Robust hydrogen-consumption-minimization strategy based salp swarm algorithm for energy management of fuel cell/supercapacitor/batteries in highly fluctuated load condition

Fathy, Ahmed; Rezk, Hegazy; Nassef, Ahmed M.

doi:10.1016/j.renene.2019.02.076

Cited by 64 publications

(23 citation statements)

References 27 publications

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“…The optimizer shifts appliances from peak to off-peak hours for cost reduction, with required operational time to complete a task. Washing machine, dishwasher, dryer, pool pump, and iron are considered as shiftable loads.. ''Expressions (13)(14)(15)(16)(17)(18) represent the appliances' ON/OFF status, the appliance cannot be up and down at the same time, remains OFF outside the customer specified time interval, maximum run hours, minimum up time for the completion of operation and dryer will operate after washing machine ends up, respectively. ''…”

Section: A: Shiftable Loadsmentioning

confidence: 99%

“…Barik and Das made attempts to achieve the active power management for renewable resources present in isolated µG in MATLAB using SSA [12]. Fathy et al presented an optimal EMS to minimize the total hydrogen consumption and fuel cell (FC) degradation using SSA by considering DER constraints [13]. Rezk et al proposed a novel methodology to minimize the operating cost and network losses of a building using SSA [14].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: A: Shiftable Loadsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Risk-Averse Home Energy Management System

Ali

Malik

Raza³

2020

IEEE Access

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“…It uses many approaches and control strategies to get maximum system performance. These control strategies may include a state machine, a classic proportional-integral (PI) control, a fuzzy logic control, an external energy maximization, an equivalent minimization, and a frequency decoupling control strategy [12]. Manoj et al [13] discussed the two main types of MG: Alternative current microgrid (ACMG) and direct current microgrid (DCMG).…”

Section: Introductionmentioning

confidence: 99%

Energy Management of a DC Microgrid Composed of Photovoltaic/Fuel Cell/Battery/Supercapacitor Systems

et al. 2019

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In this paper, a classic proportional–integral (PI) control strategy as an energy management strategy (EMS) and a microgrid stand-alone power system configuration are proposed to work independently out of grid. The proposed system combines photovoltaics (PVs), fuel cells (FCs), batteries, and supercapacitors (SCs). The system supplies a dump load with its demand power. The system includes DC/DC and DC/AC converters, as well as a maximum power point tracking (MPPT) to maximize the harvested energy from PV array. The system advantages are represented to overcome the problem of each source when used individually and to optimize the hydrogen consumption. The classic PI control strategy is used to control the main system parameters like FC current and the state-of-charge (SOC) for the battery and SC. In order to analyze and monitor the system, it was implemented in the MATLAB/Simulink. The simulation done for fuzzy logic and high frequency decoupling and state machine control strategies to validate the PI classic control strategy. The obtained results confirmed that the system works efficiently as a microgrid system. The results show that the SOC for the battery is kept between 56 and 65.4%, which is considered a proper value for such types of batteries. The DC bus voltage (VDC) is kept within the acceptable level. Moreover, the H2 fuel consumption is 12.1 gm, as the FCs are used as supported sources working with the PV. A big area for improvement is available for cost saving, which suggests the need for further research through system optimization and employing different control strategies.

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“…5,6 The most important advantages of these sources are cleanness, environmental protection, availability, and reliability. [7][8][9] Recently, many attempts were presented to enhance the efficacy of the photovoltaic (PV) technology depending on improving the semiconductor materials and improving the electronic interface circuit with the external load to increase the harvested energy. The efficiency of electric power generation based on the PV system (PVS) is low.…”

Section: Introductionmentioning

confidence: 99%

An efficient single‐sensor global maximum power point tracking method for partially shaded photovoltaic battery chargers

Rezk

2019

Int J Energy Res

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SUMMARY Solar energy and other renewables like geothermal, biomass, and wind energy can minimize the release of the CO2 and other harmful gases produced in case of fossil fuel. Low efficiency is the main drawback of the solar photovoltaic system specifically under partial shadowing condition (PSC). Commonly, with uniform solar radiation distribution, the power‐voltage graph has single maximum power point (MPP). The single MPP can be definitely extracted by any traditional tracker like perturb and observe as an example. However, during PSC, the situation is completely different since the power‐voltage curve has many MPPs (ie, multiple local points and single global point). The conventional MPP tracking methods cannot discriminate among local peaks and global peak; consequently, they can be easily trapped on the first local peak. Therefore, smart MPPTs based on modern optimization are required to track the global MPP. Most of MPPT tracking methods in the literature require both voltage and current sensors, and sometimes the control system needs an additional solar irradiance sensor and/or temperature sensor, which increase the system cost. In this paper, for the first time, a simple single‐sensor–based global MPP tracking method for partially shaded photovoltaic battery chargers is proposed. A deterministic particle swarm optimizer is utilized to extract the global MPP. Several patterns of PSC are considered to test and evaluate the proposed strategy. The obtained results confirm the efficacy of a single‐sensor–based global MPP tracking method to catch the global MPP accurately. Considering this research reduces the number of sensors, cost, and difficulty and consequently increases the power density of the MPP tracking methods under partial shadowing conditions.

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Robust hydrogen-consumption-minimization strategy based salp swarm algorithm for energy management of fuel cell/supercapacitor/batteries in highly fluctuated load condition

Cited by 64 publications

References 27 publications

Risk-Averse Home Energy Management System

Risk-Averse Home Energy Management System

Energy Management of a DC Microgrid Composed of Photovoltaic/Fuel Cell/Battery/Supercapacitor Systems

An efficient single‐sensor global maximum power point tracking method for partially shaded photovoltaic battery chargers

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