Modeling and Design of Real-Time Pricing Systems Based on Markov Decision Processes

A real-time pricing system of electricity is a system that charges different electricity prices for different hours of the day and for different days, and is effective for reducing the peak and flattening the load curve. In this paper, using a Markov decision process (MDP), we propose a modeling method and an optimal control method for real-time pricing systems. First, the outline of real-time pricing systems is explained. Next, a model of a set of customers is derived as a multi-agent MDP. Furthermore, the optimal control problem is formulated, and is reduced to a quadratic programming problem. Finally, a numerical simulation is presented.

Design of Real-time Electricity Prices and Wireless Communication Smart Meter

Under the background of smart grid’s real-time electricity prices theory, a real-time electricity prices and wireless communication smart meter was designed. The metering chip collects power consumption information. The real-time clock chip records current time. The communication between smart meter and system master station is achieved by the wireless communication module. The “freescale” micro controller unit displays power consumption information on screen. And the meter feedbacks the power consumption information to the system master station with time-scale and real-time electricity prices. It results that the information exchange between users and suppers can be realized by the smart meter. It fully reflects the demanding for communication of smart grid.

Electricity Price Influence Factors Analysis Using Stochastic Matrix for Real-Time Electricity Price Forecasting

Real-time electricity price( RTEP) influence factor extraction is essential to forecasting accurate power system electricity prices. At present,new electricity price forecasting models have been studied to improve predictive accuracy,ignoring the extraction and analysis of RTEP influence factors. In this study,a correlation analysis method is proposed based on stochastic matrix theory.Firstly, an augmented matrix is formulated, including RTEP influence factor data and RTEP state data. Secondly, data correlation analysis results are obtained given the statistical characteristics of source data based on stochastic matrix theory.Mean spectral radius( MSR) is used as the measure of correlativity.Finally,the proposed method is evaluated in New England electricity markets and compared with the BP neural network forecasting method. Experimental results show that the extracted index system comprehensively generalizes RTEP influence factors,which play a significant role in improving RTEP forecasting accuracy.

A Research of Real-Time Pricing Mechanism and Its Characteristics

Real-Time Pricing (RTP) is proposed as an effective Demand-Side Management (DSM) to adjust the load curve in order to achieve the peak load shifting. At the same time, the RTP mechanism can also raise the revenue of the supply-side and reduce the electricity expenses of consumers to achieve a win-win situation. In this paper, a real-time pricing algorithm based on price elasticity theory is proposed to analyze the energy consumption and the response of the consumers in smart grid structure. We consider a smart grid equipped with smart meters and two-way communication system. By using real data to simulate the proposed model, some characteristics of RTP are summarized as follows: 1) Under the condition of the real data, the adjustment of load curve and reducing the expenses of consumers is obviously. But the profit of power supplier is difficult to ensure. If we balance the profits of both sides, the supplier and consumers, the profits of both sides and the adjustment of load curve will be relatively limited. 2) If assuming the response degree of consumers to real-time prices is high enough, the RTP mechanism can achieve the expected effect. 3, If the cost of supply-side (day-ahead price) fluctuates dramatically, the profits of both sides can be ensured to achieve the expected effect.

Verification of Real-Time Pricing Systems Based on Probabilistic Boolean Networks

In this paper, verification of real-time pricing systems of electricity is considered using a probabilistic Boolean network (PBN). In real-time pricing systems, electricity conservation is achieved by manipulating the electricity price at each time. A PBN is widely used as a model of complex systems, and is appropriate as a model of real-time pricing systems. Using the PBN-based model, real-time pricing systems can be quantitatively analyzed. In this paper, we propose a verification method of real-time pricing systems using the PBN-based model and the probabilistic model checker PRISM. First, the PBN-based model is derived. Next, the reachability problem, which is one of the typical verification problems, is formulated, and a solution method is derived. Finally, the effectiveness of the proposed method is presented by a numerical example.

