the information about changes in the topology into the computation of the parameters of the protection devices. Specifically, the EMS communicates the status of the distributed generators, i.e., which of them are connected and injecting power to the grid. Nevertheless, as in (Coffela et al., 2014) no discussion about how the APS and the EMS must operate coordinately was included. As it can be seen from the literature review, the interaction between APS and EMS to reduce the communication complexity in the implementation of APS in microgrids was discussed yet. But, notwithstanding the efforts to define such interaction, how the APS and the EMS must be coordinated to prevent unexpected behaviors still remains as an open research question. The current paper presents a methodology for the coupled operation of APS and EMS in microgrids. The proposed methodology determines the times at which the information between APS and EMS must be exchanged, and hence guarantee a coordinated operation between both systems. The effectiveness of the proposed methodology is tested through simulations of the microgrid installed in Huatacondo, north of Chile. The remainder of this paper is organized as follows: the APS for microgrids used to formulate the proposed methodology is described in Section 2; Section 3 presents the proposed methodology; Section 4 presents the test-case; finally, the conclusions and future work are presented in Section 5.
Fig. 1. Single line diagram of a generic microgrid.
energy from DER, coordinating the energy flow to and from the main grid in grid-connected mode, and ensuring the continuity of the load supply in isolated mode (Xu et al., 2014; Olatomiwa et al., 2016). Several authors consider that this ability will contribute to improve the resiliency and reliability of electrical systems, by avoiding disruptions and serve as resources for fast recovery during main grid disturbances (Hooshyar and Iravani, 2017),(Alabdulwahab and Shahidehpour, 2016). However, the offered improved features will be seriously affected if the microgrid is not properly protected during fault events that occur within its own boundaries (Hooshyar and Iravani, 2017). 2.2. Abnormalities in microgrids In (Hare et al., 2016) a complete description of fault and failure modes, causes and effects in microgrids is presented. In this work the distribution grid and the DER are selected to define the protection zones. The protection of the PCC will not be considered in this work since different proposals can be found in the literature for this purpose. Firstly, at the distribution grid level, short-circuit faults are characterized by (Mirsaeidi et al., 2014; Brahma et al., 2014): 1 Possible bi-directional flows. 2 Lower fault current level in isolated mode. 3 Similar fault current levels at different locations along the lines. A variety of methods have been published in the last years to address microgrid protection issues presented above (Brearley and Prabu, 2017). In this proposal, an adaptive protection approach is employed based on a new protection scheme. Secondly, in the DER, the different faults will be characterized depending on the type of unit. In this work, we focus in protecting PV plants, because the possible failure modes in PV plants might be difficult to detect by conventional protection devices. A similar approach can be extended to other generation units or storage devices. In PV plants, the fault currents are highly dependent on:
2. Formulation of protection system framework This section describes the APS framework considered in this paper. Without loss of generality, the approach proposed by the authors in (Núñez-Mata et al., 2018) was selected. Despite of this particular selection, the methodology proposed in this paper can be easily adapted to any other APS proposed in the literature.
1 2 3 4 5 6
2.1. Overview Fig. 1 shows the single-line diagram of a generic microgrid. DER are divided into converter and rotating-machine coupled units. Protection devices are deployed through the microgrid among grid, distribution lines, generation units, and loads. Microgrids provide any possible means of generation and utilizing 2
The The The The The The
location of the fault within the PV array. fault impedance. type of fault. irradiance level at the moment of the fault. use of blocking diodes. influence of the Maximum Power-Point Tracker (MPPT).
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2.4. Protection of the DC zone of PV plants
With the increasing interest in monitoring PV plants, different works related to the diagnostic of these systems are emerging (Triki-lahiani et al., 2018). In this proposal, a new method for the diagnostic and fault detection in PV plants in microgrids is used. Finally, in this work it is assumed that other DER present into the microgrid include self-protection against faults such as short circuit, overload, overcharge, deep discharge, and overheating. The protective method applied in each protection zone of the microgrid is established considering the aforementioned features. These methods are described below.
