An optimal programming among renewable energy resources and storage devices for responsive load integration in residential applications using hybrid of grey wolf and shark smell algorithms

Seifi, Amir; Moradi, Mohammad H.; Abedini, Mohamad; Jahangiri, Alireza

doi:10.1016/j.est.2019.101126

Cited by 16 publications

(7 citation statements)

References 30 publications

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“…The output power of the WT generator (

) is a function of the wind velocity (

at time step

and the characteristics of the WT generator such as the cut-in speed

, cut-out speed

, and the rated speed

. The

at time step

can be computed as follows [34] :

where

denote the number of WT. A EOcycle EO10 WT, with 3 blades rated at 1 kW, 48 V DC type is chosen for the analysis.…”

Section: Modeling Of Grid-independent Microgrid Test-case Systemmentioning

confidence: 99%

“…The output power of the PV generator (

) is a function of ambient temperature

and solar irradiance. The

at time step

can be computed as follows [34] :

where

is the rated power of the PV panel

represent solar irradiance

represent the solar irradiance at reference conditions having a value of

denote the PV panel temperature coefficient, usually −3.7 × 10 -3 (1/°C) for poly and mono-crystalline silicon [35] .

denote the cell temperature,

denote temperature at standard test condition, normally set as 25 °C, and

denote the optimum number of PV module determined at each iteration by the proposed capacity planning procedure.…”

Section: Modeling Of Grid-independent Microgrid Test-case Systemmentioning

confidence: 99%

See 1 more Smart Citation

A rule-based energy management scheme for long-term optimal capacity planning of grid-independent microgrid optimized by multi-objective grasshopper optimization algorithm

Bukar

Tan

Yiew

et al. 2020

Energy Conversion and Management

132

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Highlights A nature-inspired driven approach is proposed to optimally design off-grid microgrid. Development of a rule-based energy management scheme based on queuing theory. The optimization problem minimizes energy cost and maximizes system reliability. The proposed grasshopper optimization algorithm is compared with PSO and CSA. The performance of the microgrid has been analysed against future uncertainties.

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“…The output power of the WT generator (

) is a function of the wind velocity (

at time step

and the characteristics of the WT generator such as the cut-in speed

, cut-out speed

, and the rated speed

. The

at time step

can be computed as follows [34] :

where

denote the number of WT. A EOcycle EO10 WT, with 3 blades rated at 1 kW, 48 V DC type is chosen for the analysis.…”

Section: Modeling Of Grid-independent Microgrid Test-case Systemmentioning

confidence: 99%

“…The output power of the PV generator (

) is a function of ambient temperature

and solar irradiance. The

at time step

can be computed as follows [34] :

where

is the rated power of the PV panel

represent solar irradiance

represent the solar irradiance at reference conditions having a value of

denote the PV panel temperature coefficient, usually −3.7 × 10 -3 (1/°C) for poly and mono-crystalline silicon [35] .

denote the cell temperature,

denote temperature at standard test condition, normally set as 25 °C, and

denote the optimum number of PV module determined at each iteration by the proposed capacity planning procedure.…”

Section: Modeling Of Grid-independent Microgrid Test-case Systemmentioning

confidence: 99%

A rule-based energy management scheme for long-term optimal capacity planning of grid-independent microgrid optimized by multi-objective grasshopper optimization algorithm

Bukar

Tan

Yiew

et al. 2020

Energy Conversion and Management

132

View full text Add to dashboard Cite

show abstract

“…A micro-Energies 2021, 14, 6329 2 of 15 grid controller schedules the microgrid sources (MS) and load controller (LC) to achieve optimal energy management. An appropriate combination of multiple RES operating through a distinct network for specified load patterns forming a smart microgrid can satisfy demand response management(DRM) [4][5][6]. The control technique for enhancing the stable operation of distributed generation units within a microgrid was demonstrated in various papers [7].…”

Section: Introductionmentioning

confidence: 99%

Field-Validated Communication Systems for Smart Microgrid Energy Management in a Rural Microgrid Cluster

et al. 2021

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This paper demonstrates a smart energy management scheme for solar photovoltaic-biomass integrated grid-interactive microgrid cluster system. Three interconnected microgrids were chosen as a cluster of microgrids for validation of the proposed community energy management scheme. In this work, a Global System for Mobile (GSM)-based bidirectional communication technique was adopted for real-time coordination among the renewable energy sources and loads. To realize the common phenomenon of local grid outage in rural distribution networks, a practical case study is designed in this work. The optimized scheduling of the energy sources and loadsof different microgrids and the distribution grid were implemented to ensure zero loss of power supply probability (LPSP) for dynamic load profiles. The laboratory-scale prototype of the proposed microgrid clustering was first developed in this work by establishing real-time communication among multiple energy sources and loads through different energymeters located at different places inside the academic campus. The field validation was performed with a microgrid cluster consisting of 45 kWP solar photovoltaic, 50 kVA biogas plant, community loads in a village. The developed smart energy management solution is a generalized one and applicable to satisfy scalable community energy demands as well.

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“…The simulation was performed in MAT-LAB. Results showed that cost reduced by 20% and stability increased by 18%, respectively [37]. Chamandoust et al modeled an energy scheduling problem for HEMS to minimize cost, emissions and load deviation.…”

Section: Introductionmentioning

confidence: 99%

Risk-Averse Home Energy Management System

Ali

Malik

Raza³

2020

IEEE Access

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Transformation of conventional energy systems into smart grids enables the integration of residential buildings with distributed generations, electro-thermal storages and demand response policies. Further, it improves the household comfort level and helps to preserve the ecological system. Keeping in view the techno-financial impact of residential energy management, optimal handling of residential customers may prove meaningful for peak load reduction, valley filling, and energy conservation. In this regard, this paper devises a residential energy management system (EMS) to optimally schedule appliances, energy sources and electro-thermal storage for reduction of consumption cost and greenhouse (GHG) emissions. Building integrates national grid, natural gas network and solar energy as input carriers, whereas, electricity, heat and cooling as output carriers. To resolve the risk of loss of load, conditional value at risk (CVaR) has been incorporated in the objective function. Comparison demonstrates that under risk-averse approach, energy retaining capability of electric vehicle and thermal energy storage increases by 28.56% and 53.34%, respectively. This stored energy acts as a reserve during absence of solar irradiance and outages on electric and natural gas networks. Moreover, to make EMS more efficient in tracking optimum solutions with faster convergence speed, a hybrid algorithm has been devised by concatenating the modified flower pollination algorithm with mixed-integer linear programming. The proposed algorithm has been validated by comparing its results with the Salp Swarm Algorithm, Grasshopper Optimization Algorithm, Polar Bear Algorithm, Coyote Optimization and Two Cored Flower Pollination Algorithm (FPA). Results manifest that the cost, GHG emissions and execution time drop by 8.98%, 10.81% and 35.064%, respectively. INDEX TERMS Demand response, energy management, residential buildings, smart grids, stochastic model.

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An optimal programming among renewable energy resources and storage devices for responsive load integration in residential applications using hybrid of grey wolf and shark smell algorithms

Cited by 16 publications

References 30 publications

A rule-based energy management scheme for long-term optimal capacity planning of grid-independent microgrid optimized by multi-objective grasshopper optimization algorithm

A rule-based energy management scheme for long-term optimal capacity planning of grid-independent microgrid optimized by multi-objective grasshopper optimization algorithm

Field-Validated Communication Systems for Smart Microgrid Energy Management in a Rural Microgrid Cluster

Risk-Averse Home Energy Management System

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