Day-ahead optimal dispatch considering demand response compensation and carbon trading under uncertain environment
Ze Ye, Deping Liang, Meihui Wang, Lei Chen
TL;DR
This paper proposes a model for optimizing energy dispatch that considers demand response and carbon trading to reduce emissions and costs.
Contribution
A novel low-carbon economic optimization model combining demand response and carbon trading mechanisms is proposed.
Findings
A reward-punishment laddered carbon trading model is established to reduce carbon emissions on the source side.
The triangular fuzzy method effectively handles uncertainty in new energy and load forecasting.
Simulation results verify the economic and low-carbon benefits of the proposed model.
Abstract
To fully explore the regulation resources on both sides of the source and load under uncertain environment and collaboratively achieve the energy saving and emission reduction goals, a low-carbon economic optimization dispatch model combining demand response and carbon trading mechanism is proposed in this paper. Firstly, the economic principle of demand response (DR) is analyzed, as well as the demand response compensation model is constructed for shiftable loads and curtailable loads respectively. Second, we describe the source-load synergistic low-carbon effect. The source side further reduces carbon emissions by establishing a reward-punishment laddered carbon trading model. Accordingly, the optimization model is constructed with the objective of minimizing the sum of DR compensation cost, carbon trading cost and system operation cost. The triangular fuzzy method is used to deal…
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Taxonomy
TopicsEnergy Load and Power Forecasting · Electric Power System Optimization · Integrated Energy Systems Optimization