The protection method for the DC zone of PV plants was presented by the authors in (Nuñez-Mata et al., 2017). The proposed method works together with conventional protection devices. The proposed method works together with conventional protection devices. This model-based method estimates the PV plant condition based on real measurements and model generated data and compares the measurements with the theoretical estimates. Together with the electrical analysis of the PV plant, the diagnosis is realized by means of thermal considerations using the energy balance equation (Hu et al., 2013). The purpose of the energy balance equation is to connect the electrical and thermal characteristics of a PV panel. Using the proposed diagnostic parameters, a PV plant can be diagnosed with a per-module resolution, classifying the operation condition as:
2.3. Protection of the AC distribution grid A robust optimization strategy is used to adjust and coordinate the protective devices. These devices are based on a protection scheme, in which two directional elements are operating in an interleaved manner. These are: i) directional overcurrent element; and, ii) undervoltage element. The protective device requires the internal coordination of both elements, so that the former detects any overcurrent in the microgrid being protected, and the later serves as a backup element in case of low fault-current contingencies. Low-voltage devices are selected as backup devices since (often) low fault-current contingencies might cause a decrease in the voltages in the grid. We establish an optimization problem with the objective of minimizing a certain cost function defined as ‖Ax − b‖, considering some uncertainty in the data of A and b . It is assumed that A and/or b are random variables belonging to mxn and mx1 respectively, and xε nx1. The scenario approach is used to structure uncertain input data. A scenario represents a possible realization of the uncertain input data. A specific case of the above is to model the variation in the matrix A by means of a worst-case approach (Boyd and Vandenberghe, 2004). Thus, we have a finite set of k fault scenarios defined as: A = {A1 , …, Ak } . The optimization problem is defined as:
min [ max ‖Ai x − b‖] i = 1,..., k
1 Normal: in this condition the measured and estimated values are within allowable limits. 2 Abnormal-not damaged: this condition is considered a recoverable fault. 3 Abnormal-damaged: this condition is considered an unrecoverable fault. The thresholds for the diagnostic parameters can be experimentally obtained as was shown in (Nuñez-Mata et al., 2017). Following this approach, it is possible to detect the following fault conditions in PV modules: i) open circuit; ii) line-to-line or line-to-ground short-circuit; iii) shading or excessive dust condition; iv) internal failure in the module; v) failure in bypass diode; and, vi) advanced degradation. 3. Methodological proposal for a coupled EMS-APS The current section presents the proposed methodology for the coupled operation between an APS and an EMS in microgrids. The EMS presented by the authors in (Palma et al., 2013) is selected to illustrate the proposed methodological approach. It involves the following main elements that must be included in the design of this type of systems: a prediction of the generation capacity of the variable generation technologies, a prediction of the energy demand, an estimation of the operating conditions of the energy storage system available, a measurement of the current operation state of the conventional generation technologies, an strategy to deal with the uncertainty in the expected operating conditions of the microgrid, and an optimization method that combines all the aforementioned information to determine the connection/disconnection of generation units and the power to be provided by the connected units. Fig. 2 displays a block diagram of the EMS approach described in (Palma et al., 2013). In this figure PSmax and PSmin are the expected maximum and minimum values of the solar power; PEmax and PEmin are the expected maximum and minimum values of the wind power; PLmax and PLmin are the expected maximum and minimum values of the energy load; ESOC , Vi , and Ii are the battery charge, the output voltage, and the output current of the battery energy storage system; Bgi is the status of the diesel generator (on/off). The EMS provides the power references for PS and PE (solar and wind production); PD for the diesel generator; and PI for the battery storage system (positive or negative). PUS is the expected non-supplied power and SL is the signal used for the demand response. Note that in Fig. 2 the EMS already has information that is required by the APS described in section 2. Therefore, there is an opportunity to take advantage of this fact to reduce the complexity in the communication network of a microgrid with these two systems. Indeed, the implementation of dedicated communication networks for each of these systems could be partially avoided. In this regard, in this paper it is proposed to exchange specifically the forecasts, the weather parameters, and the commitment and dispatch of the generation units used
(1)
We can cast the problem in epigraph form as:
mint x,t
s. t . ‖Ai x − b‖ ≤ t , i = 1, …, k
(2)
which can be solved in a variety of ways, with t as an auxiliary variable. If z (s, pk ) denotes the cost function related to the protection coordination problem in microgrids, where s represents the protection device settings; s ∈ S , S represents the set of permissible settings; and pk represents the fault conditions, pk ∈ P , k = 1, …, K , with K being the maximum number of fault events considered, and P the finite set of scenarios. The coordination problem can be established as:
mint s, t
s.t.
