This paper proposes a new model of scenario-based security-constrained unit commitment (SCUC) with BESSs. By formulating such a model as a mixed-integer programming (MIP) problem, we can obtain the ...optimal control strategy of units and BESSs to reduce the operating cost. To solve this MIP with the proposed model, we propose a new learning-based approach to tackle the SCUC problem. The proposed convolutional neural network (CNN)-based SCUC algorithm (CNN-SCUC) has two main stages. First, CNN-SCUC trains a CNN to obtain solutions to the binary variables corresponding to unit commitment decisions. Then, the continuous variables corresponding to unit power outputs are solved by a small-scale convex optimization problem. In contrast to existing work, CNN-SCUC eliminates the need of explicitly considering the scenario-based security constraints in the optimization problem, and thus greatly reduces the computational complexity. The average gap to the optimal solution is as small as 0.0267%. The algorithm is also scalable in the sense that the computational time is reduced from about 1236.32 seconds to 0.8379 seconds in a 10-unit and 200-scenario system. Besides, the computation time remains almost constant when the number of scenarios increases. Case studies show that compared with the traditional scenario-based SCUC model, more than 4.70% operating cost reduction is achieved by incorporating BESSs in the system.
Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has recently emerged as a cost-effective solution to provide computation service to distributed devices in the absence of terrestrial ...infrastructure. In this paper, we consider a UAV-enabled MEC system serving multiple energy harvesting (EH) devices, where the energy and task data arrive at the users stochastically. Without any future knowledge of task data and energy arrivals, our objective is to design an online algorithm to jointly optimize the UAV energy and task processing rate, meanwhile satisfying the long-term data queue stability. We formulate the problem as a multi-stage stochastic programming and propose an online algorithm, named PLOT, based on perturbed Lyapunov optimization technique. In particular, PLOT resolves the coupling effect of sequential control actions, and converts the stochastic problem into per-slot deterministic optimization problem. For each per-slot problem, we design a low-complexity algorithm to solve it. We show that the PLOT algorithm can derive a feasible solution to the original problem and achieve an <inline-formula> <tex-math notation="LaTeX">O(1/V),O(V) </tex-math></inline-formula> trade-off between the system cost and the data queue length. Simulation results justify our analysis and demonstrate that the PLOT algorithm achieves better performance in terms of system utility and maintains queue stability that is not achieved by other benchmark methods.
The rapid emergence of electric vehicles (EVs) demands an advanced infrastructure of publicly accessible charging stations that provide efficient charging services. In this paper, we propose a new ...charging station operation mechanism, the Joint Admission and Pricing (JoAP), which jointly optimizes the EV admission control, pricing, and charging scheduling to maximize the charging station's profit. More specifically, by introducing a tandem queueing network model, we analytically characterize the average charging station profit as a function of the admission control and pricing policies. Based on the analysis, we characterize the optimal JoAP algorithm. Through extensive simulations, we demonstrate that the proposed JoAP algorithm on average can achieve 330% and 531% higher profit than a widely adopted benchmark method under two representative waiting-time penalty rates.
In this paper, we study the optimal location planning of renewable distributed generation (RDG) units by taking into account the random uncertainties of renewable generation and load demand. In ...presence of the random uncertainties, location planning problem is naturally a two-stage stochastic mixed integer nonlinear programming problem, which is hard to solve efficiently. Instead of using traditional sampling methods or robust optimization methods, we propose a novel analytical approach in this paper to solve the problem efficiently and optimally. In particular, analytical expressions are derived for efficiently evaluating the performance of a candidate RDG placement decision. In this way, the stochastic mixed integer nonlinear programming problem is equivalently transformed into a deterministic integer problem, which can be solved efficiently using off-the-shelf tools. Numerical results show that the optimal RDG placement can save up to 4.2% of the long-term average cost and 80.59% of the line losses on the IEEE 13-bus test feeder. In addition, our proposed approach effectively reduces the computational time by 99.51% on the IEEE 123 node test feeder compared with other traditional sampling-based metaheuristic approaches.
