Analysis of Meta-Heuristic Algorithms to Optimal Sizing of Grid-Connected Hybrid Renewable Power Systems
Description
This dataset is used to analyze optimal sizing strategies for a grid-connected hybrid renewable energy system integrating photovoltaic and biogas generators with energy storage. It helped investigate several optimization techniques to determine the optimal size. The data are input into the Non-dominated Sorting Whale Optimization Algorithm (NSWOA), the Multi-Objective Gray Wolf Optimizer (MOGWO), the Multi-Objective Grasshopper Optimization Algorithm (MOGOA), the Multi-Objective Salp Swarm Algorithm (MSSA), and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for this purpose. Real-time evaluation data integrated with meteorological information from Debre Markos in Ethiopia are also the input for simulation. Results show that the cost of energy (COE) values for MOPSO, NSWOA, MOGWO, MOGOA, MSSA and NSGA-II are 0.091 €/kWh, 0.08625 €/kWh, 0.092 €/kWh, 0.097 €/kWh, 0.089 € / kWh and 0.087 €/kWh, respectively. Meanwhile, the net present cost (NPC) values for these methods are 3.54 × 10⁶ €, 3.15 × 10⁶ €, 3.25 × 10⁶ €, 3.95 × 10⁶ €, 3.39 × 10⁶ €, and 3.19 × 10⁶ €. Based on the simulated results, the NSWOA technique is the best for the correct system size.
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Steps to reproduce
Algorithm 1: MOPSO 1. Initialize a Population of particles with random positions and velocities, through input - Define particle position Xi - Initialize each particle with random velocity Vi - Choose appropriate parameters for the population size np - Set the maximum number of iterations - Initialize each particles current best position , at first iteration i=0 - Set the , with the best particle in initial population at iteration i=0 2. Compute the cost function for each candidate particle according to given cost function 3. Update the particles personal best and global best position at iteration g For i=1 to np End 4. Update the state of the particle in the swarm For j=1 to m End 5. Repeat step (2)-(4) for G iterations or until the algorithm meets the stop criterion 6. Output as the objective function and correlated solution as the optimal solution End Algorithm 2: NSWOA 1. Initialization of random Whale population Xi(i=1, 2, 3, ……, N) 2. Fitness estimation for all search agents 3. X* is optimal search agent 4. While (ti < maximum number of iterations) 5. for all search agent 6. Update α, A, C, l, and p 7. if1(p<0.5) 8. if2(|A| < 1) 9. Update the position of the current search agent using the equation: 10. else if2(|A| ≥ 1) 11. Choose a random search agent ( ) 12. Update the position of the current search agent using the equation: 13. end if2 14. else if1 (p ≥ 0.5) 15. Update the position of the current search using the equation: 16. end if1 17. end for 18. Verify if any search agent exceeds the search space and add it 19. Calculate the fitness for all search agents 20. Update X * if other optimal solution exists 21. ti = ti + 1 22. end while 23. Return X * Algorithm 3: MOGWO 1. Initialization of grey wolf population of XP ( P = 1,2,3,……,n); 2. Initialize α, A and Q 2. Calculate the fitness of each search agent 3. Set Xα as the best search agent 4. Set Xβ as the second best search agent 3. Set Xδ as the third best search agent 4. While (i < maximum number of iteration) 5. For each search agent 6. Update the location of the current search agent by using the equation end for 7. Update α, A and Q 8. Calculate the fitness of all search agent 9. Update Xα , Xβ and Xδ 10. i = i+1 11. end while 12. Return Xα Algorithm 4: MOGOA 1. parameter Initialization of , , UB, LB, 2. Initialization of population of grasshopper swarms Xi(i=1,2,3,….,n) randomly 3. Compute fitness value for each Grasshopper (search agent) 4. Set T as best solution of the system 5. While(t< maximum iteration) 6. Update C using the equation of 7. For each search agent 8. Normalize the separation between Grasshoppers in [1,4] 9. Update the location of the current solution using the equation 10. Bring the current Grasshopper back if it violates the boundary of search space 11. End for 12. Update T if there is a better solution in the population 13. itr=itr+1 14. end while 15. Return T
Institutions
- University of BueaSouth-West, Buea