z (s, pk ) ≤ t , ∀ k simin ≤ si ≤ simax (bounds of the setting of the i protective element) Timin ≤ Ti ≤ Timax (bounds of the operating time of the i protective device) Ti = f (si ) (characteristic function of the i protective element)
h (T ) ≥ 0 (coordination criteria)
(3)
Eq. (3) can be particularized to the problem of selecting the settings for a coordinated operation of directional overcurrent and undervoltage protection elements. The way in which the optimization problem is solved is explained in (Núñez-Mata et al., 2018). 3
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Fig. 2. Configuration of an EMS for microgrids based on a rolling horizon strategy.
Fig. 3. Proposed interaction between both APS and EMS in microgrids.
operation of the microgrid.. Then, at time t1 the APS executes a diagnosis of the operating conditions of the DER in the microgrid. For such diagnosis, the EMS sends the forecasts, the weather parameters, and the commitment and dispatch of the generation units to the APS. After that, at time t2 the APS made a decision about the tripping or not of the DER currently in operation in the microgrid At time t3 the APS informs to the EMS about the operating status of the DER in the microgrid, so that this information is available in the next computations of the EMS. At this time, there is also a transition in the coupled operation of the APS and the EMS. This transition is defined by the decision about tripping or not any DER committed and dispatched by the EMS during the black-start procedure. If a tripping decision is made, the pre-defined settings charged in the APS are sending to the protection devices. Otherwise, the APS waits until time t6 to compute the new set of settings. At times t4 and t5 the EMS computes the unit commitment and the economic dispatch of the generation units. Here, there is another transition in which the EMS informs the updated commitment and dispatch of the units to the APS and goes to time t8 . At time t6 the APS analyze again the operating conditions of the microgrid. This analysis also generates a transition: if a change in the operating conditions is detected, then the APS goes to time t7 ; otherwise, the APS goes to time t8 . At time t7 the APS updates the settings of the protection devices following the procedure introduced in Section 2, and inform to the EMS about the changes in the topology of the microgrid. Finally, at time t8 both the APS and the EMS re-start their corresponding procedures and go to time t1. It is important to notice that at time t8 the initial states for the APS and the EMS are also updated and are equal to those obtained during the execution of all procedures shown in Fig. 4. The reason for this selection is that whether a communication failure is detected, both
in (or generated by) the EMS. Furthermore, it is also proposed to exchange the microgrid topology and the status of the DER used by the APS to compute the settings of the protection devices. The remaining information required by the EMS and by the APS is taken directly from the microgrid through the devises/sensors implemented in the microgrid. Fig. 3 shows the proposed interaction between the APS described in Section 2 and the EMS presented in (Palma et al., 2013). The flows of information from/to the APS and the EMS shown in Fig. 3 are proposed because of the following factors:
• The APS require the forecasts and weather parameters to generate •
the uncertainty set associated with the current operation of the microgrid. Furthermore, the APS also require information about the committed units and their power injections to compute. The EMS requires the topology of the microgrid and the status of the DER to compute the power to be injected by the generation units so that the operating costs are reduced.
However, it is important to remark that the interaction presented in Fig. 3 not only applies for the specific APS and EMS introduced in this paper. Such interaction is general enough to be extended to any APS and EMS implemented in a microgrid. In addition to the interaction between the APS and the EMS, an adequate synchronization with both systems is required to guarantee a proper operation of the microgrid as a whole. With this aim, the timetable shown in Fig. 4 is proposed to carry out the individual calculations in the APS and the EMS. At the beginning, i.e., at time t0 both systems (the APS and the EMS) are in a safe mode. That is, a set of predefined settings for the protection devices are charged in the APS, and the EMS is ready to execute the black-start procedure to initiate the 4
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Fig. 4. Coordinated operation of a coupled EMS-APS for microgrids.