Real-time crash prediction on freeways using data mining and emerging techniques

Recent advances in intelligent transportation system allow traffic safety studies to extend from historic data-based analyses to real-time applications. The study presents a new method to predict crash likelihood with traffic data collected by discrete loop detectors as well as the web-crawl weather data. Matched case-control method and support vector machines （SVMs） technique were employed to identify the risk status. The adaptive synthetic over-sampling technique was applied to solve the imbalanced dataset issues. Random forest technique was applied to select the contributing factors and avoid the over-fitting issues. The results indicate that the SVMs classifier could successfully classify 76.32% of the crashes on the test dataset and 87.52% of the crashes on the overall dataset, which were relatively satisfactory compared with the results of the previous studies. Compared with the SVMs classifier without the data, the SVMs classifier with the web-crawl weather data increased the crash prediction accuracy by 1.32% and decreased the false alarm rate by 1.72%, showing the potential value of the massive web weather data. Mean impact value method was employed to evaluate the variable effects, and the results are identical with the results of most of previous studies. The emerging technique based on the discrete traffic data and web weather data proves to be more applicable on real- time safety management on freeways.

Investment in deepwater oil and gas exploration projects:a multi-factor analysis with a real options model

Deepwater oil and gas projects embody high risks from geology and engineering aspects, which exert substantial influence on project valuation. But the uncer- tainties may be converted to additional value to the projects in the case of flexible management. Given the flexibility of project management, this paper extends the classical real options model to a multi-factor model which contains oil price, geology, and engineering uncertainties. It then gives an application example of the new model to evaluate deepwater oil and gas projects with a numerical analytical method. Compared with other methods and models, this multi-factor real options model contains more project information. It reflects the potential value deriving not only from oil price variation but also from geology and engi- neering uncertainties, which provides more accurate and reliable valuation information for decision makers.

基于ADMM-GBS的考虑风光不确定性的智能电网实时电价策略

实时电价是智能电网需求侧管理的有效方法,对于维持电力供需平衡、削峰填谷至关重要。为提高实时电价模型的低碳经济性和精确性,在充分考虑用户与供电侧双方利益前提下提出碳交易机制,并根据新能源的发电特性构建风光出力不确定模型,建立以用户总效用最大、供电侧成本最小为目标的社会福利最大化实时电价模型。提出基于改进交替方向乘子法(alternating direction method of multiplier, ADMM),即高斯回代交替方向乘子法(ADMM with Gaussian back substitution, ADMM-GBS)的分布式优化调度方法,通过将不确定模型转化为确定模型求解。仿真结果表明,所提实时电价策略能够提升社会福利,验证了模型和算法的有效性。

Study on the roles and acting mechanism of the real estate brokerage in China second-hand housing market

As China housing reform deepens, the second-hand housing transactions become more and more popular and real estate brokerages, as an intermediary of the transactions, become an undeniable important part of this market. This article mainly explores the roles and the acting mechanism of real estate brokerages in the second-hand housing market. The results show that the participation of real estate brokers in the second-hand housing market may have positive and negative influences. On one side, brokers ease the housing transaction process and benefits buyers and sellers by shortening the sale time on the market. On the other side, if brokers are the only participants in the housing transaction, acting as buyer and seller like some firms do, housing prices will increase and this negative effect will become even stronger as the participation of many more brokers increases. Therefore, the regulation and management of the industry is vital for the long and healthy development of the housing market in China.

基于Stackelberg博弈实时定价机制的电-气综合能源系统优化调度

为了降低风电和负荷的预测误差对于综合能源系统各机组出力计划的影响,建立了基于Stackelberg博弈实时定价机制的电-气综合能源系统两阶段(日前-日内)优化调度模型。在日前调度计划中,以综合能源系统经济成本最优确定各机组次日的出力计划。在日内调度计划中,以综合运营商收益最大和用户满意度最大为目标函数,建立主从博弈模型,通过两方博弈对日前能源价格进行调整,从而引导用户积极参与需求响应。通过实际的算例仿真分析,Stackelberg博弈制定的实时能源价格相较于价格弹性系数矩阵法制定的能源价格,用户参与需求响应的程度更高,不仅可以减少对各机组日前制定出力计划的调整,而且可以降低系统运行成本,提高用户的综合效能。

