An efficient scenario-based stochastic programming method for optimal scheduling of CHP-PEMFC, WT, PV and hydrogen storage units in micro grids

Bornapour, Mosayeb; Hooshmand, Rahmat‐Allah; Parastegari, Moein

doi:10.1016/j.renene.2018.06.113

Cited by 78 publications

(43 citation statements)

References 37 publications

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“…With being the MPPT converter efficiency and the number of parallel branches. The power ℎ delivered by one series of panels is calculated by (2) (12).…”

Section: Photovoltaic Panelsmentioning

confidence: 99%

“…In (16) (12), is the flow of hydrogen produced by the electrolyser, the flow of hydrogen consumed by the fuel cell. and are the hydrogen molar mass and low heating value.…”

Section: 322mentioning

confidence: 99%

“…Its goal is also to maximise the financial revenue over the station's lifetime. The problem is resolved through the Modified Fireflies Algorithm in a first paper in [5], which is an evolutionary algorithm, and a Teaching-Learning Based Optimisation Algorithm in a second one in [12]. Torreglosa et al implemented a predictive control of a RES with hydrogen storage and compared the performance improvements compared to a myopic strategy [6].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of performance improvement by model predictive control in a renewable energy system with hydrogen storage

Morin

Stevenin²,

Grolleau

et al. 2018

International Journal of Hydrogen Energy

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Nowadays islanded microgrids mostly rely on diesel generator. In order to reduce greenhouse emissions, two islanded microgrids with hydrogen storage have been installed and are currently working autonomously in Reunion Island and France. Energy management implemented on these stations fall in the myopic control category. This study aims to determine the performance improvement that could be achieve on such stations using model predictive control. Ability to supply to the loads and energy losses minimisation are our main objectives. Lifetime degradation is also taken in consideration. Simulations shows that significant improvement can be brought, with a 76% decrease of the station's defaults time and better fill rate of hydrogen tank and batteries. In the meantime, chemical and electrical losses are reduced by 38 and 11%, and the batteries degradations are decreased by around 1%. The relevance of taking into account the electrolyser and fuel cell degradations depends on the time-step of the control. These results give a performance target in order to implement a real-time model predictive control in the microgrids, and eventually can be used to better sizing of future microgrids with similar architecture.

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“…With being the MPPT converter efficiency and the number of parallel branches. The power ℎ delivered by one series of panels is calculated by (2) (12).…”

Section: Photovoltaic Panelsmentioning

confidence: 99%

“…In (16) (12), is the flow of hydrogen produced by the electrolyser, the flow of hydrogen consumed by the fuel cell. and are the hydrogen molar mass and low heating value.…”

Section: 322mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of performance improvement by model predictive control in a renewable energy system with hydrogen storage

Morin

Stevenin²,

Grolleau

et al. 2018

International Journal of Hydrogen Energy

View full text Add to dashboard Cite

show abstract

“…The Monte Carlo method was used to simulate electric vehicle demand and renewable energy generation. Denholm et al [25] argued that a large number of variable power generation (VG) resources can improve system flexibility by changing support technologies such as grid operation and deployment of energy storage, and simulated three different proportions of wind and solar power generation scenarios; Bornapour [26] proposed a stochastic model for coordinated scheduling of renewable heat units for renewable energy power dispatching, considering proton exchange membrane fuel cells, wind and photovoltaics, etc., and then using the improved teaching-learning-based optimization (MTLBO) algorithm to solve the problem; Emanuele et al [27] believed that the integration of variable renewable energy (VRE) improved the flexibility and dispersion of power systems, and that electric vehicles (EVs) can increase the integration of VREs and capture the potential advantages of power systems; Angenendt et al [28] considered the economics of grid-connected economics from the economics of residential photovoltaic cell energy storage. The strategy to evaluate operational strategies by simulating DC-coupled PV and battery systems was expected to reduce power leveling costs by 12%.…”

Section: Introductionmentioning

confidence: 99%

Forecasting China’s Renewable Energy Terminal Power Consumption Based on Empirical Mode Decomposition and an Improved Extreme Learning Machine Optimized by a Bacterial Foraging Algorithm

