Intelligent Control for a DC Micro-Grid System

Michele Martino Aalborg University

mmarti10@student.aau.dk

Yamshid Farhat Quiñones Polytechnic University of Catalonia yamshid.farhat@estudiant.upc.edu

Pietro Raboni Aalborg University

pra@et.aau.dk

Zhe Chen Aalborg University

zch@et.aau.dk

Abstract - This paper presents the dynamic response of a DC–micro-grid (DC-MG) controlled in master-slave mode. The benefits of the micro-grids (MGs) are the low cost in terms of power electronics converters and the high reliability and quality, even in case of loss connection to the transmission system. A DC-MG in fact can survive in standalone mode if properly managed. The considered system is made by a photovoltaic array (PV), a wind turbine (WT), a gas engine (GE) and an energy storage system (ESS). The DC-MG behavior is analyzed in different scenarios to demonstrate the efficacy of the control for all the units, especially in case of variable weather conditions with different DC loads. Thus the voltage level of the system and the power flow are shown, out of a detailed description of the power electronic interfaces featuring the distributed generators (DGs). Index Terms — Distribution generation, DC micro-grid, energy management, energy storage, micro-grid, voltage regulation.

I. INTRODUCTION

The Distributed Energy Resources (DER) emerged as a promising option to match the growing customer needs in terms of electric power with an emphasis on reliability and power quality, all in the context of the traditional power system evolution. Recently an increased attention about MGs opportunities has been paid opening a new field for the electrical engineers community. A MG could ensure high reliability and power quality, being capable of maintaining the normal operation also during grid contingencies. So far the engineers have developed two alternative methods for ensuring the power balance in islanded mode. The master and slave one, sets a unit in charge for the quality control while the other generators behave like current sources in order to avoid any current recirculation between master units. With the droop control instead the sources shares automatically the load. This control strategy, even if wireless and completely decentralized, doesn’t fit to most of renewable DER, featured by almost null marginal costs and so cost-effectively operating at the maximum power point. In this sense only the installation of energy storages couple to these DER could allow their participation to the droop control [1].

In addiction in this context has arisen again the appeal of the DC distribution, in spite of the AC one, due to the diffusion of power electronics interfaced loads and sources and the concurrent fading of uncontrolled asynchronous machines based household appliances and CRT screens. Just the cost savings related to the absence of inverter could open the market of this technology [2].

Reference [3] proposes a Virtual Power Plant for islanded DC-MG running for long term simulations. Similar control

strategies have been proposed in [4], [5] for managing the status transitions, evaluating the results over simplified MG models. For larger MG instead [6] conceived a state variable approach. A model of a DC-MG with average unit models was shown in [7]. Nevertheless several issues are still unsolved like the dynamic behavior of the system during load or generation transients or the communication delay representation. Therefore this paper aims to highlight the behavior of power electronic interfaces in relation to the energy management system decisions.

II. MICROGRID ARCHITECTURE

The studied DC-MG (Fig. 1) consists of a cluster of generators, storages and loads and a power electronic interface to the three-phase AC grid. The regarded DGs are a 50kW WT, a 25kW PV and a 40kW back-up GE. A stationary Lead-Acid battery and Lithium-Ion one, for Electric Vehicle usage, compose the ESS.

The choice of the DC-MG configuration allows the easy adaptation of the traditional three wires AC distribution system, using two wires with 800V pole-to-pole voltage [8]. This is made possible assuming the same power flow and neglecting the cable insulation concerns. In fact given the rated capability of a wire we can consider the DC current equals to the RMS value of the AC one. Therefore under the hypothesis of same power flow it is found:

𝑉𝐷𝐶 = 𝑉𝐴𝐶 ∙ √3 ∙ cos𝛷 (1)

Fig. 1. Microgrid architecture

According to the European standards the AC distribution voltage is 400V (±10%) and regarding a power factor cos𝛷 = 0.9 it is found a DC pole-to-pole voltage of 623V. Nevertheless a 800V pole-to-pole voltage has been assumed in order to ensure the operation of the grid tied inverter in the linear modulation region and for offering two different voltage levels (pole-to-pole or pole-to neutral) to the loads.

III. UNIT MODELS

This section presents the units’ models including the control of the power electronic interfaces.

