国家电网公司科技项目(18-GW-03)
[1] Liu D N. An Interpretation of the Basic Rules for Mid- and Long-Term Transactions in Electric Power(Provisional)---About protection of priority generation rights and renewable energy consumption. Energy Power Industry in China, 2017, 6: 44--46. Google Scholar
[2] Zhou M, Yan Y, Ding Q, et al. Transaction and settlement mechanism for foreign representative power markets and its enlightenment for chinese power market. Autom Electr Power Syst, 2017, 41: 1--8. Google Scholar
[3] Bao M L, Ding Y, Shao C Z, et al. Review of nordic electricity market and its suggestions for china. Proc CSEE, 2017, 37: 4881--4892. Google Scholar
[4] Xue Y S, Lei X, Xue F, et al. A review on impacts of wind power uncertainties on power systems. Proc CSEE, 2014, 34: 5029--5040. Google Scholar
[5] Figueiredo N C, Silva P P D, Cerqueira P. Wind generation influence on market splitting: the Iberian spot electricity market. In: Proceedings of the 12th International Conference on the European Energy Market (EEM), Lisbon, 2015. 1--5. Google Scholar
[6] Guo H Y, Chen Q X, Zhong H W, et al. Spot market mechanism design and path planning based on standard curve for financial delivery. Autom Electr Power Syst, 2017, 41: 1--8. Google Scholar
[7] Zou P, Chen Q X, Xia Q, et al. Logical analysis of electricty spot market design in foreign countries and enlightenment and policy suggestions for china. Autom Electr Power Syst, 2014, 38: 18--27. Google Scholar
[8] Jing Z X, Zhu J S. Simulation experiment analysis on market rules for monthly centralized bidding. Autom Electr Power Syst, 2017, 41: 42--48. Google Scholar
[9] Zhao C, Zhang S H. Generation asset allocation strategies based on IGDT. Control Dec, 2017, 32: 751--754. Google Scholar
[10] Nie Z, Gao F, Wu J, et al. Contract for difference energy decomposition model for maximizing social benefit in electricity market. In: Proceedings of the World Congress on Intelligent Control and Automation, Guilin, 2016. 2449--2454. Google Scholar
[11] van Stiphout A, De Vos K, Deconinck G. The Impact of Operating Reserves on Investment Planning of Renewable Power Systems. IEEE Trans Power Syst, 2017, 32: 378-388 CrossRef ADS Google Scholar
[12] Zhang Z S, Sun Y Z, Gao D W. A Versatile Probability Distribution Model for Wind Power Forecast Errors and Its Application in Economic Dispatch. IEEE Trans Power Syst, 2013, 28: 3114-3125 CrossRef ADS Google Scholar
[13] Papavasiliou A, Oren S S. Large-Scale Integration of Deferrable Demand and Renewable Energy Sources. IEEE Trans Power Syst, 2014, 29: 489-499 CrossRef ADS Google Scholar
[14] Sharma K C, Bhakar R, Tiwari H P. Strategic bidding for wind power producers in electricity markets. Energy Convers Manage, 2014, 86: 259-267 CrossRef Google Scholar
[15] Banaei M, Oloomi-Buygi M, Zabetian-Hosseini S M. Strategic gaming of wind power producers joined with thermal units in electricity markets. Renew Energy, 2018, 115: 1067-1074 CrossRef Google Scholar
[16] Wang X, Wang L H, Zhang S H. Impacts of cooperation between wind power producer and DR aggregator onelectricity market equilibrium. Power Syst Tech, 2012, 27: 894--903 [王??, 王留晖, 张少华. 风电商与DR聚合商联营对电力市场竞争的影响. 电网技术, 2018, 42: 110--116]. Google Scholar
[17] Pinson P, Chevallier C, Kariniotakis G N. Trading Wind Generation From Short-Term Probabilistic Forecasts of Wind Power. IEEE Trans Power Syst, 2007, 22: 1148-1156 CrossRef ADS Google Scholar
[18] Chaves-ávila J P, Hakvoort R A, Ramos A. Short-term strategies for Dutch wind power producers to reduce imbalance costs. Energy Policy, 2013, 52: 573-582 CrossRef Google Scholar
[19] Wang X, Huang M T, Zhang S H. Equilibrium analysis of electricity market considering penalties for wind power's bidding deviation. Power Syst Tech, 2016, 40: 602--607. Google Scholar
[20] Brunetto C, Tina G. Wind generation imbalances penalties in day-ahead energy markets: The Italian case. Electric Power Syst Res, 2011, 81: 1446-1455 CrossRef Google Scholar
[21] Shao C C, Wang X F, Wang X L, et al. An electricity market clearing model for day-ahead pool market considering wind power. Autom Electr Power Syst, 2014, 38: 45--50. Google Scholar
