Optimizing energy consumption patterns of smart home using a developed elite evolutionary strategy artificial ecosystem optimization algorithm

Youssef, Heba; Kamel, Salah; Hassan, Mohamed H.; Nasrat, Loai

doi:10.1016/j.energy.2023.127793

Cited by 19 publications

(6 citation statements)

References 32 publications

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“…They introduced a self-adaptive operator to ensure the diversity of the mutation strategy and reduce the probability of the algorithm falling into local optimal results. Youssef et al [11] proposed an optimal stochastic mutation strategy, mutating certain genes of the optimal chromosome within the search range. The strategy provided the algorithm with a stronger exploration capability at a later stage of evolution in order to improve the ability to jump out of the local optimum.…”

Section: Literature Reviewmentioning

confidence: 99%

“…When NES provides electricity to BP and the user, the electricity balance in IES model should satisfy the below equation: (11) (2) Heat balance constraint The constraint of heat balance is as follow: (12) (3) Equipment constraint Wind turbines (WT): Wind turbines are devices that convert the kinetic energy of wind to electrical energy. As a kind of clean renewable energy, wind energy has been paid much more attention by all countries in the world.…”

Section: Constraints Of the Nes (1) Electricity Balance Constraintmentioning

confidence: 99%

“…The DEA has a unique evolutionary approach, it amplifies the differences between the individuals in the current population to construct a new variant, and adopts crossover, mutation and other operations to enlarge the range of the solution distribution. The DEA starts from a set of initial populations and generate new populations through mutation, where the worse individuals are eliminated and the better ones are retained [11] . After continuous elimination and updating, the optimal solution of the system is searched.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

See 2 more Smart Citations

Scheduling of integrated biogas energy system for rural areas using improved differential evolutionary algorithm

Lv,

Gao

2024

Infor. Syst. Smart. City

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Due to lack of rational system design, an enormous amount of energy and resources are wasted or ineffectively utilized in China’s rural areas. Therefore, it is crucial to develop a practical energy system that applies to rural areas. In this paper, a Stackelberg game model is established for optimization of integrated energy system (IES) in rural areas. As a leader, new energy supplier (NES) develops price strategy for electricity and heat, the flexible users and biogas plant (BP) as followers receive price information and make energy consumption plans. Then NES adjusts equipment output based on followers’ feedback on energy loads. The objective of our Stackelberg game is to maximize the profit of NES while taking into account the costs of followers. Furthermore, our study designs an improved differential evolutionary algorithm (DEA) to achieve Stackelberg balance. The optimization scheduling result shows that the proposed model can obviously increase the profit of NES by 5.4% and effectively decrease the cost of biogas plant by 4.5%.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Constraints Of the Nes (1) Electricity Balance Constraintmentioning

confidence: 99%

Section: Introduction 1backgroundmentioning

confidence: 99%

See 1 more Smart Citation

Scheduling of integrated biogas energy system for rural areas using improved differential evolutionary algorithm

Lv,

Gao

2024

Infor. Syst. Smart. City

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“…The author of 28 concentrates on improving the energy efficiency of buildings to meet economic and environmental goals. They utilize an elite evolutionary strategy in an artificial ecosystem optimization approach to optimize the scheduling of electrical appliances in smart homes, incorporating load conversion as a demand-side management (DSM) strategy.…”

Section: Related Workmentioning

confidence: 99%

Smart home energy management and power trading optimization using an enhanced manta ray foraging optimization

Youssef,

Kamel,

Hassan

2023

Sci Rep

Self Cite

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This paper proposes a plan to manage energy consumption in residential areas using the demand response method, which allows electricity users to contribute to the reliability of the power system by controlling their usage. Due to the growing population, the residential sector consumes a significant amount of energy, and the objectives of this study are to lower electricity costs and the peak to average ratio, as well as reduce the amount of imported electricity from the grid. The study aims to maximize profit by properly utilizing renewable energy sources and addressing energy trading. The manta ray foraging optimization (MRFO) and long term memory MRFO (LMMRFO) algorithms are used to solve this problem. Firstly, the validation of the proposed LMMRFO technique is confirmed by seven benchmark functions and compared its results with the results of the well-known optimization algorithms including hunter prey optimization, gorilla troops optimizer, beluga whale optimization, and the original MRFO algorithm. Then, the performance of the LMMRFO is checked on the optimization of smart home energy management. In the suggested approach, a smart home decides whether to purchase or sell electricity from the commercial grid based on the cost, demand, and production of electricity from its own microgrid, which consists of a wind turbine and solar panels. Energy storage systems support the stable and dependable functioning of the power system since the solar panel and wind turbine only occasionally produce electricity. Through various case studies, the proposed plan is tested and found to be effective in reducing electricity costs and the peak to average ratio while maximizing profit. Furthermore, a comparative study is conducted to demonstrate the legality and effectiveness of LMMRFO and MRFO.

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“…When smart homes actively participate in power grid operation and dispatch, ensuring user comfort is a crucial prerequisite [18]. Literature [19,20] introduces distinct smart home scheduling algorithms aiming to shift the use of electrical appliances from high electricity price periods to low electricity price periods, therefore reducing electricity bills and load peaks. The active involvement of smart homes in operation scheduling relies on the time-of-use electricity price strategy, prompting scholars to study optimal time-of-use electricity price strategies [21].…”

Section: Introductionmentioning

confidence: 99%

Low-Voltage Distribution Network Loss-Reduction Method Based on Load-Timing Characteristics and Adjustment Capabilities

Huangfu,

Wang,

et al. 2024

Energies

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The primary contributors to elevated line losses in low-voltage distribution networks are three-phase load imbalances and variations in load peak–valley differentials. The conventional manual phase sequence adjustment fails to capitalize on the temporal characteristics of the load, and the proliferation of smart homes has opened up new scheduling possibilities for managing the load. Consequently, this paper introduces a loss-reduction method for low-voltage distribution networks that leverages load-timing characteristics and adjustment capabilities. This method combines dynamic and static methods to reduce energy consumption from different time scales. To commence, this paper introduced a hierarchical fuzzy C-means algorithm (H-FCM), taking into account the distance and similarity of load curves. Subsequently, a phase sequence adjustment method, grounded in load-timing characteristics, was developed. The typical user load curve, derived from the classification of user loads, serves as the foundation for constructing a long-term commutation model, therefore mitigating the impact of load fluctuations on artificial commutation. Following this, this paper addressed the interruptible and transferable characteristics of various smart homes. This paper proposed a multi-objective transferable load (TL) optimal timing task adjustment model and a peak-shaving control strategy specifically designed for maximum sustainable power reduction of temperature-controlled loads (TCL). These strategies aim to achieve real-time load adjustment, correct static commutation errors, and reduce peak-to-valley differences. Finally, a simulation verification model was established in MATLAB (R2022a). The results show that the proposed method mainly solves the problems of three-phase imbalance and large load peak–valley difference in low-voltage distribution networks and reduces the line loss of low-voltage distribution networks through manual commutation and load adjustment.

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Optimizing energy consumption patterns of smart home using a developed elite evolutionary strategy artificial ecosystem optimization algorithm

Cited by 19 publications

References 32 publications

Scheduling of integrated biogas energy system for rural areas using improved differential evolutionary algorithm

Scheduling of integrated biogas energy system for rural areas using improved differential evolutionary algorithm

Smart home energy management and power trading optimization using an enhanced manta ray foraging optimization

Low-Voltage Distribution Network Loss-Reduction Method Based on Load-Timing Characteristics and Adjustment Capabilities

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