Nejvíce citovaný článek - PubMed ID 39152206
Chaotic self-adaptive sine cosine multi-objective optimization algorithm to solve microgrid optimal energy scheduling problems
The rising energy demand, substantial transmission and distribution losses, and inconsistent power quality in remote regions highlight the urgent need for innovative solutions to ensure a stable electricity supply. Microgrids (MGs), integrated with distributed generation (DG), offer a promising approach to address these challenges by enabling localized power generation, improved grid flexibility, and enhanced reliability. This paper introduces the Improved Lyrebird Optimization Algorithm (ILOA) for optimal sectionalizing and scheduling of multi-microgrid systems, aiming to minimize generation costs and active power losses while ensuring system reliability. To enhance search efficiency, ILOA incorporates the Levy Flight technique for local search, which introduces adaptive step sizes with long-distance jumps, improving the exploration-exploitation balance. Unlike conventional local search strategies that rely on fixed step sizes, Levy Flight prevents premature convergence by allowing the algorithm to escape local optima and explore the solution space more effectively. Additionally, a chaotic sine map is integrated to enhance global search capability, ensuring better diversity and superior optimization performance compared to traditional algorithms. Simulation studies are conducted on a modified 33-bus distribution system segmented into three independent microgrids. The algorithm is evaluated under single-objective scenarios (cost and loss minimization) and a multi-objective optimization framework combining both objectives. In single-objective optimization, ILOA achieves a generation cost of $19,254.64/hr with 0.7118 kW of power loss, demonstrating marginal improvements over the standard Lyrebird Optimization Algorithm and significant gains over Genetic Algorithm (GA) and Jaya Algorithm (JAYA). In multi-objective optimization, ILOA surpasses competing methods by achieving a generation cost of $89,792.18/hr and 10.26 kW of power loss. The optimization results indicate that, for the IEEE-33 bus system without considering EIR, the proposed ILOA algorithm achieves savings of approximately 0.0014%, 0.0041%, and 0.657% in operation costs compared to LOA, JAYA, and GA, respectively, when MG-1, MG-2, and MG-3 are operational. The analysis of real power loss reduction demonstrates that, in the IEEE-33 bus system without considering EIR, the proposed ILOA algorithm effectively minimizes power loss by approximately 0.692%, 1.696%, and 1.962% in comparison to LOA, JAYA, and GA, respectively, under the operational conditions of MG-1, MG-2, and MG-3. Additionally, reliability constraints based on the Energy Index of Reliability (EIR) are effectively incorporated, further validating the robustness of the proposed approach. Considering EIR, the real power loss analysis for the IEEE-33 bus system highlights that the proposed ILOA algorithm achieves a reduction of approximately 1.319%, 2.069%, and 2.134% in comparison to LOA, JAYA, and GA, respectively, under the operational scenario where MG-1, MG-2, and MG-3 are active. The results confirm that ILOA is a highly efficient and reliable solution for distributed generation scheduling and multi-microgrid sectionalizing, showcasing its potential for real-world applications such as dynamic economic dispatch and demand response integration in smart grid systems.
Demand-side management (DSM) enhances distribution network efficiency by shifting or reducing loads, alleviating network stress. The Load Shifting Policy (LSP) reallocates flexible loads to low-price periods without altering total demand, while the Load Curtailing Policy (LCP) incentivizes consumers to reduce peak demand. This study introduces a hybrid DSM approach that combines LSP and LCP with a smart charging strategy for plug-in hybrid electric vehicles (PHEVs). Using the hybrid load shifting and curtailment policy (HLSCP), the microgrid (MG) load profile was optimized, reducing generation costs from 707¥ for the base load to 682¥ with HLSCP and 676¥ when incorporating smart PHEV charging. Emissions decreased correspondingly, from 1267kg to 1246kg. These results demonstrate the hybrid DSM's capacity to tackle economic and environmental challenges in power systems. The Differential Evolution (DE) optimization method further validated the robustness and efficiency of this cost-effective, sustainable microgrid management approach.
- Klíčová slova
- Energy Resources, Energy engineering, Energy systems, Environmental policy,
- Publikační typ
- časopisecké články MeSH
