Optimum energy resource scheduling in a microgrid using a distributed algorithm framework

Rokni, S. G. M.; Radmehr, Masoud; Zakariazadeh, Alireza

doi:10.1016/j.scs.2017.11.016

Cited by 45 publications

(22 citation statements)

References 30 publications

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“…The constraints imposed on the upstream supplier are defined as (21) Constraints imposed on the charging and discharging of EVs, since these two phases cannot occur at the same time, are given by…”

Section: Constraintsmentioning

confidence: 99%

Solving the Problem of Large-Scale Optimal Scheduling of Distributed Energy Resources in Smart Grids Using an Improved Variable Neighborhood Search

Ramli

Bouchekara

2020

IEEE Access

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Since the last decade, power systems have been evolving dynamically due to smart grid technologies. In this context, energy management and optimal scheduling of different resources are very important. The main objective of this paper is to study the optimal scheduling of distributed energy resources (OSDER) problem. This problem is a challenging, complex and very large-scale mixed-integer non-linear programming (MINLP) problem. Its complexity escalates with incorporation of uncertain and intermittent renewable sources, electric vehicles, variable loads and markets which makes it hard to be solved using traditional optimization algorithms and solvers. However, it can be handled efficiently and without approximation or modification of the original formulation using modern optimization algorithms such as metaheuristics. In this paper, an improved version of the variable neighborhood search (IVNS) algorithm is proposed to solve the OSDER problem. The proposed algorithm was tested on two large-scale centralized day-ahead energy resource scenarios. In the first scenario, the 12.66 kV, 33-bus test system with a total of 49,920 design variables is used whilst in the second scenario, the 30 kV, 180-bus test system is used with a total of 154,800 design variables. The optimization results using the proposed algorithm were compared with five existing optimization algorithms, i.e., chaotic biogeography-based optimization (CBBO), cross-entropy method and evolutionary PSO (CEEPSO), chaotic differential evolution with PSO (Chaotic-DEEPSO), Levy differential evolution with PSO (Levy-DEEPSO), and the variable neighborhood search (VNS). For the first test system, the IVNS has achieved a score of-5598.89 while for the second test system it has achieved a score of-3180.15. A comparative study of the results has shown that the proposed IVNS algorithm performs better than the remaining algorithms for both cases.

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Section: Constraintsmentioning

confidence: 99%

Solving the Problem of Large-Scale Optimal Scheduling of Distributed Energy Resources in Smart Grids Using an Improved Variable Neighborhood Search

Ramli

Bouchekara

2020

IEEE Access

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“…Seyed et al proposed in [17] a distributed energy management system called alternating direction method of the multiplier (ADMM) in order to jointly schedule the central controller as well as local controllers. The algorithm considers optimal power flow equations within the distributed energy management problem.…”

Section: Introductionmentioning

confidence: 99%

Smart Microgrid Management: A Hybrid Optimisation Approach

Amoura¹,

Pereira²,

Lima³

et al. 2020

Preprint

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Background: The association of distributed generators, energy storage systems and controllable loads close to the energy consumers gave place to a small-scale electrical network called microgrid. The stochastic behavior of renewable energy sources, as well as the demand variation, can lead in some cases to problems related to the reliability of the microgrid system. On the other hand, the market price of electricity from mainly non-renewable sources becomes a concern for a simple consumer due to its high costs.Method: In this work, an energy management system was developed based on an innovative optimization method, combining linear programming, based on the simplex method, with particle swarm optimisation algorithm. Two scenarios have been proposed to characterise the relation price versus gas emissions for optimal energy management. The objective of this study is to nd the optimal setpoints of generators in a smart city supplied by a microgrid in order to ensure consumer comfort, minimising the emission of greenhouse gases and ensure an appropriate operating price for all smart city consumers. Results: The simulation results have demonstrated the reliability of the optimisation approach on the energy management system in the optimal scheduling of the microgrid generators power ows, having achieved a better energy price compared to a previous study with the same data. Conclusion: The energy management system based on the proposedoptimisation approach gave an inverse correlation between economic and environmental aspects, in fact, a multi-objective optimisation approach is performed as a continuation of the work proposed in this paper.

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“…15, the consensus‐based distributed energy management algorithm has been presented to help devices to estimate system global information. Lately, the ADMM algorithm has taken more into considerations, which is well‐matched for distributed convex optimization 16 . In Ref.…”

Section: Introductionmentioning

confidence: 99%

Valuation of reactive power support provided by photovoltaic systems in distribution networks

Yousefi

Gholamian

Zakariazadeh

2020

Int Trans Electr Energ Syst

Self Cite

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Optimum energy resource scheduling in a microgrid using a distributed algorithm framework

Cited by 45 publications

References 30 publications

Solving the Problem of Large-Scale Optimal Scheduling of Distributed Energy Resources in Smart Grids Using an Improved Variable Neighborhood Search

Solving the Problem of Large-Scale Optimal Scheduling of Distributed Energy Resources in Smart Grids Using an Improved Variable Neighborhood Search

Smart Microgrid Management: A Hybrid Optimisation Approach

Valuation of reactive power support provided by photovoltaic systems in distribution networks

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