Abstract

As the power systems in some large developing and developed countries are getting bigger, solving large-scale unit commitment (UC) is an urgent need and significant task to ensure their economic operation and contribute green energy consummation to society. In this article optimization models covering economy and environmental protection are established, and an improved binary artificial fish swarm algorithm (IBAFSA) is presented to solve the large-scale UC problems. The parameters of IBAFSA are improved by Levy flight and adaptive average visual distance to search space more actively, and a double threshold selection strategy is used to enhance the effectiveness of population evolution in the optimization. Meanwhile, a heuristic greedy search algorithm among the best individuals of all generations in the iterative process of the optimization is proposed, which is beneficial to improve computation convergence and reach the optimum solution. A fast constraints processing mechanism based on the heuristic modifying strategy of unit violation is established to handle the coupling between system spinning reserve constraint and unit minimum up and down time constraint. The effectiveness of the proposed approach is verified by the UC simulations of test systems of 10-1000 units, the IEEE 118-bus system, and a large-scale power system of 270 units. The numerical simulating results show that the proposed UC solution method can achieve the near-optimal solutions in a reasonable time, improve the economic and environmental benefits of a large-scale power system, and is a general method to adapt to the changes of the objective function and constraints of a UC optimization.

Highlights

  • Unit commitment (UC) is important to power system scheduling and operation

  • FUNCTION The conventional UC problem focuses on system operating costs, which is modeled as a minimization problem of total operating cost (TOC) that constitutes of fuel cost, start-up and shut-down costs in (1)

  • Constraint processing mechanism stated in Section IV-A to satisfy the unit minimum up and down time constraints and spinning reserve constraint; 3) Based on the feasible solution UF determined in step 2), λ iteration method is adopted to optimize the UC schedule; 4) Calculate the fitness of objective function according to Eq (1) or Eq (4), and save the current optimal fitness and on/off schedule of all units; 5) Use heuristic greedy search to get a better schedule based on the schedule obtained at step 4); Y

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Summary

INTRODUCTION

Unit commitment (UC) is important to power system scheduling and operation. It is a complicated optimization decisionmaking process that is coupled with the on/off schedule of generating units and the optimal output [1], [2]. Zhao proposes an improved binary cuckoo search algorithm (IBCS) based on a new binary updating mechanism and the heuristic search method for the UC problem [15] It does not consider the large-scale UC and only a small system with four units is tested in [15]. Because AFSA has the drawback of falling into local optimum, an improved binary artificial fish swarm algorithm (IBAFSA) is proposed to improve the convergence and global optimization performance in the large-scale UC. To cope with the coupling between system spinning reserve constraint and unit minimum up and down time constraint, a fast constraint processing technique based on a heuristic modification strategy is proposed to solve unit violations in the scheduling process It can efficiently handle the large-scale constraints to reduce the UC solution time.

UC PROBLEM FORMULATION
CONSTRAINTS
THE IMPROVEMENT OF BAFSA
PROPOSED SOLUTION METHODOLOGY
28: Return gbest
SIMULATION AND ANALYSIS
CONCLUSION
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