In the present work we tackle the problem of finding the optimal price tariff to be set by a risk-averse electric retailer participating in the pool and whose customers are price-sensitive. We assume that the retailer has access to a sufficiently large smart-meter dataset from which it can statistically characterize the relationship between the tariff price and the demand load of its clients. Three different models are analyzed to predict the aggregated load as a function of the electricity prices and other parameters, as humidity or temperature. In particular, we train linear regression (predictive) models to forecast the resulting demand load as a function of the retail price. Then we will insert this model in a quadratic optimization problem which evaluates the optimal price to be offered. This optimization problem accounts for different sources of uncertainty including consumer's response and renewable source availability, and relies on a stochastic and risk-averse formulation. Moreover, we consider both standard forward based contracts and the recently introduced power purchase agreement contracts as risk-hedging tools for the retailer. The results are promising as profits are found for the retailer with highly competitive prices and some possible improvements are shown if a better dataset could be produced. A realistic case study and multiple sensitivity analyses have been performed to characterize the risk-aversion behavior of the retailer considering price-sensitive consumers. It has been assumed that the energy procurement of the retailer can be satisfied from the pool and different types of contracts. The obtained results reveal that the risk-aversion degree of the retailer strongly influences contracting decisions, whereas the price sensitiveness of consumers has a higher impact on the selling price offered.
翻译:在目前的工作中,我们解决了找到最佳价格关税的问题,该关税将由参加该批的反风险电子零售商制定,其客户对价格敏感。我们假定零售商可以获得足够大、智能的数据集,从统计上可以确定关税价格与客户需求负荷之间的关系。我们分析了三个不同的模型,以预测电力价格和其他参数的累积性功能,即湿度或温度。我们特别将线性回归(预知性)模型作为零售价格的函数来预测由此产生的需求负荷。然后我们将将这一模型插入一个夸大的最佳优化度问题中,以评价最佳价格。这种优化问题说明了不同不确定性的来源,包括消费者的反应和可再生来源的供应,并依赖一种随机性和风险反风险的配方。此外,我们认为标准前期合同和最近推出的电力购买协议是零售商的风险规避工具。如果零售商发现高竞争性价格的利润,而且如果对零售商的更高合同影响有更强烈的改进度,我们将将其插入这一模型。这种优化的问题说明不同的不确定性来源,包括消费者的反应和可再生来源,并依赖于一种现实的零售风险分析。