Energy Management and Economic Operation Optimization of Microgrid under Uncertainty

Wang, Shouxiang; Ge, Leijiao; Wang, Kai; Cai, Shengxia

doi:10.5772/63802

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…The other associated components of MG are energy storage units, combined heat and power (CHP) units, thermal and electric loads, etc. 1 .…”

Section: Introductionmentioning

confidence: 99%

Data-driven optimization for microgrid control under distributed energy resource variability

Mathur,

Kumari,

Meena

et al. 2024

Sci Rep

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The integration of renewable energy resources into the smart grids improves the system resilience, provide sustainable demand-generation balance, and produces clean electricity with minimal leakage currents. However, the renewable sources are intermittent in nature. Therefore, it is necessary to develop scheduling strategy to optimise hybrid PV-wind-controllable distributed generator based Microgrids in grid-connected and stand-alone modes of operation. In this manuscript, a priority-based cost optimization function is developed to show the relative significance of one cost component over another for the optimal operation of the Microgrid. The uncertainties associated with various intermittent parameters in Microgrid have also been introduced in the proposed scheduling methodology. The objective function includes the operating cost of CDGs, the emission cost associated with CDGs, the battery cost, the cost of grid energy exchange, and the cost associated with load shedding. A penalty function is also incorporated in the cost function for violations of any constraints. Multiple scenarios are generated using Monte Carlo simulation to model uncertain parameters of Microgrid (MG). These scenarios consist of the worst as well as the best possible cases, reflecting the microgrid’s real-time operation. Furthermore, these scenarios are reduced by using a k-means clustering algorithm. The reduced procedures for uncertain parameters will be used to obtain the minimum cost of MG with the help of an optimisation algorithm. In this work, a meta-heuristic approach, grey wolf optimisation (GWO), is used to minimize the developed cost optimisation function of MG. The standard LV Microgrid CIGRE test network is used to validate the proposed methodology. Results are obtained for different cases by considering different priorities to the sub-objectives using GWO algorithm. The obtained results are compared with the results of Jaya and PSO (particle swarm optimization) algorithms to validate the efficacy of the GWO method for the proposed optimization problem.

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“…The other associated components of MG are energy storage units, combined heat and power (CHP) units, thermal and electric loads, etc. 1 .…”

Section: Introductionmentioning

confidence: 99%

Data-driven optimization for microgrid control under distributed energy resource variability

Mathur,

Kumari,

Meena

et al. 2024

Sci Rep

View full text Add to dashboard Cite

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“…There are various aspects to the optimization of MGs that include power electronic converters. In detail, the authors in [14] focused on optimizing the efficiency of energy distribution within a microgrid as well as exporting excess energy to the main power grid for economic gain. Moreover, the authors in [15] investigated this approach while also considering the uncertainties inherent in RESs and power demand.…”

Section: Introductionmentioning

confidence: 99%

Lagrange Multiplier-Based Optimization for Hybrid Energy Management System with Renewable Energy Sources and Electric Vehicles

Tran,

Ton-That,

Thao

2023

Electronics

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The issues of energy scarcity and environmental harm have become major priorities for both business and human progress. Hence, it is important and useful to focus on renewable energy research and efficient utilization of distributed energy sources (DERs). A microgrid (MG) is a self-managed system that encompasses these energy resources as well as interconnected consumers. It has the flexibility to function in both isolated and grid-connected configurations. This study aims to design an effective method of power management for a MG in the two operating modes. The proposed optimization model seeks to strike a balance between energy usage, protecting the life of batteries, and maximizing economic benefits for users in the MG, with consideration of the real-time electricity price and constraints of the power grid. Furthermore, in order to accurately account for the dynamic nature of not only the stationary battery banks used as the energy storage systems (ESS) but also the built-in batteries of electric vehicles (EVs), the model is presented as a multi-objective, multiparametric and constrained problem. The solution is proposed to be found using the Lagrange multiplier theory, which helps to achieve good performance with less computational burden. Lastly, simulation results from both the isolated and grid-connected modes also demonstrate the effectiveness of the designed method.

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“…Due to continuous increase in the price of fuel and growing concerns over global climate change, countries are deploying alternative means of generating electricity (Abdulwahab et al, 2021;Abubakar et al, 2019;Jibril et al, 2019). Renewable energy sources (RES) include: wind, solar, biomass, bio-fuel and geothermal energy (Wang et al, 2016). Electricity generated from RES is now preferred due to fossil-fuel resources depletion and rising concern about CO2 emissions which causes global warming (Einan et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

An Improved Hybrid Micro-Grid Load Frequency Control Scheme for an Autonomous System

Abdulwahab¹,

Faskari²,

Belgore³

et al. 2021

FUOYEJET

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This paper presents an improved hybrid micro-grid load frequency control scheme for an autonomous system. The micro-grid system comprises of renewable and non-renewable energy-based Power Generating Units (PGU) which consist of Solar Photovoltaic, WT Generator, Solar Thermal Power Generator, Diesel Engine Generator, Fuel Cell (FC) with Aqua Electrolizer (AE). However, power produce from renewable sources in microgrid are intermittent in supply, hence make it difficult to maintain power balance between generated power and demand. Therefore, Battery energy storage system, ultra-capacitor and flywheel energy storage systems make up the energy storage units. These separate units are selected and combined to form two different scenarios in this study. This approach mitigates frequency fluctuations during disturbances (sudden load changes) by ensuring balance between the generated power and demand. For each scenario, Moth flame optimization algorithm optimized Proportional-Integral controllers were utilized to control the micro-grid (to minimize fluctuations from the output power of the non-dispatchable sources and from sudden load change). The results of the developed scheme were compared with that of Quasi-Oppositional Harmony Search Algorithm for overshoot and settling time of the frequency deviation. From the results obtained, the proposed scheme outperformed that of the quasi-oppositional harmony search algorithm optimized controller by an average percentage improvement of 35.95% and 28.76% in the case of overshoot and settling time when the system step input was suddenly increased. All modelling analysis were carried out in MATLAB R2019b environment. Keywords—Frequency Deviation, Micro-grid, Moth flame optimization algorithm, Quasi-Oppositional Harmony Search Algorithm.

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Energy Management and Economic Operation Optimization of Microgrid under Uncertainty

Cited by 6 publications

References 10 publications

Data-driven optimization for microgrid control under distributed energy resource variability

Data-driven optimization for microgrid control under distributed energy resource variability

Lagrange Multiplier-Based Optimization for Hybrid Energy Management System with Renewable Energy Sources and Electric Vehicles

An Improved Hybrid Micro-Grid Load Frequency Control Scheme for an Autonomous System

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