Energy Management Strategies in Microgrid Systems
Summary
Microgrids are decentralised energy systems that integrate distributed generation, storage and controllable loads to serve a defined local network. They may operate connected to the main grid or in islanded mode, balancing objectives such as cost minimisation, emission reduction and reliability under variable renewable supply and demand. Energy management strategies in microgrids encompass optimisation-based scheduling, predictive control, hierarchical and multi-agent coordination, demand-side management and robust planning to accommodate uncertainties in generation, load and market conditions. Core approaches include mixed-integer linear programming for day-ahead and real-time dispatch, model predictive control for dynamic performance, and multi-objective frameworks to negotiate trade-offs between economic, environmental and technical goals. Advanced architectures partition control functions across primary (voltage and frequency regulation), secondary (power sharing) and tertiary (economic dispatch) layers. Applications range from rural electrification and remote industrial installations to urban smart-buildings and community energy systems, contributing to global decarbonisation and resilience through enhanced renewable integration, peak shaving and grid support services.
Research from Nature Portfolio
No recent Nature Portfolio content available.
Energy Management Strategies in Microgrid Systems publication trend
The graph below shows the total number of articles in energy management strategies in microgrid systems across all publications each year (not limited to Nature Index journals).
Technical terms
Microgrid: a localised network of distributed energy resources and loads capable of operating independently or in connection with the main grid.
Energy Management System (EMS): the control and information architecture that coordinates generation, storage, demand and grid interactions to achieve operational objectives.
Mixed Integer Linear Programming (MILP): an optimisation method that handles both continuous variables and discrete decisions for scheduling and resource allocation.
Model Predictive Control (MPC): a rolling optimisation technique that uses a dynamic model to forecast future states and determine optimal control actions over a moving horizon.
Multi-objective Optimisation: an approach to optimise several conflicting objectives simultaneously, often yielding a set of Pareto-optimal solutions.
References
- A rolling horizon optimization framework for the simultaneous energy supply and demand planning in microgrids. Applied Energy (2015).
- A Model Predictive Control-Based Energy Management Scheme for Hybrid Storage System in Islanded Microgrids. IEEE Access (2020).
- A Comprehensive Review of Control Strategies and Optimization Methods for Individual and Community Microgrids. IEEE Access (2022).
About these summaries
This Nature Research Intelligence Topic summary is created with the cited references and a large language model. We take care to ground generated text with facts, and have systems in place to gain human feedback on the overall quality of the process in line with our AI principles. We strive to create accurate and useful summaries for people unfamiliar with the research topic and that supports this goal. These pages are a beta release and will be updated as we learn how best to help people gain value from a research topic summary.
Turn complex research questions into confident strategic decisions
When you're under pressure to set direction, justify investment, or understand your competitive position, you need more than raw data — you need trusted insights you can act on.
Benchmark your performance against global peers using robust, methodologically sound analysis.
Combine quantitative metrics with qualitative expert insight to uncover strengths, gaps and emerging opportunities.
Gain tailored, decision-ready recommendations aligned to your strategic priorities.
Talk to us to learn more about our data dashboards and bespoke strategy reports.
Grow research skills, confidence and careers with training built for every stage of the research lifecycle.
Developed with Nature Portfolio journal Editors and internationally renowned experts. Discover three ways to learn:
Self-paced, online courses in convenient bite-sized units, covering key skills across scientific writing, publishing, grant writing, data analysis, and more.
Expert trainer-led workshops with hands-on exercises and real-time feedback across core research skills, delivered via interactive group sessions.
Editor-led workshops combining core principles in writing and publishing, personalised 1:1 feedback from Nature Portfolio Editors and hands-on exercises.
Explore course catalogues and workshop agendas, enquire about the options or request institutional pricing.