Table 1
Comparison between this paper and related works.
| Method | Typical case | Advantage | Disadvantage | ||
|---|---|---|---|---|---|
| IPCC method | Ouyangbin [4]Lu [5] | High facetedness, High transparency, Strong traceability, Wide applicability | High data requirements, Not applicable to small-scale projects, Lack of flexibility | ||
| Actual measurement | Zhou [6] | High accuracy, Wide applicability, Sustainability assessment | Costly, Data uncertainty, High degree of restriction | ||
| Material conservation method | Yan [10] | Simple and easy to follow, High comprehensiveness, Wide applicability | High data uncertainty, Difficult to quantify indirect emissions, High limitations | ||
| Carbon footprinting | Zhao [8]Shang [9] | Full in terms of assessment, Highly comparable | High data demand, High data uncertainty, Scope limitations | ||
| Carbon flow approach | Zhang [11] | High accuracy, Strong tracking capability, Wide applicability | High data demand, High data uncertainty, High complexity | ||
| Ref | Objective functions | Solution procedure | DSM strategy | Advantage | Disadvantage |
| [13] | Environment, Cost, Power | Multi approach | Load curtailment, Load shifting | The ε-constraint method guarantees a set of non-inferior solutions to the multi-objective problem | High demands on computing resources and time |
| [14] | Environment, Cost, Requirement | Multi approach | Demand curtailment, Demand shifting, Onsite generation | Optimization problems for complex systems with simultaneous consideration of multiple objectives | High computational complexity |
| [15] | Environment, Cost, Deviation | Epsilon-constraint method | Load shifting | Handling multiple conflicting objectives, applicable to various types of optimization problems | Calculation costs increase with the number of targets |
| [16] | Environment, Cost, Optimal shifting | Augmented epsilon-constraint | Optimal shifting and Strategic conversion as reserve | Efficient generation of multiple non-inferior solutions, flexible and independent | Computational costs increase with problem size |
| [17] | Cost, Emission, LOLE, Deviation | Epsilon-constraint method | Optimal shifting | Combined consideration of multiple conflicting objectives; ε-constraint method ensures a non-inferior solution set for multi-objective problems; improved practicality and robustness against renewable energy uncertainty | Computational complexity, subjectivity, and limitations in accuracy and efficiency when dealing with high-complexity uncertainties in large-scale systems |
| This paper | Cost, Emission, Deviation | Carbon emission measurement method of regional power system based on LSTM-Attention model | Optimal shifting | The support system has a comprehensive understanding of the distribution trajectory of carbon emissions in the power system; combined with neural networks, it can predict the fluctuation of carbon emissions over time | High data quality and quantity requirements and high consumption of computing resources |
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