Abstract
The integration of demand response programs (DRPs) into the energy management (EM) system of microgrids (MGs) helps in improving the load characteristics by allowing consumers to interoperate for achieving techno-economic advantages. In this paper, an improved algorithm is called LINFO is proposed for modifying search ability of the original weIghted meaN oF vectOrs (INFO) algorithm as well as avoiding its weaknesses like trapping in a local optima. The improved algorithm's efficiency is confirmed by comparing its results with those obtained by the original INFO and other optimization techniques using different standard benchmark test functions. Moreover, this improved algorithm and the original version are applied for solving the EM problem with the aim of optimizing the operation cost of the MGs in the presence DRPs. They are used to solve day-ahead EM problem for optimal operation of renewable energy resources, the optimal generation from a conventional diesel engines (DEs); taking into account the participation of customers in DRP for minimizing MG operating cost, which includes the cost of DEs fuel and the power transactions cost with the main grid. To demonstrate the efficacy of the proposed LINFO, simulation results are compared with the results of well-known and newly developed optimization techniques.
| Original language | English |
|---|---|
| Pages (from-to) | 20749-20770 |
| Number of pages | 22 |
| Journal | Neural Computing and Applications |
| Volume | 35 |
| Issue number | 28 |
| DOIs | |
| State | Published - Oct 2023 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2023, The Author(s).
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 7 Affordable and Clean Energy
Keywords
- Demand response
- Energy management
- LINFO
- Microgrid
- Optimization
ASJC Scopus subject areas
- Software
- Artificial Intelligence
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