Microgrids and Artificial Intelligence

What is a Microgrid?

One of the most comprehensive definitions of a microgrid is from the International Electrotechnical Commission which defines it as a: “group of interconnected loads and distributed energy resources with defined electrical boundaries forming a local electric power system at distribution voltage levels, that acts as a single controllable entity and is able to operate in either grid-connected or island mode.”

In simpler terms – it is a local energy grid entity that is self-sufficient and has the capacity to function in synergy with the central power grid and on its own. 

While most microgrids are usually integrated as part of the overall grid network and utilized as backup systems, some microgrids exist entirely in isolation from the central grid. These are called “remote off-grid microgrids”. Microgrids that function as stand-alone grids usually do so because of technical or economic difficulties in connecting them to the central grid. For example, the geographical region they supply may be very far from the central grid lines.

Microgrids can be powered by natural gas, biogas generators, or micro combined heat and power. They can also derive energy from renewable energy sources such as solar or wind.

Some important characteristics of a microgrid are:

Local-focused: The geographical region supplied by a microgrid is usually in close proximity to the grid. This further allows more efficient energy distribution.

Independence: Whether as a stand-alone grid or as part of a central grid, a microgrid must be able to function independently without relying on central power sources.

Intelligent: A microgrid should be intelligent enough to manage the efficient distribution of electricity, especially when supported with AI technologies.

The microgrid market is expanding as it is projected to hit $47 billion by 2025 from an estimated $28.6 billion in 2020. This represents a CAGR of 10.6% across those years. Its growth is being spurred by increasing demand for renewable energy, the need for reliable & resilient power supply, increasing cyberattacks, and the increased construction of microgrids.

Pros of Microgrids

Generally speaking, grids allow consumers to be connected to central power sources for their electricity needs. The major disadvantage of a central grid is that faults at one end can become a widespread problem for every user.

Reliable electricity: With their capacity to disconnect from the main grid, microgrids can carry the burden from the energy obtained from its local sources while the interruption is being handled. This ensures that electricity supply can be more reliable.

Electricity resilience: Microgrids are a reliable option during natural disasters and extreme weather conditions as they do not have the complex infrastructure (in terms of large assets and extensive wire systems) of central grids. This capacity of microgrids improves electricity resilience. This provides immense value during these crises as vital support systems e.g. hospitals can stay functional.

Security value: The decentralization of the electricity distribution system through smart grids is also an important security measure as terrorist groups can target central power grids.

Cost reduction: An advantage that consumers will find of particular interest is cost reduction with the use of microgrids. Obtaining energy from central power sources is typically associated with higher costs. However, microgrids – especially those that run on renewable energy sources – can ensure that electricity users pay less for electricity.  

Renewable energy: They can aid the transition to renewable energy by regulating the energy flow through the components of the microgrid. The switch to renewable energy contributes to planet conservation efforts.

In these ways, microgrids fulfil the four Ds: decarbonization, decentralization, democratization and digitization.

Microgrids and Artificial Intelligence

Artificial Intelligence offers the opportunity to build autonomous microgrids and optimize their performance. Its capacity can be deployed across all phases of microgrid infrastructure i.e. from the planning phase to daily operations.

Automated functionality – AI can ensure the automated and dynamic self-management of the smart grid. This allows the system to function independently of operators and adjust to changes in power demand from the consumers and electricity transmission from the central grids.

Energy forecasting – This can be viewed in two ways: from the perspective of energy generation from the microgrids and from the viewpoint of the expansion of the microgrid energy market. In the first instance, AI models can be built to predict the expected energy output from microgrid installations using local data from the grid. In the second instance, the analytic capabilities of AI can be applied to data gathered on microgrid performance across several geographical regions to determine energy production and consumption patterns. The insights generated can inform decisions by the pubic and private sectors to expand the microgrid market.

Predictive Maintenance – The predictive capabilities of AI can also be used to guarantee peak functionality of the smart grid by projecting equipment breakdown timelines and sending notification signals to maintenance staff. Furthermore, combined with drone technology and computer vision software, the inspection of the system can be automated and barely visible structural defects can be identified.








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