Operational decision making in compound energy systems using multi-level multi paradigm simulation based optimization

A two level hierarchical simulation and decision modeling framework is proposed for electric power networks involving PV based solar generators, various storage units (batteries and compressed air energy storage), and grid connection. The high level model, from a utility company perspective, concerns operational decision making (e.g., determined price for customers; energy amount being sold to or bought from bulk grid) and defining regulations (e.g., maximum load in general or during peak hours) for customers for a reduced cost and enhanced reliability. The lower level model concerns changes in power quality factors and changes in demand behavior caused by customers' response to operational decisions and regulations made by the utility company at the high level. The higher level simulation is based on system dynamics and agent-based modeling while the lower level simulation is based on agent-based modeling and circuit-level continuous time modeling. The proposed two level model incorporates a simulation based optimization engine that is a combination of three meta-heuristics including Scatter Search, Tabu Search, and Neural Networks for finding optimum operational decision making. In addition, a reinforcement learning algorithm that uses Markov decision process tools is also used to generate decision policies. An integration and coordination framework is developed, which details the sequence, frequency, and types of interactions between two models. The proposed framework is demonstrated with several case studies and applications, with a real utility company, where real-time or historical data are used for solar insolation, storage units, demand profiles, and price of electricity of grid (i.e., avoided cost). Challenges that are addressed in case studies and applications include 1) finding a best policy for a utility company that entitles finding the optimum price and regulation while keeping the customers electricity quality within the accepted range, 2) capacity planning of electricity systems with PV generators, storage systems, and grid for a utility company, and 3) finding the optimum threshold price in an energy management system that is used by the utility company to decide how much energy should be bought from or should be sold to grid to minimize the cost. Mathematical formulations as well as simulation and decision modeling methodologies are presented that can be used to address these challenges. Furthermore, a grid-storage analysis is performed for arbitrage, to find out if in future it is going to be beneficial to use storage systems along with grid, with future technological improvement in storage and increasing cost of electrical energy. Finally, an information model is developed that facilitates interoperability of different applications in the proposed hierarchical simulation and decision environment for energy systems, where the goal is to demonstrate generality of the proposed modeling framework and case studies and how they can be used to address a wide range of energy system management problems.