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Optimal Operation of Microgrids with Demand-Side Management Based on a Combination of Genetic Algorithm and Artificial Bee Colony

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10250020" target="_blank" >RIV/61989100:27240/22:10250020 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.mdpi.com/2071-1050/14/11/6759" target="_blank" >https://www.mdpi.com/2071-1050/14/11/6759</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3390/su14116759" target="_blank" >10.3390/su14116759</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Optimal Operation of Microgrids with Demand-Side Management Based on a Combination of Genetic Algorithm and Artificial Bee Colony

  • Original language description

    An important issue in power systems is the optimal operation of microgrids with demand-side management. The implementation of demand-side management programs, on the one hand, reduces the cost of operating the power system, and on the other hand, the implementation of such programs requires financial incentive policies. In this paper, the problem of the optimal operation of microgrids along with demand-side management (DSM) is formulated as an optimization problem. Load shifting is considered an effective solution in demand-side management. The objective function of this problem is to minimize the total operating costs of the power system and the cost of load shifting, and the constraints of the problem include operating constraints and executive restrictions for load shifting. Due to the dimensions of the problem, the simultaneous combination of a genetic algorithm and an ABC is used in such a way that by solving the OPF problem with an ABC algorithm and applying it to the structure of the genetic algorithm, the main problem will be solved. Finally, the proposed method is evaluated under the influence of various factors, including the types of production units, the types of loads, the unit uncertainty, sharing with the grid, and electricity prices all based on different scenarios. To confirm the proposed method, the results were compared with different algorithms on the IEEE 33-bus network, which was able to reduce costs by 57.01%.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    20201 - Electrical and electronic engineering

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Sustainability

  • ISSN

    2071-1050

  • e-ISSN

  • Volume of the periodical

    14

  • Issue of the periodical within the volume

    11

  • Country of publishing house

    CH - SWITZERLAND

  • Number of pages

    26

  • Pages from-to

    nestrankovano

  • UT code for WoS article

    000808841500001

  • EID of the result in the Scopus database

    2-s2.0-85132254644