Optimize energy systems through simulation – real-world examples included

Energy systems optimization

Electricity consumption and conservation have always been important issues both for countries as a whole and for each household separately. Now this challenge is increasingly being raised on the world agenda.

In this blog post, we will show how simulation helps optimize energy systems and analyze the effects of renewable energy sources, decentralized energy management, and changing social behavior on energy consumption.


  1. Existing energy problems
  2. Solutions to the energy crisis
  3. Simulation examples: distributed energy management
  4. Simulation examples: electric power consumption
  5. Conclusion

Existing problems: an energy crisis and rising electricity bills

Eurozone countries are currently facing a major challenge: an energy crisis that could have a dramatic impact on the global economy. Energy shortages and the resulting economic factors may also cause social issues.

As gas and electricity prices surge, European consumers are now spending a record amount of their income on energy. Rising electricity bills could lead to a cost-of-living crisis as well.

On top of that, manufacturers are furloughing workers and shutting down production lines because they can’t pay the gas and electric charges. Higher energy prices have made industrial firms reduce consumption. Some companies have even had to shut down production.

So, ordinary people as well as entrepreneurs are having difficulties paying their electricity bills.

Possible solutions to the energy crisis

The ways to cope with the energy crisis may be different, but the transition to renewable energy sources and energy efficiency management may become the critical solutions. In this regard, European governments are accelerating their rollout of green energy.

Citizens of European countries are taking action to cut energy consumption. The European Commission has offered to reduce demand on average by 10%-15%. Heating and lighting are being reduced in government offices and remote working is being encouraged to avoid shortages.

Renewables and distributed energy management: simulation models

Infographic showing how simulation works for testing a decentralized energy management system

Simulation modeling for testing a decentralized energy management system (click to enlarge)

At the recently held AnyLogic Conference 2022, European Institute for Energy Research (EIFER) presented the developed Virtual Demonstrator. It is a highly detailed mostly agent-based simulation model of 25 households in which they were setting a decentralized energy management system.

EIFER showed the advantages of this simulation-based digital twin, a virtual representation of the real system. It accompanies the project through various phases and enriches it throughout its life cycle. The digital twin also serves as a data repository for static and dynamic information, such as for different operating scenarios.

The simulation investigated how the proportion of locally generated and used energy can be increased by intelligently controlling the generation and consumption of electricity and heat.

Video presentation: A digital twin for highly efficient & sustainable districts →

Distributed energy model development workflow

Simulation for managing distributed energy systems – INTELAB's model development workflow

Another company, an energy lab within the RTSoft group, developed intelligent software services and solutions for distributed energy management. Using simulation, the energy lab INTELAB demonstrated the effectiveness of the platform for managing microgrids. It was easier for INTELAB to use AnyLogic than to create their software.

The energy lab tested a short-term optimization module and showed the benefits of an optimization algorithm. The goal of the optimization algorithm was to minimize the cost of generating electricity while satisfying system boundary conditions.

INTELAB compared two methods: a local control algorithm to an optimization module provided by the platform. The results showed that usage of the second method shortened the number of diesel generators operating hours by 41% and reduced the cost of diesel power plant generation up to 20%. AnyLogic simulation proved the effectiveness of the optimization module.

Case study: Modeling distributed energy management systems based on a digital platform →

Electric power consumption and conservation with simulation

Electric power consumption simulation in 3D

Electric power consumption simulation in 3D

How to encourage people to save energy? This is one of the major problems that building owners or managers face. A simulation model was created in AnyLogic to resolve this problem. It showed electric power consumption levels and a change in thinking and behavior.

This predictive model helped us understand how to push forward on education and information to fulfill the energy-saving agenda.

Case study: Simulating social behavior on electric power consumption →

Infographic showing industrial consumer’s journey on the Northern electricity market

Industrial consumer’s journey on the Northern electricity market (click to enlarge)

The University of Southern Denmark conducted an experiment applying AnyLogic simulation software to build a model for energy efficiency management. It showed how electricity costs could vary using demand response. Demand response refers to changes in the use of electricity by consumers during high periods to decrease demand on the power grid and ensure electricity reliability.

The analysis of demand response using simulation focused on a Danish industrial consumer. With simulation it is possible to compare different scenarios: to vary parameters and see how the modeled system responds. This could enable consumers to make savings in electricity.

Case study: Energy efficiency management for meat cooling facility’s production in the Nordic market →

Electric smart grid simulation

The next simulation case showed the benefits of decentralized electricity smart grids and direct local energy trading. An agent-based model of a Dutch neighborhood showed the efficiency and reliability of these changes.

With the right implementation, local energy trading could help reduce the deviations between estimated and actual consumption that occur in today’s centrally traded energy markets.

Blog post: Analyzing electricity smart grids and markets with simulation →


We’ve looked at several examples of using renewable energy sources and energy efficiency management. These approaches to energy consumption will become more and more widespread as they enable households and businesses to save energy and money.

Simulation with AnyLogic helped optimize energy systems and find solutions to overcome the energy crisis. Moreover, these initiatives provide opportunities to decrease the environmental impact of energy use worldwide.

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