Ratnesh Sharma worked at NEC Laboratories America, Inc.

Posts

Stochastic Decision-Making Model for Aggregation of Residential Units with PV-Systems and Storages

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Many residential energy consumers have installed photovoltaic (PV) panels and energy storage systems. These residential users can aggregate and participate in the energy markets. A stochastic decision making model for an aggregation of these residential units for participation in two-settlement markets is proposed in this paper. Scenarios are generated using Seasonal Autoregressive Integrated Moving Average (SARIMA) model and joint probability distribution function of the forecast errors to model the uncertainties of the real-time prices, PV generations and demands. The proposed scenario generation model of this paper treats forecast errors as random variable, which allows to reflect new information observed in the real-time market into scenario generation process without retraining SARIMA or re-fitting probability distribution functions over the forecast errors. This approach significantly improves the computational time of the proposed model. A simulation study is conducted for an aggregation of 6 residential units, and the results highlights the benefits of aggregation as well as the proposed stochastic decision-making model.

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Detection of False Data Injection Attacks in Cyber-Physical Systems using Dynamic Invariants

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Modern cyber-physical systems are increasingly complex and vulnerable to attacks like false data injection aimed at destabilizing and confusing the systems. We develop and evaluate an attack-detection framework aimed at learning a dynamic invariant network, data-driven temporal causal relationships between components of cyber-physical systems. We evaluate the relative performance in attack detection of the proposed model relative to traditional anomaly detection approaches. In this paper, we introduce Granger Causality based Kalman Filter with Adaptive Robust Thresholding (G-KART) as a framework for anomaly detection based on data-driven functional relationships between components in cyber-physical systems. In particular, we select power systems as a critical infrastructure with complex cyber-physical systems whose protection is an essential facet of national security. The system presented is capable of learning with or without network topology the task of detection of false data injection attacks in power systems. Kalman filters are used to learn and update the dynamic state of each component in the power system and in-turn monitor the component for malicious activity. The ego network for each node in the invariant graph is treated as an ensemble model of Kalman filters, each of which captures a subset of the node’s interactions with other parts of the network. We finally also introduce an alerting mechanism to surface alerts about compromised nodes.

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Coordination of PV Smart Inverters Using Deep Reinforcement Learning for Grid Voltage Regulation

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Increasing adoption of solar photovoltaic (PV) presents new challenges to modern power grid due to its variable and intermittent nature. Fluctuating outputs from PV generation can cause the grid violating voltage operation limits. PV smart inverters (SIs) provide a fast-response method to regulate voltage by modulating real and/or reactive power at the connection point. Yet existing local autonomous control scheme of SIs is based on local information without coordination, which can lead to suboptimal performance. In this paper, a deep reinforcement learning (DRL) based algorithm is developed and implemented for coordinating multiple SIs. The reward scheme of the DRL is carefully designed to ensure voltage operation limits of the grid are met with more effective utilization of SI reactive power. The proposed DRL agent for voltage control can learn its policy through interaction with massive offline simulations, and adapts to load and solar variations. The performance of the DRL agent is compared against the local autonomous control on the IEEE 37 node system with thousands of scenarios. The results show a properly trained DRL agent can intelligently coordinate different SIs for maintaining grid voltage within allowable ranges, achieving reduction of PV production curtailment, and decreasing system losses.

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Multivariate Long-Term State Forecasting in Cyber-Physical Systems: A Sequence to Sequence Approach

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Cyber-physical systems (CPS) are ubiquitous in several critical infrastructure applications. Forecasting the state of CPS, is essential for better planning, resource allocation and minimizing operational costs. It is imperative to forecast the state of a CPS multiple steps into the future to afford enough time for planning of CPS operation to minimize costs and component wear. Forecasting system state also serves as a precursor to detecting process anomalies and faults. Concomitantly, sensors used for data collection are commodity hardware and experience frequent failures resulting in periods with sparse or no data. In such cases, re-construction through imputation of the missing data sequences is imperative to alleviate data sparsity and enable better performance of down-stream analytic models. In this paper, we tackle the problem of CPS state forecasting and data imputation and characterize the performance of a wide array of deep learning architectures – unidirectional gated and non-gated recurrent architectures, sequence to sequence (Seq2Seq) architectures as well as bidirectional architectures – with a specific focus towards applications in CPS. We also study the impact of procedures like scheduled sampling and attention, on model training. Our results indicate that Seq2Seq models are superior to traditional step ahead forecasting models and yield an improvement of at least 28.5% for gated recurrent architectures and about 87.6% for non-gated architectures in terms of forecasting performance. We also notice that bidirectional models learn good representations for forecasting as well as for data imputation. Bidirectional Seq2Seq models show an average improvement of 17.6% in forecasting performance over their unidirectional counterparts. We also demonstrate the effect of employing an attention mechanism in the context of Seq2Seq architectures and find that it provides an average improvement of 57.12% in the case of unidirectional Seq2Seq architectures while causing a performance decline in the case of bidirectional Seq2Seq architectures. Finally, we also find that scheduled sampling helps in training better models that yield significantly lower forecasting error.

