Review of hierarchical control in DC microgrids

Electric Power Systems Research 122 (2015) 159–167

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Review of hierarchical control in DC microgrids C.N. Papadimitriou ∗ , E.I. Zountouridou, N.D. Hatziargyriou School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), University Campus, Zografou, Athens 15780, Greece

a r t i c l e

i n f o

Article history: Received 13 March 2014 Received in revised form 1 October 2014 Accepted 8 January 2015 Keywords: DC microgrid Primary control Secondary control Lоad sharing mechanisms Energy management Hierarchical control

a b s t r a c t DC microgrids (DC MGs) are characterized by attractive features such as high system efficiency, high power quality, reduced cost, and less complex control. The hierarchical control is extensively proposed by researchers for DC MGs. This paper reviews and classifies different primary and secondary control techniques applied to DC MGs. The load sharing mechanisms employed in primary control are distinguished in passive methods and active methods. The different methods for secondary control are also categorized. Their key points and their limitations together with solutions that have been proposed by the research community are presented and critically assessed. © 2015 Elsevier B.V. All rights reserved.

Contents 1. 2. 3.

4.

5.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hierarchical control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Primary control—Load sharing mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Passive load sharing or the droop concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1. Poor voltage regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2. Circulating currents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Active load sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Upper level: Secondary control and power management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Centralized control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Decentralized control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Hybrid power control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction Microgrids (MG) are a novel form of distribution systems, which belong to the wider concept of Smartgrids. The Microgrid can be considered as a small-scale electricity grid, which operates in low or medium voltage networks. It consists of distributed generation (DG) units, such as renewable energy generators and combined heat and power units, along with storage devices and controllable loads (e.g. air conditioners) [1]. Their unique characteristic is that they can be islanded, especially in case of faults, increasing the supply reliability. Currently, the most common application of DC

∗ Corresponding author. Tel.: +30 210 772 4378; fax: +30 210 772 3659. E-mail address: [email protected] (C.N. Papadimitriou). http://dx.doi.org/10.1016/j.epsr.2015.01.006 0378-7796/© 2015 Elsevier B.V. All rights reserved.

159 160 161 161 161 161 163 163 164 164 167 167 167

MGs is the electric power supply of isolated systems like vehicles, space crafts, data centers, telecom systems, while they have been proposed for rural areas and islands [2–4]. The DGs are interconnected via an AC link forming an AC MG, or via a DC link forming a DC MG. While a lot of work has been done in the operation and control of AC MGs, DC MGs have started attracting attention recently, due to their potential advantages over AC MGs, such as: (1) The incorporated DGs can be easier coordinated, as their control is based on DC voltage without the need for synchronization. (2) The corresponding primary control is notably less complex as the reactive power flow control is absent. Yet, the DC link can suffer from harmonic content. (3) As the DC electronic domestic loads dominate today, unnecessary AC/DC power conversions are avoided as most DGs

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Ac Grid

Green power generation

Energy Storage

Wind Turbine

Energy Storage Device

PV panel

3 -ph

AC

DC DC

DC

AC DC

DC

DC

Bidirectional Grid -Interfacing Converter

DC Bus

DC

DC DC

TV & Computer

DC DC

Lighting

DC

DC DC

DC

Refrigerator

Air conditioner & fan

DC

EV charging station

Dc Loads

Fig. 1. The single line diagram of a typical microgrid structure.

generate DC outputs. This has a direct effect on system cost and losses. Also, the converters used for the DC microsources interface, are mostly transformer-less reducing further the size and cost of the system. (4) DC protection in general is difficult due to no zero crossing to interrupt on. But the DC system does not experience high fault currents as the contribution to faults by the converters of the power electronic interfaced load or DGs is limited [2,5,6].

Fig. 1 shows a typical DC microgrid configuration with a common DC bus. Note that the DC MG topology may differ from radial single feeder configuration to two-pole or ring configuration. In these topologies either unipolar or bipolar configurations can be implemented. Bipolar configurations can provide more voltage level options in comparison with unipolar connections. With respect to the voltage levels, they can differ in accordance with the operating requirements of each system. For example, 380 V is a typical voltage level for data centers, while 20,230,325 V are typical voltage levels for house installations. Other levels could be 1500 V, ±750 V, ±230 V, ±170 V etc. The interface terminals within a MG, as shown in Fig. 1, can be mainly categorized into four types: generation (deterministic or non deterministic), load, energy storage system (ESS), and upstream grid connection using voltage-source converters (VSCs). These terminals have to be parallelized with the appropriate interfaces in order to form the MG. Paralleling the DC sources presents a number of challenges: The first challenge concerns the stability of the system that needs a proper converters design. Effective control of the DC bus voltage by the DGs is important, as electronic loads are sensitive to voltage deviations. Another important issue concerns effective load sharing among DGs, as the load should be shared “equally” or depending on DGs’ ratings or costs. In paralleling DGs, the role of the source output impedance is very important. This impedance has a considerable impact on the interaction between the source and the load [7]. This interaction and the way it affects load sharing among DGs will be analyzed later (Fig. 1). The control that is applied in DC microgrids, should ideally respond to all the aforementioned challenges. An important characteristic is that DGs should be capable to support the peer to peer scenario (local control) of operation by exercising autonomous control, especially at primary level. This feature provides modularity and improved reliability of the system. In practice, a compromise

