Control strategies for frequency regulation in microgrids: A review

Abstract

The electric power generation over the past decade has moved from conventional fossil fuel-fired thermal power plants to tiny-scale system generating power through distributed generation units. A group of such distributed generation units and loads are termed as microgrids. Microgrids can be located near the load centers to supply the load without any loss of power. Frequency regulation in a microgrid operating in autonomous mode is critical because of the intermittent nature of the renewable sources employed. To maintain the frequency regulation within a tolerance limit in a microgrid, proper control schemes have to be adopted in order to increase or decrease the real power generation. Hence, this article explores and presents a critical review of different types of control strategies employed for frequency regulation in microgrids.

Keywords

Microgrid frequency control renewable energy sources control strategies droop control internal model control robust control model predictive control

Introduction

With the growing need for electricity, the electrical energy produced from the conventional fossil fuel-fired power plants is not adequate to meet the demand and causes pollution to the environment. To reduce the levels of pollution and minimize the plant operating costs, electric power generation using renewable energy sources (RESs) such as solar photovoltaic (PV) systems, wind and biomass energy conversion systems were explored by researchers. As a result, generating energy from RESs came into existence. A group of such power generating sources can be located closer to load centers.

In a microgrid (MG), RES plays a vital role and the energy produced is not adequate to meet the demand because of the intermittent nature of wind and solar and the system frequency becomes oscillatory. The control of active power in response to the system frequency is accomplished in MGs by coordinating wind turbine generators (WTGs) with the real power outputs from diesel generation (DG), microturbine (MT), aqua electrolyzer (AE), fuel cell (FC), battery energy storage system (BESS), and flywheel (FW). The FC utilizes hydrogen gas which is produced by the AE which has advantages such as low pollution, high efficiency, and installation can be done on-site.

Motivation

When an MG and main grid are interconnected, MG exports power to the grid as there is a surplus power generation and imports power when there is a power deficit. Thereby, system frequency deviations are controlled. But, when the MG operates in an isolated mode, it should satisfy the demand. With the presence of RESs, frequency control becomes an issue. In an MG system, frequency deviation is an indication of a real power deficit. Considering the intermittent nature of the RESs and load variations, effective control methods have to be implemented for the proper generation of power from MG sources such as DG, FC, AE, BESS, FW, and MT to meet the demand and regulate the frequency.

Novelties and advantages

As MG comprises RESs which are intermittent in nature, the generation–load balance becomes significant. Non-RESs have to be incorporated to maintain the frequency deviation within the limits. Hence, an appropriate control scheme has to be implemented for the MG sources. Many research surveys have discussed MG technology, control of MG with BESS, MG power quality, MG protection, and wide-area control strategies. In this view, this article explores the control strategies required for the frequency regulation in an MG. The following sections explain briefly about the implementation of nonconventional control strategies used for the frequency control of MG. Section “Droop control method” describes the frequency control of MG by droop method. Section “MPC method” describes the frequency control of MG by the model predictive control (MPC) method. Section “FLC for frequency stabilization” discusses the frequency control by the fuzzy logic controller (FLC) method. Section “H_∞ controller” explains the H_∞ controller for frequency control of MG. Sections “Hierarchical control,” “SMC,” “Demand-side control,” and “Other control methods” describe hierarchical control, sliding mode control (SMC), demand-side control, and other control methods. Section “Conclusion” presents the conclusion.

Droop control method

Droop control method is one of the techniques to control the frequency deviations of the MG. Generally, a reference frequency is set as a small percentage of the actual frequency of the system. As the load increases, the actual frequency of the plant decreases which is caused by the reduced power generation. Hence, the difference between the reference frequency and the actual frequency increases the working fuel input to the plant to generate more power to meet the load. Briefly, the power generation of a system should change in proportion to the frequency deviations. This is executed by the droop constant R. Figure 1 shows the P/f droop characteristics. The slope R is denoted as

- R = \frac{Δ f}{Δ P}

(1)

In this view, the following literature presents the frequency control by droop method. When MG is connected to the main grid, the frequency regulation is taken care of by the main grid. When MG is disconnected from the grid, the frequency control becomes an issue. To solve this problem, BESS and supercapacitors are coordinated using the droop controller. When the load increases, the frequency falls because of the fall in generation. During this time, BESS and supercapacitor discharge accordingly with the variation of the frequency (Chae et al., 2012). Moreover, BESS combines with PV and adopts the adaptive droop control to act as a voltage source. During the charging period of BESS, the electrical load is shared among other sources. PV system supplies enough power to load when solar irradiance is at its peak. Sometimes, BESS acts as a separate storage unit to regulate the frequency (Mahmood et al., 2014). Although the adaptive droop control method provides many advantages, the conventional droop method is also used for the generation of active power and the self-recovery of frequency among the distributed generation units (DGUs).