setting are installed in the AC distribution grid. Besides, the PV plants include fuses to protect the DC zone. This protection system has problems clearing short-circuit faults, mainly, due to the significantly low fault current level during pure inverter operation, as was demonstrated in (Hare et al., 2016).
systems go to their corresponding safe-mode. That is, they keep the protection settings and the commitment and economic dispatch unchanged until the communication is restored. Furthermore, an alarm is displayed to make aware the operator of the microgrid about the anomalies taking place in the communications. This enhances the robustness of the microgrid to failures in the communication networks. In addition, going besides the interaction between the APS and the EMS, and proposing a time-scheduling for the implementation of this coupled operation significantly differentiates the proposed approach from those reported in (Ustun et al., 2012; Ates et al., 2016; Zarei and Parniani, 2017; Oudalov et al., 2014). In fact, until the literature review allowed knowing, this is the first attempt to propose a timetable to synchronize the operation of both an APS and an EMS. Moreover, such a coordinated operation takes advantage of the synergies of both systems to i) reduce the complexity of the communication network, ii) improve the performance of the EMS by having updated information about the status of the DER, and iii) enhance the adjustment of the settings performed by the APS through the exchange of information about the expected operation of the microgrid and its associated uncertainty. Next, the proposed methodology for the coupled operation between an APS and an EMS is assessed in the Huatacondo microgrid.
4.2. Criteria for the design of the EMS-APS for the ESUSCON microgrid Statistics for fault occurrence are not currently available for the ESUSCON microgrid. There are only occasional fault reports, which do not allow for the establishment of a model of likelihood of a fault happening, according to the different conditions. Given the uncertainty of the data, the reliability of the current protection system cannot be studied and ensured. For coordinating the EMS and APS, the following steps were considered:
• The APS executes the diagnostic procedure of each DER, to assess the operating condition and detect faults. • The EMS optimizes the system operation using forecast data and the related operating constraints • The EMS sends the data and information about the operation of the microgrid to the APS. • The APS decides if an update of the settings of the Protective Devices
4. Study case and discussion In this section, the fault simulation results obtained with a coupled operation of an APS and the EMS described in Section 3 are presented.
(PD) is required.
Thus, the protection system can handle the possible variability inside the 15 min’ period.
4.1. Details of the test-bench The application test was done using real data of an existing off-grid PV microgrid installed in Huatacondo, a settlement located at the Atacama Desert, Chile, also called the ESUSCON microgrid (by the acronym in Spanish of Electricidad Sustentable Cóndor) (Palma et al., 2011). The low voltage distribution grid comprises overhead power lines, with circuits in radial and meshed configuration, with a line-to-line voltage of 380[V]/50[Hz]. The microgrid considers the following DER: a 120[kW] Diesel generator; a 40[kW] lead-acid BESS with the corresponding bi-directional inverter; and two PV plants (50[kWp] and 10[kWp]) each with its own inverter. Fig. 5 shows the single line diagram. The energy sources are coordinated by means of an EMS based on approach presented in (Palma et al., 2013). The EMS oversees the energy sources defining the power setpoints for the Diesel generator and the BESS. Regarding the current protection system, circuit breakers with fixed
4.3. Obtained results and discussion To evaluate the coupled operation of the EMS-APS, two different operating conditions were selected as follows: 1 Operating condition at 2 PM: it is characterized by a low demand, which is supplied exclusively by the PV plants while the BESS is being charged. 2 Operating condition at 11 PM: it is characterized by a high demand. The Diesel generator mainly supplies the demand, and the BESS is being charged. Consequently, distinct protection settings are expected for both cases. For comparison purposes, a commonly used methodology (A) was applied to solve the coordination problem in the operating condition at 2 PM (Raj et al., 2014). In the proposed alternative methodology (B), 5
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Fig. 5. Single line diagram of the ESUSCON microgrid.
