Development of Russian electric power industry in recent years is characterized by a multitude of problems and a decrease in a number of performance indicators. It dissatisfies consumers and encourages them to implement various measures to reduce risks and costs of energy supply. This creates preconditions for the emergence of «active» consumers in the domestic electric power industry. Given this trend it would be appropriate to switch from Supply Side Management to Demand Side Management. This will require the implementation of a wide range of measures, including strategic issues of industry development, legal framework and transition to a customer-centric market model.
Development of distributed generation is one of the modern trends in the electric power industry worldwide. The main drivers are technology progress, energy policy (stimulating of development RES, cogeneration, «green energy», etc.). Furthermore, consumers' request for higher service standards is important. Liberalization of electric power industry creates additional opportunities for development of distributed generation. Also it is necessary to synchronize development of electric power industry, heat industry and consumers’ generation.
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.
Possibilities of identification and creation of the conditions, providing energy sector innovation ecosystem development are analyzed in the article. Ecosystem evolution (including energy sector ecosystems) as a form of network cooperation is ensured by specific conditions – particular type of participants, level of sector institutional development, possibilities of technological platforms (special tool for innovation infrastructure development) functions implementation, presence of developed market and demand for innovation products. During the interview, held with the energy sector representatives, and monitoring of open sources as well, availability and the problems of the conditions elaboration, which assure innovation ecosystem development, were determined.
As a result, the revealed level of ecosystem development conditions was not sufficient for the cooperation. Worked out recommendations based on the results obtained were directed towards improvement of the existing level of innovation ecosystem development conditions and to the exploration of innovation ecosystem place in Russian energy sector development initiative – Energynet.
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.
Electric power industry is under transition to intellectual energy system worldwide. There are three processes in this one: liberalization, development of renewable energy sources and distributed energy sources, development of smart grids. General conclusions of the theory of reform are confirmed in the transformation of electric power system. At the same time there are additional difficulties, for example due to overinvestment in the power generation capacities. Also the transition processes in the world economy and global energy are important. The process of electric power transformation is very difficult and each country has a lot of problems in its realization. Russia’s case is one of the hardest due to specific domestic conditions and problems and also due to external circumstances.
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.