考虑车主多模式需求响应模糊意愿的优化调度策略

现代电动汽车(electrical vehicle, EV)用户需求响应具有多样性和意愿模糊性的特点,当实施单一激励政策时,EV响应将达不到预期效果。为此,提出了考虑车主多模式需求响应及其模糊意愿的含EV微电网的主从博弈优化调度策略。微电网主体针对净负荷制定多模式动态电价激励政策,引导EV在多模式电价中做出选择,促进EV有序充放电,实现其净负荷均方差和运行成本最小。车主从体基于模糊逻辑推理意愿决策,响应多模式动态电价,极小化车主成本。采用非支配排序遗传算法(NSGA-Ⅱ)求解优化模型,获得最优多模式动态电价和EV充放电策略。仿真结果验证了所提方法的有效性。     

考虑不确定性的风-光-储合作联盟参与含需求响应的主辅联合市场的竞价交易模型

清洁能源逐渐成为电力市场的竞争主体,考虑风光出力不确定性和探究高效的市场运行机制成为电力现货市场建设进程中的关键问题。采用非参数核密度估计法和多元Gaussian-Copula函数生成典型出力场景来描述风光出力的不确定性和时空相关性。上层模型基于纳什谈判理论最大化风-光-储合作联盟收益,采用麻雀搜索算法优化风光发电商的报价策略。下层模型以购电总成本最小为目标,设计了多主体参与含价格型需求响应的主辅联合市场的出清机制。最后分析了风光出力的典型场景和市场出清结果的合理性。通过算例验证了该优化模型能够提升合作个体的收益,降低了购电总成本和负荷的峰谷差。     

融合CNN-BiLSTM-Attention的集成学习价格预测

价格预测对于大宗农产品市场的稳定具有重要意义,但是大宗农产品价格与多种因素有着复杂的相关关系.针对当前价格预测中对数据完整性依赖性强与单一模型难以全面利用多种数据特征等问题,提出了一种将基于注意力机制的卷积双向长短期记忆神经网络(CNN-BiLSTM-Attention)、支持向量机回归(SVR)与LightGBM组合的增强式集成学习方法,并分别在包含历史交易、天气、汇率、油价等多种特征数据的数据集上进行了实验.实验以小麦和棉花价格预测为目标任务,使用互信息法进行特征选择,选择误差较低的CNN-BiLSTM-Attention模型作为基模型,与机器学习模型通过线性回归进行增强式集成学习.实验结果表明该集成学习方法在小麦及棉花数据集上预测结果的均方根误差(RMSE)值分别为12.812, 74.365,较之3个基模型分别降低11.00%, 0.94%、4.44%,1.99%与13.03%, 4.39%,能够有效降低价格预测的误差.     

基于XGBoost和TCN-Attention的棉花价格多影响因素选择及预测

棉花价格受多种因素影响而复杂多变, 通过选择合适的数据特征和预测模型可提高棉花价格预测精度. 本文以棉花日现货价格数据为研究目标, 采集了供需关系、国际市场、宏观经济、产业链这4个方面的9项影响因素作为特征, 使用极限梯度提升(XGBoost)算法对棉花价格影响因素进行特征评估筛选, 选取其中5项特征后, 采用引入注意力机制(Attention)的时间卷积网络(TCN) TCN-Attention、TCN、LSTM、GRU等模型对棉花价格进行预测. 通过消融实验和对比实验, 结果表明: (1)经过XGBoost特征筛选后, TCN-Attention价格预测的平均绝对误差(MAE)和均方根误差(RMSE)为41.47和58.76, 与未筛选相比分别降低了77.57%和76.49%. (2)与TCN、LSTM、GRU相比, 本文提出的TCN-Attention模型预测结果更准确, MAE和RMSE均降低50%以上, 运行时间较LSTM、GRU缩短60%.     

电网能量流-经济流耦合分析

依托于同一电力网络的电气源流间的能流信息和市场主体间的交易信息,存在某种天然耦合关系。若不能正确处理,则易导致相关经济流分布信息掌握失准,从而影响系统运行的效能和市场运作的公正性。为此,基于电力网络电能追踪方法正确提供的实时能流信息,考虑上述耦合特性,分析了沿网络源-流对电能配送路径链分布的经济流信息,建立了核算各项费用的实时电价关系式;针对链首和链末2种能量计费方式,提出了具有广泛指导性的费用核算一致性原理,并指出：若不考虑电网实时运行状态影响而直接以上网价、过网价和政府性基金价之和作为下网电价核算相关费用,则会出现不完全满足一致性原理的现象。最后,针对典型交易模式及与其关联的多个网络源-流对,给出了量化其经济流分布的关系式。算例表明,基于考虑耦合特性的实时电价信号分析的经济流,符合核算一致性原理,可为进一步揭示交叉补贴内因提供理论依据。     

