Optimal Power Flow Solutions for Power System Considering Electric Market and Renewable Energy

Abstract

The paper applies jellyfish search algorithm (JSA) for reaching the maximum profit of IEEE 30-node and IEEE 118-node transmission power networks considering electrical market and wind turbines (WTs). JSA is compared with the particle swarm optimization (PSO), genetic algorithm (GA), moth swarm algorithm (MSA), salp swarm algorithm (SSA), and water cycle algorithm (WCA) for three study cases. The same and different electric prices for all nodes are, respectively, considered in Case 1 and Case 2, whereas Case 3 considers different prices and the placement of one WT. As a result, JSA can reach higher profit than MSA, SSA, WCA, PSO, and GA by 1.2%, 2.44%, 1.7%, 1.3%, and 1.02% for Cases 1, 2, and 3. Then, JSA is applied for optimizing the placement of from two to four WTs for the first system, and from zero to five wind farms (WF) for the second systems. Comparison of profits from the study cases indicates that the network can reach higher profit if more WTs and WFs are optimally placed. The placement of four WTs can support the two systems to reach higher profit by $130.3 and $34770.4, respectively. The greater profits are equivalent to 2.6% and 97.2% the profit of the base system. On the other hand, the obtained results also reveal the important order of location for installing wind power generators. The important order of nodes is, respectively, Nodes 5, 2, 1, and 10 for the first system, as well as Nodes 29, 31, 71, 45, and 47 for the second system. Thus, it is recommended that renewable energies are very useful in improving profit for transmission power systems, and the solutions of installing renewable energy-based generators should be determined by high performance algorithms, such as JSA.

Keywords

• optimal power flow
• wind turbines
• nodal price
• jellyfish search algorithm
• maximum profit