Renewable Resource Dispatch Policies Across Multiple Energy Markets
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The integration of renewable energy systems into existing infrastructure and energy markets is challenging due to the variability and uncertainty of renewable energy generation, which can lead to reliability issues in power grids and inefficiencies in energy market participation.
Innovation Solution
A data-driven model is introduced to compute policies for governing renewable energy generation and storage systems, optimizing resource dispatch across multiple markets under uncertain conditions, and adapting to changing market dynamics and system constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If renewable energy systems are integrated into existing power grid infrastructure, then the share of renewable electricity generation increases, but the reliability of the power grid deteriorates due to variability and uncertainty of renewable energy generation
Solution Approach 1:
The system performs day-ahead market participation and forecasting of renewable generation output in advance, allowing operators to pre-plan dispatch strategies and prepare for expected variability, thereby maintaining reliability while increasing renewable integration
Solution Approach 2:
The system continuously monitors actual renewable generation output against forecasts and adjusts real-time dispatch decisions based on deviations, creating a closed-loop control system that maintains grid reliability despite renewable variability
2Ease of operation
If manual management methods are used for operating renewable generation and storage systems across multiple energy markets, then operational complexity is manageable, but optimization of revenue and resource dispatch becomes infeasible
Solution Approach 1:
The system implements automated decision-making algorithms that independently analyze market conditions, forecast prices, and determine optimal dispatch strategies across multiple time scales without requiring manual intervention, enabling both ease of operation and revenue optimization
Solution Approach 2:
The system replaces manual operational management with automated computational models and algorithms that process market data and control dispatch decisions, transitioning from mechanical human decision-making to automated electronic optimization
3Stability of the object's composition
If energy storage systems are collocated with renewable generation systems, then the consistency of power supply improves, but the complexity of managing combined generation and storage systems across multiple markets increases
Solution Approach 1:
The system implements a unified control platform that simultaneously manages multiple functions including day-ahead market participation, real-time balancing, frequency regulation, and capacity market strategies across different time scales, reducing operational complexity despite multiple functions
Solution Approach 2:
The system divides management into distinct time-scale layers (day-ahead, real-time, frequency regulation) with specialized strategies for each, allowing complex multi-market management to be broken into manageable segments while maintaining overall optimization
Data Source
AI summary
The techniques disclosed herein enable systems to enable multi-market optimization of renewable energies using data-driven models. To achieve this, a model retrieves a current state from a resource generation system and associated resource markets. The model can then compute a policy based on the state as well physical and technical constraints. The policy defines various actions that direct operation of the resource generation system such as resource production and dispatch to markets. Applying the policy to the resource generation results in a modified state which the model extracts along with a measure of optimality which quantifies the success of the policy. Based on these metrics, the model can generate an updated iteration of the policy defining a different set of actions. In this way, the model can gradually develop an optimal policy for controlling the resource generation system.