International Journal of Smart Electrical Engineering
Volume:6 Issue: 3, Summer 2017

In an electricity market, every generation company (GENCO) attempts to maximize profit according to other participants bidding behaviors and power systems operating conditions. The goal of this study is to examine the optimal bidding strategy problem for GENCOs in energy and spinning reserve markets based on a hybrid GA-heuristic optimization algorithm. The heuristic optimization algorithm used in this study is successfully applied for validation and, it is determined that the heuristic optimization algorithm improves profits of a GENCO by 4.15-47.95% and 20.84-31.30% in single-sided and double-sided auctions, respectively.

In this paper a transformerless Static Synchronous Series Compensator (SSSC) to increase the power capacity of a 230 kV transmission line is proposed. In order to eliminate the transformer, a 15-level cascade H Bridge (CHB) inverter is employed within the proposed compensator structure to inject reactive power to the transmission line. The theory of instantaneous p-q power using in the compensator is in such a way that allows for the system to be controlled in one phase. However, this method is used with three-phase systems. The proposed control method is also able to balance the voltage of dc-link capacitors at a desirable level. Modulation technique used by the inverter is Phase-Shifted PWM. In order to validate the proposed compensator’s application, a three-phase transmission line is tested by MATLAB software. The transmitted active power of this transmission line is equal to 160 MW. The results obtained from a dynamic simulation are presented. Simulation results indicated 39% increase in the transmitted active power of the line.

In recent years, incremental rate of electrical demands made many challenges for policy makers of power systems. Besides, due to economic crisis and environmental concerns about the air pollution, the new investments in thermal power generation units have experienced a noticeable reduction. For this reason, the power system operators should propose novel approaches to make a balance between incremental rate of electrical demand and decreased rate of generation expansion planning. In this paper, in order to hedge against the aforementioned problem, Demand Response Programs (DRPs) are proposed to control the electrical demands of heavy industries which consume a considerable part of electrical demands in peak durations. In this regard, two types of incentive-based demand response programs, including spinning reserve and curtailable load program, are addressed to decrease the need of new investments in power generation during peak hours. In order to show applicability and proficiency of the proposed approach, a case study containing 23 numbers of heavy industries of Khorasan Regional Electric Company (KREC) is addressed to participate in the proposed demand response programs in summer 2016.

In this paper, a comparison study is presented on artificial intelligence and time series models in 1-hour-ahead wind speed forecasting. Three types of typical neural networks, namely adaptive linear element, multilayer perceptrons, and radial basis function, and ARMA time series model are investigated. The wind speed data used are the hourly mean wind speed data collected at Binalood site in Iran. Simulation results indicate the ability of the proposed methods in 1-hour-ahead wind speed forecasting in Binalood of Iran.

As a matter of course, power market uncertainties escalation is by product of power industry restructure on one hand and the unrivalled penetration of renewable energies on the other. Generally, the decision making process in such an uncertain environment faces with different risks. In addition, the performance of real power markets is very close to oligopoly markets, in which, some market players exercise market power to influence the power market and this matter brings some risks to other players. Hence, each market player must consider these market features to choose his best decision. So, in case of such an uncertain environment, GENCO's bidding strategy would be a complicated and error-prone process. This paper aims to ease this issue suggesting the use of probabilistic bidding strategy of generation units by the unscented transformation (UT) method. The proposed method can consider the correlation between variables, where it models the coalition between market participants. Using the proposed methodology, a market participant can choose a desired range of profit; then, set his decision to manage his profit by reducing his risks. Finally, the proposed methodology is examined through some case studies done in a standard test system. Simulation results show that executing market power by some market players disturbs the competition in the market.

In this paper, a new structure for reactive power market is presented. Considering reactive power losses caused by active power flow in the reactive power market is the main purpose of this paper. Hence, this study tries to improve reactive power market and create fair competition in reactive power generation.