[22] Zhang Y, Giannakis G B. Distributed Stochastic Market Clearing With High-Penetration Wind Power. IEEE Trans Power Syst, 2016, 31: 895-906 CrossRef ADS arXiv Google Scholar
[23] Fernandez-Blanco R, Arroyo J M, Alguacil N. On?the?Solution?of?Revenue-?and?Network-Constrained Day-Ahead Market Clearing Under Marginal Pricing-Part I: An Exact Bilevel Programming Approach. IEEE Trans Power Syst, 2017, 32: 208-219 CrossRef ADS Google Scholar
[24] Liu G Y, Chen N S, PU T J, et al. Mathematical model and clearing price analysis of co-optimization of energy regulation and operation reserves. Autom Electr Power Syst, 2014, 38: 71--78. Google Scholar
[25] Zheng T, Litvinov E. Ex Post Pricing in the Co-Optimized Energy and Reserve Market. IEEE Trans Power Syst, 2006, 21: 1528-1538 CrossRef ADS Google Scholar
[26] Sores P, Divényi D, Polgári B, et al. Day-ahead market structures for co-optimized energy and reserve allocation. In: Proceedings of 2015 12th International Conference on the European Energy Market (EEM), Lisbon, 2015. 1--5. Google Scholar
[27] Fernandez-Blanco R, Arroyo J M, Alguacil N. A Unified Bilevel Programming Framework for Price-Based Market Clearing Under Marginal Pricing. IEEE Trans Power Syst, 2012, 27: 517-525 CrossRef ADS Google Scholar
[28] Liu X, Wang B, Li Y. A transmission-constrained stochastic unit commitment model with real-time pricing for high wind power integration. In: Proceedings of Power and Energy Engineering Conference, Asia-Pacific, 2013. 1--6. Google Scholar
[29] Ma X Y. Scenario analysis and stochastic programming of wind-integrated power systems. Dissertation for PH.D. Degree. Wuhan: Wuhan University, 2014. Google Scholar
[30] Subcommittee P M. IEEE Reliability Test System. IEEE Trans Power Apparatus Syst, 1979, 98: 2047--2054. Google Scholar
c | |||||||||||
Load | |||||||||||
c | |||||||||||
The decompostion amount of long-term contracts | |||||||||||
Period | 1 | 2 | 3 | $Q^{CH}_{1t}$ | $Q^{CH}_{2t}$ | $Q^{CH}_{3t}$ | $Q^{CH}_{4t}$ | $Q^{CL}_{1t}$ | $Q^{CL}_{2t}$ | $Q^{CL}_{3t}$ | $Q^{CL}_{4t}$ |
1 | 500 | 220 | 280 | 500 | 200 | 100 | 50 | 150 | 100 | 80 | 50 |
2 | 450 | 225 | 300 | 450 | 150 | 50 | 50 | 120 | 70 | 40 | 50 |
3 | 450 | 200 | 290 | 500 | 150 | 75 | 50 | 80 | 90 | 60 | 50 |
c | ||||||||||||
High contract proportion (70%) | c | |||||||||||
Low contract proportion (30%) | No contract | |||||||||||
Period | $P^g_{1t}$ | $P^g_{2t}$ | $P^g_{3t}$ | $P^g_{wt}$ | $P^g_{1t}$ | $P^g_{2t}$ | $P^g_{3t}$ | $P^g_{wt}$ | $P^g_{1t}$ | $P^g_{2t}$ | $P^g_{3t}$ | $P^g_{wt}$ |
1 | 600.0 | 180.0 | 127.9 | 92.1 | 600.0 | 177.1 | 128.3 | 94.6 | 600.0 | 177.1 | 128.3 | 94.6 |
2 | 587.7 | 210.0 | 83.2 | 94.2 | 587.7 | 210.0 | 83.2 | 94.2 | 587.7 | 210.0 | 83.2 | 94.2 |
3 | 600.0 | 177.4 | 75.0 | 92.6 | 600.0 | 190.6 | 60.0 | 89.4 | 600.0 | 194.6 | 49.87 | 95.6 |
Bus | c | |||||||||||
Co-optimized of energy and reserve | c | |||||||||||
Orderly optimized of energy and reserve | ||||||||||||
c | ||||||||||||
LMP of energy (/MWh) | c | |||||||||||
LMP of reserve (/MWh) | LMP of energy (/MWh) | LMP of reserve (/MWh) | ||||||||||
1 | 2 | 3 | 1 | 2 | 3 | 1 | 2 | 3 | 1 | 2 | 3 | |
1 | 10.00 | 10.00 | 15.00 | 12.07 | 11.15 | 12.40 | 10.00 | 10.00 | 15.00 | 14.63 | 15.12 | 13.42 |
2 | 15.00 | 16.98 | 15.00 | 12.13 | 12.89 | 12.65 | 11.87 | 16.98 | 15.00 | 16.85 | 13.34 | 13.63 |
3 | 28.43 | 26.38 | 25.00 | 10.89 | 15.24 | 12.40 | 27.34 | 26.38 | 15.00 | 11.55 | 15.98 | 13.37 |
4 | 30.00 | 30.00 | 15.67 | 11.13 | 16.15 | 13.92 | 30.00 | 30.00 | 15.00 | 12.40 | 16.6 | 14.1 |
5 | 16.27 | 39.94 | 15.00 | 11.77 | 18.63 | 12.40 | 16.27 | 39.94 | 15.00 | 14.24 | 19.54 | 14.09 |
Total cost | 44762 | 47382 |
Unit | c | |||||||||
Long-term contract | c | |||||||||
Generation revenue ($\$$) | Revenue of reserve ($\$$) | Total revenue ($\$$) | ||||||||
revenue ($\$$) | ||||||||||
1 | 2 | 3 | 1 | 2 | 3 | 1 | 2 | 3 | – | |
Unit 1 | 6000 | 4560 | 4800 | 1000 | 2077 | 3000 | 0 | 0 | 0 | 21437 |
Unit 2 | 2550 | 2890 | 2805 | 450.0 | 679.2 | 186.0 | 0 | 0 | 29.68 | 9590 |
Unit 3 | 1395 | 1550 | 1705 | 2487 | 996.0 | 313.4 | 175.33 | 181.22 | 55.23 | 8858 |
Unit 4 | 1000 | 1000 | 1000 | 685.0 | 1765 | 639.0 | $-$175.33 | $-$181.22 | $-$84.91 | 5648 |