Model Predictive Control in Microgrid Energy Management

Microgrids integrate distributed energy generation, storage and loads within a defined electrical boundary, enabling enhanced resilience and local energy autonomy. Model Predictive Control (MPC) has become a leading technique for microgrid energy management owing to its capacity to forecast future system behaviour and manage multi-variable constraints. By solving an optimisation problem over a finite prediction horizon and implementing only the first action before re-optimising, MPC dispatches renewable sources, storage assets and conventional units in real time. The method inherently incorporates forecasts of load, generation and market prices, balancing cost, emissions and stability objectives. Recent innovations include hierarchical schemes that coordinate day-ahead scheduling with intra-day adjustment, stochastic formulations to address forecast uncertainty, and integration of hybrid storage technologies such as batteries combined with hydrogen. These approaches have demonstrated significant reductions in operational cost and carbon emissions, improved reliability in both islanded and grid-connected modes, and enhanced resilience against variability. Globally, MPC-based controllers are being deployed in rural electrification projects, urban energy communities and industrial sites, contributing to decarbonisation and energy security.

Research from Nature Portfolio

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Research from all publishers

Several recent studies illustrate the practical benefits and methodological innovations of MPC in microgrids. A case study of a renewable energy community in Austria applied a novel multi-step forecasting method within an MPC framework, demonstrating up to a 24.7 % reduction in operational costs and an 8.4 % cut in CO₂ emissions under perfect foresight; even with realistic forecast errors, cost savings of over 3 % were achieved, emphasising the need for accurate forecasts and adaptive control. In a grid-connected wind-solar microgrid with a hybrid hydrogen-battery storage system, an advanced MPC design incorporated device degradation and economic costs, extending storage lifespan while meeting dynamic power requests; simulations using real wind and solar profiles showed the controller’s effectiveness in managing storage-mode switching and optimising energy flows under variable conditions. A multi-layer MPC approach for a wind–solar microgrid participating in daily and real-time markets introduced hierarchical high-layer and low-layer controllers, integrating stochastic predictions to handle market price volatility and renewable intermittency; lab-scale experiments confirmed robust performance despite deviations between forecast and actual scenarios, demonstrating the resilience of multi-time-scale control architectures.

Model Predictive Control in Microgrid Energy Management publication trend

The graph below shows the total number of articles in model predictive control in microgrid energy management across all publications each year (not limited to Nature Index journals).

Technical terms

Microgrid: A localized group of electricity sources and loads that can operate autonomously or connected to the main grid.

Model Predictive Control: An optimisation-based control strategy that predicts future system behaviour over a finite horizon and updates control actions in real time.

Energy Storage System (ESS): Technology such as batteries or hydrogen storage that absorbs and releases energy to balance supply and demand.

Receding-horizon optimisation: A control scheme where the optimisation window moves forward in time, applying only the first action before re-optimising.

Stochastic MPC: An extension of MPC that incorporates uncertainty in forecasts or system dynamics into the optimisation problem.

Hybrid energy storage system: A configuration combining two or more storage technologies (for example, battery and hydrogen) to leverage complementary characteristics.

References

1. Optimal dispatch of a multi-energy system microgrid under uncertainty: A renewable energy community in Austria. Applied Energy (2023).
2. A Coordinated Optimal Operation of a Grid-Connected Wind-Solar Microgrid Incorporating Hybrid Energy Storage Management Systems. IEEE Transactions on Sustainable Energy (2023).
3. Optimal multi-layer economical schedule for coordinated multiple mode operation of wind–solar microgrids with hybrid energy storage systems. Journal of Power Sources (2024).