both methodologies (A and B) are shown in Tables 1 and 2. The time dial settings were defined as integer variables as part of the optimization problem, considering that the PD settings can take discrete values. In this study case, time dial settings of the overcurrent and undervoltage protection elements are adjusted in discrete steps of 0,01 [s], and the minimum values are: 0,05 [s] for overcurrent element and 0,00 [s] for undervoltage element. As shown in Table 1, the time dial settings for the overcurrent devices are lower at the 2 PM condition, compared to the 11 PM condition, which means that the methodology A selected a lower curve given the low fault currents. For undervoltage devices, the settings are higher at the 11 PM condition, since at this condition, the high fault currents allow them to be cleared by the overcurrent element. Besides, the settings of the devices obtained with the alternative methodology A were slightly lower than or the same as those of the proposed methodology B. However, the coordination margins are narrower in the methodology A, compared to the methodology B (proposed methodology). RMS fault simulations were performed to compare the effectiveness of both methodologies. The following short-circuit faults were made: three-phase (3 PH), and phase-to-phase (L-L), with zero and maximum
the protection coordination problem is formulated as a linear programming method, related with the operating time T of the PD, as follows: N
MinT =
M
L
X
∑ ∑ ∑ ⎛⎜Tijlp + ∑ Tijlxijl⎞⎟ i=1 j=1 l=1
⎝
r
x=1
(19)
⎠
where N is the total number of PD, with i as the identifier of each one of them; M is the total number of faults considered, with j as the identifier of each one of them; and, L is the total number of fault locations, with l as the identifier of each one of them. The superscript p corresponds to the primary PD, while rx represents the backup PD, with X denoting the r total number of backup PD. Tijlp and Tijlxijl are the operating times of primary PD p and backup PD rx , respectively, for the near-end fault. The constraints used in the alternative methodology were the same ones that were raised for the proposed methodology (B). Besides, the alternative methodology was applied considering a single operation scenario (base). The aim was to compare the performance of both methodologies clearing different faults using RMS simulations. The results obtained of Table 1 Obtained results for PD settings in two operating conditions. Bus No.
PD No.
Settings for 2 PM with (B) p
T1 T2 T3
T5
T8
T9
PD1.3 PD2.1 PD2.2 PD3.1 PD3.2 PD3.3 PD5.1 PD5.2 PD5.3 PD5.4 PD8.1 PD8.2 PD8.3 PD9.1 PD9.2 PD9.3
Settings for 2 PM with (A) p
Settings for 11 PM with (B)
Ii [A]
TDSi [s]
DJ [s]
Ii [A]
TDSi [s]
DJ [s]
Iip [A]
TDSi [s]
DJ [s]
60 40 40 40 40 40 40 25 10 10 15 10 40 15 40 10
0,08 0,05 0,16 0,05 0,05 0,07 0,05 0,06 0,05 0,05 0,05 0,05 0,13 0,09 0,10 0,05
0,14 0,07 0,14 0,05 0,02 0,05 0,12 0,04 0,03 0,03 0,07 0,03 0,13 0,05 0,14 0,03
60 40 40 40 40 40 40 25 10 10 15 10 40 15 40 10
0,07 0,05 0,10 0,05 0,05 0,07 0,06 0,06 0,05 0,05 0,05 0,05 0,08 0,09 0,05 0,05
0,06 0,05 0,16 0,03 0,03 0,05 0,09 0,04 0,03 0,03 0,08 0,03 0,06 0,05 0,11 0,03
125 70 70 70 70 70 70 70 10 30 70 10 70 70 70 10
0,08 0,07 0,17 0,05 0,05 0,07 0,08 0,05 0,05 0,05 0,05 0,05 0,14 0,08 0,11 0,05
0,19 0,17 0,12 0,10 0,02 0,17 0,30 0,12 0,08 0,09 0,12 0,08 0,10 0,26 0,35 0,08
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Table 2 Obtained results of RMS simulations in Line L3-5 with settings of Table 3 at 2 PM. Type
Fault resistance
PD No.
