Dimishkovska Krsteski, Natasha
Preferred name
Dimishkovska Krsteski, Natasha
Official Name
Dimishkovska Krsteski, Natasha
Alternative Name
Dimishkovska, Natasha
Main Affiliation
Email
dimishkovskan@feit.ukim.edu.mk
ORCID
10 results
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Item type:Publication, Оптимално управување со токови на енергија во микромрежи приклучени на дистрибутивната мрежа(2022-09); ; Zenku, ImerThe increased penetration of renewable energy sources, as well as the higher standards for quality electricity, actualize the implementation of microgrids into the standard power system. Microgrids provide consumers with higher reliability of power supply, and characterize with lower costs and lower power losses. Microgrids consist of many different distributed generators, more interconnected consumers, batteries and backup generators, and therefore optimization is mandatory for proper and balanced operation of the microgrid. The paper analyses the problem of optimal energy management in a hybrid system (microgrid), which consists of a photovoltaic and wind generators, connected to the distribution network. Also, the microgrid includes industrial prosumers. An upgraded genetic algorithm is applied in the optimization, which takes into account the technical limitations of the installed equipment and the voltage levels in the nodes. - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, Mean‐Guided Elite Selection Genetic Algorithm for Multi‐Objective Optimization of Operational Costs and Voltage Control in Grid‐Connected Microgrids(Institution of Engineering and Technology (IET), 2025-12-29); <jats:title>ABSTRACT</jats:title> <jats:p>This paper presents a bi‐objective optimisation approach for grid‐connected microgrids, aiming to minimise operational costs and voltage deviation at the connection nodes of distributed energy resources and loads. Existing research typically addresses these objectives separately, and the simultaneous consideration of economic performance and voltage deviation in grid‐connected community microgrids with multiple generation resources remains in an early stage of development. To advance the research in this area, a novel mean‐guided elite selection genetic algorithm (MGES‐GA) is proposed to enhance the balance between convergence and diversity in multi‐objective optimisation. The proposed algorithm enhances the selection process by re‐evaluating low‐performing individuals through gene mixing with elite solutions, thereby preserving diversity and avoiding premature convergence. Comparative analysis of the MGES‐GA with the enhanced genetic algorithm, differential evolution with heuristic, and improved differential evolutionary optimisation algorithms demonstrates its superior performance in optimising the economic dispatch of a grid‐connected microgrid. In a bi‐objective comparison with state‐of‐the‐art algorithms, tested on a modified IEEE European low‐voltage test feeder and IEEE 33‐bus network, MGES‐GA demonstrates its effectiveness in balancing conflicting objectives by producing lower voltage deviations at comparable or lower costs.</jats:p> - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Оптимизација на микромрежи поврзани на дистрибутивна мрежа со примена на подобрен генетски алгоритам(2024-10); Енергетската криза со која се соочува светот денес, како и потребата од чиста и одржлива енергија, ги ставаат микромрежите во центарот на вниманието на истражувачите. Нивната работа има позитивно влијание на работата на електроенергетските системи, но поради стохастичната природа на дисперзираните генератори од обновливите извори кои се поврзани на нив, работата на микромрежите може да има негативно влијание на електроенергетскиот систем доколку не е правилно управувана. Оттука, произлегува потребата за оптимизација на микромрежите. Во овој труд ќе биде разгледан проблемот со оптимално управување со токови на енергија во микромрежа која е поврзана на дистрибутивната мрежа. Методот на оптимизација во овој труд, предлага примена на генетски алгоритам со подобрување во делот на евалуација на потенцијалните решенија и селекција на индивидуи кои ќе генерираат нова генерација. Добиените резултати се споредени со веќе докажани методи за селекција и со тоа се оправдува предложеното подобрување. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Modified genetic algorithm for unit commitment of grid connected microgrids under real time pricing conditions(International Journal on Information Technologies and Security, 2024-09-01); <jats:p>This paper introduces a modification of the genetic algorithm aimed at enhancing the selection process for reproducing the next generation. This modification accelerates the optimization process and improves the outcome. The case study analyzes a grid-connected microgrid comprising renewable energy sources, a battery storage system, prosumers with installed photovoltaic generators, and consumers. The effectiveness of the proposed modification is validated through comparison with two selection algorithms commonly used in the standard genetic algorithms.</jats:p> - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Distributed generation placement in distributive substations analysis using Markov Chain Monte Carlo model considering the reliability of power supply(IOP Publishing, 2020-06-01); <jats:title>Abstract</jats:title> <jats:p>Proper operation of the power substations is of great importance for power network reliability, stability and uninterrupted power supply. Distributed generation provides higher reliability in power supply, but still, there are contingencies in the electric power production and supply process, which lead to outages in the power supply. In this paper, a method for substations’ reliability estimation with distributed generation is presented based on Markov Chain Monte Carlo method. The method considers the possible substation operation states and using random number generator in MATLAB, it simulates faults and calculates the substations’ reliability. The method is demonstrated on two cases of 110/35 kV substations, each consisting of two transformers and distributed generator, analysing the best placement for the distributed generation.</jats:p> - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, Optimization of Grid-Connected Microgrids with Residential Prosumers Using an Improved Genetic Algorithm(2024); With the continuous increase of the microgrids’ implementation into the power system the problem of maintaining their stability and balance arises and the necessity to adopt an appropriate energy management system emerges. This paper analyses the optimization of the grid-connected microgrid, which consists of a photovoltaic generator, a wind generator, a battery, and residential prosumers. The paper presents the application of an improved genetic algorithm, which takes into consideration the voltage levels on the connection points of the generators and the prosumers, as well as the trading with the local grid. The proposed algorithm suggests the usage of the standard genetic algorithm with the improvement in the fitness and selection process. The results of the simulation are compared with the results obtained when using a standard genetic algorithm with five different types of selection.