Natural Gas–Electricity Price Linkage Analysis Method Based on Benefit–Cost and Attention–VECM Model
Abstract
:1. Introduction
2. Gas–Electricity Price Linkage Analysis Method Based on Benefit–Cost Model of Gas Unit
2.1. Revenue Model of Gas Unit
2.2. Cost Model of Gas Market for Gas-Generating Units
3. VECM-Model-Based Gas–Electricity Price Linkage Analysis
3.1. Gas–Electricity Price Stability Test
3.2. Gas–Electricity Price Co-Integration Test
- First, the ordinary least squares (OLS) method is used to estimate the gas–electricity price fitting equation and calculate the corresponding residual value:
- Second, the stationarity of is tested. If is a stationary series, it is considered that there is a co-integration relationship between gas price and electricity price. In this case, there is no pseudo-regression problem in the equation obtained by regression.
3.3. Granger Causality Test of Gas–Electricity Price Based on VECM Model
- (1)
- Estimate the unconstrained regression model () and the constrained regression model () of electricity price , respectively:
- (2)
- Change the sequence of causality between gas price and electricity price, and use the same method in (1) to test.
- (3)
- If the test results both reject “gas price is not the reason for the change of electricity price” and accept “electricity price is not the reason for the change of gas price”, it can be concluded that “gas price is the Granger cause of electricity price”.
4. Time-Lag Analysis of Gas–Electricity Price Based on Soft Attention Mechanism
5. Case Studies
5.1. Analysis of Benefit–Cost Model of Gas Unit
5.2. Analysis of VECM Model of Gas–Electricity Price
5.3. Gas–Electricity Price Time Delay Analysis Results
6. Conclusions
- (1)
- The gas unit benefit–cost model proposed in this paper captures the quotation behavior of gas units in the market by assessing the profitability of the units. This model provides valuable gas and electricity price information for the co-ordinated operation of the gas and electricity market, thereby helping market participants to manage market risks, such as an insufficient natural gas supply.
- (2)
- Calculation reveals the co-integration relationship of long-term gas and electricity price stability. The results demonstrate a goodness of fit value greater than 0.7, indicating a strong correlation between the two. Additionally, this paper employs the VECM model to study the short-term volatility of gas and electricity prices. The results reveal the existence of an error correction mechanism, suggesting that short-term volatility will eventually tend to balance. This finding further confirms the presence of a long-term equilibrium relationship between gas and electricity prices. To analyze the causality relationship between prices in different periods, the processed gas and electricity price time series are subjected to a Granger causality test. The comparison of the results with actual market conditions reveals that the supply and demand situation in the gas and electricity market is continually changing.
- (3)
- In this paper, an attention mechanism is used to quantitatively describe the conduction delay time of gas–electricity price fluctuations. Compared with determining the lag order by the VAR model, the proposed method comprehensively reflects the influence weight of the delay in each time period, and the results are more consistent with the actual price transmission process. An analysis of the weight value of each cycle and the cumulative influence weight reveals that the time delay is mainly within the first three weeks, and there is no time delay accounting for absolute proportion in the results. Therefore, it can be qualitatively concluded that the existing gas–electricity price linkage is not strong.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Sequence of Variables | ADF Test Value | Statistical Probability at the Significance Level of 5% | Inspection Result |
---|---|---|---|
0.686 | 0.8626 | Unstable | |
−2.519 | 0.3185 | Unstable | |
−10.489 | 0.0000 | Stable | |
−12.815 | 0.0000 | Stable |
Equation Coefficient | Optimal Parameter Value | t-Test Value | Statistical Probability |
---|---|---|---|
0.9404 | 17.2245 | 0.0000 | |
2.3983 | 43.1492 | 0.0000 |
Error Correction Term | ||
---|---|---|
Coint Equation (1) | −0.4218 (0.1941) | −0.1082 (0.1136) |
−0.0782 (0.2151) | 1.3195 (0.0835) | |
2.1982 (0.0029) | −0.5194 (0.1837) |
Time | Actual Situation of Gas and Electricity Market | Null Hypothesis | Statistical Probability | Conclusion |
---|---|---|---|---|
August 2020 | Gas is cheap, and electricity supply environment is easy | Gas price is not the cause of electricity price changes | 0.79 | Electricity price causes gas price change |
Electricity price is not the cause of gas price changes | 0.08 | |||
May 2021 | Gas price is high, and electricity supply environment is tight | Gas price is not the cause of electricity price changes | 0.09 | There is a two-way causal relationship between gas price and electricity price |
Electricity price is not the cause of gas price changes | 0.06 | |||
From March to July 2022 | Gas price is high, and electricity supply environment is tight | Gas price is not the cause of electricity price changes | 0.08 | Gas price causes electricity price change |
Electricity price is not the cause of gas price changes | 0.98 | |||
From August to November 2022 | Gas price is high, and electricity supply environment is easy | Gas price is not the cause of electricity price changes | 0.85 | There is no obvious causal relationship between gas price and electricity price |
Electricity price is not the cause of gas price changes | 0.39 |
Number of Cycles | Cycle Scale Weight Value | Cumulative Influence Weight |
---|---|---|
2 | 0.24 | 0.24 |
1 | 0.21 | 0.45 |
3 | 0.19 | 0.64 |
4 | 0.14 | 0.78 |
6 | 0.08 | 0.86 |
Order of Lag | AIC |
---|---|
1 | −2.41 |
2 * | −5.13 |
3 | −4.88 |
4 | −4.64 |
5 | −3.08 |
6 | −3.32 |
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Zhou, S.; Gan, W.; Liu, A.; Jiang, X.; Shen, C.; Wang, Y.; Yang, L.; Lin, Z. Natural Gas–Electricity Price Linkage Analysis Method Based on Benefit–Cost and Attention–VECM Model. Energies 2023, 16, 4155. https://doi.org/10.3390/en16104155
Zhou S, Gan W, Liu A, Jiang X, Shen C, Wang Y, Yang L, Lin Z. Natural Gas–Electricity Price Linkage Analysis Method Based on Benefit–Cost and Attention–VECM Model. Energies. 2023; 16(10):4155. https://doi.org/10.3390/en16104155
Chicago/Turabian StyleZhou, Sheng, Wen Gan, Ang Liu, Xinyue Jiang, Chengliang Shen, Yunchu Wang, Li Yang, and Zhenzhi Lin. 2023. "Natural Gas–Electricity Price Linkage Analysis Method Based on Benefit–Cost and Attention–VECM Model" Energies 16, no. 10: 4155. https://doi.org/10.3390/en16104155