The article deals with the analysis of technological platforms in Russian energy industry, as an instrument of innovation state policy. To this end, European technological platforms have been explored, the implementation experience of which formed the basis for the formation of similar mechanisms in Russia. Their structure and place in the innovation state policy are described. Comparative analysis of European and Russian technology platforms has revealed several conceptual differences, the key of which is the dominance of a directive approach to management in Russia. This leads to incomplete use of technological platforms’ potential and causes the participants’ low interest in interaction on their site. The article suggests ways to solve the identified problems.
One of the key and pressing challenge the Russia electric power industry faces todat in its development and performance is the problem of cross subsidization. This problem, if not solved, will hinder further adequate development of market relations, effective performance of market players and of the national economy in general. This article is aimed at the comprehensive overview of the cross subsidization types currently existing in the industry (where subsidization between citizens and other consumers remains the basic one), system analysis of its causes and opportunities for the solution of this problem including potentia risk assessment as regards the price mechanism reviewed.
The electricity market liberalization in Russia has led to the emergence of the wholesale power market. Since that time, market participants operate in the competitive environment, facing everyday with market strategy planning issues. Under these conditions forecasting electricity prices has become an integral and daily challenge for most market participants. This is also true in case of high uncertainty that typically characterizes Russian electricity market. To this end, it is especially demanded to formulate and apply precise forecast models to predict market conjecture.
In this paper we consider the possibility of using neural networks for short-term electricity price forecasting on the Russian day-ahead market based only on market specific deterministic factors. The results show that the proposed set of six factors accurately describe the market conjecture and proposed model allows to get reliable month hourly price forecast in four different seasons of the year. The proposed model shows the lowest average prediction error rates for each hour of the month and, in turn, allows market participant to anticipate significant deviations of the price.
The article reviews application of incentive-based control methods with respect to service rates of power grid operators based on the example of some European countries. The article provides a classification of methods, highlights key general characteristics and individual (country-specific) applications of considered methods, and makes conclusions on opportunities and risk related to incentive-based control methods used in the power grid complex.
Using artificial neural network approach, the electricity price forecast model is created to predict the short-term dynamic of day-ahead electricity market (spot) prices in the European zone of Russian wholesale electricity market. Such model make a big contribution to reduce the uncertainty caused by recent liberalization of Russian power industry, which typically lead to high price volatility. The proposed forecasting models might be a useful tool for power producers, consumers and retailers for making their market strategy to maximize their benefits and avoid unexpected losses.
The manual focus on the innovative development of electric power based on the introduction of advanced technological and organizational solutions. The mechanisms of the industry innovation, taking into account the uncertainty of the environment and the crisis in the economy.