Location on the line based on the total distance 1%
3 PH
Rf = 0 Rf = 1,5[Ω]
L-L
Rf = 0 Rf = 1,5[Ω]
PD3.3 PD5.1 PD3.3 PD5.1 PD3.3 PD5.1 PD3.3 PD5.1
50%
99%
Clearing time with (B) [s]
Clearing time with (A) [s]
Clearing time with (B) [s]
Clearing time with (A) [s]
Clearing time with (B) [s]
Clearing time with (A) [s]
0,318 0,621 0,433 0,805 0,736 1,344 0,054 0,085
0,318 0,593 0,433 0,672 0,736 1,284 0,055 0,055
0,329 0,642 0,445 0,816 0,771 1,375 0,054 0,085
0,329 0,594 0,445 0,673 0,772 1,316 0,050 0,050
0,342 0,665 0,459 0,828 0,810 1,409 0,054 0,084
0,341 0,594 0,459 0,664 0,810 1,353 0,054 0,055
(OC) (OC) (OC) (OC) (OC) (OC) (UV) (UV)
(OC) (OC) (OC) (UV) (OC) (OC) (UV) (UV)
(OC) (OC) (OC) (OC) (OC) (OC) (UV) (UV)
(OC) (OC) (OC) (UV) (OC) (OC) (UV) (UV)
(OC) (OC) (OC) (OC) (OC) (OC) (UV) (UV)
(OC) (OC) (UV) (UV) (OC) (OC) (UV) (UV)
Fig. 6 shows the diagram of the microgrid in order to support the coupled EMS-APS proposed for the ESUSCON microgrid.
fault resistance (Rf ). Besides, three fault locations were considered, defined at 1 %, 50 %, and 99% of the line length. Table 2 shows the results obtained in a fault in line L3-5 for comparison purposes. The results showed a dissimilar performance in both methodologies. The proposed methodology correctly cleared 100 % of the evaluated fault conditions. However, the alternative methodology only cleared 25 % of the evaluated fault conditions (see bolded and underlined results in Table 2), since it operated in an uncoordinated manner. The time indicated on Table 2 on bold and underline corresponds to the clearing time of the backup device. This implied that the area of the microgrid was disconnected due to these fault conditions was larger than the optimal. A special case was presented in the L-L fault with maximum fault resistance at 1 % of the location, which caused the disconnection of the PV plant No 1, and the BESS had to assume the required generation difference.
5. Conclusions Results described in previous works reported the need and advantages of APS in microgrids. However, these studies have either been focused on the mere use of APS systems or have been just dedicated to the design of a new protection function. In this paper, the coupling between existent controllers (like the Energy Management System) and protection devices was taken into consideration to formulate a novel APS. In the proposed protection system, particular attention was paid to the information exchange between the EMS and the APS, so that the information required by the APS to adequately operate is always available and provided by the EMS. As the results show, the proposed APS performed better than similar protection systems already reported in the literature in all cases (Raj et al., 2014). This finding extends those in (Núñez-Mata et al., 2018), confirming that the information available in the EMS to compute the optimal scheduling of the dispatchable generation units support the operation of an APS. In addition, the results obtained in this paper can be easily extended to EMS and APS with capabilities that differ from the ones considered in the study case. Likewise, indicate that a co-design of the EMS and APS might be performed for a wide range of microgrids. Most notably is the fact that, notwithstanding the vast the literature on this topic, this is one of the first attempts to propose the EMS as a supporting entity of an APS. The results provided in this paper represent compelling evidence that the coupling between the EMS and the APS could be exploited to enhance the behavior of a microgrid in case of failure/contingency. However, some limitations are worth noting.
4.4. Implementation considerations For the practical implementation of the coupling between an EMS with an APS proposed in this paper in the ESUSCON microgrid, three levels, defined in accordance with the functions to be performed, should be considered: 1) The first level comprises field devices such as controllers, actuators, protective devices, and sensors. 2) The second level is the data acquisition layer, in charge of the communications within the microgrid. 3) The third level corresponds to the protection and monitoring system, where the procedures for diagnostics and adjustment of protective devices are executed.
Fig. 6. Communication diagram of the coupled EMS-APS proposed for ESUSCON microgrid. 7
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Future works should include a detailed model of the communication channels to evaluate the impact of delays and other issues associated with the information exchange in the performance of the coupled EMSAPS. Finally, a methodology to define the settings of the protective devices for the safe operation mode will be proposed.
