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

Abstract

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%.

Optimal sizing and placement of capacitors in the isolated microgrid throughout the day considering the demand response program

“…Yasin Ghadi et al (2023) presented a hybrid GA-SFLA algorithm for reconfiguring and placement of energy storage systems, electric vehicles, and distributed generation (DG) in a distribution network. Dashtdar et al (2022a) formulated and solved the problem of the optimal operation of MGs with demand-side management using the combination of the genetic algorithm and artificial bee colony optimization techniques. Dashtdar et al (2020) applied the genetic algorithm to calculate the locational marginal price (LMP) and optimal power flow problem based on congestion management.…”

Reconfiguration and displacement of DG and EVs in distribution networks using a hybrid GA–SFLA multi-objective optimization algorithm

“…DG sources are modeled as negative load, and its effect on power flow is considered as voltage constraints. So far, meta-heuristic algorithms with various efficiency and accuracy and with different objective functions have been presented for the reconfiguration and placement problem of DG such as GA (Saonerkar and Bagde, 2014;Ajmal et al, 2021;Mahdavi et al, 2021), PSO (Jena and Chauhan, 2016;Saleh et al, 2018;Rafi and Dhal, 2020b), SFLA (Arandian et al, 2014;Azizivahed et al, 2017;Onlam et al, 2019), fuzzy (Sedighizadeh and Bakhtiary, 2016;Mohammadi et al, 2017;Hosseinimoghadam et al, 2020), and ABC (Jamian et al, 2014;Quadri and Bhowmick, 2020;Dashtdar et al, 2022c).…”

Optimal configuration of grid-side energy storage considering static security of power system

“…The basic idea of traditional security assessment methods is to establish a set of expected accidents to check system security (Kundur et al, 1994), which is difficult to adapt to the security analysis of power grids after a high proportion of renewable energy is connected, so static security analysis considering uncertainty has received extensive attention from scholars in practical research and application. The static security assessment methods considering uncertainty can be divided into Monte Carlo simulation method (Song et al, 2003;Hajian et al, 2013;Zhang et al, 2015;Dashtdar et al, 2022) and analytical method (Su, 2010;Bu et al, 2012;Amraee and Ranjbar, 2013;Zhang et al, 2020). The Monte Carlo simulation method is used to obtain statistical characteristics of parameters by observing the model or process through sampling, and its computational effort is not affected by the size of the power system, which in turn is used in some complex power systems (Hajian et al, 2013).…”

“…The Monte Carlo simulation method is used to obtain statistical characteristics of parameters by observing the model or process through sampling, and its computational effort is not affected by the size of the power system, which in turn is used in some complex power systems (Hajian et al, 2013). In Dashtdar et al (2022), a combination of genetic algorithm and artificial bee colony algorithm is adopted to solve the optimization problem of power grid under demand-side management. The literature (Zhang et al, 2015) used a Monte Carlo method to extract the fault states of the system and establish static security assessment metrics to analyze the static security of the power system.…”