Dynamic Modeling of Multiple Microgrid Clusters Using Regional Demand Response Programs
Abstract
:1. Introduction
- The absence of a suitable dynamic model is one of the serious challenges for a study on LFC in multiple microgrid clusters, which restricts research activities in this field. Modeling can be done for a variety of goals. Thus, using a suitable model for a specific study goal is the preferred option. The present paper provides a suitable dynamic model for multiple microgrid clusters based on tie-lines for studying the frequency and tie-line power control.
- In this study, the Monte Carlo simulation was used to generate scenarios regarding the uncertainties of RERs and loads by considering probability distribution functions when modeling them. However, due to the uncertainties in RERs and loads, the conventional controllers, such as PID controllers, will not be suitable for LFC. Hence, adjusting the control parameters was suggested by using the ICA to solve this problem.
- Damping frequency fluctuations is one of the main issues of frequency control in power systems and microgrids. Due to its fast dynamics, the demand response program could be an attractive suggestion for damping system frequency fluctuations. Opposed to the other works, the regional demand response program is simpler and more accurate, and does not require complex and massive computational calculations. Furthermore, this method is applicable to almost all types of loads by considering the load’s participation coefficient in each region during the frequency control. Therefore, the regional demand response was applied for each microgrid in this study to reduce the frequency oscillations.
2. Case Study
3. Modeling of Units
3.1. Modeling of the Diesel Engine Generator (DEG)
3.2. Modeling of the Fuel Cell (FC)
3.3. Modeling of the Wind Turbine (WT) Generator
3.4. Modeling of the Photovoltaic (PV) Panel
3.5. Modeling of the Flywheel Energy Storage System (FESS)
3.6. Modeling of the Superconducting Magnetic Energy Storage System (SMESS)
3.7. Modeling of the Battery Energy Storage System (BESS)
3.8. Modeling of the Ultracapacitor (UC)
4. Modeling of the Microgrids
5. Modeling of the Multiple Microgrid Clusters
6. Modeling of the Uncertainties
6.1. Probabilistic Model of the Load
6.2. Probabilistic Model of the WT Power
6.3. Probabilistic Model of the PV Power
6.4. Scenario Generation and Reduction
6.5. Imperialist Competitive Algorithm
7. Modeling of Regional Demand Response Programs
8. Simulation Results and Discussions
- Scenario 1: In the first scenario, the performance of the multiple microgrid clusters was studied with non-optimal parameter values.
- Scenario 2: In the second scenario, the performance of the multiple microgrid clusters was examined with the optimal control parameters.
- Scenario 3: In the third scenario, the performance of the multiple microgrid clusters was investigated by applying RDRPs.
9. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Nomenclature
Variables | Parameters |
Tg | Governor time constant |
Tt | Turbine time constant |
RDEG/ | Speed regulation coefficient of the DEG |
TFC1/ | FC time constant |
TFC2/ | FC feed time constant |
TWTG | WT generator time constant |
TPV | PV panel time constant |
TF1 | Time constants of the measurement device |
TF2 | Time constants of the command device |
TF3 | Time constants of the converter |
F1 | Reference angular speed |
F2 | Speed regulator proportional constant |
PFESS | Number of poles of the FESS |
JFESS | Inertia of the rotational masses of the FESS |
KSMESS | SMESS gain |
TSMESS | SMESS time constant |
TBESS | BESS time constant |
RBESS | Speed regulation coefficient of the BESS |
TUC | UC time constant |
∆fi | Frequency deviation of microgrid i |
βi | Frequency bais of microgrid i |
Di | Load-damping coefficient of microgrid i |
Mi | Total inertia of microgrid i |
∆PLi | Load change in microgrid i |
∆Ptie,i | Total tie-line power change between microgrid i and other microgrids |
