Python simulation of city quarter energy systems
5 Enrollments Level : IntermediateRelevance
This BB addresses city quarter energy systems and their modeling and simulation to enable operational flexibility deployment and energy management concepts to ensure a robust and efficient operation of future power systems.
Abstract
This building block focuses on modeling and simulating energy systems in urban environments, thereby exploring state-of-the-art operational flexibility deployment and energy management concepts for city quarter energy systems. The building block can be completed independently, but the learner is expected to have basic programming skills as well as an understanding of the basic principles and theory behind mathematical optimization theory with respect to operational flexibility and energy management applications. Through a detailed video tutorial, learners will first be introduced to the open-source pycity_scheduling Python framework, empowering them to run energy management optimizations for complex city quarter energy system scenarios on their own. This includes understanding common optimization problem formulations for city quarter energy systems but also investigating optimization strategies to effectively manage and balance demand, supply, and storage within city quarter energy systems. While the emphasis is on using the framework rather than on programming aspects, participants will also be introduced to object-oriented Python programming principles for the modeling of city quarter energy system component models such as smart buildings, energy storage systems, and renewable energy sources. Interactive Jupyter Notebooks with reference simulation examples and practical hands-on tasks will then provide the learner with the opportunity to apply and deepen the acquired knowledge.
Learning Outcomes
The learner can apply the concept of object-oriented programming to model and simulate city quarter energy systems
The learner can implement optimization models in Python using open-source packages pycity_scheduling and/or pyomo
The learner can run and analyze complex energy system scenarios on the basis of mathematical optimization theory
Prior Knowledge
The learner successfully completed building block “Home as multi energy systems, interaction electricity and heat”
The learner understood the basic principles and theory behind mathematical optimization
The learner understood the principle of operational flexibility in (local) power and energy system applications
The learner brings basic programming skills in Python or in a comparable modern programming language
The learner has advanced computer skills and knows how to work with command line tools
Keywords
- City quarter energy system modeling and simulation
- Power scheduling applications
- Deployment and exploitation of operational flexibility
- Optimization problem formulation
- Object oriented Python programming