Regression Analysis of Grid Stability under Decentralized Control

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordDecentralized smart grid control (DSGC) aims to implement smart control strategies through the demand response without significant reinforcements in the grid infrastructure. This strategy aims to balance the demand and supply considering the dynamic and economic price structure of the grid. Demand response strategy is implemented based on the heterogeneous nature of response of the consumers to the electricity price. In this paper, feature selection and regression analysis have been performed to study the dependence of the stability condition and system parameters on the parameters of decentralized control like the reaction time of the consumer, power produced or consumed and the price elasticity. The analysis is performed based on the data generated considering 10,000 Monte Carlo simulations of the initial conditions, which incorporates different characteristics of the heterogeneous consumers

Economic operational analytics for energy storage placement at different grid locations and contingency scenarios with stochastic wind profiles

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.The placement of energy storage systems (ESS) in smart grids is challenging due to the high complexity of the underlying model and operational datasets. In this paper, non-parametric multivariate statistical analyses of the energy storage operations in base and contingency scenarios are carried out to address these issues. Monte Carlo simulations of the optimization process for the overall cost involving unit commitment and dispatch decisions are performed with different wind and load demand ensembles. The optimization is performed for different grid contingency scenarios like transmission line trips and generator outages along with the location of the ESS in different parts of the grid. The stochastic mixed-integer programming technique is used for optimization. The stochastic model load demand and wind power are obtained from real data. The uncertainty in the operational decisions is obtained, considering the different stochastic realizations of load demand and wind power. The data analytics is performed on ESS operations in the base and its corresponding contingency scenarios with different locations in the grid. Moreover, it is aided by non-parametric multivariate hypothesis tests to understand their dependence amongst various parameters and locations in the grid. The numerical analysis has been shown on a simple 3-bus system considering all the locational and contingency scenarios.F ERDF Cornwall New Energy (CNE

Hyperparameter optimized classification pipeline for handling unbalanced urban and rural energy consumption patterns

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record Data availability: Data will be made available on request.Energy consumer locations are required for framing effective energy policies. However, due to privacy concerns, it is becoming increasingly difficult to obtain the locational data of the consumers. Machine learning (ML) based classification strategies can be used to find the locational information of the consumers based on their historical energy consumption patterns. The ML methods in this paper are applied to the Residential Energy Consumption Survey 2009 dataset. In this dataset, the number of consumers in the urban area is higher than the rural area, thus making the classification problem unbalanced. The unbalanced classification problem has been solved in original and transformed or reduced feature space using Monte Carlo based under-sampling of the majority class datapoints. The hyperparameters for each classification algorithm family is represented as an optimized pipeline, obtained using the genetic programming (GP) optimizer. The classification performance metrics are then obtained for different algorithm families on the original and transformed feature spaces. Performance comparisons have been reported using univariate and bivariate distributions of the classification metrics viz. accuracy, geometric mean score (GMS), F1 score, precision, area under the curve (AUC) of receiver operator characteristics (ROC). The energy policy aspects for the urban and rural residential consumers based on the classification results have also been discussed.European Regional Development Fund (ERDF

Prioritized experience replay based deep distributional reinforcement learning for battery operation in microgrids

This is the author accepted manuscript. The final version is available on open access from Elsevier via the DOI in this recordData availability: Data will be made available on request.Reinforcement Learning (RL) provides a pathway for efficiently utilizing the battery storage in a microgrid. However, traditional value-based RL algorithms used in battery management focus on formulating the policies based on the reward expectation rather than its probability distribution. Hence the scheduling strategy is solely based on the expectation of the rewards rather than the distribution. This paper focuses on scheduling strategy based on probability distribution of the rewards which optimally reflects the uncertainties in the incoming dataset. Furthermore, the prioritized experience replay samples of the training experience are used to enhance the quality of the learning by reducing bias. The results are obtained with different variants of distributional RL algorithms like C51, Quantile Regression Deep Q-Network (QR-DQN), Fully Quantizable Function (FQF), Implicit Quantile Networks (IQN) and rainbow. Moreover, the results are compared with the traditional deep Q-learning algorithm with prioritized experienced replay. The convergence results on the training dataset are further analyzed by varying the action spaces, using randomized experience replay and without including the tariff-based action while enforcing the penalties for violating battery SoC limits. The best trained Q-network is tested with different load and PV profiles to obtain the battery operation and costs. The performance of the distributional RL algorithms is analyzed under different schemes of Time of Use (ToU) tariff. QR-DQN with prioritized experience replay has been found to be the best performing algorithm in terms of convergence on the training dataset, with least fluctuation in validation dataset and battery operations during different tariff regimes during the day.European Regional Development Fun

Cross-correlation based classification of electrical appliances for non-intrusive load monitoring

This is the author accepted manuscript. The final version is available from the Institute of Electrical and Electronics Engineers via the DOI in this recordOver the last few decades, residential electrical load classification and identification have been one of the most challenging research in the area of non-intrusive load monitoring (NILM) for home energy management system. The application of NILM technique in the smart grid has gained enormous attention in recent years. Several methods, including information from the given domains into NILM, have been proposed. Recently, among these methods, machine learning techniques are shown to be significantly better based on large-scale data for load monitoring. In this paper, machine learning techniques are utilized for residential load classification on novel cross-correlation based features, which are extracted from the synthetic time series data. We also present a t-distributed stochastic neighbour embedding (t SNE) based dimensionality reduction from the high dimensional feature set so that the classification can be implemented on a general-purpose microcontroller for near real-time monitoring. Our experimental results show that the extracted features are suitable for reliable identification and classification of different and the combination of residential loads.Visvesvaraya PhD scheme, Government of Indi

A readily accessible porous organic polymer facilitates high-yielding Knoevenagel condensation at room temperature both in water and under solvent-free mechanochemical conditions

A novel nitrogen-rich amorphous porous organic polymer has been synthesized using a microwave-assisted process. Its high chemical stability, reusability and poor solubility enable the use of the porous polymer as a metal-free heterogeneous catalyst for C–C bond formation at ambient temperature under environmentally benign conditions

Quality of private and public ambulatory health care in low and middle income countries: systematic review of comparative studies

Paul Garner and colleagues conducted a systematic review of 80 studies to compare the quality of private versus public ambulatory health care in low- and middle-income countries