考虑需求响应及不确定性的综合能源优化调度

如何在区域综合能源系统中建立有效的需求侧模型,对于需求响应政策的实施至关重要。为提高用户参与需求响应积极性,引入实时定价机制,同时考虑系统的经济低碳性和电气热用户的用能满意度,构建兼顾系统和用户利益的综合需求响应主从博弈模型。针对用户参与需求响应的不确定性,引入贝叶斯方法更新负荷曲线。算例分析表明,所提博弈模型及贝叶斯方法能够有效平衡系统和用户之间的利益,提高了综合需求响应的可靠性。

基于改进极限学习机的电力市场实时电价预测方法

针对实时电价数据波动性强及其影响因素复杂,导致现有预测模型稳定性及预测精度低的问题,提出了一种基于改进极限学习机的电力市场实时电价预测方法。设计了基于经验小波变换的实时电价数据分解方法,将电价序列分解为接近价格的低频信号和噪声的高频信号。同时提出基于改进随机森林的实时电价特征提取算法,根据预测重要度获取最优的电价影响因素特征组合。以此为基础,将核函数替代极限学习机隐藏层构建了R-KELM预测模型,更好地反映了多因素影响下实时电价的不确定性和波动性。以PJM实时电价数据为例,结果表明,所提方法可以有效克服电价数据强波动性及高特征冗余的问题,预测模型准确性及稳定性得到显著提升。

计及不确定性因素的家庭并网风-光-蓄协同经济调度优化方法

考虑风电、光伏出力和负荷等不确定性因素以及实时电价对家庭并网风-光-蓄发电系统优化调度的影响,构建了计及用户售电收益、购电成本、政府补贴、运行维护费用和分布式电源投资建设成本的家庭并网风-光-蓄发电优化调度模型。并提出一种结合拉丁超立方采样、场景缩减法以及自学习差分算法的优化方法对所提模型进行求解。该方法首先根据风、光、荷预测误差概率分布采用拉丁超立方采样方法生成大量样本,并采用场景缩减法对所生成的样本进行缩减。其次,针对标准差分算法在迭代后期所可能出现的＂聚集＂现象导致容易陷入局部最优的问题,提出一种融合自学习选择操作和纵向交叉操作的自学习差分算法,对所提模型进行全局寻优,得到各场景对应的最佳决策方案。最后,结合典型算例进行仿真研究,结果表明所提模型和方法具有较强的可行性和有效性。

人5-羟色胺2c受体基因实时荧光定量PCR方法的建立

目的建立5-羟色胺2c受体（5-HTR2c）基因mRNA表达水平的TaqMan real—time PcR检测方法。方法以β-actin为内参基因，根据GenBank中人5-HTR2c及β-actin基因序列，分别设计了2套特异性引物和TaqMan探针，接着对反应的退火温度、引物浓度、探针浓度、Mg^2＋浓度进行优化，然后以优化的条件建立相对定量标准曲线，并对该方法的稳定性进行了分析。结果5-HTR2c及β-actin基因的real—time PCR扩增效率分别为99.9％和100.0％；相对定量标准曲线的CT值线性范围分别为12．2～35．1和11．5～31．5，相关系数分别为0．999及1．000；批内及批问变异系数（8．0％。结论本研究所建立的针对5-HTR2c mRNA表达水平的Taqman real-time PCR检测方法具有扩增效率高、稳定性好等特点，为进一步探索5-HTR2c的功能及其mRNA表达水平的变化和各种疾病发生、发展的相关性提供了有用的方法学基础。

基于实时电价的电动汽车智能充电路径引导策略

随着电动汽车占比逐步增加,大量电动汽车在空间、时间上的随机性使得其分布极不均衡,无序充电造成电动汽车用户充电费用较高、电网负荷峰谷差加大。为此,提出一种基于实时电价的电动汽车智能充电路径引导策略。基于电动汽车智能信息交互系统,以电动汽车行驶时间、排队时间、充电时间、充电费用以及能耗费用的综合成本最小为目标,通过实时电价更新策略引导电动汽车至最优快充站。算例仿真结果表明,所提策略能实现空间上的有序快充,改善区域负荷的均衡度,减少各个充电站的拥挤度,并且有效降低了电动汽车用户的综合成本。

