2020
DOI: 10.1109/access.2020.3025673
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Optimization of Day-Ahead Energy Storage System Scheduling in Microgrid Using Genetic Algorithm and Particle Swarm Optimization

Abstract: We present a day-ahead scheduling strategy for an Energy Storage System (ESS) in a microgrid using two algorithms-Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The scheduling strategy aims to minimize the cost paid by consumers in a microgrid subject to dynamic pricing. We define an objective function for the optimization problem, present its search space, and study its structural properties. We prove that the search space has a magnification of at least 50 × (B c − B d + 1), where B c and B d … Show more

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Cited by 56 publications
(19 citation statements)
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“…In order to test the performance of the proposed optimal operation method for the microgrid, the proposed algorithm is compared with the genetic algorithm (GA) [27] and the interior point method [28]. The DDPG algorithm is implemented in Python using the TensorFlow framework.…”
Section: Performance Evaluationmentioning
confidence: 99%
“…In order to test the performance of the proposed optimal operation method for the microgrid, the proposed algorithm is compared with the genetic algorithm (GA) [27] and the interior point method [28]. The DDPG algorithm is implemented in Python using the TensorFlow framework.…”
Section: Performance Evaluationmentioning
confidence: 99%
“…is nonlinear constraint condition will be transformed by the secondorder cone relaxation method in Section 3. Equations ( 7)-( 11) are the common constraint equation of general power flow; equation (12) is the unique constraint condition of each microgrid system, which is determined by its own power load, micro source, and other internal structures of the microgrid.…”
Section: Power Flow Constraints Of Microgridmentioning
confidence: 99%
“…In order to meet the needs of new energy sources, an approximate dynamic programming algorithm is proposed to better meet the daily scheduling demand of the independent microgrid with the new power generation as the main power source [11]. For specific micro networks, particle swarm optimization and artificial swarm optimization are introduced to solve the problem of optimal scheduling of micro networks [12][13][14]. Nowadays, stochastic optimization technology [15] and bilevel optimization technology [16,17] are more and more common in the selection of optimization algorithm because of their practicability.…”
Section: Introductionmentioning
confidence: 99%
“…The primary issue of setting up a micro-grid (MG) is to determine the sizes of distributed generations (DGs) in a MG. A reliable stand-alone MG makes the most of RE resources (wind, PV, hydro [1], biomass [2], geothermal, ocean wave) which show the advantages such as power supply reliability, less of GHG emission and system cost [3][4][5]. Nonetheless, these advantages become kinds of contradictory force when to seek the multi-objective optimal configuration of DGs in a MG. Based on the contradictions, many latest research considered different uncertainties such as variations in solar irradiance, wind speed and load demand to survey the optimal allocations of DGs in an AC-MG, DC-MG or hybrid MG.…”
Section: Introductionmentioning
confidence: 99%