has to be made among load sharing, modularity and autonomous control. In [8], a review of DC microgrid control is presented, focusing however mostly on storage devices for MGs. Four typical control architectures are considered: droop control, hierarchical control, fuzzy control and multi-agent based control. This categorization seems rather untargeted, as the hierarchical control may include droop control, while it does not exclude intelligent control, such as fuzzy control. The droop control is a load sharing mechanism that can be used extensively in many different control architectures. Multi Agent System (MAS) control is mostly intelligent and can incorporate fuzzy logic. This paper provides a review and a classification of the different control methods applied to DC MGs, especially for primary and upper level of the hierarchical control. In general, the control can be divided in centralized, decentralized and hybrid. The main drawback of the control – centralized or not – that is based on communication channels is the poor reliability in case of links failure, whereas the communication free control – decentralized control – suffers from poor voltage control. The hybrid control tries to combine the advantages of the aforementioned controls. Their limitations and ways to overcome them are also discussed. The rest of the paper is structured as follows: Section 2 presents the general hierarchical control of a DC microgrid and the categorization of the power management strategies. Section 3 focuses on the primary level and the different load sharing mechanisms, while it addresses appropriate solutions to overcome their limitations. Section 4 reviews the different power management strategies and Section 5 concludes the paper. 2. Hierarchical control The hierarchical control, as in AC MGs, applies for DC MGs too and can be divided in three levels [9,10] (also in accordance to ISA95) (Fig. 2).

Tertiary control: import/export power Secondary control: restoration and synchronization Primary control: droop Fig. 2. Hierarchical control of microgrids [9].

C.N. Papadimitriou et al. / Electric Power Systems Research 122 (2015) 159–167

• Primary control: This control deals with the load sharing among the DGs. The DC–DC power converters of the DGs are responsible for this mechanism. • Secondary control: This control is responsible for voltage fluctuations regulation. It is also responsible for the synchronization process to re-connect seamlessly the microgrid to the upper grid. • Tertiary control: It sets the power flow between the DC MG and the upper grid. It is also known as energy management system and it communicates with the distribution system operator (DSO). The DSO or even the transmission system operator (TSO) might decide the schedule of power exchange with the MG. (Fig. 2). In the following, secondary and tertiary controls are discussed together. 3. Primary control—Load sharing mechanisms There are two types of methods to achieve power sharing and control of the output voltage level by the DGs: (i) passive control methods and (ii) active load sharing. It is also possible to apply hybrid control methods combining the best features of the previous methods. 3.1. Passive load sharing or the droop concept The basic principle that allows synchronously rotating AC generators to change their power output in response to a change in the system load, without an explicit communication network, is the frequency and voltage variation at the machine terminals. Normally, frequency is linked to active power, and voltage is linked to reactive power. Standard rotating generator systems inherently support these droops (natural synchronizing torque) [1]. Similar droops are also emulated at the DGs inverters for power sharing in an AC microgrid. The droop concept applied at a DC microgrid is slightly different, as the frequency and reactive power are absent and thus, the active power is linked directly to the DC voltage. The droop characteristic of a converter in a DC microgrid can be a linear function between V and I (commonly used) or between P and V (Fig. 3). This droop concept can be easily applied at the DG power converters offering independent control and modularity. Load sharing is achieved directly without the need of communication. The limitations of the droop control method are analyzed next. It should be noted that most research papers attempt to overcome one limitation at a time, depending on their study application. 3.1.1. Poor voltage regulation The conventional droop concept has an inherent trade-off between voltage regulation and current sharing, when the voltage is controlled by several converters. The sharing accuracy can be affected considerably by the DC voltage error, especially when the converters have different characteristics. Let’s assume two parallel, DC converters of equal power ratings. In Fig. 4, the unequal load sharing due to an error (V1o–V2o) in their nominal voltages is shown. From the V–I characteristics, it can be seen that voltage regulation is more accurate for a small droop

