Expansion Planning in Distribution Network with DSTATCOM using Distance Oriented Grasshopper Optimization Algorithm An Optimal Model

This paper intends to consider a multi-objective problem for expansion planning in Power Distribution System (PDS) by focusing on (i) expansion strategy (ii) allocation of Circuit Breaker (CB), (iii) allocation of Distribution Static Compensator (DSTATCOM), (iv) Contingency Load Loss Index (CLLI), and power loss. Accordingly, the encoding parameters decide for expansion, Circuit Breaker (CB) placement, DSTATCOM placement, load of real and reactive powers of expanded bus or node are optimized using Grasshopper Optimization Algorithm (GOA) based on its distance and hence, the proposed algorithm is termed as Distance Oriented Grasshopper Optimization Algorithm (DGOA). The proposed expansion planning model is carried out in IEEE 33 test bus system. Moreover, the adopted scheme is compared with conventional algorithms and the optimal results are obtained.

On the Utilization of an Ensemble of Meta-Heuristics for Simulating Energy Consumption in Buildings

Predicting energy consumption has been a substantial topic because of its ability to lessen energy wastage and establish an acceptable overall operational efficiency. Thus, this research aims at creating a meta-heuristic-based method for autonomous simulation of heating and cooling loads of buildings. The developed method is envisioned on two tiers, whereas the first tier encompasses the use of a set of meta-heuristic algorithms to amplify the exploration and exploitation of Elman neural network through both parametric and structural learning. In this regard, ten meta-heuristic were utilized, namely differential evolution, particle swarm optimization, invasive weed optimization, teaching-learning optimization, ant colony optimization, grey wolf optimization, grasshopper optimization, moth-flame optimization, antlion optimization, and arithmetic optimization. The second tier is designated for evaluating the meta-heuristic-based models through performance evaluation and statistical comparisons. Besides, an integrative ranking of the models is achieved using average ranking algorithm.

Hybrid Bare Bones Fireworks Algorithm for Load Flow Analysis of Islanded Microgrids

In this chapter, a hybrid bare bones fireworks algorithm (HBBFWA) is proposed and its application in solving the load flow problem of islanded microgrid is demonstrated. The hybridization is carried out by updating the positions of generated sparks with the help of grasshopper optimization algorithm (GOA) mimicking the swarming behavior of grasshoppers. The purpose of incorporating GOA with bare bones fireworks algorithm (BBFWA) is to enhance the global searching capability of conventional BBFWA for complex optimization problems. The proposed HBBFWA is applied to perform the load flow analysis of a modified IEEE 37-Bus system. The performance of the proposed HBBFWA is compared against the performance of BBFWA in terms of computational time, convergence speed, and number of iterations required for convergence of the load flow problem. Moreover, standard statistical analysis test such as the independent sample t-test is conducted to identify statistically significant differences between the two algorithms.