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Funding This work was supported by the FONDAP/CONICYT [grant number 15110019]. Acknowledgments This research was supported by the University of Costa Rica. References Alabdulwahab, A., Shahidehpour, M., 2016. Microgrid networking for the monitoring, control and protection of modern power systems. Electr. J. 29, 1–7. https://doi.org/ 10.1016/j.tej.2016.11.012. Ates, Y., Uzunoglu, M., Karakas, A., Rifat, A., Nadar, A., Dag, B., 2016. Implementation of adaptive relay coordination in distribution systems including distributed generation. J. Clean. Prod. 112, 2697–2705. https://doi.org/10.1016/j.jclepro.2015.10.066. Boyd, S., Vandenberghe, L., 2004. Convex optimization. Cambridge, UK. . Brahma, S., Trejo, J., Stamp, J., 2014. Insight into microgrid protection. In: IEEE (Ed.), IEEE PES Innovative Smart Grid Technologies Conference Europe. IEEE, Istanbul. pp. 1–6. Brearley, B., Prabu, R., 2017. A review on issues and approaches for microgrid protection. Renew. Sustain. Energy Rev. 67, 988–997. https://doi.org/10.1016/j.rser.2016.09. 047. Coffela, F., Booth, C., Dysko, A., 2014. An adaptive overcurrent protection scheme for distribution networks. IEEE Trans. Power Deliv. 30, 561–568. https://doi.org/10. 1109/TPWRD.2013.2294879. Habib, H., Lashway, C., Mohammed, O., 2017. A review of communication failure impacts on adaptive microgrid protection schemes and the use of energy storage as a contingency. Trans. Ind. Appl. 99, 1–8. https://doi.org/10.1109/TIA.2017.2776858. Hare, J., Shi, X., Gupta, S., Bazzi, A., 2016. Fault diagnostics in smart micro-grids: a survey. Renew. Sustain. Energy Rev. 60, 1114–1124. https://doi.org/10.1016/j.rser. 2016.01.122. Hooshyar, A., Iravani, R., 2017. Microgrid protection. Proc. IEEE 105, 1332–1353. https://doi.org/10.1109/JPROC.2017.2669342. Hu, Y., Gao, B., Song, X., Tian, G.Y., Li, K., He, X., 2013. Photovoltaic fault detection using a parameter based model. Sol. Energy 96, 96–102. https://doi.org/10.1016/j.solener. 2013.07.004. Khodayar, M., 2017. Rural electrification and expansion planning of off -grid microgrids. Electr. J. 30, 68–74. https://doi.org/10.1016/j.tej.2017.04.004. Laaksonen, H.J., 2010. Protection principles for future microgrids. IEEE Trans. Power Electron. 25, 2910–2918. https://doi.org/10.1109/TPEL.2010.2066990. Lidula, N., Rajapakse, A., 2011. Microgrids research: a review of experimental microgrids and test systems. Renew. Sustain. Energy Rev. 15, 186–202. https://doi.org/10. 1016/j.rser.2010.09.041. Lu, X., Wang, J., Guo, L., 2016. Using microgrids to enhance energy security and resilience. Electr. J. 29, 8–15. https://doi.org/10.1016/j.tej.2016.11.013. Meng, L., Riva, E., Luna, A., Dragicevic, T., Vasquez, J., Guerrero, J., 2016. Microgrid supervisory controllers and energy management systems: a literature review. Renew. Sustain. Energy Rev. 60, 1263–1273. https://doi.org/10.1016/j.rser.2016.03.003. Mirsaeidi, S., Mat Said, D., Wazir Mustafa, M., Hafiz Habibuddin, M., Ghaffari, K., 2014. An analytical literature review of the available techniques for the protection of microgrids. Int. J. Electr. Power Energy Syst. 58, 300–306. https://doi.org/10.1016/j. ijepes.2014.01.032. Nuñez-Mata, O., Palma, R., Valencia, F., Mendoza, P., Cotos, J., 2017. Integrated protection and monitoring system for safe operation of photovoltaic-based isolated microgrids. In: IEEE (Ed.), IEEE PES General Meeting. IEEE. Chicago, IL, USA, USA. pp. 1–5. Núñez-Mata, O., Palma, R., Valencia, F., Mendoza, P., Jiménez, G., 2018. Adaptive protection system for microgrids based on a robust optimization strategy. Energies 11, 308. https://doi.org/10.3390/en11020308. Olatomiwa, L., Mekhilef, S., Ismail, M., Moghavvemi, M., 2016. Energy management strategies in hybrid renewable energy systems : a review. Renew. Sustain. Energy Rev. 62, 821–835. https://doi.org/10.1016/j.rser.2016.05.040. Olivares, D., Cañizares, C., Kazerani, M., 2014. A centralized energy management system for isolated microgrids. IEEE Trans. Smart Grid 5, 1864–1875. https://doi.org/10. 