161

inverter, but load sharing, that should be equally divided, is poor. For an inverter with large droop, load sharing is tighter, while the voltage regulation is poor. Obviously, the opposite holds for the P–V droop characteristic. So, for a large DC gain, the voltage regulation is tighter, whereas the load sharing is poorer. For a smaller DC gain, the load sharing is tighter while the voltage regulation is poorer [7,10,11] (Fig. 4). A compromise between load sharing and accurate voltage regulation is therefore needed, when the droop concept is employed. This can be a real problem with storage devices in the microgrid, as the challenge of balancing the energy storage needs also to be taken into account. In order to overcome this problem, novel control strategies have been proposed. In [7], the voltage control and the load sharing between the parallel sources are achieved through Gain-Scheduling Control. The gain K (slope of the droop characteristic) changes dynamically, as it follows the gain scheduling curve, when the load power changes and the voltage need to be controlled. Load sharing is maintained at an acceptable level and eventually the system is more robust. In [11] a sophisticated voltage regulation technique based on Fuzzy Control and Gain-Scheduling Control is proposed. The control accomplishes good voltage regulation, good load sharing and good energy balance for the storage devices, simultaneously. As previously, the gain-scheduling technique, changes the gain K according to a linear function of K with respect to the output power of the sources. This function is derived from simulations and achieves better voltage regulation and better load sharing, simultaneously. Fuzzy control is employed to balance the stored energy by changing the DC bus voltage reference. 3.1.2. Circulating currents Application of the droop concept can create circulating currents among the DGs when the power converters are treated as voltage sources. In order to suppress these circulating currents, two solutions are proposed: use of (a) series resistor, (b) virtual output resistance (adaptive voltage positioning (AVP)). According to the first method, a resistor is placed in series with the DG output to provide a voltage drop in the output. The resistor value is set via a potentiometer so that the voltage drop of the output of all paralleled DGs, are made almost identical. Obviously, this method is impractical in real systems, since it results high power losses in the series resistor, if the drop in output voltage is large [12]. The second method is better applicable in the DC MG concept and has common philosophy with the virtual or fictitious impedance method in AC MGs [2,5,12]. Specifically, the DC MG droop control is based on subtracting part of the converter output current proportional to a virtual resistance (VR), Rdi , from the voltage reference at no load. This is illustrated in the two-nodes DC microgrid depicted in Fig. 5, where each converter is simplified by its Thevenin equivalent model. For DG1: ∗ ∗ Vdc1 = Vdc − idc1 Rd1

(1)

Vdc1 is the voltage reference for voltage loop, idc1 is the module ∗ output current, Rd1 is the virtual output resistance (VR), and Vdc is the output voltage reference at no load. If we consider the line

Fig. 3. Droop characteristic.

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Fig. 4. The droop concept limitations (a) V–I characteristic [10] (b) P–V characteristic.

impedances Rline1 and Rline2 , the following equations are derived (Fig. 5): ∗ −i Vload = Vdc dc1 Rd1 − idc1 Rline1 ∗ −i Vload = Vdc dc2 Rd2 − idc2 Rline2

(2)

At no load, the voltage deviation is zero. When the inverter is connected, the deviation value varies with the load current. It also depends on the value of the virtual resistance. To guarantee that the voltage deviation does not exceed its maximum acceptable value, as a stable voltage is of crucial importance in DC MGs, the value of the virtual resistance should be limited according to the following(derived from (6)):

(3)

Rdi ≤

These expressions then yield the following:





Rline2 − Rd2 /Rd1 Rline1 R idc1 = d2 + Rd1 + Rline1 Rd1 idc2

In the droop-controlled DC microgrid, the DC output current of each converter is set to be inversely proportional to its virtual resistance. Thus, the current sharing error can be eliminated if the second term of Eq. (3) is zero. In this case, the following expression is derived: Rd1 R = line1 Rd2 Rline2

(4)

In case of small DC MGs, the line impedances are quite small (order of 0.2 m), while a larger virtual resistance can be selected (order of 0.2 ). So, as Rd1 > >Rline1 and Rd2 > >Rline2 , the line impedances can be neglected and the following applies (from (2)): idc1 R + Rline2 R = d2 ≈ d2 Rd1 + Rline1 Rd1 idc2

∗ Vdci = −idci Rdi (i = 1, 2. . .)

Fig. 5. A simplified dc-microgrid with two nodes [13].

(6)

(7)

where idcfli is the output current of the i-converter at full load [9,13]. A small value of the droop gain/virtual resistance is used to restrict large variation in system voltage and the above equation is an inherent droop concept limitation. In order to overcome the voltage deviation and succeed good load sharing, some authors have proposed multiplication of measured voltage deviation to a value reciprocal to virtual resistance (VR) Rdi [15]. In the following, the relation between the droop coefficient K of the P–V droop characteristic and the VR of the V–I droop characteristic is analyzed for the system of Fig. 5. From Eq. (1): Vdci = Rdi idci → Vdci = Vdc − Rdi idci → Vdci idci 2 2 = Vdc idci − Rdi idci → Pdci = Vdc idci − Rdi idci

(5)

If the system is larger, though, Eq. (5) cannot be satisfied, while for higher virtual resistances, the system stability might be jeopardized (see (7)) [10,13]. In [14], the circulating currents are suppressed by applying a power strategy of bidirectional VSCs implemented into two Synchronous Reference Frames (SRF): positive SRF and negative SRF. It should be noted, that the droop control loop has an inherent load-dependent voltage deviation. The voltage deviation can be found from (1) as:

Vdci max idcfli

(8)

From the P–V droop characteristic, the following holds: Pdci = KVdci

(9)

From (1), (8) and (9) the following is derived: 2 KRdi idci = Vdc idci − Rdi idci → KRdi = Vdc − Rdi idci → Rdi (K + idci ) = Vdc

K=

Vdc − idci Rdi

(10)

