Miloš Pantoš

Modified topological generation and load distribution factors

In this paper, a modification of the topological generation load distribution factor method of power flow tracing is reported. The modification is needed since the existing method introduces additional fictitious nodes on system lines in order to take into account the transmission losses. Due to the expansion of a system, the existing algorithm based on the augmented matrix equation requires more mathematical effort and memory and longer calculation time to obtain a solution. The new approach successfully avoids the matrix expansion by introducing matrix decoupling, which is its main improvement. The second novelty is an introduction of an equivalent model of a line that unites the nodal reactive power with the power produced by shunt admittances. Thus, the modified method can be also applied to the reactive power flows and transmission-loss allocation.

Exploitation of Electric-Drive Vehicles in Electricity Markets

The paper presents the optimization algorithm which may eventually be used by electric energy suppliers to coordinate charging and discharging of electric-drive vehicles (EDVs) exploited in electricity markets. The research is focused on a day-ahead market and a provision of system regulation in an ancillary-service market. The proposed optimization minimizes the charging costs that can be partly compensated with profits obtained from participation in the energy markets. Due to the stochastic nature of transportation patterns, the Monte Carlo simulation is applied to model uncertainties presented by numerous scenarios. To reduce the problem complexity, the simulated driving patterns are not individually considered in the optimization but clustered into fleets using the GAMS/SCENRED tool. Uncertainties of energy requirements in the market and energy prices are presented by statistical central moments that are further considered in Hongs 2 - point +1 estimation method in order to define points considered in the optimization. Finally, each energy supplier has to offer competitive energy prices to EDV users for transportation. Due to uncertainties, the final prices cannot be deterministically calculated; thus, the paper proposes the risk-based approach applying value at risk. Case studies illustrate the application of the proposed optimization in achieving competitive prices for EDV users.

A Novel Strategy for Variable-Speed Wind Turbines' Participation in Primary Frequency Control

This paper proposes a novel strategy for participation of variable speed wind turbines in primary frequency control. The proposed strategy is based on the deployment of the kinetic energy of the rotating masses to reduce the need for deloaded operation while still being able to provide the required power reserve for the defined time frame. A steady-state deloaded operating point is optimized with respect to the amount of kinetic energy stored in the rotating masses. The parameters of the algorithm are calculated offline and stored in lookup tables. The proposed strategy is tested using a modified Nordic 32-bus test system for various operating scenarios. The results show that part of the primary frequency control power reserve of the thermal units can be replaced by the reserve provided by wind power, while retaining the desired frequency response following a disturbance. A comparison with the conventional deloaded strategy shows that between 1.16% and 2.79% energy savings can be achieved on a yearly basis, depending on the wind regime of the sites analyzed.

Ex-ante transmission-service pricing based on load-flow patterns

In this paper, a new pattern-based transmission-service pricing technique is reported. It is based on power-flow tracing techniques that determine the power contributions of participants in line power flows throughout the whole network. Since producers supply a consumer through the transmission paths considering Kirchoffs Laws, it is obvious that the actual paths are not strongly correlated with the contractual paths. This means that instead of financial paths, only the actual power-flow paths should be considered when calculating the transmission-service price. Power-flow data from previous years can be utilized for this purpose. Several power systems and different operating states that realistically represent actual system operating conditions were statistically analyzed. The analysis showed a constant pattern of power-flow shares in lines to consumers, which can be utilized in ex-ante transmission pricing for a network. Since the new approach reflects the characteristics of transaction- and nontransaction-based methods, it combines their benefits too.

Short-Term Transmission-Loss Forecast for the Slovenian Transmission Power System Based on a Fuzzy-Logic Decision Approach

In a deregulated environment, system operators are required to procure certain ancillary services, which, among others, may include compensation for active-power losses. This compensation usually involves long-term energy purchases and additional short-term energy purchases to cover the daily fluctuations. The short-term energy purchases require an accurate and quick short-term forecasting method that has to be efficiently applicable in day-ahead markets. This paper presents a novel short-term active-power-loss forecast method using power-flow analysis for the forecasted day. Specifically, this includes short-term load and generation forecasts as well as network-topology forecasts, which are used for the power-flow calculations and the resulting active-power loss calculations. To minimize the forecast errors, a fuzzy-weight grouping of the different short-term load and generation forecast results is proposed. An additional step for input-data pre-processing is presented, where the fuzzy clustering considers the patterns for training the forecasting models. The proposed approach was verified by using real data for the ENTSO-E interconnection and tested for the Slovenian power system. The forecasting results demonstrate the improved accuracy of the proposed approach.