Figure 1.

P/f droop characteristics.

The load increase or decrease differs for each DGU and the frequency deviations also differ. When these dissimilarities are given as input to the integrator of the self-frequency recovery control, it results in the miscalculation of active power sharing. Hence, considering the droop values of each DGU, compensation control is adopted to share the active power among DGU for effective frequency control (Kim et al., 2017). Likewise, the smooth switching droop control method is implemented for the coordination of supplying power between the RES and BESS. These sources may operate in voltage control mode (VCM) and power control mode (PCM). When BESS is closer to its charging limit, it is necessary to limit the charging and it is controlled in PCM. Meanwhile, RES still operates in PCM because of the absence of the communication link to notify the RES to change mode. This results in more generation of power than the required demand and hence the system frequency is increased. When the load is increased, the BESS changes the mode from PCM to VCM to discharge its power. In the same way, the change of mode happens to control the system frequency (Wu et al., 2015). Moreover, a control strategy based on a frequency droop and frequency restoration is proposed for multiple DG MG systems. This is done by the power management system which allocates the real power references to each DG units to reinstate the frequency deviations (Katiraei and Iravani, 2006). In the droop control method, every inverter contributes to load sharing and maintains the frequency deviation within the limit (You et al., 2013). Without depending on the MG central controller, decentralized control is implemented for the PV/BESS hybrid system. The control strategy is designed based on two priority levels to control the frequency deviation. At level 1, priority is to charge the battery or PV power has to be delivered considering BESS state of charge (SOC). At level 2, priority is to maintain the balance between the power generation and the load, thereby preventing the BESS from crossing their SOC limits (Mahmood et al., 2015). The combination of conventional droop and virtual inertia type of control method is adopted for BESS to supply the power to the load when connected to the MG under islanded mode. When the MG frequency profile worsens, BESS detaches from the MG mode and supplies the load without interruption (Serban and Marinescu, 2014). The microsources (MS) which adopt the droop control method can perform well with the plug-and-play concept in which load is shared among MS without communication link by taking the local information such as frequency and voltage (Huang et al., 2011). It is proved that droop method reduces the frequency deviations and also prevents sources from generating more than their prescribed capacity during load disturbances (Klem et al., 2016).

MPC method

A MG comprises RESs such as wind, solar, and FC and non-RESs such as DG, AE, MT, BESS, and FW. The block diagram of MG is shown in Figure 2.

Figure 2.

Block diagram of a microgrid.

MPC is an effective method in that the future real power output of MG can be predicted if the system’s dynamic model and the present measurements are available. Then, the required changes in input values are calculated based on both predictions and measurements. The fundamental model (Pahasa and Ngamroo, 2016) of MPC is shown in Figure 3 where y and u are the actual output and the manipulated input, respectively. At instant n, a set of M values of the inputs ${u (n + i - 1), i = 1, 2, \dots, M}$ are calculated by the MPC strategy. The set comprises u(n) current input and M future inputs. Up to P predicted outputs ${\bar{y} (n + i), i = 1, 2, \dots, P}$ attain the optimal value, the inputs are calculated.

Figure 3.

Fundamental model of MPC.

The number of predictions P is specified in the prediction horizon and the number of control moves is in the control horizon. At each sampling instant, a series of M control moves are calculated, but only the first move is essentially applied. In the next sampling instant, a new series is calculated. The first move is only applied until new measurements are available. This course of action is repeated at each sampling instant.

