Simultaneous Minimization of Energy Losses and Greenhouse Gas Emissions in AC Distribution Networks Using BESS

Molina-Martin, Federico; Montoya, Oscar Danilo; Grisales-Noreña, Luis Fernando; Hernández, Jesús C.; Ramírez-Vanegas, Carlos A.

doi:10.3390/electronics10091002

Cited by 23 publications

(10 citation statements)

References 57 publications

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“…In the case of the 69-bus test system, all the processing times required by the solution methods increased: MVO (132s), ALO (9.67s), BH (13.91s), CGA (17.38s), and PSO (13.67s). Again, the MVO exhibited the longest processing time in the 69-bus test system, with an average value close to two minutes [33,34]. Nevertheless, said time is also adequate for solving the problem of optimal power flow in the 69-bus test system because of the same reasons explained regarding its 21-bus counterpart.…”

Section: Processing Time Analysismentioning

confidence: 85%

Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks

et al. 2021

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This paper addresses the optimal power flow problem in direct current (DC) networks employing a master–slave solution methodology that combines an optimization algorithm based on the multiverse theory (master stage) and the numerical method of successive approximation (slave stage). The master stage proposes power levels to be injected by each distributed generator in the DC network, and the slave stage evaluates the impact of each power configuration (proposed by the master stage) on the objective function and the set of constraints that compose the problem. In this study, the objective function is the reduction of electrical power losses associated with energy transmission. In addition, the constraints are the global power balance, nodal voltage limits, current limits, and a maximum level of penetration of distributed generators. In order to validate the robustness and repeatability of the solution, this study used four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: ant lion optimization, particle swarm optimization, continuous genetic algorithm, and black hole optimization algorithm. All of them employed the method based on successive approximation to solve the load flow problem (slave stage). The 21- and 69-node test systems were used for this purpose, enabling the distributed generators to inject 20%, 40%, and 60% of the power provided by the slack node in a scenario without distributed generation. The results revealed that the multiverse optimizer offers the best solution quality and repeatability in networks of different sizes with several penetration levels of distributed power generation.

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Section: Processing Time Analysismentioning

confidence: 85%

Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks

et al. 2021

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“…The reduction of environmental impacts, however, is still a topic in development, with a small number of works reported in the literature. The work reported in [26] aimed to reduce CO 2 emissions and energy power losses by using a non-linear mathematical formulation, which was solved with the GAMS software. Nevertheless, the results obtained were not compared with those of other works reported in the literature.…”

Section: State Of the Artmentioning

confidence: 99%

Optimal Integration of Battery Systems in Grid-Connected Networks for Reducing Energy Losses and CO2 Emissions

2023

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This work addressed the problem regarding the optimal integration of battery systems (BS) in grid-connected networks (GCNs) with the purpose of reducing energy losses and CO2 emissions, for which it formulates a mathematical model that considers the constraints associated with the operation of GCNs in a distributed generation environment that includes BS and variable power generation related to photovoltaic (PV) distributed generation (DG) and demand. As solution strategies, three different master–slave methodologies are employed that are based on sequential programming methods, with the aim to avoid the implementation of commercial software. In the master stage, to solve the problem regarding the location and the type of batteries to be used, parallel-discrete versions of the Montecarlo method (PMC), a genetic algorithm (PDGA), and the search crow algorithm (PDSCA) are employed. In the slave stage, the particle swarm optimization algortihm (PSO) is employed to solve the problem pertaining to the operation of the batteries, using a matrix hourly power flow to assess the impact of each possible solution proposed by the master–slave methodologies on the objective functions and constraints. As a test scenario, a GCN based on the 33-bus test systems is used, which considers the generation, power demand, and CO2 emissions behavior of the city of Medellín (Colombia). Each algorithm is executed 1000 times, with the aim to evaluate the effectiveness of each solution in terms of its quality, standard deviation, and processing times. The simulation results obtained in this work demostrate that PMC/PSO is the master–slave methodology with the best performance in terms of solution quality, repeatability, and processing time.

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“…Nodes 2, 6, 15, 29, 42 and 58 are installed with charging piles. Other parameters of the case system can be referred to [42].…”

Section: Testing On a Modified Ieee 69-node Test Systemmentioning

confidence: 99%

Distribution network resilience enhancement strategy considering spatial‐temporal migration of flexible resources on supply and demand sides

Wu,

Wang,

Miao

2023

IET Renewable Power Gen

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Extreme events bring great challenges to the safety and resilience of a distribution network, which makes it difficult for traditional static resources to provide enough operation support. The continuous enrichment of spatial‐temporal flexibility resources on both the power demand and supply sides provides a new idea for the dynamic dispatching of distribution network. This paper proposes a spatial‐temporal migration method of mobile power sources (MPSs) on the supply side and internet data centres (IDCs) on the demand side, based on the power‐transportation and power‐computation network coupling models respectively. Then, the dynamic dispatching potential of MPSs and IDCs is evaluated, and the dispatching models against extreme events of MPSs and IDCs are established. With the objective of maximizing system resilience and minimizing dispatching costs, an optimization model considering spatial‐temporal migration of data loads and MPSs dynamic dispatching strategy is formulated. Finally, the proposed method is tested in the modified IEEE 33‐node test system and IEEE 69‐node test system, and the economy and resilience enhancement are verified with comparison of different dispatching schemes.

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Simultaneous Minimization of Energy Losses and Greenhouse Gas Emissions in AC Distribution Networks Using BESS

Cited by 23 publications

References 57 publications

Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks

Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks

Optimal Integration of Battery Systems in Grid-Connected Networks for Reducing Energy Losses and CO2 Emissions

Distribution network resilience enhancement strategy considering spatial‐temporal migration of flexible resources on supply and demand sides

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