Resolving Economic Dispatch with Uncertainty Effect in Microgrids Using Hybrid Incremental Particle Swarm Optimization and Deep Learning Method

Abstract

Microgrids are one example of a low voltage distributed generation pattern that can cover a variety of energy, such as conventional generators and renewable energy. Economic dispatch (ED) is an important function and a key of a power system operation in microgrids. There are several procedures to find the optimum generation. The first step is to find every feasible state (FS) for thermal generator ED. The second step is to find optimum generation based on FS using incremental particle swarm optimization (IPSO), FS is assumed that all units are activated. The third step is to train the input and output of the IPSO into deep learning (DL). And the last step is to compare DL output with IPSO. The microgrids system in this paper considered 10 thermal units and a wind plant with power generation based on probabilistic data. IPSO shows good results by being capable to generate a total generation as the load requirement every hour for 24 h. However, IPSO has a weakness in execution times, from 10 experiments the average IPSO process takes 30 min. DL based on IPSO can make the execution time of its ED function faster with an 11 input and 10 output architecture. From the same experiments with IPSO, DL can produce the same output as IPSO but with a faster execution time. From the total cost side, wind energy is affecting to reduce total cost until USD 22.86 million from IPSO and USD 22.89 million from DL.

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“…The smaller the value, the smaller the search range of the particle, the easier to find the local optimal solution. In order to take into account the convergence speed and efficiency, the paper adopts an improved particle swarm optimization algorithm(IPSO) [10][11] with decreasing inertia weight, which decreases from ω start to ω end .…”

Economic Dispatch Optimization Strategies and Problem Formulation: A Comprehensive Review

“…In the context of economic dispatch, research studies have explored the development of hybrid optimization algorithms to identify optimal solutions. These research efforts propose combining hybrid metaheuristic algorithms like GA [192], PSO and its variants [193][194][195][196][197], ant lion optimization algorithm [198], slap swarm [199], crow search algorithm [200], and firefly [201], along with traditional optimization techniques such as Lambda iteration algorithm [202], with AI-based techniques such as neural networks, fuzzy systems, and DL. This is due to the primary limitation of AI-based techniques, which require parameter tuning.…”

Model design of an architectural grid-connected photovoltaic system

“…The small-scale renewable sources can be connected to the PLN grid on both medium and low voltage levels. The integration of small-scale renewable sources into the grid system is not necessarily done directly, but through an interface consisting of power electronic devices that convert the DC to DC or DC to AC or AC to DC [8][9][10]. This conversion process creates harmonic, energy conversion efficiency, and power quality issues due to using power electronics circuits.…”