2019

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The energy consumption pattern dominated by traditional fossil energy has led to global energy resource constraints and the deterioration of the ecological environment. These challenges have become a major issue all over the world. At present, the Chinese government aims to significantly reduce the fossil energy consumption contribution in the terminal energy consumption. The development of renewable energy in the terminal energy and energy conversion links has significantly increased the proportion of clean low-carbon energy. In order to accurately get the proportion of renewable energy terminal power consumption, firstly, this paper selects a primary influencing-factors set including the gross GDP, fixed investment in renewable energy industry, total length of cross-provincial and cross-regional high-voltage transmission lines, etc. as influencing factors of China’s electricity consumption fraction produced by renewable energy based on a multitude of papers. Secondly, from the perspective of signal decomposition, the data inevitably has a lot of interference and noise. This paper uses the empirical mode decomposition (EMD) algorithm to reduce the degree of signal distortion and decomposes the signal into natural modes including several intrinsic mode functions (IMFs) and a residual term (Res); afterwards, a new extreme learning machine (ELM) forecasting model optimized by an Inverse Square Root Linear Units (ISRLU) activation function is proposed, and the ISRLU function is used to replace the implicit layer activation function in the original ELM algorithm. Then, a new bacterial foraging algorithm (BFOA) is applied to optimize the parameters of the optimized ELM forecasting model. After multiple learning and training operations, the optimal parameters are obtained. Finally, we superimpose the output of each IMF and Res training task to get the amount of China’s power consumption produced by renewable energy. Some statistical indicators including root mean squard error (RMSE) are applied to compare the accuracy of several intelligent machine forecasting algorithms. We prove that the proposed forecasting model has higher prediction accuracy and achieves faster training speed by an empirical analysis. Finally, the proposed combined forecasting algorithm is applied to predict China’s renewable energy terminal power consumption from 2018 to 2030. According to the forecasting results, it is found that China’s renewable energy terminal power consumption shows a gradual growth trend, and will exceeded 3300 billion kWh in 2030, which will represent a renewable energy terminal power ratio of about 38% in 2030.

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“…In order to cope with the uncertainties imposed by the forecasting error of PV output, a stochastic optimization programming such as a scenario-based approach has been studied and used to solve the scheduling optimization. The scenario-based approach can deal with the forecast error deriving various scenarios based on a certain probability density function and can minimize the expected cost under certain conditions [7][8][9][10]. The literature [9] provides a stochastic model for an optimal scheduling of REs and thermal units in micro grids to maximize the expected value of profit in electrical market.…”

Section: Introductionmentioning

confidence: 99%

Optimal Scheduling Method of Controllable Loads in Smart Home Considering Re-Forecast and Re-Plan for Uncertainties

et al. 2019

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Renewable energies (REs) such as photovoltaic generation (PV) have been gaining attention in distribution systems. Recently, houses with PV and battery systems, as well as electric vehicles (EV) are expected to contribute to not only the suppression of global warming but also reducing electricity bill on the consumer side. However, there are numerous challenges with the introduction of REs at the demand side such as the actual output of REs often deviating from the forecasted output, which causes fluctuation of the power flow and this is challenging for the distribution or transmission system operator. For this challenge, it is expected that smart grid technology using controllable loads such as a fixed battery or EV battery, can suppress fluctuation of power flow. This paper presents a decision method of optimal scheduling of controllable loads to suppress the fluctuation of power flow by PV output in the smart home. An optimization method to cope with uncertainties such as variability of PV power and effective forecasting methods are considered in the proposed scheme. In order to decrease the expected operational cost and to validate the robustness for the uncertainty’s optimization approach, statistical analysis is executed for the optimal scheduling scheme. From the optimization results, the proposed methodology suppressed the fluctuation of power flow in the smart home and also minimized the consumer operational cost.

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An efficient scenario-based stochastic programming method for optimal scheduling of CHP-PEMFC, WT, PV and hydrogen storage units in micro grids

Cited by 78 publications

References 37 publications

Evaluation of performance improvement by model predictive control in a renewable energy system with hydrogen storage

Evaluation of performance improvement by model predictive control in a renewable energy system with hydrogen storage

Forecasting China’s Renewable Energy Terminal Power Consumption Based on Empirical Mode Decomposition and an Improved Extreme Learning Machine Optimized by a Bacterial Foraging Algorithm

Optimal Scheduling Method of Controllable Loads in Smart Home Considering Re-Forecast and Re-Plan for Uncertainties

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