In this paper, a new optimization algorithm known as the Multiverse Optimization Algorithm (MOA) is developed for optimal economic operation of the micrigrid (MG) in the grid-connected mode. Results show the merit of the proposed technique

Bayat, Alireza

Mahdavi, Sadegh

Mirzaei, Farzad

Munich RePEc Personal Archive

Munich Personal RePEc ArchiveEconomic Operation of Grid-ConnectedMicrogrid By Multiverse OptimizationAlgorithmMahdavi, Sadegh and Bayat, Alireza and Mirzaei, FarzadElectrical and Computer Engineering Department, Azad Universityof Shiraz, Shiraz, Iran, Electrical and Computer EngineeringDepartment, Azad University of Shiraz, Shiraz, Iran, Electrical andComputer Engineering Department, Azad University of Shiraz,Shiraz, Iran2019Online at https://mpra.ub.uni-muenchen.de/95893/MPRA Paper No. 95893, posted 08 Sep 2019 10:05 UTCEconomic Operation of Grid-Connected Microgrid By Multiverse Optimization Algorithm  Sadegh Mahdavi, Alireza Bayat, Farzad Mirzaei Electrical and Computer Engineering Department, Azad University of Shiraz, Shiraz, Iran  Abstract: In this paper, a new optimization algorithm known as the Multiverse Optimization Algorithm (MOA) is developed for optimal economic operation of the micrigrid (MG) in the grid-connected mode. Results shows the merit of the proposed technique.  I. INTRODUCTION  Microgrid (MG) is the summation of the loads and distributed energy resources (DERs) in a small electricity grids [1-5]. MG has some benefits such as higher reliability, resiliency, and voltage problem, as well as the less operation cost. This is due to closeness to the consumers, that can lead to less transmission lines. [6-7]. The energy management of the MG is a mixed-integer problem, which is hard to solve. This problem is investigated in some researches so far. However, develop a powerful solution algorithm is vital in the energy management of the MG [8-11]. This paper proposes a new evolutionary algorithm known as the multiverse optimization algorithm to overcome the complexity of the energy management of the grid-connected MG operation. The proposed technique is more efficient than the well-known techniques, e.g., particle swarm optimization (PSO), teaching learning based optimization (TLBO), and genetic algorithm [12-14]. II. MICROGRID GRID-CONNECTED FORMULATIONS A. Objective function The main objective is to minimize the total operation cost as   𝑚𝑖𝑛∑ [𝐶𝑖𝑃𝑖𝑡𝐼𝑖𝑡 + 𝑆𝑈𝑖𝑡 + 𝑆𝐷𝑖𝑡]∀𝑖                                                                                                                          (1) In which:  I is a binary variable (0 or 1) that controls the status of unit i at time t.  SU and SD are the startup and shutdown costs.   In this paper, the following nomenclature is used:  𝑈𝑇 and 𝐷𝑇 are minimum up and down  𝑇(𝑜𝑛) and 𝑇(𝑜𝑓𝑓) are the number of successive on and off hours  RU and RD are the ramp up and down of the generators.                                     B. Constraints There exist some constraints associated with the MG grid-connected energy management as follows.  1. Generation unit’s capacities 𝑃𝑖𝑡,𝑚𝑖𝑛 ≤ 𝑃𝑖𝑡 ≤ 𝑃𝑖𝑡,𝑚𝑎𝑥                                                                                                                        (2) 2. Generation units ramp up and down limitations 𝑃𝑖𝑡 − 𝑃𝑖(𝑡−1) ≤ 𝑅𝑈𝑖                                                                                                                           (3) 𝑃𝑖(𝑡−1) − 𝑃𝑖𝑡 ≤ 𝑅𝐷𝑖                                                                                                                            (4) 3. Generation units minimum up and down time 𝑇(𝑜𝑛)𝑖𝑡 ≥ 𝑈𝑇𝑖(𝐼𝑖𝑡 − 𝐼𝑖(𝑡−1)                                                                                                                 (5) 𝑇(𝑜𝑓𝑓)𝑖𝑡 ≥ 𝐷𝑇𝑖(𝐼𝑖(𝑡−1) − 𝐼𝑖𝑡                                                                                                                (6) III. MULTIVERSE ALGORITHM This paper utilized a new evolutionary algorithm known as the multiverse evolutionary algorithm to overcome the complexity of the problem due to the integer variable of the generation units. This method is taken form [12]. In comparison with the well-known methods, e.g., particle swarm optimization (PSO) and genetic algorithm (GA), the proposed technique has higher accuracy and speed.  It should be noted that these techniques have been used in many fields [16-19]. IV. SIMULATION RESULTS To demonstrate the economic merit of the proposed technique, the modified IEEE 69 bus test system is selected and tested. Fig. 1 depicts the single line diagram of the model   Fig. 1. IEEE 69 bus test system diagram   Also, table I presents the DGs features within the grid, followed by the day-ahead load demand in Fig. 2.    DG1DG2DG3DG4   Table I DGs features   Minimum output power Maximum output power DG1 20 400 DG2 40 450 DG3 10                    250 DG4 10 250      Fig. 2. Microgrid load demand    The optimal output powers of the DGs are depicted in Fig. 3. According to the results the cheapest units are more committed than others. This can prove the economic aspects of the proposed method. Also, the main grid sell energy to the MG in the hours that the market price is lower than the DGs price.    Fig. 3. Produced powers by DGs    To demonstrates the economic merit, as well as the convergence speed of the algorithm, it has been compared with the PSO and GA. These are reported in Table II. Based on this table, the convergence speed and operation cost of the grid-connected MG under the proposed technique is very lower than others.   Table II Cost of operation for several methods  cost ($) Convergence (s) PSO 637335 11.1 GA 623437 9.9 Proposed method 598834 7.6   V. CONCLUSION In this paper a new optimization algorithm known as the multiverse optimization algorithm is developed for grid-connected MG energy management. This algorithm is compared with the well-known algorithms such as PSO and GA. Results show the economic merit and also high convergence speed of the proposed technique, as two main factor in utilities economic operations.   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Economic Operation of Grid-Connected Microgrid By Multiverse Optimization Algorithm

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Economic Operation of Grid-Connected Microgrid By Multiverse Optimization Algorithm

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