In practice, the DC loads are far from constant resistances and usually are converter-based constant power loads (CPL). Some researchers prove that CPL can significantly affect system stability and transient behavior, especially in large-scale DC networks [6,16–20]. The main concern is what happens with the system stability, while droop controlled sources co-exist with CPLs in the MG. Ref. [21] defines a stable operating point when a CPL is present. Note that these power electronic loads (CPLs) together with the rectifiers that interface the DC MG with the AC system can create harmonic pollution in the DC link. The simpler filter applicable would be a capacitor on the DC side of inverters and rectifiers [22]. A novel variant of the droop control is called the voltage-based power–voltage control method (VbPV) [23]. The VbPV control method provides the flattest and approximately nominal voltage profile, which is necessary for the operation of sensitive loads,

C.N. Papadimitriou et al. / Electric Power Systems Research 122 (2015) 159–167

V terminal V threshold V MaxPower

Kd

V Fault 0

I max

I Source

Fig. 6. V–I characteristic of each source [22].

regardless of the size and structure of the DC MGs. The method also preserves the voltage drops from increasing in case of long lines in the DC MGs and suppresses the circulating currents. The voltage–current characteristic, as shown in Fig. 6, is assigned to each source (Fig. 6). Where Vterminal is the terminal voltage of the DG and Isource is the injected current by the DG. Kd is the droop coefficient of the characteristic (the same as Rdi ) and the rest of parameters are explained in the following. The DG generates power when its terminal voltage becomes less than the Vthreshold . As the terminal voltage decreases, the generated power increases until its maximum generated power (Pmax ) at VMaxPower . The injected current, Isource , is calculated by: Isource =

(Vterminal − Vthreshold ) Kd

(11)

The injected current remains constant and equals Imax , as long as the terminal voltage is between VMaxPower and VFault . When the terminal voltage becomes less than VFault , the DG is switched off. The value of VFault depends on the DC MG characteristics such as the lower limit of the permitted voltage and the beginning voltage of the voltage collapse. Obviously, VFault of all the sources in the DC MG is the same. Analysis about the choice of the parameters is carried out in [23], while numerical simulations for a realistic DC MG are performed proving the good performance of this control method. 3.2. Active load sharing The active load sharing control methods can be classified into four different types and are derived from control schemes of parallel-connected DC–DC converters: (i) centralized control, (ii) master-slave control (MS), (iii) average load sharing and (iv) circular chain control (3C) [24] Although these control schemes achieve both good output-voltage regulation and equal current sharing, they need communication links among the modules. In the following, the four methods are briefly discussed.

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Centralized control (Fig. 7a): A central control board (CCB) is necessary in this scheme in order to set the reference current for each module. The measured load current is driven in the CCB where is divided by the number of the modules in parallel (N), forming the reference current (ij∗ ) of each module. Then the reference current is subtracted from the current of each module. The error is processed through a current control loop (CL). An outer control loop in the centralized control adjusts the load voltage. The main drawback of this method, apart the central controller, is the need to measure the total load current, so the application of this scheme in a large distribution system is difficult. Master–slave control (Fig. 7b): One inverter (master) regulates the voltage and sets the current references of the other units (slaves). So, the master inverter operates in voltage control mode and the rest of the units in current control mode. The main drawback of this method is the single point failure and the requirement of a supervisory control. The system is also difficult to expand, failing to satisfy the plug and play functionalities. The method is further categorized depending on the role of the master: (i) dedicated (ii) rotary and (iii) high-crest current. Average load sharing (Fig. 7c): A single wire is used, which contains the average current information computed by a resistor connected to the current sensor of every single module. In addition, adjusting the resistor to a proper value, we can parallel converters with different power ratings by suppressing the circulating currents. The average current of all the modules is the reference for each individual one. This control scheme is more reliable as no Master–Slave philosophy is present. In addition, the approach is highly modular and expandable. Circular chain control (Fig. 7d): In this scheme, the current reference of each module is taken from the other module, forming a control ring. Obviously, in order to form the circular chain the current reference of the first unit is obtained from that of the last unit. An interesting variant of the circular chain control is the current limitation control. In this case, the master–slave logic is present. The voltage is controlled by the master module (inverter under voltage control) and the slave modules share the load current (inverters under current control). The circular chain in this case is formed only by the slaves modules and the master module is exempted. The current command of the slave is generated by its previous module and limited in amplitude forming the circular chain. Note that every module can become the master (Fig. 7). 4. Upper level: Secondary control and power management The key point of power management in DC MGs is to maintain the power balance between energy sources, storage devices

Fig. 7. Active load sharing methods: (a) centralized control (b) master–slave control (c) Average load sharing (d) 3C control.