1109/TSG.2013.2294187. Oudalov, A., Degner, T., Overbeeke, Fvan, Yarza, J.M., 2014. Microgrid protection. In: IEEE (Ed.), Microgrids Architectures and Control. Wiley, pp. 117–164. Palma, R., Benavides, C., Lanas, F., Severino, B., Reyes, L., Llanos, J., Sáez, D., 2013. A microgrid energy management system based on the rolling horizon strategy. IEEE Trans. Smart Grid 4, 996–1006. https://doi.org/10.1109/TSG.2012.2231440. Palma, R., Ortiz, D., Reyes, L., Jiménez, G., Garrido, N., 2011. A social SCADA approach for a renewable based microgrid –The Huatacondo Project–. In: IEEE (Ed.), Power
Oscar Núñez-Mata. He received his B.Sc. on Electrical Engineering from the Universidad de Costa Rica, San José, and the M.Sc. and Ph.D. degrees from the Universidad de Chile, Santiago, in 2014 and 2018 respectively. He is visiting professor at the School of Electrical Engineering, University of Costa Rica. He is with the Electric Power and Energy Research Laboratory (EPERLab), University of Costa Rica. Co-founder of the initiative Comunidad Solar R9 - IEEE. His research field is the operation of electrical systems, renewable energy, education and micro grids. Rodrigo Palma-Behnke. He received his B.Sc. and M.Sc. on Electrical Engineering from the Pontificia Universidad Católica de Chile and a Dr.-Ing. from the University of Dortmund, Germany. He is associate professor at the Electrical Engineering Department, University of Chile. He is with the Energy Center, University of Chile, and Director of the Solar Energy Research Center. Secretary in the IEEE Task Force Micro Grids Stability Analysis and Modeling. Co-founder of the initiative Comunidad Solar R9 - IEEE. Director of the Ayllu Solar. His research field is the planning and operation of electrical systems, solar energy, smart grids and microgrids. Felipe Valencia. He received the Master’s and Ph.D. (magna cum laude) degrees in Control Engineering from the Universidad Nacional de Colombia. He is a Control Engineer with the Universidad Nacional de Colombia. He is currently a Full Time Researcher with the Solar Energy Research Center Solar Energy Research Center, Department of Electrical Engineering, University of Chile. His current research interests include design of distributed and hierarchical strategies for controlling large-scale systems, researching on different fields such as power energy generation, transmission, and distribution systems, transportation, and smart grids. Alex Urrutia-Molina. He received the B.Sc. degree in Electronic Engineering from the Universidad de la Frontera, Temuco, in 2013. He is currently a PhD student in Electrical Engineering at Department of Electrical Engineering, University of Chile. His main research interests are renewable energy, distributed generation, smart grids and micro grids. Patricio Mendoza-Araya. He received the B.Sc. degree in Electrical Engineering from the Universidad de Chile, Santiago, in 2007 and the Ph.D. degree from the University of Wisconsin-Madison. He is currently an assistant professor at Universidad de Chile, Santiago. He is a researcher with the Solar Energy Research Center Solar Energy Research Center, Department of Electrical Engineering, University of Chile. His main research interests are power electronics, renewable energy, distributed generation, smart grids and micro grids. Guillermo Jiménez-Estévez. He received the B.Sc. degree in electrical engineering from the Escuela Colombiana de Ingeniería, Bogotá, in 1998 and the M.Sc. and Ph.D. degrees from the Universidad de Chile, Santiago, in 2003 and 2010 respectively. He was Director of the Energy Center of Physical and Mathematical Sciences Faculty of the Universidad de Chile and currently is visiting professor at Universidad de los Andes, Colombia. His main research interests are power systems planning and operation, renewable energy, distributed generation, smart grids, micro grids and regulation.
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