Abstract

Optimal planning of integration the Photovoltage Distributed Generation (PV-DG) and DSTATCOM is a crucial task due to the stochastic variations of PV output power and the load demand which are related to solar irradiance variations and the activities of the customers, respectively. In this article, the optimal planning problem of the PV-DG and DSTATCOM system is solved. The proposed model considers the uncertainties of the solar irradiance and the load demand for a multi-objective function, including the cost reduction, the voltage profile, and stability index improvement. Modified Ant Lion Optimizer (MALO) is proposed to enhance the basic ALO searching ability using two strategies. The first strategy is based on Levy Flight Distribution (LFD) to strengthen the exploration of the algorithm and avoid the premature of the basic ALO. In contrast, the second strategy is based on updating the solutions in a spiral orientation to improve the exploitation of the algorithm. The IEEE 69-bus and 118-bus radial distribution systems are used to demonstrate the effectiveness of the proposed method, and the yielded simulations are compared with the basic ALO and other well-known optimization techniques for power loss minimization under deterministic conditions. The simulation results demonstrate that the techno-economic benefits can be increased considerably by optimal inclusion of two PV-DGs and DSTATCOMs compared with a single system.

Highlights

  • SIMULATION RESULTS the proposed algorithm is applied for solving the optimal power problem for optimal allocation of a hybrid system (PV-DG and the DSTATCOM) under uncertain conditions

  • CASE 1: OPTIMAL INSTALLATION OF PV SYSTEM UNDER THE DETERMINISTIC CONDITION In this case, to state the Ant Lion Optimizer (ALO) and the Modified Ant Lion Optimizer (MALO)’s effectiveness, these techniques have been examined on the standard IEEE 69-bus to assign the optimal locations and ratings of the PV units for power loss minimization

  • The trends of the power losses vs. iteration number with incorporating a single DG are converged at iteration numbers 5, 15, 10 and 43 by applying the proposed method, ALO, and the modified teaching–learning-based optimization (MLBO) algorithm [57]

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Summary

INTRODUCTION

B. CONTRIBUTION OF PAPER It should be highlighted here that most of the presented works related to optimal allocation of DG and DSTATCOM problem have been solved at the deterministic condition or solving the optimal power planning considering the technical and the economic perspective separately. The optimal power planning problem with integration of the PV-DG and DSTATCOM has been solved considering uncertainties of the solar irradiance and load demands of four seasonal (summer, winter, spring, autumn) variations of these parameters. An improved version of the ALO is proposed to solve the planning problem based on Levy flight distribution and spiral orientation. Proposing a modified ALO version for solving the presented problem based on two improvements includes the spiral movement around the best solution and the levy flight-based motion. The obtained outcomes clearly indicate that scenario 2 (Optimal installation of two-hybrid systems) is more effective in optimal planning

PAPER LAYOUT
THE SYSTEM CONSTRAINTS
COMBINED MODEL OF PV AND LOA
ANT LION OPTIMIZER
SLIDING ANTS TOWARDS ANT LIONS
ELITISM
SIMULATION RESULTS
CASE 2
CONCLUSION
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