工业园区配售电公司分时电价双层优化模型

随着增量配电网业务的放开,配售电公司在激烈的竞争中通过峰谷分时电价套餐吸引电力用户成为其运营的重要方式,而电价套餐各参与方利益的合理分配是配售电公司顺利推进此项服务的关键.考虑增量配电工业园区内配售电公司拥有配电业务的特殊性以及大工业用户调整生产的不适应成本,首先构建了工业用户与配售电公司的成本收益模型;并在此基础上提出了工业园区配售电公司峰谷分时电价双层优化模型,上层优化模型以配售电公司收益最大化为目标、下层优化模型以大工业用户用电成本最小化为目标;最后对某一试点园区数据进行仿真测算,结果表明,由该模型得到的分时电价套餐在起到削峰填谷效果的同时,能够保证配售电公司和工业用户的利益,有利于引导双方参与峰谷分时电价套餐的积极性.     

基于鲁棒均值-下半偏差模型的供电公司购电组合策略

电力市场中,日前市场购电电价的随机波动,给供电公司的投资带来了一定的收益风险,因而供电公司需要在不同的市场中合理分配购电电量分散投资,以实现自身收益率尽可能大的同时承受的风险最小.供电公司在多市场中购电电价呈随机波动的特性,本文用均值-下半偏差作为购电风险测度,并用鲁棒优化处理电价的不确定性,建立了供电公司鲁棒均值-下半偏差(Robust Mean Semi-Deviation)购电策略优化模型.最后利用广西电网公司提供的数据进行实证分析,验证了模型的有效性和适用性,表明此模型对供电公司的投资组合决策具有一定的参考价值和指导意义.     

可再生能源配额制与碳排放权交易并行实施的政策效果

中国将同时实施可再生能源配额制与碳排放权交易,并以售电商为配额制的考核主体。本文建立两级供应链模型分析两者相互作用的政策效果。研究结果表明,引入碳排放权交易将导致火电价格上涨与可再生能源电力价格下降,有利于实现两者在同一价格水平竞争。然而,碳排放权交易亦将造成零售电价上涨,电力需求减少,且变化幅度与碳排放权价格和减排成本成正比。若配额比例与售电商差异性提高,在不影响环境成本内化于火电批发价格的同时,可降低售电商的转嫁成本。从而减小了可再生能源电力价格降幅与零售电价涨幅,并平抑碳排放权价格与减排成本上涨造成的电价与需求波动。     

基于厂商策略的电价建模

发电侧厂商的行为是影响电力市场竞价交易的重要因素,发电商的行为策略是造成市场出清价的不稳定性的关键原因,而其行为主要是通过操控市场力来提升价格.本文以美国加利福尼亚州电力市场99年电价一容量数据的分析对象,企图对数据去噪、聚类建立模型,特别是针对由于发电商的容量持留造成市场出清价格的振荡这种情况,依托数据挖掘中混合聚类思想建立混合模型,以从中反映发电厂商的行为.     

基于多层博弈的智能电网住宅电力实时需求响应机制

通过建立多层博弈模型刻画智能电网系统中电力公司、家庭电力管理中心和家庭内各个电器设备间的电力实时需求响应过程。在此模型中,每个时段电力公司将电价通知各个家庭,每个家庭的电力管理中心接收到实时电价信息后为家庭内各个电器设备分配虚拟电价,各个设备则确定各自最优用电量并将其反馈给家庭电力管理中心,由其确定该家庭总用电量并发送至电力公司,电力公司再计算得到最优电价。证明了所建多层博弈模型的均衡存在唯一性,并得到均衡解。仿真结果验证了模型的有效性,并给出其实际应用价值。     

基于标杆管理优化算法的供电商购电组合策略

针对开放式电力市场环境下,供电商的购电风险管理问题,结合金融输电权拍卖市场和电能交易市场,构造了一个考虑阻塞风险的供电商最优购电组合模型.由于此模型的非可微性和非凸性,使用了一种新型的智能计算方法——标杆管理优化算法对该模型进行了求解计算.仿真实例表明,提出求解计算方法是切实可行的,具有一定的实用性和灵活性.     

