article · IET Generation Transmission & Distribution
Managing electric power systems requires balancing competing demands, including lowering fuel costs, cutting power losses, improving voltage profiles, and maintaining system stability. An optimisation method called the forced initialised multi-objective differential evolution algorithm addresses this optimal power flow challenge. The technique pairs a specific differential evolution variant with an epsilon-constraint strategy to produce well-distributed Pareto-optimal solutions across a single run. By adaptively shifting the epsilon value, the algorithm circumvents the heavy computational processing usually required for Pareto ranking and updating, while fuzzy set theory identifies the best balanced solution among the trade-offs. Testing across standard IEEE thirty-bus and fifty-seven-bus electrical test networks showed that the algorithm delivers high convergence speeds and diverse solutions. Comparisons with other evolutionary optimisation techniques confirm its ability to resolve complex non-linear power system problems at acceptable economic and technical levels.
Power grid operators must continuously balance operational costs against electrical performance and grid stability. Solving these trade-offs mathematically is computationally demanding. Developing faster and more balanced multi-objective algorithms helps system planners identify operational set points that keep electricity generation economical while preventing line overloads and voltage instability across the network.
The method could be integrated into grid optimisation and energy management software used by electrical transmission utilities and system operators. Because it has only been tested on standard academic benchmarks such as the IEEE 30-bus and 57-bus models, the technology remains early-stage research that requires validation on larger, real-world utility networks before practical commercial deployment.
AI-generated from the published abstract. Always read the original work before citing.
This study proposes a multi‐objective differential evolution algorithm (MO‐DEA) based on forced initialisation to solve the optimal power flow (OPF) problem. The OPF problem is formulated as a non‐linear MO optimisation problem. The considered objective functions are fuel cost minimisation, power losses minimisation, voltage profile improvement, and voltage stability enhancement. For solving the MO‐OPF, the proposed approach combines a new variant of DE (DE/best/1) with the ɛ ‐constraint approach. This combination guarantees high convergence speed and good diversity of Pareto solutions without computational burden of Pareto ranking and updating or additional efforts to preserve the diversity of the non‐dominated solutions. The proposed approach has the ability to generate Pareto‐optimal solutions in a single simulation run through adaptive variation of the ɛ ‐value. In addition, the best compromise solution is extracted based on fuzzy set theory. The effectiveness of the proposed MO‐DEA is tested on the IEEE 30‐bus and IEEE 57‐bus standard systems. The numerical results obtained by the proposed MO‐DEA are compared with other evolutionary methods reported in this literature to prove the potential and capability of the proposed MO‐DEA for solving the MO‐OPF at acceptable economical and technical levels.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1049/iet-gtd.2015.0892
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
