Distributed, Renewable, and Mobile Resources

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    The Environmental Potential of Hyper-Scale Data Centers: Using Locational Marginal CO2 Emissions to Guide Geographical Load Shifting
    ( 2021-01-05) Lindberg, Julia ; Roald, Line ; Lesieutre, Bernard
    Increasing demand for computing has lead to the development of large-scale, highly optimized data centers, which represent large loads in the electric power network. Many major computing and internet companies operate multiple data centers spread geographically across the world. Thus, these companies have a unique ability to shift computing load, and thus electric load, geographically. This paper provides a "bottom-up" load shifting model which uses data centers' geographic load flexibility to lower CO2 emissions. This model utilizes information about the locational marginal CO2 footprint of the electricity at individual nodes, but does not require direct collaboration with the system operator. We demonstrate how to calculate marginal carbon emissions, and assess the efficacy of our approach compared to a setting where the data centers bid their flexibility into a centralized market. We find that data center load shifting can achieve substantial reductions in CO2 emissions even with modest load shifting.
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    Stochastic Synthetic Data Generation for Electric Net Load and Its Application
    ( 2021-01-05) Liu, Mengwei ; Reed, Patrick ; Anderson, C. Lindsay
    The increasing integration of renewable energy in electric power systems focuses attention on realistic representation of ”net load” because it aggregates the information from both demand and the renewable supply side; net load is the remaining demand that must be met by non-renewable resources. However, the net load data is not readily accessible because of cost, privacy, and security concerns. Furthermore, even if historical data is available, multiple stochastic scenarios are often needed for a wide range of power system applications. To address these issues, this paper proposes a stochastic synthetic net load profile generation approach. A seasonal detrending technique is combined with the modified Fractional Gaussian Noise method to deal with the complex multi-periodic seasonal trends in the net load profile. A thorough statistical validation and temporal correlation check are performed to show the quality of the synthetic data. The benefits of the synthetic data are demonstrated by a microgrid energy management problem.
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    Resilient Distributed Control Approach for Online Voltage Regulation in Distribution Networks under Adversaries
    ( 2021-01-05) Zhao, Tianqiao ; Wang, Jianhui
    This paper proposes a resilient distributed control approach for the voltage regulation problem in distribution networks with high penetration of photovoltaic systems. Aiming to reduce the network power loss and curtailment of photovoltaic active power generation, an objective function is formulated while subjecting to physical operation constraints. With feedback-based information, the proposed solution to optimal voltage regulation can be implemented in an online and distributed manner that ensures a real-time regulation response to fast voltage fluctuations. The proposed approach provides a cyber-secure solution that mitigates attack impacts on voltage control based on a weighted mean subsequence reduced technique. The proposed approach further addresses potential cyber-threats to the information and communication-based control of distributed PV inverters. Numerical studies on the IEEE 37-bus distribution system verify that the proposed approach achieves the optimal voltage regulation performance while ensuring the resilience.
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    On Estimation of Equipment Failures in Electric Distribution Systems Using Bayesian Inference
    ( 2021-01-05) Peerzada, Aaqib ; Begovic, Miroslav M. ; Rohouma, Wesam ; Balog, Robert
    This paper presents a new statistical parametric model to predict the times-to-failure of broad classes of identical devices such as on-load tap changers, switched capacitors, breakers, etc. A two-parameter Weibull distribution with scale parameter given by the inverse power law is employed to model the survivor functions and hazard rates of on-load tap changers. The resulting three-parameter distribution, referred to as IPL-Weibull, is flexible enough to assume right, left, and even symmetrical modal distribution. In this work, we propose an inferential method based on Bayes’ rule to derive the point estimates of model parameters from the past right-censored failure data. Using the Monte Carlo integration technique, it is possible to obtain such parameter estimates with high accuracy.
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    Multi-Agent Learning in Repeated Double-side Auctions for Peer-to-peer Energy Trading
    ( 2021-01-05) Liu, Andrew ; Zhao, Zibo
    Distributed energy resources (DERs), such as rooftop solar panels, are growing rapidly and are reshaping power systems. To promote DERs, feed-in-tariff is usually adopted by utilities to pay DER owners certain fixed rates for supplying energy to the grid. Such a non-market based approach may increase electricity rates and create inefficiency. An alternative is a market based approach; i.e., consumers and DER owners trade energy in a peer-to-peer (P2P) market, in which electricity prices are determined by real-time market supply and demand. A prevailing approach to realize a P2P marketplace is through double-side auctions. However, the auction complexity in an energy market and the participants’ bounded rationality may invalidate many well-established results in auction theory and hence, cast difficulties for market design and implementation. To address such issues, we propose an automated bidding framework based on multi-agent, multi-armed bandit learning through repeated auctions, which is aimed to minimize each bidder’s cumulative regret. Numerical results suggest the potential convergence of such a multi-agent learning game to a steady-state. We also apply the framework to three different auction designs (including uniform-price versus Vickrey-type auctions) for a P2P market to study the impacts of the different designs on market outcomes.