福建省电力投资预期收益率分析

电力销售价格是电力市场体系中的重要因素，影响着电力市场的正常运作。本文在比较福建省销售电价与商品零售价格指数的基础上，使用资本资产定价模型（CAPM）作为度量方法，对不同售电对象进行β系数和预期收益率计算，结果发现，福建当前的售电价格体现出不同售电对象具有不同的预期收益率。从我国的现实情况和电力市场的发展趋势来看，这种状况既有其相应的合理性，也存在一定的弊端。     

灰色系统预测模型GM(1,1)背景值重构研究

分析了灰色系统预测模型GM(1,1)对高增长指数序列建模适应性较差的原因,通过重构背景值计算公式,建立了一个适应性极强的灰色系统预测模型NGM(1,1),该模型具有对建模结果进行优化的能力.算例结果表明该模型对低增长指数序列和高增长指数序列建模都能获得最佳的拟合和预测精度,对经济、工程和自动控制等领域中的预测问题有较高的理论价值和实践意义.     

基于Delphi-AHP和加权集值统计的高校突发事件预警评估

科学预防高校突发事件发生对维护高校乃至社会稳定具有重要意义。为建立和完善预警机制,提高应急管理能力,本文针对高校突发事件监测预警复杂性,提出一种预警评估新方法。该方法首先采用Delphi法建立预警评估的指标集,并将Delphi和AHP方法集成(DelphiAHP)来确定各指标权重;在预警评估阶段采用加权集值统计法(weighted set valued statistics, WSVS),避免了以往评价中采用点估计所带来的不准确性。最后,以学生公寓突发火灾事件为例表明,采用该模型确定预警等级并进行预警发布具有可行性与有效性。     

基于优化的灰色马尔可夫内蒙古电力需求预测模型

针对电力需求随机性和波动性的特点,建立优化的灰色马尔可夫预测模型.利用该模型,对内蒙古自治区2006-2015年的电力消费数据,进行数值模拟.使用三种检验方法:残差、关联度和后验差对结果进行检验.实验结果表明:平均相对残差达到1.0%,关联度满意,后验差预测等级为好.在此基础上,对内蒙古未来五年的电力需求进行预测,并提出建议与对策,对内蒙古制定可行的电力发展规划方案具有参考价值.     

计及需求响应不确定性的综合能源系统多目标优化调度管理

需求响应作为电力系统的重要调节手段,可显著提升系统灵活性和经济性。利用价格弹性构建了包含价格与激励措施的需求响应模型,并在此基础上考虑需求响应的不确定性,以综合能源系统经济性和环保性为优化目标,构建了综合能源系统多目标优化调度模型。利用E约束法将多目标优化模型转化为单目标优化模型,得到Pareto最优解集,运用模糊决策法从中选取最优方案。基于实际案例进行测算,结果表明价格型与激励型需求响应手段的结合能够实现削峰填谷,有效降低系统的运行成本和碳排放量。     

A fractional programming approach for retail category price optimization

We present a new mixed-integer programming (MIP) approach to study certain retail category pricing problems that arise in practice. The motivation for this research arises from the need to design innovative analytic retail optimization techniques at Oracle Corporation to not only predict the empirical effect of price changes on the overall sales and revenue of a category, but also to prescribe optimal dynamic pricing recommendations across a category or demand group. A multinomial logit nonlinear optimization model is developed, which is recast as a discrete, nonlinear fractional program (DNFP). The DNFP model employs a bi-level, predictive modeling framework to manage the empirical effects of price elasticity and competition on sales and revenue, and to maximize the gross-margin of the demand group, while satisfying certain practical side-constraints. This model is then transformed by using the Reformulation–Linearization Technique in tandem with a sequential bound-tightening scheme to recover an MIP formulation having a relatively tight underlying linear programming relaxation, which can be effectively solved by any commercial optimization software package. We present sample computational results using randomly generated instances of DNFP having different constraint settings and price range restrictions that are representative of common business requirements, and analyze the empirical effects of certain key modeling parameters. Our results indicate that the proposed retail price optimization methodology can be effectively deployed within practical retail category management applications for solving DNFP instances that typically occur in practice.     