3.1. Photovoltaic Array The PV model is composed by 11 parallel strings, each one

devised by 12 modules connected in series, for an overall nominal power of 25kW. The aggregated PV cell module is defined by a Four-Parameter Model. The optimal operation of the PV is ensured by the Incremental Conductance Maximum Power Point Tracking (MPPT) algorithm, fitting to rapidly changing irradiance conditions [9]. It aims “climbing” the typical hill-shaped P-V curve, pursuing the zero slope condition:

𝑑𝑃𝑑𝑉

= 0 (2)

The (2) in turns allows achieving the Maximum Power Point (MPP) when:

� 𝑑𝐼𝑑𝑉�𝑖=𝐼𝑀𝑃𝑃𝑣=𝑉𝑀𝑃𝑃

= −𝐼𝑀𝑃𝑃𝑉𝑀𝑃𝑃

(3)

Therefore the algorithm conceived in [10] and shown in Fig. 2 is used to drive the boost converter interfacing the PV system to the DC-MG.

3.2. Wind Turbine The needs of cost-effective, low noise and low

maintenance lead the manufacturer of small scale WTs to

operate without gearboxes and so with low speed generators [11]. In this paper a Permanent Magnet Synchronous Generator (PMSG) is connected to a two-level inverter, allowing the WT operation at variable speed and the power transfer to the DC-MG. The designed control for wind speeds below the nominal one pursues the WT operation at the maximum power coefficient, setting reference rotational speeds proportional to the wind speeds as highlighted in Fig. 3. Instead for stronger winds the method ensures the nominal power production as shown in the flat region of the optimal tracking curve of Fig. 4. This is achieved by setting a reference rotational speed fulfilling the proper reduction of the power coefficient. Such speed regulation is attained with a PI regulator in an outer speed loop applied to the inverter control. This operates with inner loops controlling the machine currents transformed in a reference frame rotating at the PMSG electrical speed. Such speed can be easily obtained with an encoder and known the number of pair poles of the machine. The overall control scheme is reported in Fig. 5. For the sake of the simulation burden a low inertia value, as reported in table 1, has been considered, which allows verifying the WT behaviour with short term simulations.

Fig. 3. Power curve dependency by the ratio between rotational speed and

wind speed

Fig. 4. WT Optimal Tracking Curve (dashed line)

Fig. 2. Maximum Power Point Algorithm for PV boost control

TABLE I WT NOMINAL RATINGS AND MAIN PARAMETERS

PnPMSG [kW] 50 vn [m/s] 12 Vn [V] 400

Pair Poles 12 J [kg·m²] 100

Fig. 5. WT control scheme

3.3. Gas Engine In this paper the 40 kW GE operates as an Uninterruptable Power Supply (UPS) in charge for ensuring the DC-MG grid bus bar voltage in case of contingencies. The model consists of a PMSG connected to a six pulse diode bridge rectifier feeding a boost converter, controlled in Current Programmed Mode with an outer voltage loop [12][13], as shown in Fig. 6. Due to the inertia of the generator of the GE unit, an unbalanced power between the load and the sources could happen during the transient time.

3.4. Energy Storage System The ESS is composed by a stationary lead-acid battery,

whit low capital cost, and an electric Vehicle Li-Ion battery, with higher energy density, very high efficiency and long life cycle. Each model has been designed with 6.5Ah capacity and respectively 150V and 200V nominal voltage. Each unit is connected to the DC-MG by means of a bidirectional buck-boost converter. It operates like a boost whenever the power transfer is towards the grid, discharging the battery, while as a buck during the opposite condition. Fundamental condition for safe and reliable operation is the complementary operation of the controlled switches S1 and S2 in Fig. 7 [14].

Fig. 6. GE control scheme

Fig. 7. Bi-directional converter ESS

3.5. Voltage Source Converter The MG is connected to the grid through a three-level

inverter. Thus in grid connected mode this three-level inverter keeps the voltage level regardless from the power flows. Its control scheme is similar to the aforementioned described for the WT with the exception for the outer DC voltage loop.

IV. CONTROL STRATEGY

An Energy Management (EM) has been considered for the optimal operation of the MG both in grid connected and islanded modes. The overall control is shown in Fig. 8 and an overview is given below.