Tij | Tie-line synchronizing torque coefficient |
PL | Load power |
μ | Mean deviation value |
σ | Standard deviation value |
V | Wind speed |
Y | Scale parameters of the Weibull distribution function |
x | Shape parameters of the Weibull distribution function |
PWT(V) | Power generated at wind speed V by the WT generator |
Pr,WT | Rated power of the WT generator |
Vci | Low cut speed of the WT generator |
Vco | High cut speed of the WT generator |
Vr | Rated speed of the WT generator |
PPV(R) | Power generated by the PV panel |
Pr,PV | Rated power of the PV panel |
R | Solar radiation |
RC | Certain radiation point |
RSTC | Solar radiation in the STC |
γi | Participation factor of the demand response program of microgrid i |
τi | Demand response program time delay of microgrid i |
RDRPi | Calculated load for the demand response program of microgrid i |
Kp | Proportional gain of the PID controller |
Ki | Integral gain of the PID controller |
Kd | Derivative gain of the PID controller |
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Parameter | Value | Parameter | Value | Parameter | Value | Parameter | Value |
---|---|---|---|---|---|---|---|
β1 (p.u./Hz) Tg1 (s) TSMESS (s) F1 | 10 0.05 0.3 1 | D1 (p.u./Hz) Tt1 (s) TF1 (s) F2 | 1 0.5 0.1 0.5 | M1 (p.u. s/Hz) RDEG1 (Hz/p.u.) TF2 (s) PFESS | 3 0.05 0.1 4 | TWTG (s) KSMESS TF3 (s) JFESS | 0.04 0.98 0.01 2 |
Parameter | Value | Parameter | Value | Parameter | Value | Parameter | Value |
---|---|---|---|---|---|---|---|
β2 (p.u./HZ) Tt2 (s) HVirtual | 10 0.4 0.139 | D2 (p.u./Hz) RDEG2 (Hz/p.u.) TUC (s) | 0.012 0.5682 0.2 | M2 (p.u. s/Hz) TBESS2 (s) | 0.298 0.1 | Tg2 (s) RBESS2 (Hz/p.u.) | 0.18 0.705 |
Parameter | Value | Parameter | Value | Parameter | Value | Parameter | Value |
---|---|---|---|---|---|---|---|
β3 (p.u./HZ) Tg3 (s) TFC2 (s) | 10 0.08 0.04 | D3 (p.u./Hz) Tt3 (s) TBESS3 (s) | 0.015 0.4 0.08 | M3 (p.u. s/Hz) RDEG3 (Hz/p.u.) RBESS3 (Hz/p.u.) | 0.1667 3 3 | TPV (s) TFC1 (s) | 0.04 0.026 |
Parameter | Value | Parameter | Value | Parameter | Value |
---|---|---|---|---|---|
Kp11 Kp12 Ki11 Ki12 Kd11 Kd12 | −0.0083 −1.972 −4.9991 −4.996 −4.1601 −2.6442 | Kp21 Kp22 Ki21 Ki22 Kd21 Kd22 | −2.0719 −0.1249 −3.605 −0.15 −1.7581 −0.5424 | Kp31 Kp32 Ki31 Ki32 Kd31 Kd32 | −4.09 −4.09 −2.1081 −2.1081 −1 −1 |
Parameter | Value | Parameter | Value | Parameter | Value |
---|---|---|---|---|---|
Kp11 Kp12 Ki11 Ki12 Kd11 Kd12 | −0.042 −3.9327 −7.9876 −7.8101 −4.815 −3.1939 | Kp21 Kp22 Ki21 Ki22 Kd21 Kd22 | −4.8144 −3.2097 −4.8601 −1.8405 −2.9884 −0.8321 | Kp31 Kp32 Ki31 Ki32 Kd31 Kd32 | −5.6655 −5.6655 −3.4699 −3.4699 −2.951 −2.951 |
Parameter | Value |
---|---|
Number of iterations or decades Number of initial countries Number of imperialist countries Number of colonies | 50 17 5 12 |
Parameter | Limitation | Parameter | Limitation | Parameter | Limitation |
---|---|---|---|---|---|
Kp11 Kp12 Ki11 Ki12 Kd11 Kd12 | −0.1 to −0.05 −5 to −1 −10 to −2 −10 to −1 −10 to −2 −5 to −1 | Kp21 Kp22 Ki21 Ki22 Kd21 Kd22 | −5 to −1 −5 to −0.5 −5 to −1 −2 to −0.05 −5 to −0.5 −2 to −0.1 | Kp31 Kp32 Ki31 Ki32 Kd31 Kd32 | −10 to −1 −10 to −1 −5 to −1 −5 to −1 −5 to −0.1 −5 to −0.1 |
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Share and Cite
Rostami, Z.; Ravadanegh, S.N.; Kalantari, N.T.; Guerrero, J.M.; Vasquez, J.C. Dynamic Modeling of Multiple Microgrid Clusters Using Regional Demand Response Programs. Energies 2020, 13, 4050. https://doi.org/10.3390/en13164050
Rostami Z, Ravadanegh SN, Kalantari NT, Guerrero JM, Vasquez JC. Dynamic Modeling of Multiple Microgrid Clusters Using Regional Demand Response Programs. Energies. 2020; 13(16):4050. https://doi.org/10.3390/en13164050
Chicago/Turabian StyleRostami, Ziba, Sajad Najafi Ravadanegh, Navid Taghizadegan Kalantari, Josep M. Guerrero, and Juan C. Vasquez. 2020. "Dynamic Modeling of Multiple Microgrid Clusters Using Regional Demand Response Programs" Energies 13, no. 16: 4050. https://doi.org/10.3390/en13164050