DER Scheduling Across Long-, Mid-, and Real-Time Grid Optimization
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Solution Overview
Problem
Existing energy management systems for electrical power systems with integrated distributed energy resources face challenges in achieving optimal multi-timescale optimization, cost efficiency, and addressing uncertainties in renewable energy generation and load demand fluctuations, with existing algorithms failing to provide flexible and robust solutions that adapt to system changes.
Innovation Solution
A multi-timescale coordinated optimization scheduling method that includes long-, mid-, and short-timescale optimization scheduling, utilizing cloud and edge computing to integrate forecast and real-time data, with model predictive control for DERs, ensuring robustness and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single optimizer with a single time interval is used for scheduling, then the system structure is simple, but it cannot address multiple needs from long-term operation planning to real-time dispatching control
Solution Approach 1:
The scheduling system is segmented into three distinct optimizers operating at different timescales: a long-term optimizer for operation planning and market participation, a mid-term optimizer for day-ahead scheduling, and a real-time optimizer for dispatching control. Each optimizer handles specific scheduling needs with appropriate time intervals, enabling the system to address multiple requirements simultaneously while maintaining clear functional boundaries that prevent excessive complexity
Solution Approach 2:
The system introduces a timescale dimension by organizing optimizers hierarchically across different time horizons (long-term, mid-term, real-time). This dimensional approach allows each optimizer to specialize in specific time intervals and scheduling tasks, transforming a potentially complex monolithic system into a structured multi-layered architecture where each layer operates independently at its appropriate timescale
2Manufacturing precision
If different levels of optimization are used for long-term planning to real-time control, then each level can be optimized for its specific time interval, but coordination and information flow among multi-timescale engines becomes complex
Solution Approach 1:
The long-term optimizer performs preliminary action by establishing operation planning and market participation strategies in advance. These pre-determined plans serve as input constraints and guidance for the mid-term and real-time optimizers, allowing each subsequent optimizer to focus on its specific time interval without needing to reconsider long-term strategic decisions, thereby simplifying coordination while maintaining optimization precision at each level
Solution Approach 2:
The system uses an energy management system as an intermediary that coordinates information flow among the multi-timescale optimizers. The EMS manages the hierarchical data exchange, ensuring that long-term plans are properly transmitted to mid-term scheduling, and real-time control actions are aligned with higher-level plans. This intermediary structure standardizes coordination protocols and simplifies the complex information flow between optimizers operating at different timescales
3Ease of manufacture
If heuristic online algorithms are used for real-time dispatching, then the system is simple to implement, but they cannot achieve optimal solutions and are not flexible to system changes
Solution Approach 1:
The real-time optimizer employs dynamic programming or model predictive control that adapts to system changes by continuously updating its model and constraints based on current system state and forecast data. This dynamic approach maintains flexibility to accommodate new distributed energy resources and changing operational conditions while achieving optimal solutions, overcoming the rigidity of heuristic algorithms without sacrificing implementation feasibility
Data Source
AI summary
A multi-timescale coordinated optimization scheduling method for an electrical power system that includes a number of distributed energy resources (DERs) is disclosed. The method includes performing long-timescale optimization scheduling for the electrical power system based at least on renewable energy generation forecast data of the DERs to obtain long-timescale operation planning data. The method further includes performing mid-timescale optimization scheduling for the electrical power system based on the long-timescale operation planning data and measured data of the DERs to obtain mid-timescale operation planning data. The method further includes performing at least close to real-time optimization scheduling for the electrical power system based on the mid-timescale operation planning data, the measured data of the DERs and grid signals of the electrical power system to obtain short-timescale power setpoints for the DERs. An electrical power system in which the method is used is also disclosed.