含储能参与的日前市场价值公平分配机制

现有的节点边际电价机制中,由于传统发电商具有市场操控力,当储能独立参与市场出清时,各发电商采取策略性报价,打压并挤占储能电站的市场份额,阻碍了储能电站参与市场出清,间接导致市场出清总成本增大。为此,文中提出一种包含传统机组以及储能电站参与的市场竞争机制。首先,分析现有市场结算机制的弊端以及阻碍储能参与市场出清的原因;其次,建立含储能参与的市场出清模型,采用样本均值近似求解二阶段随机规划模型;接着,基于VCG结算机制,提出适应储能参与的日前市场价值分配机制;最后,提出解决激励相容而造成的系统收支不平衡问题的策略。文中采用修改后的IEEE30节点为例,证明该机制满足激励相容、收支平衡以及削弱传统发电商的市场操控力等性质,同时储能的参与将会减小系统出清总成本,降低市场价格剧烈波动的风险。     

中国电力中长期市场分时段交易价格形成机制及模型

电力中长期市场分时段交易是中国电力市场改革的重要举措之一。针对当前电力中长期市场分时段交易价格形成机制不合理、不完善的问题,提出了中国电力中长期市场分时段交易价格形成机制及其模型。首先,分析影响中国电力中长期分时段交易价格形成的因素,提出电力中长期交易的4种典型场景;然后,综合考虑生产成本和用户效用,探索性地提出适用于中长期分时段交易的系统平均成本定价、用户失负荷价值定价、系统边际成本定价和发电企业失负荷价值定价4种成本定价机制,构建了基于成本定价的分时段交易定价模型;最后,以某省实际数据为例进行测算,验证了模型的有效性。     

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.     

基于区块链的电力交易模型及博弈定价方法

为解决传统能源中心化交易模式缺少灵活性、透明性和可监督性等问题,满足新型电力系统中电网各主体间电力交易市场化、定价灵活化等新要求,提出一种基于区块链的电力交易模型及博弈定价方法。该电力交易模型具有分布式、去中心化、不可篡改和加密安全等优势。首先,建立基于区块链的电力交易模型,协调发、供、用等主体间的生产和消费行为,形成统一的市场机制;其次,提出了基于博弈的多时间尺度电力交易竞价机制,利用蚁群优化(ACO)算法求解可得每小时的最佳竞拍价格。最后,通过仿真验证了交易模型及博弈定价方法,在激励政策下各交易方收益最优化。结果表明,交易模型及定价方法在新能源参与交易背景下能有效地平衡市场各主体的效益;基于区块链的智能合约可实现电力交易过程的智能化、透明化和可追溯性。     

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。     

Disaggregating growth in future retail electricity rates

The retail rate impacts of a number of emerging trends (e.g., rapid deployment of electric vehicles and storage, transmission build-out for large-scale renewables deployment, and grid modernization) are unknown. Importantly, decision-makers are concerned about the potential future rate impacts on energy affordability and equity. We disaggregate the key drivers of retail electricity rates and assess their impacts on future rate growth considering their interactions and uncertainty. Specifically, we develop ranges of future cost growth for a generic investor-owned and vertically-integrated electric utility representing typical cost and operating characteristics. The rate driver growth rate ranges are applied in isolation and jointly to quantify the uncertainty and variability in future retail electricity rates. The results identify what rate drivers and factors may minimize and/or decrease uncertainty in retail rate growth and their linkages to industry trends.     

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。     

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.