Renewable Energy-Based Load Frequency Stabilization of Interconnected Power Systems Using Quasi-Oppositional Dragonfly Algorithm

Abstract

It is already established that the renewable integration effects to the power system are nonzero and become more important with large penetrations. Thereby, the impacts of renewable energy sources (RESs) after integration are studied in this work to stabilize grid frequency of the studied test power system model. Initially, the two-area power system model is studied as the test system. The purpose is to show the tuning efficiency of non-conventional quasi-oppositional dragonfly algorithm (QODA) algorithm as compared to conventional way of tuning technique. It is showed that QODA algorithm is quite effective to find the optimal parameters of proportional–integral–derivative (PID) controller in load frequency control performance. Further, the three-area power system model integrated with RESs is studied. The work done here is to study the impacts of wind turbine generation, solar thermal power generation and solar photovoltaic on system frequency oscillations. The PID controller is employed as the supplementary control task, and its parameters are tuned by QODA algorithm. The integral of time absolute error is chosen as the objective function, and further performance indices are determined at the end of the execution of the program to examine the performance of the designed QODA-based PID controller. Following the integration of RESs, the impacts on frequency deviation through simulation results are also presented. The simulation results showed that the RESs are quite effective in regulating the power system frequency deviation understudied.

Appendix

1.1 Nominal Parameters of Two-Area System with Non-reheat Turbine (Shiva et al. 2015).

$P_{{\text{r}}} = 2000$ MW(rating); $f_{{{\text{sys}}}} = 60$ Hz; $B_{1} = B_{2} = 0.425$ p.u.MW/Hz; $R_{1} = R_{2} = 2.4$ Hz/p.u; $T_{\rm g1} = T_{\rm g2} = 0.03$ s; $T_{\rm t1} = T_{\rm t2} = 0.3$ s; $K_{\rm p1} = K_{\rm p2} = 120$ Hz/p.u.MW; $T_{\rm p1} = T_{\rm p2} = 20$ s; $T_{12} = 0.545$ p.u; $a_{1} = a_{2} = - 1$.

1.2 Nominal Parameters of Unequal Three-area Hydro-Thermal Power System (Shiva and Mukherjee 2015)

$f_{{{\text{sys}}}} = 60$ Hz; $R_{{{\text{th}}}} = R_{{\text{h}}} = 2.4$ Hz/p.u; $T_{{{\text{g}}_{1} }} = T_{{{\text{g}}_{2} }} = 0.08$ s; ${\Delta} P_{{{\text{tie}}\max }} = 200$ p.u.MW; $T_{{{\text{r}}_{1} }} = T_{{{\text{r}}_{2} }} = 10$ s; $K_{{{\text{r}}_{1} }} = K_{{{\text{r}}_{2} }} = 0.5$; $H_{1} = H_{2} = H_{3} = 5$ s; $B_{1} = B_{2} = B_{3} = 0.425$ p.u.MW/Hz; $P_{{{\text{r}}_{1} }} = P_{{{\text{r}}_{2} }} = P_{{{\text{r}}_{3} }} = 2000$ MW; $T_{{{\text{t}}_{1} }} = T_{{{\text{t}}_{2} }} = 0.3$  s; $K_{{{\text{pe}}}} = 1$; $K_{{{\text{de}}}} = 4$; $K_{ie} = 5$; $T_{{\text{w}}} = 1$  s; $T_{{\text{r}}}$ = 5 s; $K_{{{\rm ps}_{1} }} = K_{{{\rm ps}_{2} }} = K_{{{\rm ps}_{3} }} = 120$ Hz/p.u.MW; $T_{{{\rm ps}_{1} }} = T_{{{\rm ps}_{2} }} = T_{{{\rm ps}_{3} }} = 20$ s; $D_{1} = D_{2} = D_{3} =$ 8.33 × 10^–3 p.u.MW/Hz; $T_{12} = T_{23} = T_{31}$ = 0.086 p.u.MW/rad; $a_{12} = a_{23} = a_{31} = - 1.$

1.3 Parameters of GA

Number of parameters depends on problem variables (AGC configuration), number of bits = (number of parameters)*8, population size = 50, maximum number of iteration cycles = 100, mutation rate = 0.04, crossover rate = 80%.

1.4 Parameters of QODA (Mirjalili 2016)

Number of parameters depends on problem variables (LFC configuration), population size = 50, maximum number of iteration cycle (\max_{\rm iter}) = 100, $e = 0.1 \times \left( {1 - \frac{{2 \times {\text{iter}}}}{{\max \_{\text{iter}}}}} \right)$,$s = a = c = 2 \times {\text{rand}}*e,$ $f = 2 \times {\text{rand}},$ and $w = 0.9 - \frac{{0.5 \times {\text{iter}}}}{{\max \_{\text{iter}}}}$.