The optimization problem is solved at the current time for finite future time steps. Therefore, the system can be represented by finite impulse response as

y (n + 1) = y (n) + A \sum_{i = 0}^{k_{T}} β_{i} u (n - i)

(2)

y(n) is the vector of manipulated moves at time n, u(n − i) is the input at a time (n − i), k_T is the number of coefficients based on impulse response adopted to the system, and β_i is the coefficient number and is stated as

β_{i} = h_{i + 1} - h_{i}, \forall i = 0, \dots, k_{T}

(3)

where h_i is scalar. MPC has to find the input at instant k as the solution to the quadratic program which is stated as

\min \sum_{j = 1}^{M} {[y (n + j) - r (n + j)]}^{T} W_{x} [y (n + j) - r (n + j)] + {[u (n) - u (n - 1)]}^{T} W_{e} [u (n) - u (n - 1)]

(4)

Subject to

y (n + 1) = y (n) + A \sum_{i = 0}^{k_{T}} β_{i} u (k - 1) - Δ u_{\max} + u (n - 1) \leq u (n) Δ u_{\max} + u (k - 1)

(5)

where r(n + j) is the required profile, W_x and W_e are the weighting matrices and it is multiplied by the identity matrix. With the predictions and measurements, MPC produces the necessary control signals.

In an MG, WTG and plug-in hybrid electric vehicles (PHEVs) are coordinated using the MPC method. MPC-based control method is used to produce a control signal for the PHEVs to control the frequency deviations. As the frequency deviations are high, it is required to connect more PHEVs. To reduce the number of PHEVs, wind power (WP) is leveled by pitch angle control using the MPC method and coordinated with PHEVs (Pahasa and Ngamroo, 2016). Figure 4 shows the block diagram of MG with the MPC controller applied to PHEV. Due to the load and wind speed change, few WTGs are kept as a reserve by deloading. Hence, to face these changes, the MPC method is used to provide torque compensation for the deloaded WTGs to participate in the frequency regulation (Wang et al., 2017). In Pahasa and Ngamroo (2015), the multiple model predictive control (MMPC) is implemented for PHEVs where the MG consists of WTG, PHEV, and BESS. MMPC controls over the SOC and charging or discharging cycles of PHEVs and regulates the frequency. In the work by Verma et al. (2016), the MPC model is implemented for the BESS. When the load fluctuates, the frequency gets deviated. If there is a high penetration of PV power, the frequency can be controlled. In the absence of required power from PV, the MPC method is implemented for BESS to regulate the frequency. Also, in the work by Khalid and Savkin (2010b), BESS acts as a primary controller. Some of the constraints which exist for BESS are SOC limit, the capacity of the battery, the life span of the battery, charging, and discharging level. But, for frequency control, maintaining the nominal frequency is one of the constraints. Hence, the MPC method is chosen for BESS to regulate the frequency. MPC is also used to smoothen (Khalid and Savkin, 2010a) the WTG output along by optimizing the BESS. The controller is able to predict the wind speed some steps in advance. Due to this, frequency deviations are settled well within the limits. In the work by Khalid and Savkin (2010a), MPC is implemented to smooth the WP, whereas in the work by Khalid and Savkin (2012), a control method is implemented for BESS which is based on the MPC method and frequency predictor (FP). The need for the FP is to optimize the MPC controller performance with several steps ahead of predictions. Due to this FP, BESS regulates the frequency well. In the work by John and Ping Lam (2017), multiarea microgrid (MMG) is proposed to meet the large demand in which the MGs in different locations are interconnected. MPC method is adopted for DGUs and battery present in the MMG to provide effective control over the frequency deviations. The frequency deviations occur due to the change in RESs such as wind and solar and also due to load. Due to some constraints, the optimal values of controllers cannot be found exactly. Consequently, the MPC method is applied to the MT and the AE to damp the frequency oscillations (Jafari et al., 2012) where MT produces substantial power and AE produces hydrogen gas and stores when there is more power in the MG.

Figure 4.

Block diagram of a microgrid with MPC controller applied to PHEV.

