Energy consumption has increased exponentially, resulting in increased consumption of fossil fuels and a concomitant increase in greenhouse effect gas emissions. Globally, energy systems are beginning to exploit small- and large-scale renewable energy sources such as tidal, wind, solar and biomass energy to address the aforementioned issues. Energy consumption worldwide is anticipated to increase by more than a quarter until 2040, when renewable energy sources will account for 40% of the overall energy mix. Renewable energy source reliability is a significant issue, owing mostly to an imbalance between load supply and demand. Therefore, the rise in energy demand and the redesigning of power networks have resulted in energy being generated close to points of consumption and expanded deployment of microgrids.

What is a microgrid?

A microgrid is made up of small-scale power generating plants, electrical loads and energy storage systems. It may be described more broadly as a medium- or low-voltage distribution grid with distributed generation that includes renewable and conventional energy sources (hybrid systems) and storage devices that provide electrical energy to end consumers. The storage enhances the microgrid's dependability and is used to compensate for the intermittent nature of the photovoltaic- and wind-generated electricity. Such microgrids are equipped with communication technologies that enable real-time control. Microgrids can also function alone or in conjunction with a grid and can be classed as AC, DC or hybrid, depending on the type of source handled.

Stability is critical in a microgrid because the output of intermittent distributed sources such as wind turbines and solar cells is hard to forecast and their power generation may vary considerably as a function of wind speed and solar irradiation. Whenever the microgrid operates in stand-alone mode, only limited generation is available to balance the load demand; therefore, the supply-demand imbalance problem gets even more critical. Moreover, energy management optimization is frequently viewed as an offline process in microgrids.

What is an energy management system?

Microgrids powered by renewable energy sources can be characterized as a collection of systems that exchange information between customers and generation from distributed energy sources. An energy management system is an information system that, when backed by a platform, offers the required functionality to guarantee that energy generation, transmission and distribution occur at the lowest possible cost. Energy management in microgrids entails the use of control software to ensure that the system operates optimally. This is accomplished by taking into account the price and the two microgrid operation modes (connected to the grid or functioning alone). When analyzing microgrids with renewable energy sources, the unpredictability of sources such as wind speed and solar irradiance must be taken into account.

Energy management using optimization techniques

Energy management in a microgrid entails a complete automated system that is mainly concerned with optimum resource scheduling. It is centered on sophisticated information technology and is capable of optimizing the control of battery banks and distributed energy sources. Generally, the microgrid energy management optimization issue entails the following objectives:

• Reduce environmental expenses.
• Increase the life expectancy of energy storage devices.
• Ensure maximum generators' output power at a specific time.
• Reduce the microgrid's running expenses.

Among the most well-known energy management optimization techniques are mixed integer linear and non-linear programming. In linear programming, the constraints and objective functions are linear functions with whole-valued and real-valued decision variables. Dynamic programming techniques are utilized to find solutions for more complicated problems that can be sequenced and discretized. Such complex optimization problems are generally broken down into sub-problems that are then individually optimized. Eventually, such optimal solutions are combined to provide an optimal solution to the initial issue.

[See also: A resilient microgrid planning resource for local governments]

Metaheuristics are another significant technique for optimizing microgrids. In such techniques, evolutionary algorithms, statistical mechanisms and biological evolution are used to estimate the ideal solution for microgrid energy operation and control. Predictive control methods are utilized in situations where it is important to forecast generation and loading in order to manage stored energy effectively. This is generally accomplished through the use of both stochastic programming and control. The most notable of these approaches are those for forecasting the degradation of grid components, primarily the battery banks. On microgrids, optimization approaches based on a multi-agent architecture enable decentralized administration of the microgrid by dividing it into parts with autonomous behavior that perform tasks with stated objectives. These agents, which comprise distributed generators, loads, and energy storage systems, collaborate to achieve the lowest possible cost. Robust programming, stochastic methods and enumeration algorithms are other popular techniques that are used for managing energy in microgrids.

As already stated above, a microgrid includes various renewable energy sources, which can be interconnected with the main power grid through a common point. In such microgrids, an energy management system includes many elements such as control and data acquisition systems, optimization techniques, human machine interfaces and load forecasting. Numerous scholars have investigated energy management using a variety of methods. All efforts, however, have been directed at identifying the most optimum and efficient microgrid functioning.

Conclusion

A microgrid is a local generation grid made up of small-scale renewable power generating plants, electrical loads and energy storage systems. The energy management issue in microgrids due to the intermittent nature of solar and wind energies is an optimization problem, which can be both a mono- or multi-objective problem. The problem is mono-objective when a single cost function is defined and becomes a multi-objective when it provides optimal solutions to environmental, economic and technical problems.