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4.2. Decentralized control

Hierarchical Control

Centralized

Hybrid

Decentralized

Communication link available

No communication

Power Management Strategies in Dc Microgrids Fig. 8. Power management strategies classification.

and loads at any time, which is represented by the stable DC bus voltage. The power management scheme dictates in which mode the DC microgrid operates, in order to optimize its performance. In interconnected mode, power exchange with the upstream grid is included. If the power is not enough for the loads, the DC MG will absorb power from the upper grid. If the power generated within the MG is more than needed, it will transfer power to the upper grid. Keeping this in mind, many research papers present strategies for grid-connected, islanded and transition operation [3,8,25,26]. Most energy management strategies are based on Hierarchical Control. These management strategies can be divided into three major categories – depending on the way the secondary control is implemented – centralized, decentralized and hybrid. Fig. 8 shows this classification. The next subsections analyze each category (Fig. 8). 4.1. Centralized control In this method a centralized controller is employed in order to overcome the voltage deviation caused by the primary controllers and to achieve power balance among the DGs and loads in the DC MG. This method can achieve optimal control, but it requires some form of real-time communication. Ref. [9] describes a centralized controller that restores the voltage level by using low bandwidth communication channels (LBC). The voltage level in the MG bus is compared with the voltage reference, and the error ıV, processed through a compensator, is sent to all the modules via the LBC to restore the output voltage. So, Eq. (1) for each DG becomes: ∗ ∗ Vdci = Vdc + ıV − idci Rdi

where

i = 1, 2. . .

(12)

In [2], a centralized control, called supervisory, is applied in order to avoid voltage deviations. An interesting approach is presented by changing the virtual resistance (VR) values, Rdi , online, in a DC MG with two batteries connected to the main bus. The virtual resistance of their droop control is adapted online by the supervisory control; so that the batteries of the islanded system have the same SOC at all times and therefore preserve equally their cycle life. The value of Rdi corresponds to the current SOC and capacity of the battery i. Higher Rdi will cause lower charge/discharge rate and vice versa. Therefore, when batteries are charging, higher Rdi are given to a battery with higher SOC. On the other hand, when discharging, higher Rdi is given to a battery with lower SOC. A symmetric function for computing charge and discharge VRs is proposed, taking into account the batteries’ SOCs and their rate of change. Application of Centralized Control is reported in [26], where an intelligent multi-layer supervision subsystem is suggested to achieve power balance in a DC MG of a tertiary building. The supervision system dictates power setpoints for the DGs and decides load shedding actions according to the end-user demands, forecasts of PV production and load, energy cost management, etc.

In this control strategy, the DGs are autonomously controlled using local real-time feedbacks. The methods for autonomously controlled MGs can be further categorized into (1) with communication (2) no-communication. Microgrid with communication: Communication between DGs is required, but the operational decisions are taken in a decentralized way, usually at the DGs level. That is the substantial difference with the centralized secondary control. In [13], a LBC is used for the exchange of the DC voltage and current information of the converters, and all of the calculations and operations are realized locally. The aim of the proposed strategy is to solve the two main problems produced by droop primary control: poor current sharing and voltage deviation. Two signals are formed locally and affect the droop equation of the primary level control. The error between the voltage reference and the calculated average value of the DC voltage is driven through a compensator and forms the first signal. It is responsible for the DC bus voltage restoration. The error between the current reference and the calculated average value of the current is driven through a compensator and forms the second signal, which is responsible for the precise current sharing. In [10], a decentralized approach is presented to address the primary control voltage deviation. Droop V–I characteristic is shifted along the voltage axis by addition of a small voltage signal u. This shift control is termed as digital average current sharing (DACS) control. The signal’s value is determined through the utilization of a LBC as follows: The controller of each source communicates with the controller of other sources and sends the magnitude of its current (in per unit). Using this information, the individual source controller determines the average value of the current supplied by all the sources. This current multiplied by a shift gain forms the voltage signal u. Distributed control based on average current sharing (ACS) is also presented in [27]. The local controllers communicate with each other using a common bus of information. The measured value of each source current is converted to voltage signal, via a resistance Rj which is connected to the ACS bus (analog). If resistances of all modules are equal, the voltage appearing on the bus corresponds to the currents average value. This signal is added to the droop equation. The scheme offers equal load sharing among sources and tight voltage regulation of the DC bus. The main drawback is that the current sharing bus has to be distributed within the DC MG region along with power lines. This may inject significant external noise in the bus. In [28] a decentralized secondary control that is based on power-line signaling (PLS) is proposed. In this case, the power network serves as a communication channel where the exchanging messages come in the form of PLS signals directly injected from the converters’ primary control loops. The localized controller in each source receives the PLS signal, extracts the needed information and broadcasts by giving rise to the respective voltage references of the primary controllers. This way, the sources change mode of operation whereas the DC voltage bus can deviate from its nominal value but in a certain range. This deviation can be optionally canceled by secondary control action without affecting system’s proper operation. The main drawbacks of this scheme are the slow communication through PLS and the possible mismatch in the electromagnetic compatibility (EMC) with the electronic devices in the MG. Agent based control is predominantly decentralized. MultiAgent Systems (MAS) are composed of intelligent entities which can be software or hardware units with only local knowledge and limited abilities, but can be able to interact with each other to achieve a global target [1]. Agents can work with the help of conventional control strategy, as well as advanced control methods, e.g. artificial intelligent based techniques and expert systems. Especially, DC MG present a unique challenge to agent modeling due

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Fig. 9. Power management strategies (a) centralized control (b) decentralized control with communication (c) decentralized control without communication (d) hybrid power control.