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VeCharge: Intelligent Energy Management for Electric Vehicle charging

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2018’s 1.2 million North American charging ports will grow ten times to over 12.6 million by 2027, according to Navigant, which could overwhelm the nation’s grids. DC Fast charging requires grid upgrade to supply the new charging demand. However, since the utilization ratio of those charging station is currently low. Demand charge cost can reach up to 90% of the total bill. Combining fast charging with energy storage can mitigate grid impacts and reduce demand charges. EV specific pricing is proposed for EV charging by many energy suppliers. Without managed charging, EV owner will lose the benefit of lowering charging cost by avoiding peak hour charging or missing the period when renewable energy generation is abundant.

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Aggregation of BTM Battery Storages to Provide Ancillary Services in Wholesale Electricity Markets

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The behind the meter battery energy storage systems (BTM-BESSs) have been deployed widely by indus-trial/commercial buildings to manage electricity transaction with utilities in order to reduce customers’ electricity bills. Commercial BTM battery storages are mainly employed to cut the customers’ monthly demand peaks, which is rewarded by significant decrease in the monthly demand charge. However, given complexity of demand charge management problems, the rates of return on investments for installation of BTM-BESSs are not appealing enough. In this paper, an aggregation model for BTM-BESSs is proposed in order to provide the opportunity for the BTM-EMS units to participate in the multiple wholesale markets to provide ancillary services, in addition to the demand charge management, to maximize owners’ payoff from installation of BTM-BESSs. Finally, the efficiency of the proposed aggregation model is validated through the simulation studies on the real value data.

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Decentralized Transactive Energy Auctions with Bandit Learning

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The power systems worldwide have been embracing the rapid growth of distributed energy resources. Commonly, distributed energy resources exist in the distribution level, such as electric vehicles, rooftop photovoltaic panels, and home battery systems, which cannot be controlled by a centralized entity like a utility. However, a large number of distributed energy resources have potential to reshape the power generation landscape when the owners (prosumers) are allowed to send electricity back to the grids. Transactive energy paradigms are emerging for orchestrating the coordination of prosumers and consumers by enabling the exchange of energy among them. In this paper, we propose a transactive energy auction framework based on blockchain technology for creating trustworthy and transparent transactive environments in distribution networks, which does not rely on a centralized entity to clear transactions. Moreover, we propose intelligent decentralized decision-making strategies by bandit learning for market participants to locally decide their energy prices in auctions. The bandit learning approach can provide market participants with more benefits under the blockchain framework than trading energy with the centralized entity, which is further supported by the preliminary simulated results conducted over our blockchain-based platform.

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Energy Predictive Models with Limited Data using Transfer Learning

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In this paper, we consider the problem of developing predictive models with limited data for energy assets such as electricity loads, PV power generations, etc. We specifically investigate the cases where the amount of historical data is not sufficient to effectively train the prediction model. We first develop an energy predictive model based on convolutional neural network (CNN) which is well suited to capture the interaday, daily, and weekly cyclostationary patterns, trends and seasonalities in energy assets time series. A transfer learning strategy is then proposed to address the challenge of limited training data. We demonstrate our approach on a usecase of daily electricity demand forecasting. we show practicing the transfer learning strategy on the CNN model results in significant improvement to existing forecasting methods.

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Learning from Rules Performs as Implicit Regularization

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In this paper, we study the generalization performance of deep neural networks in learning problems where the given task is governed by a set of rules. We consider two settings of supervised learning and rule-based learning. In supervised learning, the network is trained with pairs of inputs and the corresponding solutions that satisfy the problem constraints. In rule-based learning, the constraints are encoded into a neural network module that is applied on the output of the solver network. In this approach, instead of training with any actual solutions of the problem, the model will be trained to explicitly satisfy the constraints. We perform the experiments on two problems of solving a system of nonlinear equations and solving Sudoku puzzles. Our experimental results show that, compared to supervised approach, rule-based learning results in higher training error, but significantly lower validation error, especially when training data is small, thus performing as an implicit regularization.

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Transactive Energy Management with Blockchain Smart Contracts for P2P Multi-Settlement Markets

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Integration of renewables and energy storage, leading to rise of prosumers, has created localized bidirectional flows. As the result, the utility demand has decreased and traditional centralized controller can no longer realize the optimal performance of ever growing distribution systems. To achieve scalable control, exploiting the potential of smart loads and Distributed Energy Resource (DER) controllability, a framework for decentralized Peer-To-Peer (P2P) energy management has been developed to manage localized micro-energy markets. Such decentralized management approach could, in theory, sustain diverse prosumer and utility business models. We have been developing an autonomous decentralized management solution that maximizes the benefit of prosumers while protecting utility assets. This P2P energy trading market leverages Blockchain technology and its Smart Contract framework. This paper presents 1) transactive energy market for P2P multi-settlement markets, 2) architecture of blockchain-based energy management system, 3) smart contract design that solves an economic dispatch problem of DERs to maximize the profit of pro/consumers.

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