Energy System Optimization
Optimized economic scheduling of demand response in integrated energy systems considering dynamic energy efficiency and dynamic carbon trading
This research introduces an optimized economic dispatch model for Integrated Energy Systems (IES) to address uncertainties in demand, static energy efficiency assumptions, and carbon cost inefficiencies. The model integrates dynamic energy efficiency and dynamic carbon trading, along with a 'Distributed Robust Optimization (DRO)-Model Predictive Control (MPC)' framework. Key findings demonstrate significant cost reductions (13.07% total, 11.57% carbon trading) and improved tracking accuracy (14.66% and 6.13% compared to conventional methods) under uncertainty. This provides a robust, low-carbon economic dispatch solution for IES.
Key Impact Metrics
Our analysis reveals the direct, measurable benefits for enterprises leveraging this advanced optimization model.
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Total System Cost Reduction
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Carbon Trading Cost Reduction
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Tracking Accuracy Improvement (vs. no feedback)
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Tracking Accuracy Improvement (vs. no rolling optimization)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
13.07%
Reduction in Total System Costs
The coupled dynamic energy efficiency and carbon trading model reduces total system costs by a significant margin compared to the conventional approach. This highlights the economic benefits of integrating these dynamic considerations into IES dispatch.
Enterprise Process Flow
Day-ahead DRO Optimization
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Intraday MPC Scheduling
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Electricity Price Elasticity Matrix Calculation
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Rolling Optimization & Feedback Correction
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Optimized Dispatch Schedule
The proposed 'distributed robust optimization (DRO)-model predictive control (MPC)' collaborative framework addresses time-scale mismatch and uncertainties. The flowchart illustrates the key stages of this two-stage optimization process, from day-ahead planning to real-time adjustments.
| Approach | Total System Cost | Carbon Trading Cost | Tracking Accuracy |
|---|---|---|---|
| Conventional (static efficiency, annual quota) | 393,617 CNY | 248,340 CNY | Baseline |
| Dynamic Efficiency + Dynamic Carbon Trading (Proposed) | 342,145 CNY | 219,607 CNY | Improved (14.66% vs. no feedback, 6.13% vs. no rolling opt.) |
A comparative analysis of the proposed model against conventional approaches demonstrates superior performance across key metrics. The table summarizes the cost and accuracy improvements achieved by integrating dynamic energy efficiency and carbon trading.
Impact of Dynamic Carbon Trading on Seasonal Costs
Scenario: A summer day simulation revealed that dynamic carbon trading significantly reduces carbon costs compared to annual average quotas. This is due to its ability to adapt to seasonal variations in equipment efficiency and carbon emissions, leading to more precise quota allocation.
Outcome: The dynamic carbon trading model led to a 10.7% reduction in carbon trade costs during summer and an overall 9.89% reduction in annual IES costs compared to fixed quota models, highlighting its effectiveness in optimizing costs based on real-world operational variations.
The study included a case study on the impact of dynamic carbon trading across different seasons. This module outlines a specific scenario and its outcomes, illustrating the practical benefits of the proposed dynamic carbon trading mechanism.
Calculate Your Potential ROI
Estimate the direct financial benefits and productivity gains your enterprise could achieve by implementing optimized energy system scheduling.
Annual Savings Potential
Annual Hours Reclaimed
Implementation Roadmap
A phased approach to integrate these advanced optimization strategies into your existing infrastructure.
Phase 1: Data Integration & Baseline Modeling (1-2 Months)
Integrate historical energy consumption, renewable generation, and carbon emission data. Develop baseline IES economic dispatch models with static efficiency and annual carbon quotas. Establish initial electricity price elasticity matrix based on historical market data.
Phase 2: Dynamic Model Development (2-3 Months)
Implement dynamic energy efficiency models for key equipment (e.g., HFC, GB, GT, EL). Develop the tiered dynamic carbon quota allocation strategy considering seasonal variations. Integrate the price elasticity coefficient matrix for PDR and real-time pricing calculation.
Phase 3: DRO-MPC Framework Implementation (3-4 Months)
Construct the data-driven Distributed Robust Optimization (DRO) model for day-ahead scheduling, including scenario generation via LHS and k-means clustering. Implement the Model Predictive Control (MPC) with rolling optimization for intraday adjustments and feedback correction of electricity price elasticity.
Phase 4: Validation, Testing & Deployment (2-3 Months)
Conduct extensive simulations under various conditions (normal, extreme, seasonal) to validate cost reductions, tracking accuracy, and robustness. Refine model parameters and integrate the solution into existing IES control systems for real-world deployment.
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