Integrated Energy Dispatch Using Decomposed Stochastic Planning
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Solution Overview
Problem
Energy system operators face challenges in managing production plans for systems with multiple generation and storage units due to variability and uncertainty in renewable energy sources, which existing formulations fail to model, leading to inefficient and costly dispatch of generation units.
Innovation Solution
A dynamic programming approach is used to determine a production plan by decomposing a stochastic system dynamic program into unit-specific programs, applying stochastic value functions, and using a price model to define bounds, ensuring efficient dispatch under uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing production plan formulations are used, then the system can manage multiple generation and storage units, but they fail to model uncertainty in renewable energy sources, leading to inefficient dispatch
Solution Approach 1:
The patent transforms the deterministic production plan formulation into a stochastic dynamic programming formulation by introducing probabilistic parameters for renewable energy generation and demand. This allows the system to model uncertainty while optimizing dispatch decisions, resolving the contradiction between reliability (uncertainty modeling) and productivity (dispatch efficiency).
Solution Approach 2:
The patent implements a dynamic programming approach that adapts production plans based on realized renewable generation and demand conditions. The formulation transitions from static deterministic planning to dynamic stochastic optimization, enabling the system to respond efficiently to uncertain conditions while maintaining optimal dispatch performance.
2Adaptability or versatility
If complex optimization formulations are used to manage vertically integrated utilities, then more generation and storage units can be coordinated, but the computational complexity increases and uncertainty is not modeled
Solution Approach 1:
The patent decomposes the complex system-wide optimization problem into unit-specific stochastic dynamic programs. Each generation and storage unit has its own simplified DP formulation that considers local state variables and constraints, reducing overall computational complexity while maintaining the ability to coordinate diverse units through the stochastic framework.
Solution Approach 2:
The patent simplifies the complex optimization formulation by changing from deterministic parameters to stochastic parameters with known probability distributions. This transformation enables the use of dynamic programming techniques that are computationally tractable for large-scale systems while capturing uncertainty in renewable generation and demand.
3Ease of manufacture
If deterministic production plans are created, then the planning process is simpler, but the plans do not account for variability in renewable energy and demand, leading to suboptimal operations
Solution Approach 1:
The patent transitions from deterministic parameters to stochastic parameters in the production plan formulation. By incorporating probability distributions for renewable generation and demand, the planning process remains structured and manageable while significantly improving operational efficiency through uncertainty-aware optimization.
Solution Approach 2:
The stochastic dynamic programming formulation incorporates feedback from realized renewable generation and demand conditions into subsequent decision-making. This allows the system to adapt plans based on actual system state while maintaining the structured approach of dynamic programming, balancing planning simplicity with operational efficiency.
Data Source
AI summary
A method for dynamically managing an energy system includes determining a production plan by determining a first stochastic system dynamic program (SSDP) based on a state of and a forecasted energy demand in the energy system, determining a second SSDP by relaxing the first SSDP, decomposing the second SSDP into energy unit-specific SSDPs, applying the unit-specific SSDPs with a price model to define a bound on the first SSDP, and determining a forward-looking dynamic economic dispatch plan based on the second SSDP by identifying actions for the energy units corresponding to reachable production levels, applying current unit-specific states and the identified actions to the production plan to generate an updated production plan including unit-specific actions and expected continuation values based on the second SSDP that modify subsequent actions, and dispatching the identified unit-specific actions to the energy system.