Energy systems optimization using simulation modeling

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 analyze the effects of using renewable energy sources, decentralized energy management, and changing social behavior on energy consumption.

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 reducing 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 energy systems management: simulation models

At the recently held AnyLogic Conference 2022, European Institute for Energy Research (EIFER) presented the developed Virtual Demonstrator. It is a highly detailed agent-based simulation model that maps and connects the individual plant components of generation, storage, and demand for the electricity and heat sectors.

EIFER showed the advantages of this 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 →


Another company, an energy lab within the RTSoft group, developed intelligent software services and solutions for distributed energy resources (DERs). 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 own 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: 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 intelligent control systems based on a digital platform →


Energy consumption and conservation with simulation

How to motivate people to conserve energy is one of the major problems that building owners or managers face. An agent-based model was created in AnyLogic to resolve this problem. The developers created an influence and predictive model showing a change in thinking and a new behavior.

The predictive model helped understand how to push forward on education and information in order to fulfill the energy-saving agenda.

Case study: Modeling social behavior on energy consumption →


At the University of Southern Denmark, an experiment applying AnyLogic simulation software demonstrated how electricity costs could vary using demand response. Demand response refers to changes in the use of electricity by consumers during high periods in order 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: A simulation for a meat cooling facility’s production in the Nordic electricity market →


The next modeling case showed the benefits of decentralized electricity smart grids and direct local energy trading. An agent-based simulation of a Dutch neighborhood demonstrated 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 →


Conclusion

We’ve looked at several examples of using renewable energy sources and energy efficiency management. These approaches in 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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