An interlink converter (IC) presents in a hybrid microgrid allowing a bidirectional power exchange between AC and DC subgrids. In this paper, a model predictive control (MPC) based IC current controller is employed, taking the current constraint into account to ensure the safety of IC. To reduce online computation burden of the MPC, a tabular form of MPC named Explicit MPC is applied. The simulation result shows that the proposed IC controller successfully maintains the power supplydemand balance of the hybrid microgrid. Moreover, the proposed method keeps the power exchange value in the acceptable range, particularly under high loading condition.
Over the past century, generating clean energy has become an important issue due to the increasing awareness of the environmental issue. On the other hand, the number of renewable energy resources, such as wind turbine, photovoltaic and fuel cell, rapidly grow as the impact of science and technology. A microgrid is considered as a practical solution to integrate the renewable resources and provide the demand power. Microgrid is an energy system consisting of distributed energy sources and loads, and typically operates parallel with a power grid, although it can also work independently or synchronously with another microgrid.
Traditionally, microgrid consists of several AC distributed generators. Power converters are used to supply DC loads and connect DC power sources. In the process of energy conversion, there would be power loss inevitably. As a consequence, the efficiency decreases as the increasing number of the power converter. Some studies consider a hybrid AC/DC microgrid to incorporate both AC and DC power sources [1, 2]. The hybrid AC/DC microgrid consists of two independent microgrids, AC and DC microgrid, which are linked by one or more interlink converter (IC). The necessary power conversion of AC/DC hybrid microgrid is less than the conventional one as the AC subgrid mainly provides the AC load, and the DC subgrid serves for the DC load primarily. The IC delivers power exchanged when one of subgrid cannot manage its power supplydemand balance. For instance, when AC subgrid experiences imbalance power, the IC transmits the necessary power from DC to ACsubgrid.
In gridconnected mode, the utility grid is connected to the microgrid. The frequency stability of ACsubgrid is guaranteed as the utility grid performs as the primary source. When a fault occurs in the utility grid, microgrid disconnects from the utility grid and changes its operating mode to the standalone mode. In this mode, the microgrid has to maintain the frequency, in AC subgrid, and voltages, in both subgrids. The IC takes an essential role as it provides a sufficient power exchange to maintain the power supplydemand balance of both subgrids for any operating mode. In this regard, several control methods are studied, for instance, a droopcontrol is proposed by [1, 2] to determine the power flow from one subgrid to the other. In [3], the coordination of IC and the battery controller is studied. Robust current control for the IC is investigated by [3]. Some literatures [4–7] show interest in the optimal control method for the IC controller. For example, an adaptive hysteresis control method is designed by [4] such that the switching loss of the traditional hysteresis is reduced. Another used a finite state model predictive control (MPC) to include the switching state in the optimization cost function [5]. While [6] shows that the linear quadratic regulator (LQR) method have superior performance compared to the proportional integrator (PI) controller. Also, an unconstrained MPCbased IC current controller is applied in [7]. In these literatures [1–3, 5–8], the control signal is saturated to prevent overmodulation in the signal modulator.
The focus of this paper is placed on employing an optimal control such that it regulates the output current while respecting the input constraint. A MPC has been widely used, for instance in [9, 10] to solve a control problem while satisfying a set of constraints. In this paper, a constrained MPC is considered to include the input constraint in the current controller. By doing that, the saturation function applied in [1–3, 5–8] can be eliminated. Furthermore, due to safety reason, the current output has to be limited in order to keep the power exchange below, or equals, to its maximum value. Conventionally, a saturation function is used in the reference value. Hence, under the assumption that the current controller yields a small tracking error, the power exchange is maintained in its acceptable value. However, this method is trivial, especially when the maximum power is required. Thus, this paper includes the state constraint in the design of MPC to avoid large current output. In order to consider constraints in the MPC, the controller has to solve the optimization problem every sampling time which may require huge computation burden. However, the IC hast fast sampling time. To this end, this paper employs an Explicit MPC to reduce online computation burden. The Explicit MPC solves the optimization problem offline and represents the optimal solution in a tableform. The simulation results demonstrate the proposed control successfully maintains the stability of microgrid while preventing the power exchange from exceeding the maximum value.