基于强化学习的电动汽车换电站实时调度策略优化

随着电动汽车的应用推广,换电站的调度优化逐渐成为研究热点。传统的基于换电需求预测值的调度策略在实际应用中面临着难以适应动态干扰因素、预测误差累积等问题。为了解决这些问题,提出了一种基于带基线的蒙特卡罗策略梯度法的换电站实时调度策略,用于优化换电站的充放电策略以及响应电池数量。提出了带基线的蒙特卡罗策略梯度强化学习,并为换电站实时调度问题选取合适的状态空间和动作空间;设计了奖励函数对智能体进行离线训练,从电池状态数据、分时电价和排队电动汽车数量中学习得到最优策略网络;在离线训练好的模型基础上进行实时调度策略测试。基于换电站的服务可用率和经济效益验证了所提调度策略的有效性和经济性,算例结果表明所提策略能对电网负荷起到一定的削峰填谷作用。     

面向精准价格牌识别的多任务循环神经网络

为了促进智能新零售在线下业务场景的发展, 提高作为销售关键信息价格牌的识别精度. 本文对价格牌识别问题进行研究, 有效地提高了价格牌的识别精度, 并解决小数点定位不准确的难题. 通过深度卷积神经网络提取价格牌的深度语义表达特征, 将提取到的特征图送入多任务循环网络层进行编码, 然后根据解码网络设计的注意力机制解码出价格数字, 最后将多个分支的结果整合并输出完整价格. 本文所提出的方法能够非常有效地提高线下零售场景价格牌的识别精度, 并解决了一些领域难题如小数点的定位问题, 此外, 为了验证本文方法的普适性, 在其他场景数据集上进行了对比实验, 相关结果也验证了本文方法的有效性.     

The price of anarchy in closed-loop supply chains

This paper measures the worst-case efficiency of price-only contracts in closed-loop supply chains (CLSCs) with the price of anarchy (PoA). We model a single-period Stackelberg game in which a manufacturer sells new products to a retailer and collects used products with exogenous retail price and collection price via three alternative reverse channels: (a) the manufacturer collects directly from customers, (b) the retailer collects for the manufacturer, and (c) a third party is awarded a collection subcontract from the manufacturer. We carry out a comprehensive investigation under push–pull configurations to observe how reverse channel structures with different gaming sequences of CLSC members influence the worst-case performance when the demand distribution is over the set of increasing generalized failure rate distributions. From our PoA analysis, we find that the pull system does not always outperform the push system, especially when the retailer is the leader, in contrast to the results for forward supply chains. While the PoA of the push system is dramatically sensitive to the quality condition of used products, the pull system has a constant efficiency loss that is independent of the quality condition. Instead, the PoA of the pull system solely changes with the gaming sequence of the manufacturer. We also find that manufacturer's direct collection is a better reverse channel choice compared to retailer's collection. Additional managerial insights are summarized for discussion.     

Joint pricing and advertising strategy with reference price effect

Consumers are susceptible to reference price effects when they make purchase decisions for a certain product. Meanwhile, the sales price and advertisement are the determinable factors that have impact on consumers’ reference price which are also fundamental marketing strategies. Therefore, how to determine an appropriate sales price and advertising effort level to maximise firms’ profits is an essential task. A joint pricing and advertising problem for a monopolistic firm with consideration of reference price effect is investigated, where consumer demand rate is price-sensitivity and depends on the gap between the sales price and the reference price in consumers’ mind. An optimisation model is established to maximise the firm’s total profit by making a joint pricing and advertising strategy. The static and dynamic joint strategies are obtained by applying Pontryagin’s maximum principle. Results show that the dynamic strategies dominate the static ones. Furthermore, the dynamic pricing and dynamic advertising strategies are strategic complements. Additionally, the length of the sales period plays a key role in determining the superiority of the two dynamic strategies. Specifically, a relatively short sales period highlights the value of the dynamic advertising while a long sales period strengthens the function of the dynamic pricing.     

Optimal markdown policy of perishable food under the consumer price fairness perception

Markdown policies are widely used by retailers to sell perishable food. Consumers purchase food at different prices during different sales periods. Some consumers may compare their experience with others who purchase the same items. Price unfairness or inequity is perceived when different prices are quoted without reasonable explanations. This study develops an optimal markdown model for perishable food pricing to optimise the food retailer revenue and enable a maximum aggregated consumer utility considering individuals’ price fairness perception. The model serves as the first step in evaluating trade-offs between food retailer revenue and consumer utility. In addition, it enables consumer utility to be depicted through perceived price fairness by including the effects of food perishability and scarcity. Another innovative feature is the equalisation of the consumer average aggregated utility during different sales periods as a condition of intertemporal price fairness perception. The proposed model is compared with two benchmark models to justify the effectiveness and advantages in the example. Finally, a sensitive analysis based on the food deterioration rate, consumer food desire rate and consumer average reservation price is conducted to justify the manner in which these factors influence the optimal pricing policy, and managerial insights are suggested for food retailers.