有功与无功功率最优实时定价新方法

本文给出了一种基于社会收益最大化的有功与无功功率最优实时定价新方法 .传统的研究仅仅考虑了电力系统运行成本最小这一目标 .本文则把有功运行成本、无功运行成本与消费者的收益结合起来 ,利用把消费者的反映表示为需求函数的逆形式的手段 ,使最大化电力市场参与者的社会收益目标变为可能 ,每条母线上的有功与无功功率定价结果也可相应求出 .     

Multi-period price promotions in a single-supplier, multi-retailer supply chain under asymmetric demand information

This paper considers a two-stage supply chain in which a supplier serves a set of stores in a retail chain. We consider a two-stage Stackelberg game in which the supplier must set price discounts for each period of a finite planning horizon under uncertainty in retail-store demand. As a mechanism to stimulate sales, the supplier offers periodic off-invoice price discounts to the retail chain. Based on the price discounts offered by the supplier, and after store demand uncertainty is resolved, the retail chain determines individual store order quantities in each period. Because the supplier offers store-specific prices, the retailer may ship inventory between stores, a practice known as diverting. We demonstrate that, despite the resulting bullwhip effect and associated costs, a carefully designed price promotion scheme can improve the supplier’s profit when compared to the case of everyday low pricing (EDLP). We model this problem as a stochastic bilevel optimization problem with a bilinear objective at each level and with linear constraints. We provide an exact solution method based on a Reformulation-Linearization Technique (RLT). In addition, we compare our solution approach with a widely used heuristic and another exact solution method developed by Al-Khayyal (Eur. J. Oper. Res. 60(3):306–314, 1992) in order to benchmark its quality.     

Dual role of price and myopia in a marketing channel

In this paper we study a dynamic two-player channel where the manufacturer controls the wholesale price and the investment in quality and the retailer chooses the retail price. We consider that the retail price affects both the demand and the perceived quality of the brand and that its variations contribute to the building of an internal reference price. One of the model’s distinctive features is that it accounts for the two meanings of price, i.e., its classical objective measure of the cost of acquiring a particular quantity of the product, and its subjective roles as an assessment of the quality of the product and an evaluation of gains or losses (deal vs. sacrifice) resulting from buying a “cheap” or an “expensive” product. This dual computation is done with respect to the internal reference price.     

Should retail stores also RFID-tag ‘cheap’ items?

Despite their implementations in a wide variety of applications, there are very few instances where every item sold at a retail store is RFID-tagged. While the business case for expensive items to be RFID tagged may be somewhat clear, we claim that even ‘cheap’ items (i.e., those that cost less than an RFID tag) should be RFID tagged for retailers to benefit from efficiencies associated with item-level visibility. We study the relative price premiums a retailer with RFID tagged items can command as well as the retailer’s profit to illustrate the significance of item-level RFID-tagging both cheap and expensive items at a retail store. Our results indicate that, under certain conditions, item-level RFID tagging of items that cost less than an RFID tag has the potential to generate significant benefits to the retailer. The retailer is also better off tagging all items regardless of their relative price with respect to that of an RFID tag compared to the case where only the expensive item is RFID-tagged.     

Conditions of reverse bullwhip effect in pricing for price-sensitive demand functions

Supply chain mechanisms that exacerbate price variation needs special attention, since price variation is one of the root causes of the bullwhip effect. In this study, we investigate conditions that create an amplification of price variation moving from the upstream suppliers to the downstream customers in a supply chain, which is referred as the “reverse bullwhip effect in pricing” (RBP). Considering initially a single-stage supply chain in which a retailer faces a random and price-sensitive demand, we derive conditions on a general demand function for which the retail price variation is higher than that of the wholesale price. The investigation is extended to a multi-stage supply chain in which the price at each stage is determined by a game theoretical framework. We illustrate the use of the conditions in identifying commonly used demand functions that induce RBP analytically and by means of several numerical examples.