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Grid Modeling & Database Management for Energy Systems Analysis

This comprehensive guide by Ashish Gupta focuses on the development of infrastructure and software tools for analyzing energy systems. It covers extracting and converting data from power plants, implementing optimization algorithms, and planning PJM data schematics. Key topics include machine learning techniques for effective data management, locational marginal pricing for energy costs, and solving the economic dispatch problem. With practical insights into processing tools such as Byte Index and SQL databases, this resource is essential for professionals in energy system modeling.

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Grid Modeling & Database Management for Energy Systems Analysis

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  1. Grid Modeling & Database Management for Energy Systems Analysis Ashish Gupta

  2. Developing The Infrastructure… • Extracting Power Plant Data • Converting Databases • Developing Software Tools to Analyze Data • Researching and Implementing Optimization Algorithms

  3. PJM Data Schematic Planning PSSE Model Bus ID, Bus Name EMS PSSE Model fk_ta_id Creating This Link via Machine Learning Techniques Pnodes 5-Minute Load Data Pnodes ID Bid Data Pnode Names

  4. A Peek at the Code….

  5. PJM Load Data • 52+ Gigabyte File • 2 Main Tools: 1. Byte Index 2. SQL Database

  6. The Processing Tools in Action

  7. Locational Marginal Pricing • Method for calculating energy cost at a particular node

  8. Power Transfer Distribution Factors • Determines increases in flows on branches given power injections at given nodes P: Node injections (the variable). D: Branch susceptances. A:node-arc incidence. B’: Powerflow. Theta: Nodal Phase Angles. PB is power transfer distribution factor matrix.

  9. Economic Dispatch Problem • Finding the optimal way to meet the load of the system • Must adhere to cost curves of each generator

  10. Lambda Iteration Method • Algorithm to efficiently compute generation power corresponding to a particular price

  11. Aggregate Bid Curve

  12. Putting it All Together

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