Karlis Baltputnis

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.

Impact of smart electric thermal storage on transmission grid limitations

This paper examines the potential benefits smart and quickly controllable load can provide for transmission network congestion management if end-users give the transmission network operator or an independent aggregator service access to disconnect their devices in case of contingencies. The necessary amount of available quickly controllable load for the fulfilment of N-1 criteria is found for the case study of the Latvian power system. Of particular interest is the possibility to increase the maximum available transfer capacity between the Latvian and Estonian bidding areas by removing the N-1 provision from the calculation of the cross-border trading capacity if it can be supplied by the controllable load instead. For this purpose, a steady-state power flow model of the 330 kV transmission network and its interconnections with neighboring countries is used for simulations.

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.

Modeling of water utilization in hydroelectric power plants on the Daugava River

In this paper the task of modeling water utilization in the hydroelectric power plants on the Daugava River is outlined. Difficulties caused by the complexity of the task are discussed and the necessity to decompose it is considered. Unit commitment proves to be especially important subtask as it introduces several constraints and limitations. A possibility to solve it using dynamic programming approach has been offered and the veracity of the mathematical model of reservoirs has been tested in this paper.

The costs of enviromental limitations of HPPs in cascade

When performing the optimization of hydroelectric power plants (HPP), it is necessary to consider all the technological and environmental constraints. Usually, restrictions are taken into account as a permanent set of parameters. We describe an optimization problem solution in the Multi-Objective Stochastic form, which presents the results as a Pareto set that can be used for making more reasonable final decisions. An example on Daugava HPP cascade is depicted. The mathematical statement of the optimization problem is described. The case study shows that an increase in the permitted reservoir water level range may considerably influence the income gained, while not having a significant influence on the environment.

ANN-based city heat demand forecast

This paper discusses the importance of accurate forecasting tools in solving power system planning, modelling and optimization tasks. While artificial neural networks are widely considered to be one of the best prediction methods, their accuracy can vary greatly depending on the network structure and parameters. A method of experimentally finding the best ANN parameters has been offered and tested on heat demand forecasting. Some value of the benefits of increased prediction accuracy on the operation of CHP plants has been identified.

Short-Term Optimization of Storage Power Plant Operation under Market Conditions

This paper deals with the optimization of storage power plant operation with a particular focus on market situation in the Latvian bidding area of the Nord Pool. Some currently already available storage options such as hydropower are considered, but attention is given to an emerging technology - hydrogen storage - as well. An algorithm for storage plant scheduling optimization is devised. In the case study, it is concluded that both technologies are capable of exploiting the price spread in the day-ahead electricity market. Another operational strategy apart from the price arbitrage is studied in this paper as well - cooperation with wind farms. Coordinated operation allows to decrease expenses caused by inaccurate wind generation forecasts.