FLC for frequency stabilization

FLC is one of the promising controllers suitable for the needs of the industry. The FLC has three sections, namely, fuzzification, fuzzy rule base, and defuzzification, which are shown in Figure 5. In Figure 6 (Marzband et al., 2011), f_ref and f denote the reference frequency and actual frequency of the MG, respectively. The difference between these frequencies produces an instantaneous error signal e(t). This e(t) is compared with the previous state error e(t − 1) to generate deviation in error Δe(t). Now, e(t) and Δe(t) are the inputs to the FLC. With the implementation of the fuzzy rule base, FLC produces an output Δm(t). Again, the output of the FLC is added to the previous state output m(t − 1) and, finally, the output is m(t). The membership functions are defined according to system behavior. Hence, linguistic variables are selected and have values such as PB (positive big), PS (positive small), PM (positive medium), Z (zero), NB (negative big), NS (negative small), and NM (negative medium) which form the fuzzy rule base. The FLC uses a set of IF. . .THEN rules and is shown in Figure 7.

Figure 5.

Fuzzy inference system.

Figure 6.

Fuzzy logic controller (Marzband et al., 2011).

Figure 7.

Fuzzy rule base.

Figure 8 shows the block diagram for an FLC in an MG. In this view, FLC is proposed for the isolated MG consists of WTG, PV, and synchronous generator (SG) supplying various loads such as heater, resistive, inductive and capacitive (RLC) load, and induction motor load. Due to the random variation of MG loads, controlling the frequency becomes an issue and tracking of frequency is needed. Instead of designing an entire FLC, fuzzy logic table control (FLTC) is implemented to share the generation of power among the sources to control the frequency deviations. Also, the dynamic response of MG shows that FLTC performs better than proportional–integral (PI) controller (Mahdi and Ahmad, 2017).

Figure 8.

Block diagram of an FLC in an MG.

The disturbances in the MG may occur due to the increase or decrease of power generation by RESs and also due to the load variations. For these uncertainties, fuzzy gain scheduled proportional–integral–derivative (FGSPID) controller gives the solution to the frequency regulation. The rules for FGSPID are framed based on the frequency response of the MG and hence this method exhibits its superiority in terms of quick settling time (Kumar et al., 2016). The same FGSPID is suggested for the MG consisting of WTG, PV, DG, FC, and BESS for the effective control of frequency deviations. This method uses a combination of conventional proportional–integral–derivative (PID) control and fuzzy control. In this method, PID values are set in the fuzzy rule and the fuzzy system regulates PID values according to the changes in the operating conditions. Also, it is compared with the conventional PID controller and the robustness is proved for the changes in the load and variation of generation due to the wind and PV (Bisht and Suhag, 2014).

Supplying power to remote areas in power systems from central generating stations is expensive. Hence, electrical energy required for these remote MG loads is generated from DGs which are located closer to them. If a particular remote MG is having high power generation from WTG, the requirement of DG becomes less and there is no need to store the power using BESS. In this regard, high-penetration no-storage wind diesel (HPNSWD) is developed and the power generation by DG is controlled by FLC. This type of MG controlled using FLC is compared with the PID controller and superiority is proved in terms of effective frequency control (Marzband et al., 2011). Once the PID controller values are tuned and fixed, it can be retuned using adaptive neuro-fuzzy interface strategy (ANFIS). This type of FLC is proposed for the MG comprising WTG, PV, DG, FC, and BESS for the control of frequency deviations rapidly (Singh and Suhag, 2016). BESS is used as a power smoothing device. As RES generating power increases or decreases, BESS regulates the frequency by absorbing or delivering its active power. In this view, as PV power is utilized in the MG, it causes frequency deviations. BESS is usually employed to smooth the power and then supplied to the loads. But, in the work by Datta et al. (2011), by the implementation of FLC, power control of PV supplying the load and maximum power capture through isolation are attained. With the inputs as insolation and change in frequency, FLC produces the command signal to control the PV power. The fuzzy control algorithm is adopted for BESS in the MG to provide power in all load conditions. To show the dynamics of the fuzzy algorithm, it is compared with the fixed and adaptive algorithm. The fixed algorithm instructs BESS to provide power when the demand exceeds the predetermined value, whereas the adaptive algorithm provides power without considering SOC of BESS (Khooban et al., 2017). MG is developed with PV, FC, and BESS to meet the load. Whenever there is a rapid variation in the load and power output of PV, the system experiences frequency deviations. When PV power is high or low, the function of the BESS is to charge or discharge the power, whereas FC is controlled by FLC to generate its maximum power, when the SOC of BESS reaches its minimum value (Vigneysh and Kumarappan, 2015).