In all the operating conditions the MPPT sources are operated as slave ones. In case of grid connected, the Voltage Source Converter (VSC) is able to work as “master” and the bidirectional converter of the ESS as ”slave”.

Fig. 8. Energy Management Control flow chart

Therefore the ESS should be capable of storing electricity or energy to produce electricity and releasing it for use when the use or cost is most beneficial, i.e. if the State of Charge (SOC) is too low for a safe operation of the MG.

From a power flow point of view in grid connected mode the VSC behaves like a power slack node, taking care only about the voltage regulation.

Whenever a fault happens in the main grid the MG must be capable of running in islanded mode. In this case the EM copes the unbalances between power production from MPPT units and load by means of the ESS, if their SOCs is sufficient, or with the GE. In this case must be noticed that the GE response is much more slower respect to the ESS due to the mechanical inertia and ramp-up time [15][1]. However as long as the SOC is sufficient the ESS keeps the control of the voltage regulation. In case of ESS failure however the master unit becomes the GE. The last countermeasure in case of load greater than the available generation is the load rejection.

V. SIMULATIONS

This paragraph describes the two simulated scenarios by using MATLAB/Simulink. Case A.

In the case A the DC-MG is implemented to work in Islanded Mode, where the ESS is the “master”, to control the voltage level on the DC bus and to balance the power flow of the MG. In this scenario the input of the PV is the fluctuation irradiance and the input of WT is the wind speed variations and in both units the MPPT methods are implemented to control the electronic converters.

Case B.

In the case B until time 0.5s the DC-MG is connected to the AC-grid, therefore the with the VSC operating as “master” unit for controlling the voltage level. At time 0.5s a fault in the grid is hypothesized and the MG starts working in islanded mode with the ESS as the “master” control. Finally at the time 1.25s the ESS is disconnected and the GE converter behaves like the “master” unit, until the end of the simulation at time 2s.

The case B shows how the intelligent control is able to maintain the voltage level and the power flow of the MG in case of cascaded faults in the AC-grid and to the ESS. For the case B is showed the voltage level fluctuations due to the “master” control.

VI. RESULTS

The results of the simulations are presented, from the Fig. 9 to the Fig. 12 for the case A and in the Fig. 13 for the case B. In Fig. 9 the behavior of nominal and reference speed of the WT is shown and it is clear that the control works properly. The Fig. 10 displays the inputs and output of the PV, respectively the irradiance and the power production. The power flows in the DC-MG is displayed in the Fig. 11, here it

is interesting noticing the ESS power flow variation at time 1.25s, when a second DC load is connected to the DC-MG. In particular the ESS copes the power unbalance passing from charging to discharging mode. Also the fluctuation of the WT, due to the PMSG, is regulated by the ESS.

Fig. 12 shows the DC voltage bus profile. This is constant with a fluctuation around 2% until the time 1.25s, when one more DC load is connected to the DC-MG by producing a voltage drop which is balanced automatically by ESS. In Fig. 13 is scoped the DC-MG voltage profile. In the first region there is a slight oscillation due to grid tied inverter regulators, while in the intermediate one the voltage profile looks flat. The transition between the ESS mastering to the GE one is affected by an initial transient which is damped in less than 100ms.

Fig. 9. Case A: WT rotational speed reference and instantaneous value

Fig. 10. Case A: Irradiance and PV power production (MPPT Control)

Fig. 11. Case A: Power flows in the MG (ESS control)

Fig. 12. Case A: DC voltage in the MG bus (ESS control)

Fig. 13. Case B: DC voltage in the MG bus (Grid/ESS/GE control)

VII. CONCLUSIONS

This paper presents a DC-MG based power generation system with a PV, WT, GE and ESS generation and connected to the grid. Different kinds of control methods are proposed to increase the injected power, balance the power flow and the voltage level of the MG. The proposed control strategy based on the MPPT control for the PV and WT models and the voltage and power control for the GE and the ESS has been implemented in Simulink. From the simulation results, it is confirmed that the PV and the WT can work in the MPP mode and the ESS is able to balance the power flow and stabilize the voltage level. Also if there is a fault on the ESS or almost out of charge the GE converter is able to ensure the secured power supply and efficient operation of the MG. The model could be further validated with pole-to-neutral loads and with the operation of weather dependant units in master mode, as long as provided by a proper control.

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