Энергосбережение - теория и практика: труды Шестой Всероссийской школы-семинара молодых учёных и специалистов
With limited energy resources and increased costs for their extraction, to meet the growing demand, it is necessary to search for sources of supply growth and to analyze various approaches to the development of energy. World energy leaders pay great attention to traditional sources, but also show interest in renewable energy technologies (renewables). The reason for this interest is not only the depletion of old ones and the absence of new large deposits. This is dictated by environmental requirements, the need to diversify energy supply sources, energy security policy and the formation of strategic reserves, as well as companies' readiness for modern trends in energy and the development of new technological solutions. The development of renewables is becoming a factor of competition for technological leadership. According to the draft of the energy strategy of Russia until 2035 (ES-2035), the technologies of the "energy revolution" include renewables and energy storage. It is projected that the generation of energy using renewables, equal to 1% of the country's total energy generation, will be achieved by 2020, and this conflicts with declarations of the importance of renewables.
Russian oil and gas companies are represented in corporate strategies as energy, which should mean increased attention not only to the hydrocarbon production sector, but also the development of other energy sectors, including renewable energy sources. Modern development strategies and programs sparingly reflect measures to develop alternative energy sources. This is due to the fact that the efficiency of the proposed green technologies is still low and for Russian conditions, the generation of energy by traditional methods is more effective than renewables: no additional resources, special efforts and innovations, and creativity of managerial decisions are required.
Russian companies, unlike the Western ones, invest little in the development of "green" energy at the industrial scale. Among the renewable assets, for example, BP has the largest biogas station in Brazil, 16 onshore wind farms in the US, and solar power plants in Germany and the United States. In the long term, the lack of such a direction of investment can mean the loss of technological prospects by Russian energy companies and even the withdrawal from the position of world energy leaders.
To ensure the share of renewable energy in the energy balance in accordance with the project ES-2035 (more than 3% of the total power generation by 2035), it is necessary to invest at least $ 1.7 billion annually. Achieving 3% by 2035 will only bring Russia closer to the indicators already achieved by developed economies.
The development of public-private partnerships, other instruments of state support, the acceleration of technological progress will narrow the gap in technological development, reduce the likelihood of Russian companies losing their share in the world energy market and the risk of occupying their place by those who are already actively investing in the development of clean energy.
Introduction. The gained experience in the field of building automation and IoT technologies yields a new approach to the management of engineering subsystems that provides stated parameters of operation quality throughout the entire building lifecycle. This paper explores compensatory and predictive algorithms in the scope of the aforementioned approach to manifest control over building climate parameters utilizing IoT controllers. This research aims to improve the management efficiency of smart house engineering subsystems through the implementation of a control system (CS) capable to compensate disturbances and predict their variations using an IoT controller and an analytical server.
Materials and methods. In order to improve the quality of control, various algorithms based on analysis of data collected from controllers can be employed. The collected data about the object accumulated over the entire period of operation can be used to build a model for the purposes of predictive control. The predictive control allows forecasting the parameters having an effect on the object and compensating it beforehand under the inertia conditions. The continuous adaptation and adjustment of the CS model to operating conditions allows permanent optimizing the settings of the control algorithm ensuring the efficient operation of local control loops.
Results. The CS is based on an IoT controller and able to predict and compensate potential disturbances. The compensation algorithm is updated depending on the behavior of the object properties, quality of control and availability of data most suitable for identification.
Conclusions. The capabilities of the control system based on the IoT controller and generation of a compensatory and predictive control signal with the algorithm hosted at a cloud server are demonstrated on the indoor temperature control model. The following simulation models of the indoor temperature variation process are considered: model without CS, model with proportional plus integral controller with disturbance compensation and model with IoT controller-based CS with disturbance compensation. Structural and parametric identification of the models are accomplished by means of active experiment.