A hybrid microgrid is decomposed by two subgrids: AC and DC subgrids, as shown in Figure 1. The power sources and loads are placed in the subgrid, depending on their inherent current type. An IC is used to incorporate both subgrids and allows bidirectional power to flow from both the subgrids alternately. When there is a power supplydemand imbalance in one subgrid, the necessary power is transferred from the other subgrid. With that in that mind, the IC establishes enough power exchange such that the power supplydemand balance is achieved in both subgrids.
Figure 2 shows a typical block diagram in IC controller based on currentcontrol mode. The controller mainly consists of three control blocks: a reference generator, a current controller, and pulsewidth modulator (PWM). By denoting the IC current in dqframe as i_{od} and i_{oq}, the power exchange is determined by imposing the IC current references
where P_{AC or}_{(}_{DC}_{)} and Q are the AC (or DC) active and reactive power, respectively, while f and V_{AC or}_{(}_{DC}_{)} denote the frequency and AC (or DC) voltage. The nominal value of f is written as f_{0}, and similarly for other variables. The droop coefficients are represented by k_{i}.
The droop control for a hybrid microgrid is formulated by combining the subgrids’ droop characteristic. Intuitively, comparing the frequency and DC voltage deviations indicates which subgrid has more severe active power imbalance. PI control is implemented to achieve equal power loading of the AC and DC subgrids. The frequency and DC voltage are converted to perunit (pu) values since both have a different unit base. Hence, the power exchange reference is given by:
where c_{1,2,3} are welltuned droop coefficient,
The resulting IC current reference will be used by the current controller to determine the input signal for the PWM block. The objective of this paper is to implement a current controller such that the current reference
Then, by assuming the switching signals g_{1}, g_{2}, g_{3} bring the IC voltage v_{id}_{,}_{q} to its reference value
where x = [i_{od} i_{oq}]^{T} is the state vector,
where A:= e^{A}^{c}^{T}^{s},
The objective of IC current control is to regulate the IC current. To this end, the following function is minimized
A MPC minimizes desired cost function over a finite horizon. By considering the system dynamic (
where N is the prediction horizon, x(ik) and u(ik) denote the predicted state and control input, respectively. By assuming the predicted disturbance equals to the measured disturbance variable, the predicted state is given by:
The matrices Q and R are the positivedefinite weighting for the reference tracking error and increment control input, respectively. Moreover, to avoid overmodulation in the PWM block control, the input signal constraint is considered:
The state constraint is also taken into account to limit the current output
Thus, the MPC problem can be written as follows:
By transforming the problem into equivalent quadratic programming (QP) formulation, the problem can be solved using any standard nonlinear programming method [
The computational complexity to solve the MPC grows as the number of constraint and prediction horizon increase. The problem (
Substituting predicted control input (
where
and
In this paper, it is assumed that the predicted state references equal to the state referece at time sampling k,
where
(PWA) [
where ℛ^{j} for j = 1, …, q are the resulting subsets in which the control law K_{j}θ(k) + c_{j} is valid when θ(k) ∈ ℛ^{j}. Then, the input signal u(k) = u(k − 1) + Δu_{0} is applied to the system where Δu_{0} is the first two component of the vector ΔU(k). Note that the optimal control input (
In this section, the simulation result of the Explicit MPCbased IC current control is discussed. The explicit optimal input signal is obtained by using the multiparametric toolbox (MPT) in MATLAB with the parameters described in Table 1. Then, it is applied to regulate the IC current of hybrid AC/DC microgrid. The system parameters are taken from [3]. Two simulation cases are employed to show the performance of the proposed method.