to the effect of the very fast transients that occur in the distribution lines, while the collaborative decision making process among agents requires some time [29]. In [30], a DC-MG lab is presented that provides a flexible platform for different control schemes with the help of Rockwell’s agent based distributed control software. In [29], a small scale DC MG is used to implement and test several intelligent distributed control concepts. These are implemented using a combination of real-time control and intelligent agents that communicate with each other. In this distributed information sharing process, each agent is responsible for solving a portion of the problem, such as load priority, voltage balancing and battery charging. In [31], a DC MG containing three constant power loads (CPL) is introduced. As already mentioned in Section 3.1, such a system could become unstable under some conditions. Local stabilizing agents on CPLs are implemented and are designed in an appropriate way to ensure the system stability, even during faults, such as loss of one of the stabilizing agents and failure of one or several loads. No-communication microgrid: In this category, all operations can be performed without communications. The method mostly used is the DC Bus Signaling (DBS) technique. This method deviates from the concept of accurate power sharing with minimal voltage deviations on the bus. Significant voltage deviations from nominal are permitted, since it is assumed that the system is power electronics based and the source and load interfaces can be designed to operate satisfactorily within a broader specified voltage range. The DC bus voltage is used as an information carrier to distinguish different modes of operation for the DGs. In this case, voltage thresholds that dictate the different modes of operation and priority of the devices and loads have to be set [32]. The main advantage is that the transition between different

modes and the corresponding changes of control methods for converters can be achieved without additional communication links. The benefits are cost reduction and reliability enhancement [33]. The voltage thresholds have to be fairly distinctive, but also tight enough in order not to destabilize the system. For DC networks with longer transmission cables and considering possible DC measurement errors, the DC voltages at different terminals can be slightly different. So, care must be taken when selecting the voltage thresholds to ensure normal load and generation variation will not cause wrong transitions. One should take also into account the different control methods used for different terminals [21,34]. The thresholds for the DGs converters are calculated beginning with the highest threshold. Each successive threshold is calculated to ensure that when the sources assigned to the previous threshold are online, voltage drop in the system, caused by distribution line resistance and voltage droop, do not prematurely activate sources assigned to the next threshold. In general, the first voltage threshold, V0 , is set to the nominal operating voltage of the system. Each successive voltage threshold, Vn, is calculated by subtracting the voltage drop and a margin of error from the preceding threshold. For load shedding, the shutdown thresholds must be calculated such that the shutdown priority of the loads remains unaffected by the unequal propagation of the DC bus voltage due to long cables throughout the system. In this case, the shutdown threshold for the lowest priority load is calculated first. Refs. [33,35,36] present different power management strategies for different DC MGs focusing on extreme conditions. In [33], the DC bus signaling is employed for controlling a DC MG based on modular photovoltaic generation. The paper deals with extreme conditions in islanding mode, such as fully charged or fully discharged batteries. Ref. [35] designs a power sharing strategy based on DBS for a laboratory DC MG including PV, batteries and fuel cell.

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Passive load sharing (communication free) Decentralized control with communication Hybr id

Decentralized control without communication

Secondary Control

Centralized control

Active load sharing (with communicat ion)

Primary Control

Voltage control

Limitations to overcome Accurate Circulating power Currents sharing

Proposed solutions

fair

good

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Gain scheduling technique[7] Gain scheduling technique and Fuzzy control[11] control strategy implemented into two Synchronous Reference Frames (SRF): positive SRF and negative SRF [14] multiplication of measured voltage deviation to a value reciprocal to virtual resistance [15] The voltage-based power-voltage control method (VbPV)[22] (i) centralized control, (ii) masterslave (MS), (iii) average load sharing and (iv) 3C[23]

good

good

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good

-

Good suppressing

-

good

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excellent

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good

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good

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excellent

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good

good

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-

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DACS[10]

good excellent

good excellent

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ACS[26] PLS[27]

excellent

excellent

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Multi- agent based control systems[2830]

good

good

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Supervisory control with adaptive droop[2] Voltage restoration level enabled [9] intelligent multilayer supervision subsystem[25] Improved droop control[13]

DBS[4,21, 32-35]

good

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N o vel hierarchical c o ntr o l[38]

Fig. 10. Synopsis of references on DC microgrids control.

The designed controller takes effective control to limit the influence of diverse load. Ref. [36] presents a novel adaptive DBS for a DC MG containing batteries. The batteries swap modes of operation (droop mode and constant current charging mode), through their I–V characteristics, to avoid that the bus voltage reaches its maximum or minimum value. Another use of the DBS for power management involves load shedding. Ref. [4] proposes load shedding based on predefined load priority levels, during abnormal or island conditions. Load shedding is activated by insufficient power generation or insufficient energy storage. Ref. [21] also presents

a control strategy based on DBS that involves load shedding and generation curtailment. The main drawback of the DBS methods is that the number of sources and storages within the system is restricted by the number of voltage levels, which cannot be divided unlimitedly due to the DC bus voltage tolerance. Also, adding sources with higher priority involves changing the states of all other sources with a lower priority, which restrains “plug and play” operation. Moreover, voltage level at different locations varies due to resistive drop across the interconnecting cables causing erroneous operation transitions

