The power grid is rapidly transforming, and while recent grid innovations increased the utilization of advanced control methods, the next-generation grid demands technologies that enable the integration of distributed energy resources (DERs)and consumers that both seamlessly buy and sell electricity. This paper develops an optimization model and blockchainbased architecture to manage the operation of crowdsourced energy systems (CES), with peer-to-peer (P2P) energy trading transactions. An operational model of CESs in distribution networks is presented considering various types of energy trading transactions and crowdsourcees. Then, a two-phase operation algorithm is presented: Phase I focuses on the day-ahead scheduling of generation and controllable DERs, whereas Phase II is developed for hour-ahead or real-time operation of distribution networks. The developed approach supports seamless P2P energy trading between individual prosumers and/or the utility. The presented operational model can also be used to operate islanded microgrids. The CES framework and the operation algorithm are then prototyped through an efficient blockchain implementation, namely the IBM Hyperledger Fabric. This implementation allows the system operator to manage the network users to seamlessly trade energy. Case studies and prototype illustration are provided.
Abstract-Phasor measurement units (PMUs) can be effectively utilized for the monitoring and control of the power grid. As the cyber-world becomes increasingly embedded into power grids, the risks of this inevitable evolution become serious. In this paper, we present a risk mitigation strategy, based on dynamic state estimation, to eliminate threat levels from the grid's unknown inputs and potential cyber-attacks. The strategy requires (a) the potentially incomplete knowledge of power system models and parameters and (b) real-time PMU measurements. First, we utilize a dynamic state estimator for higher order depictions of power system dynamics for simultaneous state and unknown inputs estimation. Second, estimates of cyber-attacks are obtained through an attack detection algorithm. Third, the estimation and detection components are seamlessly utilized in an optimization framework to determine the most impacted PMU measurements. Finally, a risk mitigation strategy is proposed to guarantee the elimination of threats from attacks, ensuring the observability of the power system through available, safe measurements. Case studies are included to validate the proposed approach. Insightful suggestions, extensions, and open problems are also posed.
Before drinking water leaves water treatment plants, chemical disinfection is typically applied to ensure the microbiological safety of the treated water. Water utilities worldwide rely on chlorine-based disinfectants due to their strong antimicrobial activity and low cost. Excess chlorine is usually applied at the treatment plant to prevent microbial recontamination of the treated drinking water as it moves through the pipes of water distribution networks (WDN).Residual chlorine concentrations are routinely monitored to verify that a sufficient residual is maintained throughout WDN. Maintenance of a detectable residual is also typically mandated by state and federal regulations in many countries. For instance, water utilities in the US are required to preserve detectable chlorine residual throughout their WDNs under the Surface Water Treatment Rule (SWTR) (Haas, 1999), and many states have established even more stringent numerical thresholds on the minimum residual concentration (Roth & Cornwell, 2018).Nevertheless, determining the appropriate chlorine dosage to ensure a sufficient residual, particularly at the far ends of WDNs where the water age is the highest, is rather challenging. Applying large doses of chlorine-based disinfectants at the treatment plant has been associated with multiple issues, including the excessive formation of disinfection byproducts as well as aesthetic issues with water taste and odor (Fisher et al., 2011;Hua et al., 2015). Alternatively, the disinfectant can be injected in smaller doses at multiple locations in the network, a practice commonly known as booster disinfection, to maintain a uniform disinfectant concentration throughout the WDN (Tryby et al., 1999). Most recently, to solve the problem of low disinfectant concentrations at critical dead-end nodes with no need of increasing disinfectant dose at sources or installing additional booster stations, the modulation of nodal outflows in WDN is proposed (Avvedimento et al., 2020). For more context of real-time control of water quality in WDN (see Creaco et al., 2019).
Literature ReviewOver the past two decades, many studies have investigated the water quality control problem (WQC) of optimizing the locations and/or dosing schedules of booster disinfection systems.A wide range of optimization-based methods was used to solve the WQC problem, including linear programming (LP), quadratic programming (QP), heuristic algorithms such as genetic algorithm (GA), and
This paper introduces the Time Synchronization Attack Rejection and Mitigation (TSARM) technique for Time Synchronization Attacks (TSAs) over the Global Positioning System (GPS). The technique estimates the clock bias and drift of the GPS receiver along with the possible attack contrary to previous approaches. Having estimated the time instants of the attack, the clock bias and drift of the receiver are corrected. The proposed technique is computationally efficient and can be easily implemented in real time, in a fashion complementary to standard algorithms for position, velocity, and time estimation in off-the-shelf receivers. The performance of this technique is evaluated on a set of collected data from a real GPS receiver. Our method renders excellent time recovery consistent with the application requirements. The numerical results demonstrate that the TSARM technique outperforms competing approaches in the literature.
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