In the first case, the utility grid is connected at time t ∈ [1, 10] s. Suppose that the AC subgrid has 65 kW power source and 20 kW load, while DC microgrid experiences power sourceload imbalance at time t = 4 s. Figure 4 shows the DC voltage and AC frequency responses for this simulation case. The power imbalance happens at t = 3 s. As a result, the DC voltage deviates to 490 V. The IC controller successfully restores the DC voltage to the nominal value 500 V at t = 7 s. Besides, the frequency is maintained in 60 Hz when the microgrid is in gridconnected mode. However, when the utility grid is disconnected from the microgrid, the frequency deviates to 58.4 Hz. Since AC subgrid has a sufficient power source, the frequency can be brought back to 60 Hz. Figure 5 shows the active power exchange has negative value, which means the active power flows from AC to DC subgrid. In addition, the current output and control signal responses are shown in Figures 6 and 7, respectively. Figure 6 shows that the proposed current control steers the current output to the reference value while maintaining the input signal in the acceptable range (Figure 7).
In the second case, the AC subgrid experiences high loading condition in the standalone mode. At first, utility grid is connected to the microgrid for 1.3 s before it is detached. Furthermore, at t = 10 s, the AC load increases from 84 kW to 126 kW. Meanwhile, the battery in DC subgrid is assumed to have state of charge (SOC) of 60% which is enough for the DC subgrid. In this case, the performance of proposed current control is compared to the existing modelbased current control: LQR [6]. Figure 8 shows the frequency response of Explicit MPC and LQR control methods. Both controllers maintain the frequency both in the transient and steady state.
The current constraints are taken into account with the value defined in Table 1. This value is obtained by assuming that the IC has an acceptable maximum power of 100 kW. Figure 9 demonstrates the resulting active power exchange. The positive value represents the power exchange flows from DC to AC subgrid. As can be seen in Figure 9, when microgrid is in maximum load condition, the power exchange using LQR method goes around 120 kW, which is larger than the maximum value. Meanwhile, the Explicit MPC can maintain the power exchange in the acceptable value. Furthermore, in view of optimality, the Explicit MPC yields less control input variation compared to the LQR method as can be seen in Figure 10.
In this paper, an optimal control method is studied for the interlink converter controller to regulate the inverter current output. The MPC method is used to achieve current reference tracking with optimal control effort. In addition, the method gives an advantage as it allows the controller to include input and state constraints. Thereby, the safety of component can be guaranteed. Furthermore, an Explicit MPC is employed to avoid the controller solves the optimization problem at each sampling time. Hence, the proposed controller is more suitable for practical use than the conventional MPC as the online computation load is reduced. A computer simulation of a hybrid AC/DC microgrid is employed to show the performance of the proposed controller.
This study was supported by the Research Program funded by the Seoul National University of Science and Technology (SeoulTech).
No potential conflict of interest relevant to this article was reported.
An AC/DC hybrid microgrid.
An AC/DC hybrid microgrid.
Topology of interlink converter (IC).
DC voltage and AC frequency responses for the first case. The DC voltage deviates at
Active power exchange for the first case. Around 3.8 kW active power is transmitted fromAC to DC subgrid in order to restore the DC voltage to its nominal value.
IC current output for the first case: reference tracking is achieved.
Control input for the first case. The control input is constrained in its maximum value 1.2 at
Frequency response for the second case: both controllers show comparable results.
Active power exchange under high loading condition in AC subgrid. The exchanged power of LQR exceeds the maximum value while the Explicit MPC limits the power exchange in its maximum value at
Control input for the second case: the Explicit MPC lessens the control input of the LQR method.
Explicit MPC parameter
Parameter  Value 

0.5 

120 

7  
[1.2;1.2]  
[−260;−30] 
Email: ismirosyiana@gmail.com
Email: jungsu@seoultech.ac.kr