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Optimizing Energy Systems with AnyLogic Simulation Modeling

Preface:




European Institute for Energy Research (EIFER), the joint research center of Karlsruhe Institute of Technology and EDF Company, is an organization that deals with the decentralization of energy systems on various territories, and promotes renewable energy sources. Unlike classical energy systems, localized ones are less hierarchical, thus providing the prospects of power storage, renewable energy inclusions, and demand forecasting. EIFER engineers chose AnyLogic simulation modeling to find out how the localized systems should be planned and operated. It helped design multiscale models, which included all parts of the energy system, and aimed at understanding causes and effects throughout the systems’ scales. With AnyLogic, the company defied to handle emergency phenomena that might happen in decentralized systems.


Case #1: Smart Grid Modeling on Island Systems – Demand Side Flexibility




Problem:




Energy production on island territories is expensive and depends on the cost of oil. EIFER aimed to test how the inclusion of photovoltaic elements would affect the classical energy system at such territories, thus trying to reduce costs and oil dependency.


Solution:




The current system comprised three scales based on the voltage areas. The area with the lowest voltage (final customer level) was chosen for the implementation of photovoltaic elements. After those were distributed in the model, and experiments were run, the data was re-aggregated to capture the impact on the whole system and avoid any breakages and power overconsumption.


It was decided to model the scenario when the island would be partly covered with clouds to test if the system’s balance would be disturbed. It turned out that, due to cloud coverage, there was higher production at the thermal plant located at non-cloudy areas, which could lead to the system’s imbalance.


Outcome:




AnyLogic modeling helped simulate the influence of photovoltaics on the island’s current energy system. The cloud coverage scenario was implemented to test the model’s flexibility and show meteorological impact on the system.

Energy System Simulation Modeling


Energy System Simulation Modeling


Energy System Simulation Modeling

Case #2: Optimization for Local Energy System Management




Problem:




Multi-energy systems might include various energy carriers, like solar power, gas, electricity, liquid fuel, etc. Thus, when optimizing such systems, one should consider energy as a flow to see how energy sources interact. EIFER engineers used AnyLogic to model and optimize sources connected into a single system.


Solution:




In the agent-based model, the production plant generated electricity and heat, while ordinary people and the tertiary sector consumed it. The heat went to storages if they were empty, whereas electricity went to households or to grids. The output statistics showed how production affected power consumption. This allowed them to compare power consumption at different time frames and analyze capital and operational expenditures.


Outcome:




AnyLogic helped represent the energy system as a holistic one, connecting separate parts in one model. With AnyLogic, a modeling library was created to make it simpler to reuse certain blocks and agents in other models. User-friendly interface simplified the interaction with the model for unskilled AnyLogic users.


Output statistics allowed modelers to capture seasonality factors affecting the energy system, and analyze economic indicators.


Conclusion:




Energy systems tend to be multiscale and may be easily affected by various factors. With AnyLogic simulation modeling, it is possible to simplify complex systems and capture the issues connected with variabilities, demonstrating how alterations in one scale may affect the whole system.


To learn more on how EIFER performs energy systems’ decentralization and applies renewable energy sources, read the following papers:

 

More Case Studies

  • Optimization of Utility Companies’ Mutual Assistance Using Agent-Based Modeling
    When people are impacted by a natural or man-made disaster, utility companies look for ways to provide resources as soon as possible, and reduce outage time. To assist and better coordinate with each other, Canadian companies from closely located territories created alignments for mutual assistance. Engineers from York University applied simulation modeling to provide the utility companies with a better decision making tool for managing the mutual assistance process.