A new optimal operation framework for balanced microgrids considering reconfiguration and generation scheduling simultaneously

Sedighizadeh, Mostafa; Shaghaghi‐shahr, Gholamreza; Aghamohammadi, Mohammad Reza; Esmaili, Masoud

doi:10.1002/2050-7038.12302

Cited by 11 publications

(8 citation statements)

References 58 publications

(112 reference statements)

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“…Recent attempts have accounted for the operating costs [30], including the distributed energy resource (DER) shutdown/start-up costs, as well as the uncertainty costs [31]. Additionally, researchers enhanced the reliability and power quality of the MG [32,33].…”

Section: Microgrid Reconfigurationmentioning

confidence: 99%

A Review of Energy Management and Power Management Systems for Microgrid and Nanogrid Applications

2021

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In the past few years, the application and research community has expressed a lot of interest in managing energy and power while using distributed generation systems. Electricity generation and its usage coordination are vital aspects of energy efficiency that can help in saving energy, decreasing energy costs, and fulfilling global emission objectives. Owing to the relevance of the topic, here, the researchers have presented a comparative and critical review of recent developments in the fields of energy management systems (EMSs) and power management systems (PMSs). Furthermore, the researchers also reviewed the various EMS and PMS methods that could be used for reviewing microgrid (MG) and nanogrid (NG) systems. The EMS for MG and NG systems helps in addressing important economic objectives like minimisation of operational costs after optimising the fuel costs, emission costs, and battery degradation costs, while also improving the life of the MG devices. Alternatively, the PMS helps in addressing technical objectives like improving the stability, flexibility, reliability, and quality of MG and NG systems. The researchers have also discussed the drawbacks and challenges affecting the widespread application of EMSs and PMSs.

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Section: Microgrid Reconfigurationmentioning

confidence: 99%

A Review of Energy Management and Power Management Systems for Microgrid and Nanogrid Applications

2021

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“…In that technique, the costs that has to do with the installation of DGs in a distribution system are made up of operating cost, running and investment costs. The method of Optimized Genetic Algorithm has been widely adopted to reduce transmission losses as it is simple and user friendly [6]. Local particle swarm optimization (PSO) is another useful technique but lacks the numerical accuracy since it is a population-based stochastic optimization technique [7].…”

Section: Extent Of Past Related Workmentioning

confidence: 99%

Improving Loss Minimization in 33kv Power Distribution Network Using Optimized Genetic Algorithm

Ngang¹,

Kazeem²,

Ikechukwu³

et al. 2021

IJSE

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The epileptic power supply from the national grid due to instability is a concern to energy consumer. This instability in power supply experienced in power distribution network could be minimized by introducing Optimized Genetic Algorithm (OGA). It is achieved by characterizing 33KV distribution network, running the load flow of the characterized 33KV distribution network, determining the distribution losses from the load flow. Minimizing the determined losses in 33kv distribution network using (OGA), and designing SIMULINK model for improving loss minimization in 33kv power distribution network using OGA. Finally, validating and justifying the percentage of loss reduction in improving loss minimization in 33kv power distribution network without and with OGA. The results obtained are conventional percentage power loss in 33KV distribution network, 75%, while that when OGA is incorporated in the system is 72.9%. With these results obtained, the percentage improvement in loss reduction in 33KV distribution network when OGA is used is 2.1%. The conventional percentage of power loss in 33KV distribution network is 80%. The percentage power loss in the distribution network now is 72.9%; hence, power loss reduction in distribution network. Unmitigated power loss was 76.7% when OGA is introduced we had 74.63%. The percentage power loss in distribution network in bus 8 is 81.7% while that when OGA is applied is 79.49%. The percentage power loss in bus 9 of 33KV distribution network is 86.7%. Finally, when optimized genetic algorithm is incorporated in the system the percentage power loss in the network was reduced to 84.36%.

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“…The objective of the proposed method was to reduce resistive line losses under normal operating conditions. Sedighizadeh et al [12] modelled three demand response (DR) programs for a NR problem. To improve tractability, the model was formulated as a mixed-integer second-order cone programing (MISOCP) problem in order to minimize operational cost subject to technical and financial constraints.…”

Section: Introductionmentioning

confidence: 99%

“…The reviewed papers in [6,7,8,9,10,11,12,13], show that PDSs are greatly affected by power loss and poor VP [14]. A number of studies have used different approaches to reduce power loss in PDSs such as distributed generation placement [15], optimal capacitor placement [16], and electric distribution network reconfiguration (DNR) [17].…”

Section: Introductionmentioning

confidence: 99%

“…A number of studies have used different approaches to reduce power loss in PDSs such as distributed generation placement [15], optimal capacitor placement [16], and electric distribution network reconfiguration (DNR) [17]. Out of these different approaches, the DNR approach has shown remarkable results for power loss reduction and VP improvement [5,6,7,8,9,10,11,12,13]. DNR by placing DGs and capacitor units optimally is very effective in reducing APLs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal network reconfiguration for power loss minimization and voltage profile enhancement in distribution systems

Salau

Gebru

Bitew

2020

Heliyon

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This paper presents an optimal method for optimizing network reconfiguration (NR) problems in a power distribution system (PDS) for the purpose of power loss reduction and voltage profile (VP) improvement. Furthermore, a modified algorithm was presented to address this problem in order to provide a more efficient PDS. Various works which used NR to improve VP and reduce power loss were discussed and summarized in detail. In particular, a modified Selective particle swarm optimization (SPSO) method was used for NR in existing networks considering different loading conditions. The main objective of this study is to minimize real power losses and enhance VP of a distribution system using the proposed SPSO method. The SPSO method was programmed in MATLAB R2016b software and tested using IEEE 33-bus radial distribution system (RDS). The obtained test results show that the real power was enhanced by 99.341%, 97.289%, and 95.389% for the light, normal, and heavy load conditions, respectively. Also, the minimum voltage level in the worst case was significantly enhanced from 0.8841 p.u. to 0.9510 p.u. Towards the end, a comparative analysis of the proposed SPSO with existing methods for distribution network reconfiguration (DNR) is presented. The comparative results show that the proposed SPSO was found to be more efficient in reducing voltage deviation (VD) and power losses in the system.

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A new optimal operation framework for balanced microgrids considering reconfiguration and generation scheduling simultaneously

Cited by 11 publications

References 58 publications

A Review of Energy Management and Power Management Systems for Microgrid and Nanogrid Applications

A Review of Energy Management and Power Management Systems for Microgrid and Nanogrid Applications

Improving Loss Minimization in 33kv Power Distribution Network Using Optimized Genetic Algorithm

Optimal network reconfiguration for power loss minimization and voltage profile enhancement in distribution systems

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