Open AccessEffects of Changing Consumer Demand on Distributed Generation and Grid Optimization
Author(s) -
Meisam Mahdavi,
Amir Bagheri,
Alireza Soleimani,
Anna Pinnarelli,
Francisco Jurado,
Augustine Awaafo
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3573588
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Integrating Distributed Generations (DGs) within adaptive power grids is a highly effective approach to reducing electricity losses and minimizing generation and switching costs in contemporary electric systems. The efficiency of these strategies is heavily influenced by consumer load profiles, which significantly affect energy losses, generation expenses, and overall purchasing costs. This connection underscores the crucial role of consumer load variations in determining electricity losses, operational expenditures, and energy pricing, thereby impacting the network’s topology and the optimal placement of DGs. Considering load variability in DG placement within reconfigurable networks adds considerable computational complexity, resulting in extended processing times. Ignoring these variations, however, can lead to inaccurate estimations of losses and costs. This research examines how consumer load variations affect the allocation of DG and the reconfiguration strategies of distribution grids. Utilizing the Mathematical Programming Language (AMPL) as a traditional optimization tool, the study evaluates the proposed model across diverse distribution networks. The simulation results demonstrate that while consumer load variations do not change the total number of DG units required, they significantly influence the optimal locations, switching combinations, and total grid costs. From an energy policy perspective, these findings emphasize the need for utility operators to incorporate consumer load dynamics into their strategies. This integration ensures better alignment with regulatory frameworks that promote efficient energy.