Integrated Energy Control With Dual-Loop RMPC Across Time Scales
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Solution Overview
Problem
Existing integrated energy systems (IES) face challenges in efficiently managing uncertainty due to the inherent randomness and volatility of renewable energy sources and load demands, with current scheduling policies failing to fully utilize multi-energy complementarity and source-load coordination, leading to inefficiencies and reliability issues.
Innovation Solution
A dual-loop feedback robust model predictive control (RMPC) framework is implemented, integrating multi-time scale optimization, robust optimization algorithms, and advanced prediction models to achieve dynamic adaptive adjustment of uncertainty, utilizing a dual-loop feedback mechanism to coordinate source-load interactions and enhance control reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If advanced scheduling policies (robust optimization, stochastic optimization, MPC) are applied alone to manage uncertainty in IES, then certain aspects of uncertainty management are improved, but overall system reliability and economy are insufficient
Solution Approach 1:
The patent combines multiple scheduling policies (robust optimization, stochastic optimization, and MPC) into a unified multi-time scale compound control framework. The robust optimization provides uncertainty bounds, stochastic optimization generates probabilistic scenarios, and MPC implements real-time control adjustments. This merging allows the system to simultaneously achieve reliable uncertainty management and economic efficiency by leveraging the complementary strengths of each method across different time scales.
Solution Approach 2:
The patent segments the control framework into multiple time scales (day-ahead, intra-day, and real-time control layers). Each time scale applies appropriate scheduling policies tailored to its specific requirements: day-ahead uses robust optimization for conservative planning, intra-day uses stochastic optimization for scenario-based adjustments, and real-time uses MPC for rapid responses. This segmentation resolves the contradiction by allowing economy-optimized control at longer time scales and reliability-optimized control at shorter time scales.
2Productivity
If demand response policies are implemented to stimulate interaction between demand-side resources and renewable energy, then consumption capacity of renewable energy is improved, but flexibility to utilize complementary characteristics of IDR and renewable energy is insufficient
Solution Approach 1:
The patent implements dynamic demand response policies that adapt to real-time system conditions and renewable energy availability. The multi-time scale framework allows demand response strategies to be adjusted dynamically: day-ahead policies establish baseline demand response commitments, intra-day policies adjust based on renewable energy forecast deviations, and real-time policies respond to actual system state. This dynamic approach fully utilizes the complementary characteristics of integrated demand response and renewable energy, achieving both high consumption capacity and flexibility.
3Ease of operation
If multi-time scale optimization is applied to coordinate source-load interactions, then system coordination is improved, but anti-interference ability is insufficient
Solution Approach 1:
The patent incorporates feedback mechanisms at each time scale to enhance anti-interference ability. The day-ahead robust optimization uses feedback from uncertainty realizations to adjust confidence levels, the intra-day stochastic optimization uses feedback from prediction errors to refine scenarios, and the real-time MPC uses feedback from actual system deviations to correct control actions. This multi-layer feedback structure maintains excellent source-load coordination while providing strong anti-interference capability against disturbances and uncertainties.
Data Source
AI summary
The present invention belongs to the technical field of integrated energy system (IES), and provides a method and system of multi-time scale compound control of IES based on dual-loop feedback robust model predictive control (RMPC), comprising: analyzing multi-time scale characteristics of typical equipment and integrated demand response (IDR), establishing a compound control framework for equipment-side-load-side coordination, carrying out differential control on different time scales through intra-day three-layer controller aiming at the difference in regulation rate and response characteristics between the multi-energy equipment and the IDR, modifying, layer by layer, a reference of upper layer and issuing control strategies in real-time, which fully stimulates flexibility on both sides of supply and demand.