A cuckoo load scheduling optimization approach for smart energy management

Shaban, Ahmed; Maher, Hagag; Elbayoumi, Mahmoud; Abdelhady, Suzan

doi:10.1016/j.egyr.2021.06.099

Cited by 16 publications

(4 citation statements)

References 48 publications

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“…Individuals from the swarm also share information, which influences their behavior in the following iterations. Some examples that we found are: particle swarm optimization [103], ant colony optimization [76], grey wolf optimization, bacterial foraging [77], lion's algorithm [50] and cuckoo search [54]. According to our cumulative statistics, these types of algorithms are frequent in optimizing the PAR and energy consumption cost.…”

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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“…EMS optimally schedules the sources and the loads in the two micro grids keeping the energy cost at its minimum. Cuckoo search algorithm is used for load scheduling minimizing the peak load, user inconvenience and energy cost (Shaban et al, 2021). It also helps in maximizing the output of the renewable generators considering time of use pricing.…”

Section: Energy Management In Micro Grid/distribution Networkmentioning

confidence: 99%

Nature-Inspired Algorithms for Energy Management Systems

Meera

Lavanya

2023

International Journal of Swarm Intelligence Research

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The electric grid is being increasingly integrated with renewable energy sources whose output is mostly fluctuating in nature. The load demand is also increasing day by day, mainly due to the increased interest in electric vehicles and other automated devices. An energy management system helps in maintaining the balance between the available generation and the load demand and thus optimizes the energy usage. It also helps in reducing the peak load, green-house gas emissions, and the operational cost. Energy management can be performed at different levels and is essential for realizing smart homes, smart buildings, and even smart grid. The different objectives considered for designing energy management systems are reduction of emissions, energy cost, operational cost, peak demand, etc. Many traditional and hybrid nature-inspired algorithms are used for optimizing these various objectives. This paper intends to give an overview about the various nature-inspired algorithms used for optimizing energy management systems in homes, buildings, and micro grid.

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“…In [17], a FLC is designed to make an energy balance between PV and FC, and the parameters of FLC are optimized by genetic algorithm. The energy management is studied by cuckoo algorithm in [18], to compensate PV power shortage in necessary times. In [19], a FLC is proposed to handle the uncertain dynamics of PV and FCs, and by comparison with conventional controllers the good proficiency of FLCs is demonstrated.…”

Section: Introductionmentioning

confidence: 99%

A New Immersion and Invariance Control and Stable Deep Learning Fuzzy Approach for Power/Voltage Control Problem

et al. 2022

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Background: The use of renewable energies is extended due to their valuable features such as abundant and clarity. The microgrids that include the renewable energies are widely used in various applications such as power supplying of remote areas, increasing the network reliability, reducing the greenhouse gas emission, reducing the consumption demand, eliminating the consumption peaks, and so on. But, energy management in the these systems in an challenging problem. Because, there are some natural perturbations such as variation output load, grid-side faults and changes of irradiation and temperature. Aim and Objective: The problem is to design a controller to regulate the output voltage/energy under aforementioned disturbances. Methods: The paper presents a new approach for energy management in Photovoltaic (PV)/Battery/Fuel Cells (FC) systems. The uncertainties are compensated by the new optimization rules based on Immersion and Invariance (I&I) theorem and proposed deep learning type-2 fuzzy logic compensator (T2FLC). The objective function of T2FLC is to minimize the tracking error in presence of perturbations. The adaptation rules are derived such that the I&I stabilization criterions are satisfied. Both rules and fuzzy sets (FSs) of T2FLCs are optimized by guaranteed stability rules to tackle the effect of perturbations and estimation errors. Results and Discussion: It is shown that a well voltage/energy regulation performance is achieved under variation of temperature, suddenly changes of load and variation of irradiation. A comparison with similar controllers demonstrates the superiority of the suggested approach. Conclusion: The suggested regulator do not depend on the mathematical models, and results in good accuracy under difficult conditions, then it can be used in various applications.INDEX TERMS Energy management, immersion and invariance, deep learning, fuzzy systems, voltage control, stability.• Both rules and FS parameters are optimized.• The superiority of the designed method is examined under various conditions and comparison with other conventional approaches.

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A cuckoo load scheduling optimization approach for smart energy management

Cited by 16 publications

References 48 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

Nature-Inspired Algorithms for Energy Management Systems

A New Immersion and Invariance Control and Stable Deep Learning Fuzzy Approach for Power/Voltage Control Problem

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