Microgrids are becoming popular in distribution systems because they can improve the power quality and reliability of power supplies and reduce the environmental impact. Microgrid operation can be classified into two modes: grid-connected and islanded modes. In general, microgrids are comprised of distributed energy resources (DERs) including renewable energy sources, distributed energy storage systems (ESSs), and local loads [1
]. However, the use of renewable energy sources such as wind and solar power in microgrids causes power flow variations owing to uncertainties in their power outputs. These variations should be reduced to meet power-quality requirements [4
]. This study focuses on handling the problems that are introduced by wind power.
To compensate for fluctuations in wind power, various ESSs have been implemented in microgrids. Short-term ESSs such as superconducting magnetic energy storage (SMES) systems [6
], electrical double-layer capacitors (EDLCs) [7
], and flywheel energy storage systems (FESSs) [8
] as well as long-term ESSs such as battery energy storage systems (BESSs) [11
] are applied to microgrid control. ESSs can also be used to control the power flow at point of common coupling in the grid-connected mode as well as to regulate the frequency and voltage of a microgrid in the islanded mode. Among these ESSs, BESSs have been implemented widely owing to their versatility, high energy density, and efficiency. Moreover, their cost has decreased whereas their performance and lifetime has increased [13
In practice, BESSs with high performance such as smooth and fast dynamic response during charging and discharging are required for microgrid control. This performance depends on the control performance of the power electronic converter. Proportional-integral (PI) control is a practical and popular control technique for BESS control systems. However, PI control might show unsatisfactory results for nonlinear and discontinuous systems [10
]. Meanwhile, model predictive control (MPC) is considered an attractive alternative to promote the performance of future energy processing and control systems [14
]. Predictive strategies are based on the inherent discrete nature of a power converter. Owing to the finite number of switching states of a power converter, all possible states are considered for predicting the system behavior. Then, each prediction is used to evaluate a cost function. Consequently, the switching state with the minimum cost function is selected and applied to the converter [15
]. One of the advantages of an MPC is the easy inclusion of constraints and nonlinearities. Therefore, MPC has been widely applied to drive applications [15
] and power converters such as active front-end rectifiers [19
], matrix converters [20
], and multilevel converters [21
]. Recently, it has been applied to a bidirectional AC-DC converter for use in BESSs [22
Only a few literatures were found on the application of MPC to microgrid control. Most existing studies focused on MPC for a distributed generator in a microgrid with voltage and/or power control [25
]. A modified MPC method for voltage control of a BESS in the islanded mode operation of a microgrid was presented in [27
]. However, this study did not deal with frequency control in the islanded mode operation of a microgrid. MPC based on PI control in the outer control loop and predictive current control (PCC) in the inner control loop for BESS was presented in [28
]. Coordinated predictive control of a wind/battery microgrid system was proposed to maintain the system voltage and frequency by adjusting the output power of BESS. PCC was used to control the current in the inner control loop, whereas PI regulators were used to regulate the voltage and power in the outer control loop. Owing to the use of PI regulators in the outer control loop, the dynamic response time under such MPC techniques was similar to that under PI control techniques with outer and inner control loops using PI regulators.
Another MPC technique is based on predictive power control (PPC), in which the power is predicted and controlled directly. This MPC technique could be applied to microgrid control because it affords advantages such as fast dynamic response for power control; however, studies have not yet explored the application of the PPC-based MPC technique to microgrid control. Furthermore, this MPC technique can only be used for power control. To overcome this problem, PI regulators can be used in an additional control loop to control the frequency and voltage. Therefore, this MPC technique uses PI regulators in the outer control loop and PPC in the inner control loop. It is similar to previous MPC techniques in which PI control is used in the outer control loop and PCC is used in the inner control loop. However, an MPC technique based on PI and PPC requires more computation time than does one based on PI and PCC, owing to the predicting powers in the inner PPC control loop. Therefore, in a microgrid with a single BESS, MPC based on PI and PCC is a suitable alternative for microgrid control. Another approach to overcome this limitation of the MPC control technique is to use a droop control scheme. Thus, a PPC-based MPC technique can be applied to microgrids consisting of multiple BESSs with different functionalities. This study deals with the effective application of an MPC technique to a microgrid with two BESSs as an example of multiple BESSs in a microgrid.
