Smart Autonomous MicroGrids

Smart Autonomous MicroGrids

Smart Autonomous MicroGrids
Smart Autonomous MicroGrids like the Island El Hierro

MicroGrid Research project binnen het STW Perspectief programma ‘Robust Cyber-Physical Systems

Scientific description of the MicroGrid project

Problem and goal In recent years, more and more distributed generation has been installed on a neighborhood level. When enough (renewable) generation like PV panels, biomass installations and windturbines and storage are installed, it is possible to create a self

supplying neighborhood in a so-called energy autonomous smart MicroGrid.

A neighborhood can be

  • a collection of residential buildings
  • small business parks
  • a university campus
  • a pop festival
  • a village in a remote area

Since (renewable) distributed generation is not (always) producing the energy when it is needed, and consumption is also not always predictable, a MicroGrid behaves highly stochastic.

On the other hand, an energy autonomous neighborhood should always be in balance, i.e. at all times the energy consumption should be approximately the same as the production, where up to a certain level, temporary mismatches between consumption and generation can be bridged by energy storage.

In a less extreme scenario, when there is temporary shortage of energy the main electricity grid could be used for back up and similarly the neighborhood might provide excess energy to the grid when energy is expensive.

This means that the autonomous grid must be able to switch to/from the main grid, without interruption of the power supply.

MicroGrid Challenges

1. Stochastic modeling of an energy neutral neighborhood

The type of generators that can be used in a neighborhood is very much dependent on the environment where it is used.

For example, in a suburban neighborhood with a lot of farms energy can be obtained from bio wastage of animals and crops, in a neighborhood close to the sea wind energy might be a good option and neighborhoods close to a river might use water energy.

Unfortunately, energy from the environment is not always available and thus generation is inherently unreliable.

An important challenge is to stochastically model and predict how much energy can be generated, which depends on the used generation method and may be depending amongst others on time of day and weather conditions.

In order to control the MicroGrid, it is also important to develop a systematic and flexible model of the elements of the grid

  • various types of energy generators
  • various static or dynamic loads
  • batteries and other energy-storage devices
  • transmission lines
  • switches
  • as well as the network structure

The stochastic model of the uncertainties can then be superimposed on this physical layer. As the demand may be depending on human behavior, the demand has also a stochastic behavior.

As mentioned before, to cover mismatch between generation and demand energy storage is needed. Based on the stochastic demand and supply models possible options for the energy infrastructure can be derived. The ICT system has to cope with various forms of uncertainties.

2. Matching demand and supply

Within an autonomous MicroGrid, supply and demand must be in balance. This balancing must be done on different levels:

  • when the MicroGrid is cut off from the main grid (operating autonomously or islanded)
  • the grid stability must be maintained on a (micro) second level, such that supply and demand within the MicroGrid is balanced

When this is not done properly high peak voltages may appear in the MicroGrid and connected appliances may be damaged.

To design control strategies, which prevent these high peak voltages, accurate models of the grid components are essential.

In an autonomous MicroGrid, the buffers also take care of grid balancing.

  • When no conventional generation is available (i.e. the MicroGrid is disconnected from the main grid), there is also no spinning reserve available and very fast changes in demand should be taken care of with batteries.
  • Spinning reserve in conventional power plants is a mechanical way of keeping up with fast changes, but for an autonomous (autarchic) MicroGrid a fast control algorithm is required to maintain the fine grain stability supplied by the batteries.

This algorithm should be a fast, as it has to deal with the real-time fluctuations in demand and supply.

On a course grain level (minutes), the supply and demand should be balanced with different methods, for example

  • by shifting demand in time or temporarily switching off appliances (e.g. a freezer)
  • the remaining mismatch should be solved by the energy buffer

How much buffering is required, which type of buffering and where in the grid it should be placed is investigated in this project.

Next, the control algorithms must be capable of using this buffering capacity in a near optimal way.

A first version for such an algorithm (TRIANA) is developed at the University of Twente, but this algorithm needs be tailored for this autonomous situation.

To guarantee the good behavior of the demand and supply balancing mechanism, information must flow among and within the MicroGrids. Algorithms, which control or provide accurate information to the physical infrastructure, constitute the cyber component of the system.

Approach, workplan and results

This project consists of three parts:

  1. the stochastic modeling of an autonomous neighborhood
  2. the development of multi-level control algorithms
  3. creating a support tool for planning future MicroGrids

Finally, the system must be validated in a realistic environment.

In detail


Development of a versatile simulation and analysis model of an energy autonomous neighborhood incorporating stochasticity and uncertainty.

The main research challenge is: given the stochastic behavior of demands and generation how much energy storage is needed to fulfill all demand. And what is the probability that we run out of energy and what are possible back-up scenarios. Another challenge is how to model aging of batteries.

WP2 (UT)

Develop balancing algorithms for an energy autonomous MicroGrid. For example: stochastic model predictive control, a special implementation of optimal control, may have numerical advantages. The methods should use different time granularities and multiple aggregation levels (e.g. building, MicroGrid etc.).

The algorithms should balance supply and demand using energy buffering, and maintain grid stability.

WP4 (UT + Alliander)

Create a Decision Support Tool for planning and dimensioning of future autonomous MicroGrids. In this part methods and support tools will be developed to determine important system parameters of a smart autonomous MicroGrid: e.g. how much generation is required, what is a sustainable mix of technologies, size of energy buffers, dimension of biogas installations, amount of back-up generation etc. to become energy autonomous.

Furthermore all three PhDs will work together in WP3 on a demonstrator of a complete system (generation, storage and supply) to test the approach in a realistic environment provided by Alliander.

In particular Alliander has a test facility near Zutphen (holiday park Bronsbergen).

Therefore, the result of this project is a Decision Support method and tool to dimension future autonomous neighborhoods. This tool will be validated in a real test-site of Alliander.

Utilisation plan

Alliander is responsible for building, maintaining and managing the electricity- and gas networks of a large part of the Netherlands.

Alliander primary goal is to distribute electricity and gas to the end-users in the most reliable and safest way. Due to the increasing desire to reach a more sustainable energy infrastructure, Alliander is responsible for preparing the distribution network for new forms of decentralised energy production, like PV and wind turbines.

Like many other DSOs is Alliander continuously active in reinforcing and modernizing the distribution networks in an affordable way. Via ICT Alliander tries to improve the dependability of the networks, match demand and supply on a local level and reduce failures and their corresponding costs.

Alliander will contribute to this project with their knowledge and experience on Smart Grid and Smart Energy from other projects.

Furthermore, Alliander contributes a researcher responsible for researching the possibilities of using distributed energy storage for grid stabilization. The field trials, created in other projects, can be used within this project. Furthermore, real-life measured data will be provided for simulation and model-development purposes.

Alliander already has some experience with energy buffering and stabilization of (autarchic) neighbourhoods. Furthermore, Alliander’s facilities for field trail will be available for validation.

Alliander will utilise the results of this project for their future energy infrastructures. Alliander cooperates with other networking companies in organisations like SETS (Smart Energy Transport and Systems), SEC (Smart Energy Collective) and the project group Smart Grids from all network operators.


Arjan van der Schaft, FWN/JBI
P. 050-3633731

Collaborating partners

ENTEG (Claudio De Persis), UTwente (G.J.M. Smit), Alliander, DNV GL, Alphen


Spring 2014 – spring 2018


Budget: Requested from STW: k€ 400.984 Contribution by users: k€ 200.000 (cash) & k€ 145.000 (in kind)

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