A stochastic multi-range robust approach for low carbon technology participation in electricity markets
A stochastic multi-range robust approach for low carbon technology participation in electricity markets
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DOI：
10.1016/j.ijepes.2024.109825
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发表时间：
2024-06
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影响因子：
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通讯作者：
Arman Alahyari;C. Patsios;Natalia-Maria Zografou-Barredo;Timur Saifutdinov;Ilias Sarantakos
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文献类型：
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作者：
Arman Alahyari;C. Patsios;Natalia-Maria Zografou-Barredo;Timur Saifutdinov;Ilias Sarantakos
雄心勃勃的减排目标要求在配电网中培育更多的低碳技术（lct）。对未来能源使用的预测表明，这些技术将在居民区得到大量应用。尽管如此，他们不能单独有效地参与电力市场。本研究考察了住宅lct （rlct）通过聚合器在多个电力市场中的潜在参与，包括批发提前日、实时和本地能源市场（LEM）。我们提出了一个随机加权多范围鲁棒模型，为RLCT聚合器在这些市场中同时充当卖方和买方，在LEM中充当价格制定者，在批发市场中充当价格接受者提供策略。该模型考虑了报价/投标对LEM市场出清价格影响的不确定性，以及汇总lct的可用性模式。使用现实数据的案例研究结果表明，与风险中性和风险厌恶稳健方法相比，所提出的方法可产生更高的总体利润。此外，所引入的模型对预测误差具有弹性，当预测误差增加20%时，采用所提出的方法的利润减少了12%，而采用风险中性策略的利润减少了26%。
Ambitious emission reduction targets require fostering more low-carbon technologies (LCTs) in distribution networks. Projections for future energy use predict a significant implementation of these technologies in residential areas. Despite this, individually they cannot effectively participate in electricity markets. This study examines the potential participation of residential LCTs (RLCTs) in multiple electricity markets, including wholesale day-ahead, real-time, and local energy markets (LEM), through the aggregators. We propose a stochastic weighted multi-range robust model to provide a strategy for RLCT aggregators to function as both sellers and buyers in these markets, as price-makers in LEM and price-takers in wholesale markets. The proposed model accounts for the uncertainty associated with the effect of offers/bids on the market clearing price of LEM and the availability patterns of aggregated LCTs. Results of a case study using realistic data reveal that the proposed approach results in higher overall profits compared to both risk-neutral and risk-averse robust methods. Furthermore, the introduced model is resilient to forecast errors, as evidenced by a 12% decrease in profits with the proposed approach compared to a 26% decrease with a risk-neutral strategy when the forecast error was increased by 20%.