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of DGs. For this reason, most of the works reported in this area describe applications of DBS to superconducting DC MG or small MG with very short electrical lines [10,32]. To address the limitation of the varying voltage level due to line resistances, a small AC signal over the DC signal is injected in a method reported in [37,38]. The frequency of the AC signal acts as a means of communication. This method is prone to noise on power cables. Further, it requires circuits for accurate injection and detection of the AC signal. This limits the viability of the scheme. 4.3. Hybrid power control Hybrid control architectures combine the aforementioned strategies at different levels, so as to achieve better results. In [39] a novel hierarchical control is implemented. The first level is based on the DBS technique and is activated when the communication links fail. The second level is activated under normal operation where the Communication link provides full observability over the DC MG including real time bus voltage, power flow and operation status of converters. Fig. 9 depicts the general power management strategies that are presented in Section 4, while Fig. 10 summarizes the references at both primary and secondary/tertiary level (Fig. 9). 5. Conclusion This paper reviews and classifies the primary and secondary control methods applied to DC MGs. The hierarchical control, as known in AC MGs, is also applied to DC MGs. It is divided into three main categories – depending on the way the secondary control is implemented – centralized, decentralized and hybrid. The decentralized control is further divided into techniques requiring communication and without communication. The above control methods are implemented by different load sharing mechanisms, such as passive methods (droop control, VbPV) or active methods and different control techniques such as DBS, fuzzy, MAS etc. Their key points and their limitations are presented and critically assessed. References [1] N. Hatziargyriou, Microgrids: Architectures and Control, Wiley, IEEE Press, Athens, 2014. [2] T. Dragicevic, J.M. Guerrero, J.C. Vasquez, D. Skrlec, Supervisory control of an adaptive-droop regulated DC microgrid with battery management capability, IEEE Trans. Power Electron. 29 (2) (2014) 695–706. [3] D. Salomonsson, L. Soder, A. Sannino, An adaptive control system for a DC microgrid for data centers, IEEE Trans. Ind. Appl. 44 (6) (2008) 1910–1917. [4] Lie Xu, Dong Chen, Control and operation of a DC microgrid with variable generation and energy storage, IEEE Trans. Power Delivery 26 (4) (2011) 2513–2522. [5] Y. Ito, Y. Zhongqing, H. Akagi, DC micro-grid based distribution power generation system, in: Power Electronics and Motion Control Conference, vol. 3, 2004, pp. 1740–1745. [6] A.A.A. Radwan, Y.A.-R.I. Mohamed, Linear active stabilization of converterdominated DC microgrids, IEEE Trans. Smart Grid 3 (1) (2012) 203–216. [7] Zhihong Ye, D. Boroyevich, Kun Xing, F.C. Lee, Design of parallel sources in DC distributed power systems by using gain-scheduling technique, in: Power Electronics Specialists Conference. vol. 1, 1999, pp. 161–165. [8] Z.H. Jian, Z.Y. He, J. Jia, Y. Xie, A review of control strategies for DC microgrid, in: Fourth International Conference on Intelligent Control and Information Processing (ICICIP), 2013, pp. 666–671. ˜ M. Castilla, Hierarchical [9] J.M. Guerrero, J.C. Vasquez, J. Matas, L.G. de Vicuna, control of droop-controlled AC and DC microgrids—a general approach toward standardization, IEEE Trans. Ind. Electron. 58 (1) (2011) 158–172. [10] S. Anand, B.G. Fernandes, M. Guerrero, Distributed control to ensure proportional load sharing and improve voltage regulation in low voltage DC microgrids, IEEE Trans. Power Electron. 28 (4) (2013) 1900–1913. [11] H. Kakigano, A. Nishino, T. Ise, Distribution voltage control for DC microgrid with fuzzy control and gain-scheduling control, in: IEEE Eighth International Conference on Power Electronics and ECCE Asia (ICPE & ECCE), 2011, pp. 256–263.