This study discusses the effective application of two MPC techniques to BESSs for microgrid control based on the characteristics of the MPC techniques as well as the functionalities of BESSs. One BESS is based on PI control in the outer and PCC in the inner control loops (PI (outer) + PCC (inner)); it is used for smoothing wind power fluctuations both in the grid-connected and the islanded modes. The other BESS is based on PPC (one loop); it controls the tie-line powers at the point of common coupling in the grid-connected mode and the frequency in the islanded mode. Additionally, to reduce the power losses of converters, the reduction of the switching frequency of the converter is considered an additional control variable in the MPC algorithm. The control performances of the two types of MPC techniques are compared to the PI control technique using PI regulators in the outer and inner control loops (PI (outer) + PI (inner)). The tuning of PI regulator parameters must be taken into account to effectively compare the control performance of MPC techniques to the PI control technique. Several tuning techniques have been used to select the PI regulator parameters. In this study, the tuning technique provided by MATLAB/Simulink software is used. The efficacy of the proposed control system is verified via simulations in the MATLAB/Simulink environment.
The remainder of this paper is organized as follows. Section 2
introduces the discrete-time model of the converter for prediction and MPC algorithms. Two types of MPC techniques are introduced in this section. Section 3
describes the microgrid system used to test the performance of the proposed control strategies. Section 4
presents a comparison of the MPC and PI control techniques and the considerations for the effective application of MPC-based BESSs to microgrid control. Section 5
presents the simulation results for microgrid control in the grid-connected and islanded modes. The performances of the MPC techniques are compared to those of the PI control technique. Finally, Section 6
summarizes the main conclusions of this study.
3. Test Microgrid
The test microgrid system (Figure 4
) used in this study includes several components: A diesel generator, a consumer load, a wind generator, and two BESSs. Table 2
shows the parameters of the test microgrid system. In this study, the fixed-speed wind energy conversion system (WECS), a type of WECS [34
], is used for simplicity. Two BESSs with different control strategies according to the operation mode of the microgrid, as shown in Table 3
, are used. In the grid-connected mode, the voltage and frequency of the microgrid is set by the utility grid. Therefore, the main function of the BESS is to control the real and reactive powers. On the other hand, in the islanded mode, the microgrid is disconnected from the utility grid and controls its own frequency and voltage.
Configuration of microgrid.
Configuration of microgrid.
Parameters of test microgrid.
Parameters of test microgrid.
|Wind generator||150 kVA|
|Load||500 kW; 100 kVAR|
|Diesel generator||500 kVA|
|Mean wind speed||9 m/s|
|System frequency||60 Hz|
|Transformer||700 kVA; 6.6 kV/380 V|
Control strategies of BESSs.
Control strategies of BESSs.
|Grid-connected||Tie-line powers at point of common coupling||Smoothing wind power|
|Islanded||Frequency control||Smoothing wind power|
|Reactive power at point of common coupling||Voltage control|
This study discusses the effective application of two types of MPC techniques to BESSs for microgrid control: MPC based on PPC and MPC based on PI control in the outer control loop and PCC in the inner current control loop. In addition, PI control using a PI regulator in the outer and inner control loops for BESS was compared to these two types of MPC techniques. A reduction switching frequency is implemented in the cost function to reduce the power losses of converters. The simulation results show that the response time, power ripples, and frequency spectrum could be improved significantly by using MPC techniques. Both the average switching frequency and the THD obtained by using MPC techniques were lower than those obtained by using PI control. Using MPC based on PI control in the outer and PCC in the inner control loops did not improve the response time under power changing compared to PI control; however, it could significantly improve the power and voltage ripples under the steady-state condition. Moreover, using PPC-based MPC could reduce the response time under power changing compared to other control techniques. Therefore, in microgrids with multiple BESSs, the PPC-based MPC technique should be applied for BESSs that control the power at the point of common coupling and the frequency of the microgrid, and an MPC technique based on PI in the outer control loop and PCC in the inner control loop should be applied for BESSs that play the role of smoothing wind power fluctuations. Besides, in case of microgrids with a BESS, PCC-based MPC technique should be a suitable alternative for the BESS owing to its flexible characteristic. MPC technique is easy to implement and it can eliminate the tuning controller parameters effort that has to be done in the PI technique. Furthermore, various control objectives can be included in the MPC strategies.
In the future, we plan to include additional control variables such as considering the state of charge of the battery and coordination control of multiple ESSs in the MPC algorithm.