An MG in which the MT is combined with FC is used to reduce the frequency deviations. The fuzzy logic–based PI controller is adopted for BESS to solve the frequency deviation problem (Li et al., 2008) in which the MG consists of DG and BESS (Lee et al., 2014). When MG is operated in the islanded mode, BESS controlled by FLC is used to provide active power instantaneously (Kim et al., 2011). The fuzzy-based control action is proposed for long-time power supply to the customers by the MG with the availability of RESs and BESS (Marinescu and Serban, 2011). The frequency fluctuations are caused by the power generation by the RESs. Electric vehicles (EVs) are employed to improve frequency regulation by replacing BESS. Hence, FLC is used to tune PI controller adaptively for large-scale systems (Oliveira et al., 2017). An adaptive controller is adopted for the doubly-fed induction generator (DFIG) to control the active power of the WTG so that the frequency deviations caused by the load changes are controlled. The results show that the adaptive controller is better than the conventional method (Sa-ngawong and Ngamroo, 2013).

H_∞ controller

H_∞ control method synthesizes the controllers in order to stabilize the system and give a good performance. This method solves the control problem as mathematical optimization.

Figure 9 shows an MG plant with feedback. Δ(s), P(s), and K(s) represent the transfer functions of unstructured uncertainty, plant model, and feedback control. Considering that the system is not subjected to disturbances, the term Δ(s) is neglected, and the model becomes as shown in Figure 10.

Figure 9.

Standard model with uncertainty.

Figure 10.

Model without plant disturbance Δ(s).

P(s) can be written in a matrix form as follows

P (S) = [\begin{array}{l} P_{11} (s) P_{12} (s) \\ P_{11} (s) P_{12} (s) \end{array}]

(6)

With the z, w, u, y, and P(s), a matrix is framed as

[\begin{matrix} z \\ y \end{matrix}] = [\begin{matrix} P_{11} P_{12} \\ P_{21} P_{22} \end{matrix}] [\begin{matrix} w \\ u \end{matrix}]

(7)

Hence

z = P_{11} w + P_{12} u

(8)

and

y = P_{21} w + P_{22} u

(9)

z = [P_{1} 1 + P_{1} 2 K {(1 - P_{2} 2 K)}^{(- 1)} P_{2} 1] w = F_{1} (P, K) w

(10)

The function $F_{1} (P, K)$ must be minimized to minimize the error z. In this view, to reduce the frequency deviations in an MG, the following literature present the details of the implementation of the H_∞ controller.

The H_∞ control method is implemented as a secondary loop control for MT, DG, and FC for the frequency deviation control in which MG consists of WTG, PV, MT, DG, FC, BESS, and FW. Moreover, the uncertainty by wind, solar, and load is considered for synthesizing the controller (Bevrani et al., 2016). In an MG, to avoid the calculations of gain in which more control inputs are needed, linear matrix inequality (LMI) conditions are implemented for the design of H_∞ for DG and BESS. With the H_∞ method, the DG suppresses the low-frequency factor, whereas BESS is used to suppress the high-frequency factor (Masui and Namerikawa, 2015). Similarly, the H_∞ controller is implemented for BESS in which MG consists of PV, DG, and BESS (Mongkoltanatas et al., 2013).

The error in modeling affects the performance of the system. The model uncertainty is the result of the difference between the actual system and its mathematical model derived. Hence, a robust control named µ-synthesis provides a solution to the system with uncertainties. Compared to the H_∞ method, µ-synthesis performance is less but offers robustness against model uncertainties. In this view, µ-synthesis method is adopted for an MG consisting of WTG, PV, BESS, DG, and fossil fuel generators. DG delivers power during the low-frequency period and BESS controls the system frequency deviations during the high-frequency period (Han et al., 2013, 2015). Correspondingly, the µ-synthesis method is implemented for three areas where MG consists of PV, WTG, and BESS in each area. With the help of this method, the frequency deviations are controlled quickly (Azizi and Khajehoddin, 2016).

