article · Electricity
An energy management framework coordinates interconnected microgrids under both standard and emergency conditions. The system uses a two-tier architecture where local controllers manage power dispatch independently during routine operations. When a fault or generation shortfall occurs, a higher-level global controller activates, allowing neighbouring microgrids to share electricity across physical tie-lines while exchanging operational data via cyber links. A metaheuristic technique called the pufferfish optimisation algorithm guides this process to reduce overall operational costs, including the financial impact of network power losses. One-day simulation studies conducted in MATLAB and DIgSILENT benchmarked the technique against established algorithms such as particle swarm optimisation, genetic algorithms, and the grey wolf optimiser. The pufferfish optimisation algorithm demonstrated superior performance by achieving the lowest average operating expenses alongside consistent computational stability and faster convergence across normal and emergency scenarios.
Modern electrical grids increasingly rely on clustered microgrids that can support one another during disruptions. By automatically sharing power when equipment fails or generation drops, networked microgrids can prevent blackouts and keep essential services running. Optimising these power transfers using advanced computational techniques lowers overall operational costs and minimises energy lost during transmission, making decentralised energy networks both more resilient and economically viable.
This approach could enable smart grid operators and microgrid energy management software developers to lower operational expenses and automate outage recovery across interconnected facilities. Evaluated purely through one-day software simulations in MATLAB and DIgSILENT, the technology remains at an early computational research stage. Real-world application would require physical prototyping and validation under dynamic grid conditions before integration into commercial utility automation platforms.
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This paper presents an approach for optimal operation of self-healing networked microgrids (NMGs) under both normal operation and emergency conditions using the pufferfish optimization algorithm (POA). The proposed methodology is based on an energy management system (EMS) with two levels and independent functions. The lower-level is designed for normal operation, where the local controller of each microgrid (MG) performs the optimal dispatch of power from the dispatchable sources. During an emergency case in any MG, the higher-level EMS is activated, and the global controller is brought into operation. Physically, the NMGs are connected by tie-lines, while cyber links are established to exchange information and control signals for coordinated operation. Each MG operates to supply its local demand during normal operation conditions, resulting in no electrical power exchange between MGs. İn case of generation deficiency or a fault leading to generation outage, electrical power can be exchanged through the existing interconnections, enabling the affected microgrid to receive support from neighboring MGs. The main objective of POA is to minimize the total operating cost, in which the economic impact of network power losses is incorporated into the single objective function. Simulation studies were conducted using MATLAB and DIgSILENT software over one day. The performance of POA was compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO) under the same computational settings. Statistical and convergence analyses show that POA achieves the lowest mean operating cost across all studied cases, with low run-to-run variability and favorable convergence behavior. The results demonstrate the effectiveness of the proposed approach in improving the economic operation of NMGs under both normal and emergency conditions.
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DOI: 10.3390/electricity7030099
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