Renewable Energy Delivery Scheduling for Time-Matched Grid Demand
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Solution Overview
Problem
Renewable energy consumers face challenges in matching the timing of renewable energy production with electricity consumption due to the variability of renewable energy sources and the need for energy storage, while also navigating grid operational constraints and location-based energy pricing, which can limit the ability of renewable energy power plants to deliver electricity and create renewable energy credits efficiently.
Innovation Solution
A system and method for determining an energy delivery schedule for multiple renewable energy power plants connected to an electric grid, which involves forecasting energy production and demand, identifying optimal delivery schedules that consider state of charge limits and grid needs, and selecting a schedule that maximizes fit with demand and minimizes risk, allowing for efficient allocation of energy delivery from multiple plants to satisfy contractual obligations and grid requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If renewable energy is produced during daylight hours (e.g., solar energy), then renewable energy production is enabled, but energy delivery is limited to specific time windows when demand may be low
Solution Approach 1:
The system performs preliminary actions by producing renewable energy during periods when it is available (e.g., daylight hours for solar), storing it in energy storage systems, and preparing it for later delivery when demand occurs, thus resolving the timing mismatch between production and consumption
Solution Approach 2:
The system dynamically adjusts energy delivery schedules based on real-time grid needs, demand patterns, and energy availability, allowing flexible allocation of energy from multiple power plants to different time windows and locations to optimize both production utilization and delivery timing
2Adaptability or versatility
If energy storage systems are used to bridge timing gaps between renewable energy production and consumption, then energy delivery flexibility is improved, but system cost increases
Solution Approach 1:
The system merges the capabilities of multiple renewable energy power plants into a coordinated network, allowing energy to be delivered from different locations at different times to satisfy overall demand, thereby reducing the need for excessive storage capacity at any single plant while maintaining delivery flexibility
Solution Approach 2:
The energy management system performs multiple functions: it coordinates energy production scheduling, optimizes delivery timing, manages storage operations, and allocates energy across multiple plants and time windows, replacing the need for oversized storage systems with a multi-functional coordination approach
3Productivity
If renewable energy power plants deliver electricity to the grid, then renewable energy credits are generated, but grid operational constraints and location-based pricing limit delivery efficiency
Solution Approach 1:
The system dynamically optimizes energy delivery schedules by considering real-time grid constraints, location-based pricing signals, and demand patterns, automatically adjusting which plants deliver energy when and where to maximize credit generation while operating within grid constraints
Solution Approach 2:
The system uses feedback from grid operational constraints and location-based pricing to continuously refine energy delivery schedules, learning from grid responses and pricing signals to improve the efficiency of renewable energy credit generation over time
4Manufacturing precision
If the time window for matching renewable energy production and consumption is narrowed, then renewable energy authenticity is improved, but energy delivery reliability decreases
Solution Approach 1:
The system combines the outputs of multiple renewable energy power plants with different production patterns and locations, creating a aggregated energy supply that can reliably meet demand within narrower time windows while maintaining the precision of timing matching through coordinated scheduling
Data Source
AI summary
Methods and systems determine an energy delivery schedule for a first renewable energy power plant (REPP) and a second REPP connected to an energy grid at different points of delivery. The method includes forecasting energy production by a first REPP and a second REPP during a time period; identifying one or more possible delivery schedules for the first REPP and the second REPP; forecasting energy demand corresponding to points of delivery at the energy grid to which energy produced by the first REPP and the second REPP are provided; selecting a delivery schedule from the one or more possible delivery schedules for the first REPP and the second REPP; and sending a first control signal to the first REPP and a second control signal to the second REPP to implement the selected delivery schedule at the first REPP and the second REPP.