The secondary control scheme is adopted for SG and PV in which the PI controllers are found by minimizing the H_∞ norm (Gong et al., 2015). Control and monitoring system (CMS) type of control strategy is adopted in such a way that MT and AE are continuously rolled by PI controllers in which the controllers are found by the H₂/H_∞ technique to reduce the frequency deviations caused by the frequent charging and discharging of PHEVs (Vachirasricirikul and Ngamroo, 2012). As an MG consists of intermittent energy sources, the frequency deviations can be reduced in a smart way by controlling the power consumed by heat pump (HP) and charging of PHEV at the user side. For this, the PID controller is designed using H₂/H_∞ technique and adopted for HP and PHEV (Vachirasricirikul and Ngamroo, 2011). H_∞ method is implemented where the MG comprises WP, PV, MT, AE, and FC. The controller stabilizes the frequency by tuning the MT, AE, and FC power outputs (Jian and Li, 2016).

Hierarchical control

As MG is capable of operating in both grid-connected and islanded modes, advanced control strategies are required for stable operation of the system at a frequency closer to the nominal value. The control technique adopted should provide the following requirements (Bidram and Davoudi, 2012):

A good frequency regulation during grid-connected and islanded modes;

Load sharing among the distributed energy resources (DERs);

Capable of reconnecting with grid;

Power export and import to or from the grid.

The above requirement can be fulfilled by hierarchical control structure which consists of primary, secondary, and tertiary control level. In primary control level, voltage and frequency are maintained during the islanding process and the loads are shared among DER units. But, in the steady state, primary control cannot settle the frequency to the nominal value. This leads to the implementation of secondary level control to restore the frequency deviations. In this view, the following literature present the implementation of hierarchical control to restore the frequency in MG. Figure 11 shows the hierarchical control structure for an MG.

Figure 11.

Hierarchical control structure for a microgrid.

To provide frequency control by WTG under all wind conditions, storage of its mechanical power is needed. This mechanical power can be stored using FW. The storing and releasing of excess WP are initiated by the local controller (LC) and supervised by the central controller in order to make WTG take part in frequency control (Díaz-González et al., 2015). MG comprises BESS, superconducting magnetic energy storage (SMES), DG, MT, and renewable sources, and the control of frequency is executed by the hierarchical structure. The microgrid central controller (MGCC) fixes the set point for MS and provides primary control for the MG. The secondary control is provided by LC and microsource controller (MC) for MG. A MG comprises DG, MT, BESS, and SMES and, among these sources, BESS and SMES provide frequency control as the MG is cut off from the grid (Kim et al., 2010). MGCC acts as a regulatory unit for coordinating the MS when the demand decreases or increases. MGCC provides safe operational modes for BESS like avoiding uneven degradation of BESS, limiting the power from RESs, thereby avoiding overcharging or discharging of BESS (Díaz et al., 2017).

The secondary control restores the frequency deviations caused by the primary controller action. Also, tertiary control contributes significantly to coordinate the generation of the various MG sources with one another (Olivares et al., 2014). During islanded operation, for DERs, a decentralized control strategy is needed for regulating the balance between the generation and the system load (Yazdanian and Mehrizi-Sani, 2014). Among the three layers of control, the secondary control provides control over frequency deviations (La Bella et al., 2017) and takes proper care when primary control fails (Bidram and Davoudi, 2012). A two-layer structure which is based on an agent control model is proposed. The agent gets information about the generation and load level. To meet the demand, among the network, the agent communicates with their nearby agents to regulate the DGUs generation. Through communication between the agents, the frequency fluctuations are minimized (Li et al., 2016).

For islanded MG, a decentralized control scheme is adopted where DGUs are interconnected. LC at the point of common coupling (PCC) regulates the frequency. To make LC change its setting when the load fluctuates, DGU is made to plug in or out (Riverso et al., 2015). The secondary control is executed by the distributed cooperative control among the multi-agent systems such that DGUs share their active power to restore the frequency of MG (Bidram et al., 2013). For secondary frequency control, a cooperative method is proposed. The controllers communicate with the neighboring DGUs by getting the information locally and regulate the frequency quickly by sharing the active power of each DGU (Dorfler et al., 2015). A consensus algorithm is executed for frequency regulation which uses a rule and group of data for the secondary control scheme which comprises load frequency control and a communication time delay (Coelho et al., 2016). In the tertiary control of hierarchical control structure, an additional controller for PV generates active power at the tertiary level (Wei et al., 2014).

