Hydro-Wind-Solar Dispatch Using Multi-Regional Load Profiles
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Solution Overview
Problem
Current methods fail to accurately characterize localized load fluctuations and sequential flexibility requirements in hydro-wind-solar power systems, leading to excessive regulation stress and operational bottlenecks due to inadequate modeling of multi-temporal hydropower-electricity coupling and heterogeneous load demands across multiple receiving-end grids.
Innovation Solution
A mid-term coordinated dispatch method is developed, incorporating multi-regional daily load profiles to reconstruct power demand trajectories and establish a multi-objective nested scheduling model, optimizing power transmission and generation across multiple temporal scales.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional load characterization indices are used, then global load characteristics are captured, but localized load fluctuations and sequential flexibility requirements are not accurately described
Solution Approach 1:
The load curve is segmented into multiple scheduling periods with distinct load characteristics. Each period is characterized by specific features (peak, flat, valley segments) that capture localized load fluctuations. This segmentation enables accurate representation of sequential flexibility requirements without requiring overly complex models, as each segment can be independently characterized and optimized.
2Measurement precision
If load reconstruction methods are applied, then localized load fluctuations are described, but irreparable outliers and single-interval mutations occur causing frequent oscillations
Solution Approach 1:
The patent introduces dynamic adjustment mechanisms that allow the dispatch plan to adapt to load fluctuations while maintaining stability. The scheduling model dynamically adjusts power transmission and generation based on reconstructed load curves, preventing frequent oscillations by incorporating smoothing and constraint mechanisms that filter out irreparable outliers while preserving essential load characteristics.
Solution Approach 2:
The load reconstruction is performed in advance to identify and correct potential outliers before they affect the dispatch plan. By preprocessing the load data and establishing corrected reference curves beforehand, the system prevents single-interval mutations from causing frequent oscillations in the final dispatch schedule.
3Adaptability or versatility
If multi-day extreme operating scenarios are considered, then renewable energy integration challenges are addressed, but operation complexities intensify
Solution Approach 1:
The multi-day scheduling horizon is segmented into discrete scheduling periods, each with its own load characteristics and renewable generation patterns. This segmentation allows the system to handle extreme operating scenarios by breaking down complex multi-day problems into manageable periodic units, reducing operational complexity while maintaining adaptability to extreme conditions.
Solution Approach 2:
The patent employs parameter changes in the scheduling model to adapt to different extreme scenarios. By adjusting key parameters (such as load reconstruction weights, transmission constraints, and generation priorities) based on the specific extreme conditions being modeled, the system maintains high adaptability without requiring completely different models for each scenario, thus controlling operational complexity.
4Measurement precision
If refined load modeling is implemented, then heterogeneous load demands are accurately characterized, but computational requirements and model complexity increase
Solution Approach 1:
Heterogeneous load demands across multiple receiving-end grids are segmented by geographic region and temporal period. Each region's load characteristics are modeled independently with region-specific parameters, allowing accurate characterization of heterogeneous demands while managing complexity through modular, region-by-region optimization rather than a single monolithic model.
Solution Approach 2:
The scheduling model incorporates local quality by applying region-specific load reconstruction parameters and characteristics to different receiving-end grids. Each region's load curve is reconstructed with local features (peak times, valley patterns, flat periods) that reflect its unique demand profile, enabling accurate heterogeneous characterization without requiring a uniformly complex model across all regions.
Data Source
AI summary
This invention advances power grid operational planning by introducing a mid-term scheduling framework for integrated hydro-wind-solar systems that accounts for heterogeneous daily load profiles across multiple receiving-end power grids. The proposed approach utilizes an adaptive variable-step search algorithm to segment loads into peak, flat, and valley intervals. By synthesizing five key metrics, including mean daily load, daily load factor, peak-valley differential ratio, load rates during peak/valley periods, and timing of peak/valley occurrences, the method accurately captures region-specific load patterns and peak-shaving demands. This enables a refined reconstruction of load profiles of receiving-end power grids. A nested multi-temporal scheduling model that couples medium- and short-term horizons to simultaneously maximize total energy production and minimize transmission imbalances among power grids. The model is addressed by using the mixed-integer linear programming (MILP) to obtain medium- and short-term generation schedules and power transmission schedules.