A Novel Solution for Optimized Energy Management Systems Comprising an AC/DC Hybrid Microgrid System for Industries

Asghar, Faran; Zahid, Adnan; Hussain, M. Imtiaz; Asghar, Furqan; Amjad, Waseem; Kim, Jun-Tae

doi:10.3390/su14148788

Cited by 9 publications

(3 citation statements)

References 30 publications

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“…In the works that we surveyed, these approaches are used, but usually with another approximate algorithm. Some instances that we found include linear programming, nonlinear programming, dynamic programming and integer programming [4,40,44,116]. However, exact methods are inefficient and very slow in more complex (NP-hard ones, for example) problems with many local optima, stochastic or unknown search space, many objectives (e.g., with user preference inclusion) and renewable sources integration.…”

Section: Rq1 What Are the Most Used Algorithms And Techniques For Pea...mentioning

confidence: 99%

Optimization Approaches for Demand-Side Management in the Smart Grid: A Systematic Mapping Study

Mimi,

Ben Maissa,

Tamtaoui

2023

Smart Cities

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Demand-side management in the smart grid often consists of optimizing energy-related objective functions, with respect to variables, in the presence of constraints expressing electrical consumption habits. These functions are often related to the user’s electricity invoice (cost) or to the peak energy consumption (peak-to-average energy ratio), which can cause electrical network failure on a large scale. However, the growth in energy demand, especially in emerging countries, is causing a serious energy crisis. This is why several studies focus on these optimization approaches. To our knowledge, no article aims to collect and analyze the results of research on peak-to-average energy consumption ratio and cost optimization using a systematic reproducible method. Our goal is to fill this gap by presenting a systematic mapping study on the subject, spanning the last decade (2013–2022). The methodology used first consisted of searching digital libraries according to a specific search string (104 relevant studies out of 684). The next step relied on an analysis of the works (classified using 13 criteria) according to 5 research questions linked to algorithmic trends, energy source, building type, optimization objectives and pricing schemes. Some main results are the predominance of the genetic algorithms heuristics, an insufficient focus on renewable energy and storage systems, a bias in favor of residential buildings and a preference for real-time pricing schemes. The main conclusions are related to the promising hybridization between the genetic algorithms and swarm optimization approaches, as well as a greater integration of user preferences in the optimization. Moreover, there is a need for accurate renewable and storage models, as well as for broadening the optimization scope to other objectives such as CO2 emissions or communications load.

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Section: Rq1 What Are the Most Used Algorithms And Techniques For Pea...mentioning

confidence: 99%

Optimization Approaches for Demand-Side Management in the Smart Grid: A Systematic Mapping Study

Mimi,

Ben Maissa,

Tamtaoui

2023

Smart Cities

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“…The mathematical modeling of a photovoltaic (PV) system involves the equations that describe the behavior of the PV panels and the conversion of solar irradiance into electrical power. In the context of using an adaptive genetic algorithm for optimization, we focus on the equations that calculate the PV system's output power and the fitness function to be used in the genetic algorithm [27]. The PV system is designed based on a Canadian solar panel of mono-perc half-cut 330 W, while the inverter is the ABB UNO Trio Series of 50 kTL.…”

Section: B Photovoltaic Systemmentioning

confidence: 99%

Optimal Energy Management System for Grid-Tied Microgrid: An Improved Adaptive Genetic Algorithm

Majeed,

Phichaisawat,

Asghar

et al. 2023

IEEE Access

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Grid-tied microgrids play a crucial role by connecting renewable energy sources to the main power grid, contributing to sustainability and resilience in a balanced and effective manner. However, the dynamic interplay between the intermittent nature of renewable energy sources and the volatility of load fluctuations presents a multifaceted array of intricate energy management complexities. This study aims to formulate optimization techniques for energy management systems based on renewable energy resources and standalone diesel systems. The proposed system consists of a wind turbine, a photovoltaic system, a standalone diesel generator, and a battery energy storage system, along with flexible and non-flexible loads tied to the local grid. Battery energy storage acts as a primary backup system, while diesel generators act as a standalone secondary backup system. The performance of the proposed optimization technique is validated using Matlab/Simulink, substantiating its performance and robustness, thus affirming its pragmatic suitability for real-world implementation. A comparison has been made with other optimization techniques and found that the proposed technique gives enhanced efficiency, improved resource allocation, load scheduling, and greater adaptability to varying demand and supply dynamics. Moreover, the proposed system exhibits a superior ability to achieve optimal energy utilization and realize noteworthy cost savings in comparison to the alternatives that underwent evaluation.INDEX TERMS Adaptive Genetic Algorithm, grid-tied microgrid, bidirectional converter, cost optimization, renewable energy resources, peak average ratio, load scheduling, resource allocation.

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“…Peak-to-average ratio was not considered. [33] The authors proposed a novel strategy for enhanced energy management systems, which includes an AC/DC hybrid microgrid system for industries.…”

Section: References Groups Reference Contributions Shortcomingsmentioning

confidence: 99%

A Multi-Objective Improved Cockroach Swarm Algorithm Approach for Apartment Energy Management Systems

Alhasnawi,

Jasim,

Jasim

et al. 2023

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The electrical demand and generation in power systems is currently the biggest source of uncertainty for an electricity provider. For a dependable and financially advantageous electricity system, demand response (DR) success as a result of household appliance energy management has attracted significant attention. Due to fluctuating electricity rates and usage trends, determining the best schedule for apartment appliances can be difficult. As a result of this context, the Improved Cockroach Swarm Optimization Algorithm (ICSOA) is combined with the Innovative Apartments Appliance Scheduling (IAAS) framework. Using the proposed technique, the cost of electricity reduction, user comfort maximization, and peak-to-average ratio reduction are analyzed for apartment appliances. The proposed framework is evaluated by comparing it with BFOA and W/O scheduling cases. In comparison to the W/O scheduling case, the BFOA method lowered energy costs by 17.75%, but the ICSA approach reduced energy cost by 46.085%. According to the results, the created ICSA algorithm performed better than the BFOA and W/O scheduling situations in terms of the stated objectives and was advantageous to both utilities and consumers.

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A Novel Solution for Optimized Energy Management Systems Comprising an AC/DC Hybrid Microgrid System for Industries

Cited by 9 publications

References 30 publications

Optimization Approaches for Demand-Side Management in the Smart Grid: A Systematic Mapping Study

Optimization Approaches for Demand-Side Management in the Smart Grid: A Systematic Mapping Study

Optimal Energy Management System for Grid-Tied Microgrid: An Improved Adaptive Genetic Algorithm

A Multi-Objective Improved Cockroach Swarm Algorithm Approach for Apartment Energy Management Systems

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