SMC

An adaptive SMC is implemented for a voltage controller for regulating the voltage and frequency of the master DGU closer to the reference point (Rezaei and Soltani, 2015). The observer-based SMC is implemented for an MG comprising PV, MT, and PHEVs. PV output power is predicted using SMC which helps in regulating the frequency (Mu et al., 2016). Even the droop control method can be implemented for frequency control based on SMC for an alternating current or direct current (AC/DC) MG. As load variations cause uncertain voltage and frequency drop, SMC-based droop control mechanism maintains the stability of the MG (Alam et al., 2016). Adaptive sliding mode control (ASMC) is used to find the real power for the MG comprising wind, solar, micro-hydro turbine, and BESS in order to keep up the power balance among the sources. The RESs are combined using single-phase voltage source converter (VSC). The ASMC estimates the reference current and controls the VSC by which the frequency is regulated (Kalla et al., 2017).

Demand-side control

In addition to various control methods mentioned above, the frequency control can be performed by controlling the load-side demand. The demand-side control is to make the customers consume less power for particular appliances during peak hours. Otherwise, they can be made to consume power at nighttime. In this regard, decentralized demand-side (DDC) control strategy is used for a group of refrigerators that are controllable. When the frequency is higher or lesser than the nominal value, the refrigerators consume more or less power (Qi et al., 2013). Due to wind variations and lack of spinning reserve in the MG, demand-side response control strategy can be used (Pourmousavi Kani and Nehrir, 2012).

Nowadays, electric power generation using fossil-fueled power plants is reduced and renewable power generation is increased. Hence, demand-side management and control are required for primary frequency control. As the frequency is within the limit, the load gets uninterrupted power supply. When system frequency is not within the specified limits, the appliances such as freezers, refrigerators, and air conditioners can be switched off (Molina-Garcia et al., 2010). Due to the intervention of RESs, load frequency control in a MG has become complicated. Hence, the demand-side management control strategy is implemented (Molina-Garcia et al., 2014). Instead of keeping the power from the generating sources as a reserve, demand response gives a solution for the frequency control since the communication exists within the MG (Khederzadeh, 2012).

Other control methods

MG consists of DGUs and loads. BESS gets charged whenever high penetration of energy from RESs occurs. Hence, controlling these sources becomes a problem and the conventional controllers like PI is not suitable. This leads to the implementation of an emotional controller which is based on the emotional process of the human brain and is self-tuning in nature (Khalghani et al., 2016). Another method called the internal model approach is used to find PI parameters in which the controller and the plant can be rearranged in a feedback form. The sources such as DG, FC, AE, and BESS are operated with the aid of the PI controllers to reduce the frequency deviations. In Figure 12, r(s) is the reference input, u(s) is the controlled output, g(s) is the plant, $\tilde{g} (s)$ is the plant model, and d(s) is the disturbance signal. This $\tilde{g} (s)$ can be rearranged as feedback to the controller q(s). q(s) and $\tilde{g} (s)$ can combine together to form the controller structure, as shown in Figure 13 (Jeya Veronica and Senthil Kumar, 2017c). Even the feedback structure of internal model control (IMC) shown in Figure 13 can be used as a controller for MG sources (Jeya Veronica and Senthil Kumar, 2017b).

Figure 12.

Structure of IMC (Jeya Veronica and Senthil Kumar, 2017c).

Figure 13.

Rearrangement of IMC (Jeya Veronica and Senthil Kumar, 2017c).

