ANFIS-Based Peak Power Shaving/Curtailment in Microgrids Including PV Units and BESSs

Nikolovski, Srete; Baghaee, Hamid Reza; Mlakić, Dragan

doi:10.3390/en11112953

Cited by 41 publications

(21 citation statements)

References 30 publications

(39 reference statements)

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“…The training set is made up of the first 3000 samples, and the testing set is composed of the remaining 650. In order to compare the effectiveness of the model, six other methods were used in the comparative experiments: an RBF neural network, [36], an ANFIS [37], the original BP model, FABP, FABP with CEEMDAN decomposition (CEEMDAN-FABP), and FABP with VMD decomposition (VMD-FABP). The experimental environment involved the Windows 7 operating system, and all experiments were carried out using MATLAB R2016a on a 3.50 GHz, Intel(R) Core i3-4150M CPU with 6 GB RAM.…”

Section: Resultsmentioning

confidence: 99%

A Hybrid Model Based on a Two-Layer Decomposition Approach and an Optimized Neural Network for Chaotic Time Series Prediction

Ren

2019

Symmetry

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The prediction of chaotic time series has been a popular research field in recent years. Due to the strong non-stationary and high complexity of the chaotic time series, it is difficult to directly analyze and predict depending on a single model, so the hybrid prediction model has become a promising and favorable alternative. In this paper, we put forward a novel hybrid model based on a two-layer decomposition approach and an optimized back propagation neural network (BPNN). The two-layer decomposition approach is proposed to obtain comprehensive information of the chaotic time series, which is composed of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD). The VMD algorithm is used for further decomposition of the high frequency subsequences obtained by CEEMDAN, after which the prediction performance is significantly improved. We then use the BPNN optimized by a firefly algorithm (FA) for prediction. The experimental results indicate that the two-layer decomposition approach is superior to other competing approaches in terms of four evaluation indexes in one-step and multi-step ahead predictions. The proposed hybrid model has a good prospect in the prediction of chaotic time series.

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Section: Resultsmentioning

confidence: 99%

A Hybrid Model Based on a Two-Layer Decomposition Approach and an Optimized Neural Network for Chaotic Time Series Prediction

Ren

2019

Symmetry

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“…Given proper control of storage units and communications with the electricity market, the non-dispatchable renewable power can be smoothed and used on demand, therefore reducing the difficulty of power scheduling in the main grid operation [3]. In many studies that focus on the control of renewable power systems [4][5][6][7][8][9][10][11][12][13][14][15], the battery energy storage system (BESS) is essential for controlling the actual power dispatched to the local customers and the grid. Utilizing forecasting data on renewable power and power demand to arrange BESS actions over different periods, the power constraints in the microgrid and the operating parameters in the main grid can be satisfied.…”

Section: Motivationsmentioning

confidence: 99%

Profit Maximizing Control of a Microgrid with Renewable Generation and BESS Based on a Battery Cycle Life Model and Energy Price Forecasting

Zhuo

Savkin

2019

Energies

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In this paper, an optimal control strategy is presented for grid-connected microgrids with renewable generation and battery energy storage systems (BESSs). In order to optimize the energy cost, the proposed approach utilizes predicted data on renewable power, electricity price, and load demand within a future period, and determines the appropriate actions of BESSs to control the actual power dispatched to the utility grid. We formulate the optimization problem as a Markov decision process and solve it with a dynamic programming algorithm under the receding horizon approach. The main contribution in this paper is a novel cost model of batteries derived from their life cycle model, which correlates the charge/discharge actions of batteries with the cost of battery life loss. Most cost models of batteries are constructed based on identifying charge–discharge cycles of batteries on different operating conditions, and the cycle counting methods used are analytical, so cannot be expressed mathematically and used in an optimization problem. As a result, the cost model proposed in this paper is a recursive and additive function over control steps that will be compatible with dynamic programming and can be included in the objective function. We test the proposed approach with actual data from a wind farm and an energy market operator.

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“…Renewable energy seems to be one of the most significant solutions to energy shortages. Various studies have discussed how to use hybrid renewable energy systems for improving energy efficiency and reducing CO 2 emissions [4][5][6][7][8].…”

Section: Literature Reviewmentioning

confidence: 99%

A Study of Regional Power Generation Efficiency in China: Based on a Non-Radial Directional Distance Function Model

Zhu

Zhou

et al. 2019

Sustainability

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With the deterioration caused by environmental pollution, studies on the low-carbon economy have received more attention. As far as we know, electricity production accounts for a large portion of total carbon emissions. Therefore, based on panel data from 2005 to 2014, we used the non-radial directional distance function model based on the data envelope analysis (DEA) to evaluate the static electricity product efficiency of various provinces in China. Further, the global Malmquist index was introduced to analyze the main factors. The relationship between the environmental policy stringency and the regional power production efficiency was also investigated. Static results show that China's regional power production efficiency has a significant difference between coastal developed and inland areas. Dynamic evaluation further proved that the difference is mainly due to technological change. Finally, regression results show that technological change is the only factor directly affecting regional environmental policy stringency.

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ANFIS-Based Peak Power Shaving/Curtailment in Microgrids Including PV Units and BESSs

Cited by 41 publications

References 30 publications

A Hybrid Model Based on a Two-Layer Decomposition Approach and an Optimized Neural Network for Chaotic Time Series Prediction

A Hybrid Model Based on a Two-Layer Decomposition Approach and an Optimized Neural Network for Chaotic Time Series Prediction

Profit Maximizing Control of a Microgrid with Renewable Generation and BESS Based on a Battery Cycle Life Model and Energy Price Forecasting

A Study of Regional Power Generation Efficiency in China: Based on a Non-Radial Directional Distance Function Model

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