Machine intelligent forecasting based penalty cost minimization in hybrid
            <scp>wind‐battery</scp>
            farms

Dhiman, Harsh S.; Deb, Dipankar; Muyeen, S. M.; Abraham, Ajith

doi:10.1002/2050-7038.13010

Cited by 12 publications

(5 citation statements)

References 31 publications

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“…The authors of ref. [27] discussed a machineintelligence-based forecasting-based technique for penalty cost minimization in hybrid wind-battery farms. In ref.…”

Section: Literature Reviewmentioning

confidence: 99%

Mitigating Generation Schedule Deviation of Wind Farm Using Battery Energy Storage System

et al. 2022

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Meeting the generation schedule in a wind farm is a major issue. This work utilized battery energy storage systems (BESS) integrated wind farms (WF) to supply energy to the power grid at a pre-determined generation schedule, which was set previously based on the meteorological forecast and BESS characteristics. This study proposed the integration of two independently controlled BESS into the WF to balance stochastic power deviations between actual wind power and scheduled power. By utilizing linear optimization and solving in MATLAB, simulation models of the operations of BESS-integrated WF have been developed. The technical performance of the BESS-integrated wind farm on meeting the generation schedule, along with the cost benefits and profit attributed to the BESS, is therefore measured by a series of indices. The simulation on a practical wind farm, i.e., Adama-I WF, Ethiopia shows that even though it depends on the type of state exchanging strategy adopted, the developed methodology of integrating BESS into the WF is effective and BESS profits can totally cover the cost. Technical and economic indices that resulted from the integration of two separate BESSs with independent control were compared with indices that resulted from integrating a single BESS. Simulation results show that operating the wind farm with two independently controlled batteries has better performance as compared to operating with a single battery. It also shows that the discharging and charging state exchanging approaches of the BESS (in the case of two battery integration), as well as the number of batteries integrated into the wind farm, have significant impacts on the performance of the WF integrated with BESS.

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“…The authors of ref. [27] discussed a machineintelligence-based forecasting-based technique for penalty cost minimization in hybrid wind-battery farms. In ref.…”

Section: Literature Reviewmentioning

confidence: 99%

Mitigating Generation Schedule Deviation of Wind Farm Using Battery Energy Storage System

et al. 2022

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“…BESSs are experiencing increasingly being used in various grid-scale applications due to technology development and incentive policies [5]- [8]. BESS could furnish the deficit power in renewable energy power forecasting because of the prediction error [9] and could be applied to determine the best operating strategy of the cluster of multi-hybrid wind farms [10]. Unlike traditional power generation plants, the lifetime of BESSs resulting from aging degradation is highly sensitive to BESS types and dispatch strategies [11].…”

Section: Introductionmentioning

confidence: 99%

Improved Cycle Aging Cost Model for Battery Energy Storage Systems Considering More Accurate Battery Life Degradation

Zhang¹,

et al. 2022

IEEE Access

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Battery energy storage systems (BESSs) have been widely used in power grids to improve their flexibility and reliability. However, the inevitable battery life degradation is the main cost in BESS operations. Thus, an accurate estimation of battery aging cost is strongly needed to cover the actual cost of BESSs. The existing models of battery life degradation either are not fully accurate to estimate the actual cost or are not solved easily because of their computation nonlinearity. In this paper, a piece-wise linear battery aging cost model with an accurate estimate of battery life degradation for BESSs is proposed to extend battery life and improve battery profits. In our method, the widely-used Arrhenius law is modified to quantify the battery life degradation affected by the depth of cycle. Further, a nonlinear battery cycle aging cost model is developed by finding the derivative of battery life degradation with respect to discharging power, which indicates the battery life degradation rate due to depth of cycle. To reduce the complexity of computation, a piece-wise linearization method is proposed to simplify the battery cycle aging cost model. Finally, the cycle aging cost model with an accurate estimation of battery life degradation is applied to the optimization dispatch in the day-ahead energy and auxiliary service market. The results show that the error of estimating the battery cycle aging cost of BESSs is less than 5% under proper piece-wise segment numbers. The profits are increased by 27% and the battery life is extended by 11% than the fixed cost method.

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“…Compared to SVM, ANN and ELM, hybrid TSVR, RFR and CNN models showed improved ramp event prediction. In considering hybrid wind-battery farms, the authors in [8] proposed a penalty-cost solution based on machine intelligent wind forecasting. They compared a wavelet-Twin support vector regression (TSVR)-based windpower forecasting model to Random Forest, ε Twin support vector regression, and Gradient-boosted machines, aiming to mitigate penalty cost.…”

Section: Introductionmentioning

confidence: 99%

Optimized Feature Selection Based on a Least-Redundant and Highest-Relevant Framework for a Solar Irradiance Forecasting Model

2022

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Exogenous and endogenous variables are typically evaluated several times during the selection trial of a predictive model for Global Horizontal Irradiance (GHI). This is accomplished using various statistical measures (e.g., univariate statistical analysis, correlation analysis, etc.) that are applied to gauge redundancy and relevancy in specific variables. The main benefits of these approaches include lower computational cost, fast screening times, accurate measuring of linear and monotonic degrees of variable pairs, and the removal of features with low relevance. However, they cannot identify instances where single or groups of predictor variables are non-monotonically associated with the response variable, nor can they discern whether variables are predictive in combination with other variables or in isolation. The present study attempts to overcome these challenges by first describing monotonic and non-monotonic (Spearman's rho and Hoeffding's D, respectively) correlation statistics in combined usage for locating groups with major non-monotonic endogenous variable changes. The proposed work's novelty is subset evaluation that determines relevance using Weather Recursive Feature Elimination (WRFE). This is a novel hybrid feature reduction method that optimizes feature selection using a Least-Redundant/Highest-Relevant framework. The proposed WRFE utilizes feature importance for measuring variance reduction in Random Forest Regression (RFR) and as data perturbation in Long Short-Term Memory (LSTM). The simulation results of GHI hourly predictions demonstrate that the proposed optimal features of the training subset make the greatest contributions to the prediction target, proving that the high variability of irradiance conditions lowers training subset reliability.The results showed that the proposed WRFE is superior compared to the other models with 1.0927 % for the RMSE and the R 2 coefficient is exceeding 98%. INDEX TERMSFeature importance, redundancy and relevancy measures, exogenous and endogenous variables, recursive feature elimination, long short-term memory, random forest regression, GHI forecasting, data perturbation, variance reduction. NOMENCLATURE RH mean Average relative humidity. µ Mean. ρ P Pearson correlation. ρ S Spearman correlation. σ Standard deviation.

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Machine intelligent forecasting based penalty cost minimization in hybrid wind‐battery farms

Cited by 12 publications

References 31 publications

Mitigating Generation Schedule Deviation of Wind Farm Using Battery Energy Storage System

Mitigating Generation Schedule Deviation of Wind Farm Using Battery Energy Storage System

Improved Cycle Aging Cost Model for Battery Energy Storage Systems Considering More Accurate Battery Life Degradation

Optimized Feature Selection Based on a Least-Redundant and Highest-Relevant Framework for a Solar Irradiance Forecasting Model

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