Unified Approach to Reliability Assessment in an Isolated RES System

Due to their unique characteristics, renewable energy sources (RES) are an ideal energy source for small isolated power systems (SIPS). At the same time, the reliability impacts of RES electricity supply should be carefully studied, especially in SIPS with significant RES installed capacity. One of the most potent methods for this purpose is system well-being analysis, accounting for deterministic and stochastic reliability factors. In the paper, we present a novel method for calculation of system well-being indices based on contingency enumeration (CE) method. We also investigate the energy-oriented energy not served (ENS) index and propose an improved method for its calculation. The indices are then used to analyze the impacts of different hybrid generation structures and RES penetration levels on supply reliability of the system. We investigate the suitability of different reliability assessment schemes and pertaining indices for reliability of hybrid energy sources in SIPS. The results show-that due to the ability of the ENS index to measure the severity of the risky state, both types of indices are needed to satisfactorily observe the supply reliability, especially in hybrid-supplied SIPS

Participation of DFIG wind turbines in frequency control ancillary service by optimized rotational kinetic energy

The increase in renewable and distributed energy resources has given rise to new concerns about frequency stability of electric power systems, as renewable generation is considered an intermittent source, which by its nature does not participate in frequency control. In some countries, system operators demand wind turbines to provide some sort of ancillary services, such as reactive power control and frequency/active power control in order to increase system stability. This paper focuses on the provision of primary frequency control by wind generators and proposes a novel control strategy for active power reserve provision of doubly fed induction generator wind turbines. The proposed control strategy aims at maximizing the rotational kinetic energy, using the differential evolution. The control variables in the optimization process are the rotor speed and the pitch angle, considering the given power reserve factor. The proposed control strategy is compared to the strategies proposed by other authors. The results show that, compared to the existing methods at the same de-loaded power, the proposed method yields more rotational kinetic energy, that is released into the system in case of frequency deviations, thus increasing frequency stability of a power system.

Optimal Sizing of Biomass-Fired Organic Rankine Cycle CHP System with Heat Storage

A novel methodology for Optimal Sizing of Biomass-Fired Organic Rankine Cycle (ORC) Combined Heat and Power (CHP) System with Heat Storage is presented in this paper. The ORC technology received a lot of attention recently, especially for medium scale applications (e.g. district heating) where market potential is substantial. Another push for the technology is provided in the context of recent EU regulations to tackle the rising environmental problems, i.e. the 20-20-20 directive, which should provide another boost for the use of renewables in electric power systems. In this study, the simulation of the operation of a biomass fired ORC CHP system is discussed first, where the dispatch of the plant is optimized to maximize the profits from electricity and heat sales. Based on the optimal dispatch values, an economic evaluation is carried out to determine the feasibility of the proposed scheme for investment purposes; this evaluation is based on calculations of modified internal rates of return for a realistic scenario. Lastly, an optimization procedure for optimal sizing of the generation architecture with heat storage is convolved with the optimal operation and economic evaluation models, to achieve maximal rates of return on the ORC CHP investment. The results of the present studies demonstrate that the heat storage is not economically feasible in the assumed setup making profits from increasing the flexibility and thus utilization of the ORC CHP operation. Furthermore, the results show favorable economic parameters for the ORC CHP setup without the heat storage for annual heat demands higher than 5 GWh and biomass prices lower than 17 EUR/MWh.

Probabilistic short-term transmission-loss forecasting based on a two-point estimate method

In a competitive electricity market, system operators are required to procure certain ancillary services, which may among others include compensation of active-power losses. The compensation is most often done as a yearly, monthly and daily purchase of energy. The yearly and monthly bulk purchases do not take daily variations into account and daily purchases are required to cover the discrepancies between them. This requires an accurate and fast short-term forecasting method that has to be efficiently applicable in day-ahead markets. This paper presents a novel probabilistic short-term transmission-loss forecast method. Specifically, the method includes deterministic short-term load, generation and power transit forecasts as well as network configuration forecasts, which can be used for deterministic power-flow calculations to forecast transmission losses. However, the uncertainty of system loading conditions and inherent nonlinearities in power systems may cause inaccurate transmission-loss forecasts. By using deterministic forecasts, no additional information as to the possible forecast deviations can be given, as transmission losses do not show a clear correlation with these uncertainties. To account for the uncertainties, probabilistic power flow approach is proposed to define the probability distribution of the forecasted losses, which may help system operators to decide on the most efficient strategy on the day-ahead market. Hongs point-estimate method is used to solve the probabilistic power flow problem. The proposed approach has been verified by using real data for the ENTSO-E interconnection and tested for the Slovenian power system. The forecasting results demonstrate the usefulness of the proposed approach.

A remark on forecasting spikes in electricity prices

This paper presents a hybrid model for electricity price forecasting with focus on price spikes predictions. Nowadays, short-term forecasts have become increasingly important since the rise of the competitive spot electricity markets. A two-layered model is introduced for forecasting 7-days ahead hourly electricity price values of electricity spot market. Due to the importance of improved analysis of spikes for risk management, price segmentation into normal range and price spike module is applied. Price spike module consists of two segments: obtaining the probability of price spike occurrence and predicting the value of price spike. To avoid reliance on a single classifier, the compound classifier is proposed in the paper, which combines three individual classification methods: a support vector machine (SVM) classification, decision trees (DT) and probabilistic artificial neural network (PANN). The k-nearest neighbors algorithm (k-NN) is applied for the price spike value prediction.