In an MG, where PV, DG, and FC are coordinated without the use of any storage devices such as BESS or super capacitor (SC), PV power is kept as a reserve. Usually, the PV generator delivers a reduced power than its maximum power absorbed and can be made controllable. As load increases, PV quickly releases the reserved power to restore the frequency to its nominal value when the power from DG and FC is not sufficient (Sekhar and Mishra, 2016). To regulate the frequency, a day-ahead load and power from RESs are calculated to generate the power signal. With the help of this signal, EVs are made to charge and discharge. When the reserve power in MG is high, EVs are charged, and when the power reserve is low, EVs are discharged (Kermani et al., 2016). As more DGUs and RESs are present in MG, controlling of each source is important. In this regard, fuzzy rules are implemented for the PI controller. But, for the optimal values of the PI controller, the particle swarm optimization (PSO) algorithm is used which is performed online (Bevrani et al., 2012). When MG operates in islanded mode, BESS acts as the most important source of power. Hence, a novel control method with the voltage/frequency (V/f) and real/reactive (P/Q) droop control is adopted for the BESS and the system frequency becomes stable (Tang et al., 2016). To make the conventional droop control to work well, a supplementary control signal can be implemented along with it. This control strategy makes the controller to suppress the frequency deviations and works well for the large disturbances (Zhao et al., 2015). The same way, instead of conventional droop method, voltage and current feedforward control is introduced for the BESS (Shao et al., 2012). As load is uncertain in the MG, 2-degree-of-freedom (DOF) feedback–feedforward controller is proposed. First, the control design is changed to a nonconvex optimization problem. Second, the nonconvex is reduced to a convex LMI. Through these steps, the optimal performance of the MG is achieved (Babazadeh and Karimi, 2013).

The wind speed is unpredictable and causes frequency fluctuations. By estimating the load of the MG by minimal order observer, the small frequency factor is minimized by the pitch angle control system of WTG, whereas high-frequency factor is minimized by BESS (Howlader et al., 2012). In the work by Howlader et al. (2012), only the system load is estimated. But, the prediction of wind speed a few steps ahead is also needed for effective frequency control. In this regard, an integral control method which comprises load estimation and wind speed prediction is used. With this method, flexible power control and reduced frequency deviations are achieved (Kaneko et al., 2011). Using disturbance observer, the load is predicted to change the output power of PV so that the frequency fluctuations are reduced (Mi et al., 2013), whereas disturbance observer is used to provide reference inputs to DG and BESS considering the variations in load and wind velocities (Hiranaka et al., 2016). Another method named coefficient diagram matrix (CDM) is designed for HP and EV in an MG. CDM arranges the closed-loop poles of a transfer function and gives the optimal response in the time domain. This method works well even with the system parameter uncertainties and load variations (Ali et al., 2014). To change the mode from grid connected to islanded or vice versa, an appropriate strategy is needed which is provided by SMC. This strategy controls the VSC of PV. DG regulates the frequency and PV regulates the voltage at PCC. Sometimes, the DG becomes uncontrollable and then extra PV power is added to maintain the frequency deviations (Mishra et al., 2013).

A mathematical model is developed which uses direct-quadrature (DQ)-frame current control and phase-locked loop (PLL) for an MG. This control strategy needs minimum software adjustments for the operation of DGU and provides a black start capacity. To make the system stable, feedforward technique is used (Delghavi and Yazdani, 2009). Linearized feedback control is used for BESS. This method is adopted after the system undergoes disturbances and also provides better frequency regulation (Kazempour et al., 2015). A simple search method is used as an optimization technique to find the PI parameters for DG, FC, AE, and BESS in an MG (Jeya Veronica and Senthil Kumar, 2017a). To restore the frequency deviations to its nominal value, active power should be distributed among DGUs according to their rated capacity. For this, a finite time control type is used (Bidram et al., 2014).

Conclusion

This article makes a conclusion on the researches that make efforts on control strategies for the frequency control of MG. The intervention of renewable sources such as wind and solar induces frequency deviations. This necessitates the addition of other sources such as DG, FC, MT, BESS, and FW to stabilize the frequency. However, the implementation of control strategies is very essential for the proper generation of power from these sources when WP and PV change. The control strategies are required for the sources to generate within their capacity. In addition, the review done shows the scope of demand-side control which utilizes real-time communication of customer power demands to the MGCC.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

N Senthil Kumar

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Control strategies for frequency regulation in microgrids: A review

Abstract

Keywords

Introduction

Motivation

Novelties and advantages

Droop control method

MPC method

FLC for frequency stabilization

H∞ controller

Hierarchical control

SMC

Demand-side control

Other control methods

Conclusion

Footnotes

Declaration of conflicting interests

Funding

ORCID iD

References

H_∞ controller