基于“负荷-电价”的热电联产系统风电消纳策略

针对现今"三北"地区冬季供暖期弃风严重的问题,提出了一种基于实时电价的热电联产系统风电消纳策略。该策略基于每时刻用电负荷占总负荷比例把日总电费分摊到各时刻,建立负荷与电价的关系模型,并在热负荷中心安装电锅炉解耦"以热定电"约束,同时引入辅助服务费用对联合系统负荷转移和热电解耦过程产生的损失费用进行补偿,建立以消纳弃风上网费用及辅助服务费用之差最大为目标的调度模型。通过"三北"某地区电网仿真结果表明,该策略实现了日负荷整体动态跨时段转移和热电解耦的最优协调控制,使联合系统减少了弃风,同时负荷峰谷差异也得到了明显改善。

考虑多能供电差异的智能电网实时定价

可再生能源和传统能源联合发电是未来电网的发展趋势,不同类型能源发电的定价机制是智能电网研究中的一个重要问题。考虑供电侧同时包含传统能源和可再生能源发电、用户配备电力存储设备,为兼顾供需双方的利益和碳减排,提出了一种基于社会福利最大化模型的实时定价机制。鉴于传统能源和可再生能源的发电方式和发电成本等存在较大差异,在利用拉格朗日对偶算法求解时,引入两个全局拉格朗日乘子作为相应的影子价格,即最优实时电价。数值仿真结果表明,与固定分时定价相比,所提实时定价机制能够有效地削峰填谷、提高社会福利,在经济性方面取得了良好的效果。

氢储能电站运营的模式优选与激励机制

可再生能源发电需要大规模的储能设施进行跨季调节,氢储能具有储存成本低、自放电率低的优势,是跨季储能的最佳选择。当前,氢储能如何实现盈利是阻碍其发展的主要问题。通过混合整数线性规划模型计算氢储能在各商业模式下的机组最佳装机容量及收益,探索氢储能的最佳商业模式,结果发现:氢储能电站通过电价差套利、售氢及建设可再生能源的方式可获得最大收益;在电价上限为2.56元/kW·h时,氢储能可以实现收支平衡;在2030-2037年间,氢储能可以实现商业化盈利;氢燃料电池成本下降对氢储能系统收益影响最大。当前,应通过加快推进电力现货市场建设、完善氢储能进入电力市场政策、重视氢燃料电池发展等措施,大力推动氢储能快速发展。

基于ADMM的完全去中心化P2P能源交易机制

为研究完全去中心化的点对点(peer-to-peer,P2P)能源市场中产消者的最优清算问题,重点解决产消者内部的协作和在P2P市场中实现社会福利最大化的挑战,采用了一种新的平行、分布式的交替方向乘子法(alternating direction method of multipliers,ADMM),推导出P2P市场的交易机制。该方法考虑每个产消者的效用函数,并引入分布式发电机(distributed generator,DG)和电能存储系统(battery energy storage system,BESS)。算法中每个产消者通过迭代与其相邻的产消者同步交换少量信息,并优化以满足不同的需求。通过对6-peers系统的数值验证,证明了所提出方法的有效性。与基于池的交易机制相比,完全去中心化的P2P问题在单位时间内交易电量提升了160%,社会福利从-9.47元增加到32.43元。

基于MD3QN算法的微电网能量协调优化

可再生能源出力的波动性、间歇性、用户电力负荷的随机不确定性,使微电网的能量调度极具挑战性.为此,该文提出激励竞争双深度Q网络(motivation dueling double deep Q-network,简称MD3QN)算法,对微电网能量进行协调优化.仿真分析结果表明:采用MD3QN算法对微电网进行能量调度,能实现削峰填谷,使微电网的经济效益最大化;相对于其他4种算法,MD3QN算法具有更高的综合性能.因此,MD3QN算法具有有效性.