Zane Broka

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.

Modelling residential heat demand supplied by a local smart electric thermal storage system

This paper presents an inverse modelling approach for deriving equivalent thermal parameters of buildings. A simplified thermal network based on electrical analogy was developed to replicate building thermal dynamics and model residential heat demand. The study employs data driven blackbox modelling based on the measured indoor and outdoor temperature. To validate the proposed method, virtual and physical experiments were conducted and performance of the simplified thermal network model was compared to two more complex RC models and measurements in an existing building. The simplified model was able to replicate the thermal dynamics of the complex models and the building with a high accuracy at the same conditions under which model parameters were estimated implying that for accurate modelling a large amount of experimental data obtained under various conditions is required. Such data will be gathered in the upcoming studies from 50 buildings in Latvia. The obtained data will then be used to model the aggregate heating demand at a national scale for assessment of the impact of smart electric thermal storage appliances on the overall power system.

Biogas power plant operation considering limited biofuel resources

The problem of operational planning of a biogas combined heat and power (CHP) plant under conditions of limited biofuel resources is analyzed in this paper. The power plant operation mode depends on the biomass amount and its delivery. Consideration of parameters with high dispersion, such as the ambient temperature, thermal energy consumption and energy price, makes the problem solution much more complicated and requires new algorithms and software tools. This paper proposes an algorithm of stochastic optimization to estimate the maximum value of income by taking into account the random nature of variables. When the biomass resource is limited, the need of its optimal distribution for all the time periods considered becomes of top importance. In this paper, a case study is conducted on a realistic biogas CHP plant that demonstrates the feasibility of our stochastic optimization algorithm. It is concluded that the proposed algorithm can increase the efficiency of the biogas power plant operations under conditions of limited resources. The main distinction of this paper is that a biogas CHP plant optimization model is considered from the point of view of income maximization under market conditions taking into account the limited biomass resources.

Impact of smart electric thermal storage on distribution grid

The paper presents a preliminary assessment on impact of smart electric storage heating on distribution grid in comparison to conventional electric heating if heating electrification is partly introduced. Real-world data of electric load, heating demand, electricity prices and ambient temperature was used for simulations to conclude that while a small penetration of smart storage heating can facilitate efficient usage of the existing distribution grid, larger penetrations can require grid reinforcements due to a significantly increased electric load. These effects could be partly mitigated using smart charging algorithms for storage heaters. From the economic point of view, smart storage heating provides cheaper energy for end-users and lower cost of distribution losses for the system operator.

Modelling heat demand in buildings with an experimental approach

Smart electric thermal storage heating devices can be used for demand response, congestion management and incorporated within unit commitment and dispatch of generation resources for more efficient control of power systems. This paper presents an experiment-based thermal modelling approach of residential buildings which will be scaled to a national aggregate level to be used in overall power system modelling involving smart electric thermal storages. A simplified thermal network based on electrical RC-circuit analogy was developed to replicate buildings thermal dynamics and model residential heat demand at national scale. To obtain the equivalent parameters for the RC model, physical experiments were conducted during which buildings were let to cool down and then heated for several times and indoor and outdoor temperature, heat consumption and solar radiation was recorded. The identified model exhibits a good performance which improves when solar gains are considered within it. Different control strategies of the heating equipment were examined and the hourly heat demand over the year was estimated.

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.