Optimal Allocation of Hybrid Energy Storage Systems for Smoothing Photovoltaic Power Fluctuations Considering the Active Power Curtailment of Photovoltaic

Ma, Wei; Wang, Wei; Wu, Xuezhi; Hu, Ruonan; Tang, Fen; Zhang, Weige; Han, Xiaoyan; Ding, Lu

doi:10.1109/access.2019.2921316

Cited by 62 publications

(42 citation statements)

References 40 publications

(70 reference statements)

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“…In [14], solar active power curtailment is implemented along with designing an optimal allocation model for the hybrid ESS to smooth the power fluctuations.…”

Section: Related Workmentioning

confidence: 99%

Saviztky–Golay Filtering for Solar Power Smoothing and Ramp Rate Reduction Based on Controlled Battery Energy Storage

Atif

Khalid

2020

IEEE Access

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Energy is present in every touch we make in our modern life. However, with the increase of energy-consuming devices, there is a burden to meet this demand with a clean power generation. Although solar and wind are considered as clean and renewable energy resources, many issues engender with their higher penetration into electricity grids. However, when renewable energy resources are integrated with battery energy storage systems (BESS), more smoothed and easily dispatchable power can be obtained. This paper investigates the smoothing quality of the solar photovoltaic power output with the help of BESS using a couple of approaches such as low pass filtering (LPF), moving average (MA) filtering, Gaussian filter (GF) and Saviztky-Golay (S-G) filter. Obviously, the smoothed dispatchable power has been achieved with all mentioned methods, however, the performance of moving average and low pass filters is not acceptable comparatively especially when longer window size and time constant are used, which consequently deteriorates the performance of storage system. In contrast, the paper introduces using the Savitsky-Golay filter to reduce battery ramp rate and battery charging and discharging power while smoothing the solar power fluctuations. The simulation results depict the performance of the proposed smoothing filter and compare its performance against MA, LPF and GF.

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“…In [14], solar active power curtailment is implemented along with designing an optimal allocation model for the hybrid ESS to smooth the power fluctuations.…”

Section: Related Workmentioning

confidence: 99%

Saviztky–Golay Filtering for Solar Power Smoothing and Ramp Rate Reduction Based on Controlled Battery Energy Storage

Atif

Khalid

2020

IEEE Access

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“…When grid-tied, the zero cost always win in ED problem. When running in the islanded mode, the main goal of the microgrid operation is to stabilize the fluctuation, ensure the critical load power supply, keep the system safe and stable, its priority is higher than the economic target [32].…”

Section: B Agent Descriptions and Cost Functions 1) Renewable Energymentioning

confidence: 99%

Multi-Agent Based Fully Distributed Economic Dispatch in Microgrid Using Exact Diffusion Strategy

Wang

2020

IEEE Access

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To solve the economic dispatch (ED) problem of multiple distributed generations in the microgrid, this paper proposes a fully distributed method by using the exact diffusion strategy which can achieve exact convergence under sparse communication network. Firstly, a multi-agent hierarchical control architecture for the microgrid is designed. The microgrid central controller (MGCC) agent does not act as centralized control but instead participates in the distributed optimization between the bottom layer agents. Furthermore, an economic dispatch optimization model is established which aims at minimizing the operation cost, optimizing the active power of all dispatchable agents including distributed generators and flexible loads on demand side, and realizing the economic utilization of resources. A modified diffusion algorithm supporting full distributed optimization is proposed to solve it, which does not depend on the ''leader unit'' and is applicable to the actual problem. The modified algorithm has faster convergence speed, higher stability and stronger expansibility than consensus algorithm. Six scenarios are adopted to verify the proposed method. The result shows that the agent framework and the algorithm can cope with changing of communication topology and realize the ''plug and play''. The output will converge to the optimal dispatch solution even if communication fails. INDEX TERMS Consensus, diffusion algorithm, economic dispatch problem, fully distributed control, multi-agent system.

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“…In [10], a deterministic forecasting module is presented in a smart energy management system to optimize the operation of the microgrid. In addition, the PV forecasting technology is widely used in hybrid integrated energy systems [11][12][13], real-time electric vehicle charging scheduling and management [14,15], multi-objective optimization tasks [16,17], robust planning electric vehicle charging facilities [18], and smart home energy management [19].…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic Power Forecasting With a Hybrid Deep Learning Approach

et al. 2020

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Solar energy is the key to clean energy, which can generate large amounts of electricity for the future smart grid. Unfortunately, the randomness and intermittency of solar energy resources bring difficulties to the stable operation and management of the power systems. To reduce the negative impact of photovoltaic (PV) plants accessing on the power systems, it is great significant to predict PV power accurately. In light of this, we propose a hybrid deep learning approach based on convolutional neural network (CNN) and long-short term memory recurrent neural network (LSTM) for the PV output power forecasting. The CNN model is leveraged to discover the nonlinear features and invariant structures exhibited in the previous output power data, thereby facilitating the prediction of PV power. The LSTM is used to model the temporal changes in the latest PV data, and predict the PV power of next time step. Then, the prediction results in the two models are comprehensively considered to obtain the expected output power. The proposed approach is extensively evaluated on real PV data in Limberg, Belgium, and numerical results demonstrate that the proposed approach can provide good prediction performance in PV systems. INDEX TERMS Solar energy, deep learning, photovoltaic (PV) power forecasting, power systems.

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Optimal Allocation of Hybrid Energy Storage Systems for Smoothing Photovoltaic Power Fluctuations Considering the Active Power Curtailment of Photovoltaic

Cited by 62 publications

References 40 publications

Saviztky–Golay Filtering for Solar Power Smoothing and Ramp Rate Reduction Based on Controlled Battery Energy Storage

Saviztky–Golay Filtering for Solar Power Smoothing and Ramp Rate Reduction Based on Controlled Battery Energy Storage

Multi-Agent Based Fully Distributed Economic Dispatch in Microgrid Using Exact Diffusion Strategy

Photovoltaic Power Forecasting With a Hybrid Deep Learning Approach

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