Abstract-We present an architecture for peer-to-peer energy markets which can guarantee that operational constraints are respected and payments are fairly rendered, without relying on a centralized utility or microgrid aggregator. We demonstrate how to address trust, security, and transparency issues by using blockchains and smart contracts, two emerging technologies which can facilitate decentralized coordination between nontrusting agents. While blockchains are receiving considerable interest as a platform for distributed computation and data management, this is the first work to examine their use to facilitate distributed optimization and control. Using the Alternating Direction Method of Multipliers (ADMM), we pose a decentralized optimal power flow (OPF) model for scheduling a mix of batteries, shapable loads, and deferrable loads on an electricity distribution network. The DERs perform local optimization steps, and a smart contract on the blockchain serves as the ADMM coordinator, allowing the validity and optimality of the solution to be verified. The optimal schedule is securely stored on the blockchain, and payments can be automatically, securely, and trustlessly rendered without requiring a microgrid operator.

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Abstract-This paper studies a state estimation scheme for a reduced electrochemical battery model, using voltage and current measurements. Real-time electrochemical state information enables high-fidelity monitoring and high-performance operation in advanced battery management systems, for applications such as consumer electronics, electrified vehicles, and grid energy storage. This paper derives a single particle model with electrolyte (SPMe) that achieves higher predictive accuracy than the single particle model (SPM). Next, we propose an estimation scheme and prove estimation error system stability, assuming the total amount of lithium in the cell is known. The state estimation scheme exploits dynamical properties such as marginal stability, local invertibility, and conservation of lithium. Simulations demonstrate the algorithm's performance and limitations.

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Newcastle University ePrints -eprint.ncl.ac.uk Wu X, Hu X, Moura S, Yin X, Pickert V. Stochastic control of smart home energy management with plug-in electric vehicle battery energy storage and photovoltaic array. AbstractEnergy management strategies are instrumental in the performance and economy of smart homes integrating renewable energy and energy storage. This article focuses on stochastic energy management of a smart home with PEV (plug-in electric vehicle) energy storage and photovoltaic (PV) array. It is motivated by the challenges associated with sustainable energy supplies and the local energy storage opportunity provided by vehicle electrification. This paper seeks to minimize a consumer's energy charges under a time-of-use tariff, while satisfying home power demand and PEV charging requirements, and accommodating the variability of solar power. First, the randomvariable models are developed, including Markov Chain model of PEV mobility, as well as predictive models of home power demand and PV power supply. Second, a stochastic optimal control problem is mathematically formulated for managing the power flow among energy sources in the smart home. Finally, based on time-varying electricity price, we systematically examine the performance of the proposed control strategy. As a result, the electric cost is 493.6% less for a Tesla Model S with optimal stochastic dynamic programming (SDP) control relative to the no optimal control case, and it is by 175.89% for a Nissan Leaf.

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9Electric vehicles enable clean and efficient transportation, however concerns about range anxiety 10 and battery degradation hinder EV adoption. The common definition for battery end-of-life is 11 when 70-80% of original energy capacity remains, however little analysis is available to support 12 this retirement threshold. By applying detailed physics-based models of EVs with data on how 13 drivers use their cars, we show that EV batteries continue to meet daily travel needs of drivers 14 well beyond capacity fade of 80% remaining energy storage capacity. Further, we show that EV 15 batteries with substantial energy capacity fade continue to provide sufficient buffer charge for 16 unexpected trips with long distances. We show that enabling charging in more locations, even if 17 only with 120V wall outlets, prolongs useful life of EV batteries. Battery power fade is also 18 examined and we show EVs meet performance requirements even down to 30% remaining 19 power capacity. Our findings show that defining battery retirement at 70-80% remaining 20 capacity is inaccurate. Battery retirement should instead be governed by when batteries no longer 21 M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT satisfy daily travel needs of a driver. Using this alternative retirement metric, we present results 22 on the fraction of EV batteries that will be retired with different levels of energy capacity fade. 23 24 Key Words: electric vehicles, battery degradation, capacity fade, power fade, battery second 25 life, battery retirement 26