技术商品的价值与价格模型研究

技术商品是一种特殊商品 ,它与一般商品相比有着许多特殊性。从技术商品的概念入手 ,分析了技术商品的特征 ,并将其按照技术的研究对象和应用效果及技术所应用的行业不同进行了分类。阐述了技术商品的价值和使用价值 ,论述了技术商品的价格形成。利用Cobb -Douglas生产函数 ,构造出了技术商品的价格模型。说明技术商品的价格决定于技术商品的影子价值和其消费风险     

价格系统的混沌行为研究

本文首先研究了价格系统的混沌行为,然后建立并分析了一个具有混沌行为的价格系统模型,从而在价格系统混沌行为的研究方面取得了进展。     

Electricity market price volatility: The case of Ontario

Price volatility analysis has been reported in the literature for most competitive electricity markets around the world. However, no studies have been published yet that quantify price volatility in the Ontario electricity market, which is the focus of the present paper. In this paper, a comparative volatility analysis is conducted for the Ontario market and its neighboring electricity markets. Volatility indices are developed based on historical volatility and price velocity concepts, previously applied to other electricity market prices, and employed in the present work. The analysis is carried out in two scenarios: in the first scenario, the volatility indices are determined for the entire price time series. In the second scenario, the price time series are broken up into 24 time series for each of the 24 h and volatility indices are calculated for each specific hour separately. The volatility indices are also applied to the locational marginal prices of several pricing points in the New England, New York, and PJM electricity markets. The outcomes reveal that price volatility is significantly higher in Ontario than the three studied neighboring electricity markets. Furthermore, comparison of the results of this study with similar findings previously published for 15 other electricity markets demonstrates that the Ontario electricity market is one of the most volatile electricity markets world-wide. This high volatility is argued to be associated with the fact that Ontario is a single-settlement, real-time market.     

Aggregate industrial energy consumer response to wholesale prices in the restructured Texas electricity market

The aggregate response of consumers to wholesale price signals is very limited in the restructured Electric Reliability Council of Texas (ERCOT) market. An overall average own-price elasticity of demand of − 0.000008 for industrial energy consumers served at transmission voltage is estimated using a Symmetric Generalized McFadden cost function model. To date, ERCOT has sought to promote demand response to price signals without reliance on “stand alone” demand response programs, but with a market structure that is designed to facilitate economic demand response. This very limited responsiveness to wholesale price signals may prove problematic in light of policy decisions to pursue an “energy only” resource adequacy mechanism for ERCOT.     

The Fetish of Flexibility: Farrell/Grimshaw Partnership, 125 Park Road,London

Completed in 1970, this residential building on a prime London site is recognised as a pioneering example of the British High Tech style. More importantly for this issue of AD, it was designed so that leaseholders could devise their apartments' layouts themselves, with maximum allowance for future adaptation – in contrast with the high-spec, investor-driven typology that is prevalent for such sites today. Writer and architect Edwin Heathcote describes the building's structure, the co-ownership housing society model that made it possible, and how it has fared over time.     

分时段短期电价预测

分时段电价序列比顺序电价序列的变化特征更单一，有利于电价的分析建模，从而提高预测精度，因此采用各时段电价分别预测的分时段预测方法。该文将相关系数作为选取电价影响因素的标准，考虑了历史电价、负荷、负荷率等影响电价的因素。以小波分析和神经网络作为工具，对不同输入因素和不同预测方法下的电价预测精度进行了研究，并重点比较了基于分时段电价序列的预测方法和基于顺序电价序列的预测方法。算例采用美国新英格兰电力市场历史数据，对其2002年第4季度的电价进行了连续预测。与基于顺序电价序列的预测方法相比，分时段短期电价预测方法能够使平均相对百分比误差下降约3个百分点。     

引入保险机制降低电力市场金融风险的研究

电力市场的运行给保险公司开拓新业务带来了新的机遇。电价的高度波动性是电力市场区别于其它市场的主要特点,它使得市场成员面临着巨大的收益损失风险,因此为了稳定收入,在电力市场中必须建立有效的风险规避机制。针对发电商收入损失风险的特点,作论述了引入收入保险规避风险的必要性和可行性。通过原理阐述并结合某省电力市场的具体算例,论证了发电企业购买收入保险降低经营风险的可实现性和良好前景。     

Electricity market price spike analysis by a hybrid data model and feature selection technique

In a competitive electricity market, energy price forecasting is an important activity for both suppliers and consumers. For this reason, many techniques have been proposed to predict electricity market prices in the recent years. However, electricity price is a complex volatile signal owning many spikes. Most of electricity price forecast techniques focus on the normal price prediction, while price spike forecast is a different and more complex prediction process. Price spike forecasting has two main aspects: prediction of price spike occurrence and value. In this paper, a novel technique for price spike occurrence prediction is presented composed of a new hybrid data model, a novel feature selection technique and an efficient forecast engine. The hybrid data model includes both wavelet and time domain variables as well as calendar indicators, comprising a large candidate input set. The set is refined by the proposed feature selection technique evaluating both relevancy and redundancy of the candidate inputs. The forecast engine is a probabilistic neural network, which are fed by the selected candidate inputs of the feature selection technique and predict price spike occurrence. The efficiency of the whole proposed method for price spike occurrence forecasting is evaluated by means of real data from the Queensland and PJM electricity markets.