Optimal Reactive Power Dispatch in Power Systems

Optimal reactive power dispatch (ORPD) constitutes a cornerstone of modern power‐system operation, balancing voltage regulation, network losses and system stability. By judiciously adjusting control variables such as generator bus voltages, transformer tap settings and shunt capacitor injections, ORPD seeks to minimise active power losses and voltage deviations whilst satisfying security and equipment constraints. The problem is inherently non‐linear and often mixed‐integer, reflecting both continuous and discrete control actions. Historically addressed through linear and non‐linear programming techniques, recent decades have witnessed a surge in metaheuristic and hybrid algorithms that exploit nature-inspired search behaviours to overcome local optima and accelerate convergence. The proliferation of renewable energy sources and power‐electronic devices further complicates dispatch owing to variability and uncertainty, prompting scenario-based and stochastic formulations. Practical applications span bulk transmission networks, distributed generation clusters and microgrids, with standard IEEE bus systems serving as benchmarks. Multi-objective extensions facilitate trade-off analysis among cost, loss reduction and voltage stability, supported by integrated decision-making frameworks that extract operator-preferred solutions from Pareto fronts.

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Recent years have seen the emergence of comprehensive surveys and innovative algorithms that push the boundaries of reactive power optimisation. A contemporary survey in Artificial Intelligence Review has evaluated four modern metaheuristics—mantis search, spider wasp, nutcracker optimisation and artificial gorilla optimisers—and proposed a hybrid variant of the nutcracker algorithm (HNOA). This hybrid method demonstrated superior performance in minimising power losses and voltage deviations across small-, medium- and large-scale IEEE test systems, particularly excelling in large networks by robustly escaping local minima and accelerating convergence. Further developments in handling uncertainty have been explored through the application of a scenario-based marine predators algorithm (MPA), which accounts for stochastic variations in load demand and wind-solar generation. By generating deterministic scenarios from probability distributions, the MPA achieves efficient minimisation of power losses and voltage deviations under renewable intermittency, reinforcing its suitability for modern grids with high renewable penetration. Another promising advance is the adaptation of a simple Rao-3 optimisation algorithm to the ORPD problem under time-varying demand and renewable uncertainty. This approach extends the method to both single- and multi-objective frameworks, optimising active power loss, voltage deviation and stability indices for standard IEEE 30-, 57- and 118-bus systems. The Rao-3 algorithm proved competitive against recent heuristics by offering ease of implementation and robust handling of stochastic conditions in power networks.

Optimal Reactive Power Dispatch in Power Systems publication trend

The graph below shows the total number of articles in optimal reactive power dispatch in power systems across all publications each year (not limited to Nature Index journals).

Technical terms

Reactive power dispatch (RPD): Adjustment of reactive power sources in a network to optimise voltage levels and minimise losses.

Metaheuristic algorithm: A high-level, problem-independent strategy guiding subordinate heuristics to explore and exploit complex search spaces for near-optimal solutions.

Voltage deviation: Difference between actual bus voltage and its reference, used to quantify voltage quality.

Bus: Node in a power system where lines, loads or generators interconnect.

Tap changer: Transformer device that alters its turns ratio to regulate voltage.

Scenario-based method: Technique converting probabilistic uncertainties into a set of deterministic scenarios for optimisation under variability.

Multi-objective optimisation: Simultaneous treatment of two or more conflicting objectives, yielding a Pareto-optimal set of solutions for decision-making.

References

1. Artificial intelligence-based optimization techniques for optimal reactive power dispatch problem: a contemporary survey, experiments, and analysis. Artificial Intelligence Review (2024).
2. Solving the Optimal Reactive Power Dispatch Using Marine Predators Algorithm Considering the Uncertainties in Load and Wind-Solar Generation Systems. Energies (2020).
3. Optimal Reactive Power Dispatch With Time-Varying Demand and Renewable Energy Uncertainty Using Rao-3 Algorithm. IEEE Access (2021).