Improved PSO based home energy management systems integrated with demand response in a smart grid

Yang, Hong-Tzer; Yang, Chiao-Tung; Tsai, Chia‐Chun; Chen, Guan-Jhih; Chen, Szu-Yao

doi:10.1109/cec.2015.7256902

Cited by 30 publications

(16 citation statements)

References 15 publications

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“…To meet the practical implementation of the systems, the PSO algorithm is mostly applied in recent studies. Similarly, various bio‐inspired algorithms such as modified FPA, modified firefly algorithm, modified genetic and firefly algorithm, glowworm swarm optimization, multicore PSO, bat algorithm, improved shuffled frog leaping algorithm, enhanced simulated annealing, monkey king evolution algorithm, and artificial fish swarm algorithm are used for tracking MPPT in the solar systems under partial shading conditions.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy‐elephant herding optimization technique for maximum power point tracking in the hybrid wind‐solar system

Veeramanikandan¹,

Selvaperumal²

2019

Int Trans Electr Energ Syst

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Summary In recent years, the attention towards the utilization of solar and wind power systems are becoming more popular because of its nonpolluted and nondepleting nature. However, these power sources rely on environmental conditions for power generation. So, extracting maximum power available from the wind and solar systems is essential. Many of the existing maximum power point tracking (MPPT) techniques' performances are not convenient in the hybrid operation of wind and solar power systems. So, to provide an optimum MPPT operation, this paper proposes a fuzzy logic controll er (FLC)–based elephant herding optimization (EHO) technique for MPPT in the hybrid wind‐solar system. The proposed fuzzy‐EHO (FEHO) method has the tendency to track the maximum power under the partial shading condition of the photovoltaic (PV) system and low wind speed situations. The results are compared with the conventional MPPT methods, and it shows that the proposed optimization method efficiently tracks the maximum power point of the wind‐solar power systems even with the variations in the climatic conditions. MATLAB tool is used in the design and operation of the proposed method. Furthermore, the performance of the proposed system is tested with an experimental setup and provides better efficiency.

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Section: Introductionmentioning

confidence: 99%

A fuzzy‐elephant herding optimization technique for maximum power point tracking in the hybrid wind‐solar system

Veeramanikandan¹,

Selvaperumal²

2019

Int Trans Electr Energ Syst

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“…Two different pricing schemes were used, such as DHP and RTP. In [12], an optimization model such as GA for balancing electric load was proposed by the authors. They took time horizon T equal to 24 h. The load profile generator tool was used to calculate energy consumption and the pattern of load in each time horizon.…”

Section: Related Workmentioning

confidence: 99%

“…X p represents the position of prey, while X is the position of the gray wolf at the t th iteration, which is given by Equation (12). The vectors A and C are calculated according to Equations (14) and (15):…”

Section: Encircling Preymentioning

confidence: 99%

Efficient Power Scheduling in Smart Homes Using Hybrid Grey Wolf Differential Evolution Optimization Technique with Real Time and Critical Peak Pricing Schemes

et al. 2018

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With the emergence of automated environments, energy demand by consumers is increasing rapidly. More than 80% of total electricity is being consumed in the residential sector. This brings a challenging task of maintaining the balance between demand and generation of electric power. In order to meet such challenges, a traditional grid is renovated by integrating two-way communication between the consumer and generation unit. To reduce electricity cost and peak load demand, demand side management (DSM) is modeled as an optimization problem, and the solution is obtained by applying meta-heuristic techniques with different pricing schemes. In this paper, an optimization technique, the hybrid gray wolf differential evolution (HGWDE), is proposed by merging enhanced differential evolution (EDE) and gray wolf optimization (GWO) scheme using real-time pricing (RTP) and critical peak pricing (CPP). Load shifting is performed from on-peak hours to off-peak hours depending on the electricity cost defined by the utility. However, there is a trade-off between user comfort and cost. To validate the performance of the proposed algorithm, simulations have been carried out in MATLAB. Results illustrate that using RTP, the peak to average ratio (PAR) is reduced to 53.02%, 29.02% and 26.55%, while the electricity bill is reduced to 12.81%, 12.012% and 12.95%, respectively, for the 15-, 30-and 60-min operational time interval (OTI). On the other hand, the PAR and electricity bill are reduced to 47.27%, 22.91%, 22% and 13.04%, 12%, 11.11% using the CPP tariff.

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“…The proposed scheme did not consider the user comfort in the problem formulation. In [26], the authors proposed an improved-PSO (IPSO) for the solution of cost minimization problem. Results illustrate that the proposed IPSO brings the user load curve near to the objective curve, where the objective curve and electricity price have an inverse relationship.…”

Section: Reduction Of Load In Peak Hoursmentioning

confidence: 99%

“…Moreover, an acceptable trade-off between the UC and cost reduction is also achieved. [26] Grid station stability…”

Section: Related Workmentioning

confidence: 99%

An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources

et al. 2017

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Traditional power grid and its demand-side management (DSM) techniques are centralized and mainly focus on industrial consumers. The ignorance of residential and commercial sectors in DSM activities degrades the overall performance of a conventional grid. Therefore, the concept of DSM and demand response (DR) via residential sector makes the smart grid (SG) superior over the traditional grid. In this context, this paper proposes an optimized home energy management system (OHEMS) that not only facilitates the integration of renewable energy source (RES) and energy storage system (ESS) but also incorporates the residential sector into DSM activities. The proposed OHEMS minimizes the electricity bill by scheduling the household appliances and ESS in response to the dynamic pricing of electricity market. First, the constrained optimization problem is mathematically formulated by using multiple knapsack problems, and then solved by using the heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), bacterial foraging optimization (BFO) and hybrid GA-PSO (HGPO) algorithms. The performance of the proposed scheme and heuristic algorithms is evaluated via MATLAB simulations. Results illustrate that the integration of RES and ESS reduces the electricity bill and peak-to-average ratio (PAR) by 19.94% and 21.55% respectively. Moreover, the HGPO algorithm based home energy management system outperforms the other heuristic algorithms, and further reduces the bill by 25.12% and PAR by 24.88%.

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Improved PSO based home energy management systems integrated with demand response in a smart grid

Cited by 30 publications

References 15 publications

A fuzzy‐elephant herding optimization technique for maximum power point tracking in the hybrid wind‐solar system

A fuzzy‐elephant herding optimization technique for maximum power point tracking in the hybrid wind‐solar system

Efficient Power Scheduling in Smart Homes Using Hybrid Grey Wolf Differential Evolution Optimization Technique with Real Time and Critical Peak Pricing Schemes

An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources

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