Roman Petrichenko

A stochastic approach to hydroelectric power generation planning in an electricity market

The paper discusses the planning of hydroelectric power generation. A stochastic optimization procedure is offered to solve the complex task of planning the operation of three hydroelectric power plants. The proposed stochastic optimization algorithm is based on time average revenue maximization, taking into account the random nature of the future energy prices and river water inflows. Random variables are predicted by using an algorithm based on artificial neural networks. For computing within the operational planning, the task is divided into three parts. First, middle-term planning is used to solve the water resources distribution task. The second and third parts are related to day-ahead operational and unit commitment planning; in those cases, nonlinear programming tools are used. A case study presents the results of electric power generation in the case of optimum water resource distribution in the storage basins of hydroelectric power plants cascade. The paper proves the workability of the developed algorithm for maximizing the income value and is intended to enable and support improved planning and decision-making for electric power producers.

Development of Smart Underfrequency Load Shedding System

The paper describes possibility to apply innovation technology of Smart Grid for power system emergency automation. Operational characteristics of existing emergency automation and new suggested one are compared in the paper. The method for liquidation of drawback of existing automation is suggested. The paper describes the developed mathematical model of intellectual underfrequency load shedding system and its operational algorithms.

Smart load shedding system

This paper describes a methodology of application of smart grid technology for improvement of existing emergency automation system. Existing and new developed automation systems are considered in the paper. Authors approved advantage of suggested automation system using mathematical simulation of emergency processes in the power system. The paper describes the developed mathematical model of intellectual load shedding system and its operational algorithms.

A multi-objective stochastic approach to hydroelectric power generation scheduling

In this paper, we propose a novel stochastic approach to multi-objective optimization of hydroelectric power generation short-term scheduling. Maximization of profit is chosen as the main objective with additional sub-objective-to reduce the number of startups and shutdowns of generating units. The random nature of future electricity prices and river water inflow is taken into account. We use an artificial neural network-based algorithm to forecast market prices and water inflow. Uncertainty modeling is introduced to represent the stochastic nature of parameters and to solve the short-term optimization problem of profit-based unit commitment. A case study is conducted on a real-world hydropower plant to demonstrate the feasibility of the proposed algorithm by providing the power generation company with the day-ahead bidding strategy under market conditions and a Pareto optimal hourly dispatch schedule of the generating units.

ANN-based forecasting of hydropower reservoir inflow

Reservoir inflow forecasting with artificial neural networks is presented in this paper. Different types of ANN input data were considered such as temperature, precipitation and historical water inflow. Performance of the hourly inflow forecasts was assessed based on a case study of a specific hydropower reservoir in Latvia. The results showed that all the approaches had similar prediction errors implying that for optimal hydropower scheduling uncertainties need to be modelled which is also proposed in this study through generation of several forecast realisations in addition to point predictions.

Development and integration of adaptive underfrequency load shedding into the smart grid

The objective of underfrequency load shedding (UFLS) is to balance generation and load in case of a significant drop in frequency. Conventional UFLS schemes rely on parameters that are obtained by analyzing predefined scenarios that might not coincide with the actual emergency process. Improved UFLS scheme is presented in the paper that estimates accurate values of active power deficiency thus presenting an opportunity to enhance the efficiency and reliability of power system operation. The theory of the presented scheme is verified by computer modelling. Integration of the new UFLS scheme into the smart grid where it can be most effective by taking advantage of digital communication systems and smart meters is also discussed.

District heating demand short-term forecasting

This paper discusses various forecasting tools that can be used in predicting the thermal load in district heating networks, focusing on day-ahead hourly planning as it is particularly important for cogeneration plants participating in electricity wholesale markets. Forecasts obtained by employing an artificial neural network are compared to a polynomial regression model. Their ability to supplement each other in a combined forecasting tool has been considered as well. Prediction inaccuracy cost is observed and suggested as evaluation criterion. The case studies are based on the district heating network in Riga, Latvia. Recorded data sets of temperature and heat demand are applied for thermal load prediction.

Detection and Management of Large Scale Disturbances in Power System

Recent blackouts of major power systems (PSs) clearly demonstrate the topicality of the blackout problem when rapid multiple tripping of vital PS elements leads not only to PS collapse but also essentially affects the operation of other critical infrastructures. To prevent such situations special types of automation such as PS splitting and frequency control automation are employed. Taking into account the drawbacks of existing devices the new methods and technical solutions to increase sustainability to such events are discussed.

Coexistence of different load shedding algorithms in interconnected power system

This article deals with the approbation of new smart automation that is proposed for load shedding. The existing load shedding automation has been considered, an innovative load shedding method has been proposed, and an issue regarding the integration problem of new automation into the power system has been considered. The authors have proven the superiority of the suggested automation system using mathematical simulation of emergency processes in the power system.

Underfrequency load shedding in large interconnection

Possible interconnection of the European Network of Transmission System Operator for Electricity (ENTSO-E) and joint power system of the former USSR (IPS/UPS) will cause the necessity to evaluate the security state of large transmission systems. One important problem for a large interconnection is the frequency control. This paper considers distinctions of frequency control in interconnected power systems during emergency situations with a deep frequency drop. To restore the system to its normal operating frequency, automatic underfrequency load shedding (UFLS) serves as a last-resort tool to prevent the system from collapse. This paper presents results of analysis of frequency behavior for different algorithms of UFLS and possible solutions of frequency control problem. The frequency behavior is analyzed for different active power deficiencies in different sides of interconnection. Due to the difference in the UFLS structure in both systems either overfrequency or low frequency situations can take place during operation of load shedding. For more reliable frequency restoration process smart uniform philosophy of the load shedding system should be preferable.