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[12] Gulin Marko, Control of a DC Microgrid, 2012, https://www.fer.unizg.hr/ download/repository/KDI Marko Gulin.pdf. [13] Xiaonan Lu, J.M. Guerrero, Kai Sun, J.C. Vasquez, An improved droop control method for DC microgrids based on low bandwidth communication with DC bus voltage restoration and enhanced current sharing accuracy, IEEE Trans. Power Electron. 29 (4) (2014) 1800–1812. [14] M. Kumar, S.N. Singh, S.C. Srivastava, Design and control of smart DC microgrid for integration of renewable energy sources, in: Power and Energy Society General Meeting, 2012, pp. 1–7. [15] P. Karlsson, J. Svensson, DC bus voltage control for a distributed power system, IEEE Trans. Power Electron. 18 (6) (2003) 1405–1412. [16] R.S. Balog, Autonomous Local Control in Distributed DC Power Systems, Dept. Elect. Comput. Eng., Univ. Illinois at Urbana-Champaign, Champaign, IL, 2006 (Ph.D. Dissertation). [17] S.D. Sudhoff, S.F. Glover, P.T. Lamm, D.H. Schmucker, D.E. Delisle, Admittance space stability analysis of power electronic systems, IEEE Trans. Aerosp. Electron. Syst. 36 (3) (2000) 965–973. [18] C.H. Rivetta, A. Emadi, G.A. Williamson, R. Jayabalan, B. Fahimi, Analysis and control of a buck DC–DC converter operating with constant power load in sea and undersea vehicles, IEEE Trans. Ind. Appl. 42 (2) (2006) 559–572. [19] A. Griffo, J. Wang,D. Howe, Large signal stability analysis of DC power systems with constant power loads, in: Vehicle Power and Propulsion Conference, 2008. VPPC ’08. IEEE, 3–5 Sept. 2008, pp. 1–6. [20] C.N. Onwuchekwa, A. Kwasinski, Dynamic behavior of singlephase full-wave uncontrolled rectifiers with instantaneous constant power loads, in: Energy Conversion Congress and Exposition (ECCE) IEEE, 2011, pp. 3472–3479. [21] D. Chen, L. Xu, L. Yao, DC voltage variation based autonomous control of DC microgrids, IEEE Trans. Power Delivery 28 (2) (2013) 637–648. [22] Andrey Lana, Kaipia Tero, Partanen Jarmo, Investigation into harmonics of LVDC power distribution network using EMTDC/PSCAD software, in: International Conference on Renewable Energies and Power Quality (ICREPQ’11), 2010, pp. 1–6. [23] R. Asad, A. Kazemi, A novel decentralized voltage control method for direct current microgrids with sensitive loads, Int. Trans. Electr. Energy Syst. (2013), http://dx.doi.org/10.1002/etep.1833. [24] J.M. Guerrero, Lijun Hang, J. Uceda, Control of distributed uninterruptible power supply systems, IEEE Trans. Ind. Electron. 55 (8) (2008) 2845–2859. [25] H. Kakigano, Y. Miura, T. Ise, R. Uchida, DC micro-grid for super high quality distribution-system configuration and control of distributed generations and energy storage devices, in: Power Electronics Specialists Conference PESC’06, 2006, pp. 1–7. [26] Baochao Wang, M. Sechilariu, F. Locment, Intelligent DC microgrid with smart grid communications: control strategy consideration and design, IEEE Trans. Smart Grid 3 (4) (2012) 2148–2156. [27] W. Qiu, Z. Liang, Practical design considerations of current sharing control for parallel VRM applications, in: 20th Applied Power Electronics Conference and Exposition. vol. 1, 2005, pp. 281–286. [28] T. Dragicevic, J.M. Guerrero, J.C. Vasquez, A distributed control strategy for coordination of an autonomous LVDC microgrid based on power-line signaling, IEEE Trans. Ind. Electron. 61 (7) (2014) 3313–3326. [29] F.P. Maturana, R.J. Staron, D.L. Carnahan, K.A. Loparo, Distributed control concepts for future power grids, in: Energytech IEEE, 2013, pp. 1–6. [30] D.H. Moore, J.M. Murray, F.P. Maturana, T. Wendel, K.A. Loparo, Agent-based control of a DC microgrid, in: Energytech IEEE, 2013, pp. 1–6. [31] P. Magne, B. Nahid-Mobarakeh, S. Pierfederici, A design method for a faulttolerant multi-agent stabilizing system for DC microgrids with constant power loads, in: Transportation Electrification Conference and Expo (ITEC) IEEE, 2012, pp. 1–6. [32] J. Bryan, R. Duke, S. Round, Decentralized generator scheduling in a nanogrid using DC bus signaling, in: IEEE Power Engineering Society General Meeting. vol. 1, 2004, pp. 977–982. [33] Zhang Li, Tianjin Wu, Xing Yan, Kai Sun, J.M. Gurrero, Power control of DC microgrid using DC bus signaling, in: 26th Applied Power Electronics Conference and Exposition (APEC) IEEE, 2011, pp. 1926–1932. [34] J.K. Schonberger, Distributed Control of a Nanogrid Using DC Bus Signalling, University of Canterbury, Christchurch, New Zealand, 2005 (Ph.D. Dissertation). [35] Xiaofeng Sun, Zhizhen Lian, Baocheng Wang, Xin Li, A hybrid renewable DC microgrid voltage control, in: IEEE Sixth International Power Electronics and Motion Control Conference, IPEMC, 2009, pp. 725–729. [36] Xunwei Yu, A. Huang, R. Burgos, Jun Li, Yu Du, A fully autonomous power management strategy for DC microgrid bus voltages, in: 28th IEEE Applied Power Electronics Conference and Exposition (APEC), 2013, pp. 2876–2881. [37] D.J. Perreault, R.L. Selders, J.G. Kassakian, Frequency-based current-sharing techniques for paralleled power converters, IEEE Trans. Power Electron. 13 (4) (1998) 626–634. [38] A. Tuladhar, K. Jin, A novel control technique to operate DC/DC converters in parallel with no control interconnections, in: 29th Annual IEEE Power Electronics Specialists Conference. vol. 1, 1998, pp. 892–898. [39] Jin Chi, Peng Wang, Jianfang Xiao, Tang Yi, Hoong Choo Fook, Implementation of hierarchical control in DC microgrids, IEEE Trans. Ind. Electron. 61 (8) (2014) 4032–4042.