To exploit massive amounts of data generated at mobile edge networks, federated learning (FL) has been proposed as an attractive substitute for centralized machine learning (ML). By collaboratively ...training a shared learning model at edge devices, FL avoids direct data transmission and thus overcomes high communication latency and privacy issues as compared to centralized ML. To improve the communication efficiency in FL model aggregation, over-the-air computation has been introduced to support a large number of simultaneous local model uploading by exploiting the inherent superposition property of wireless channels. However, due to the heterogeneity of communication capacities among edge devices, over-the-air FL suffers from the straggler issue in which the device with the weakest channel acts as a bottleneck of the model aggregation performance. This issue can be alleviated by device selection to some extent, but the latter still suffers from a tradeoff between data exploitation and model communication. In this paper, we leverage the reconfigurable intelligent surface (RIS) technology to relieve the straggler issue in over-the-air FL. Specifically, we develop a learning analysis framework to quantitatively characterize the impact of device selection and model aggregation error on the convergence of over-the-air FL. Then, we formulate a unified communication-learning optimization problem to jointly optimize device selection, over-the-air transceiver design, and RIS configuration. Numerical experiments show that the proposed design achieves substantial learning accuracy improvement compared with the state-of-the-art approaches, especially when channel conditions vary dramatically across edge devices.
Millimeter-wave (mmWave) is highly susceptible to obstacles and requires significant directivity of beams. To address these challenges, a promising solution is to deploy antenna arrays at base ...stations (BSs) and reconfigurable intelligent surfaces (RISs). Antenna arrays enable 3D beamforming and provide highly directional beams, while RISs create favorable transmission environments. In this paper, we propose an analytical framework to quantify the coverage performance of the RIS-assisted mmWave cellular network with 3D beamforming. Modeling obstacles and RISs with a line Boolean model and BSs with a Poisson point process (PPP), we provide the distance distribution between a UE and its associated RIS by evaluating the impact of the RIS orientation. Moreover, we adopt a 3D sectorized flat-top antenna model to characterize the interference introduced by dynamic directional beams (i.e., mainlobe/sidelobe). Furthermore, we derive the signal-to-interference ratio (SIR) coverage probability, where an equivalent PPP is proposed to enhance the computation efficiency. Numerical results validate the accuracy of our analysis and show that the proposed network achieves significant coverage improvement. We also investigate the impacts of the key parameters on the coverage probability, providing useful insights for deploying RISs and antenna arrays in mmWave cellular networks.
Background: The diagnosis of hypertension should be based on the mean of two or more properly measured BP readings on each of two visits for clinical practice, but a one-visit strategy was applied in ...most epidemiological surveys. The impact of hypertension definition based on two visits on estimates of hypertension burden is unknown. This study aims to assess the impact of hypertension diagnosis based on a two-visit strategy for estimating hypertension burden in China. Methods: The one-visit and two-visit strategies were applied to investigate the incidence of hypertension in a cohort study based on the China Health and Nutrition Survey (CHNS) 1989–2011. Additionally the prevalence of hypertension was investigated in a cross-sectional study based on the CHNS 2006–2009/2011 and the hypertension burden in China was estimated with data from the 2012–2015 China hypertension survey. Results: Overall, the age-adjusted incidence of hypertension based on the two-visit strategy (1.82%; 95% confidence interval CI, 1.74–1.90%) was 62.1% lower than estimation based on the one-visit strategy (4.80%; 95% CI, 4.68–4.93%). Similar results were found in the prevalence of hypertension (one-visit: 18.13% 95% CI, 17.34–18.92%; two-visit: 9.47% 95% CI, 8.87–10.07%). When the two-visit strategy was applied to the 2012–2015 China hypertension survey, the hypertension burden was predicted to be overestimated by 25.5–47.8% (based on JNC 7) and 23.5–48.2% (based on the 2017 ACC/AHA). Conclusion: The hypertension burden would decrease from 244.5 million persons to 127.5–182.3 million persons in China if the two-visit strategy was applied.
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This paper develops a systematic approach to derive the optimal bidding strategy for prosumers in a distribution-level energy market. In particular, the optimal bidding curve describes the ...cost-minimizing buying/selling strategy of prosumers and is composed of a limited number of linear segments. As such, only a small number of end points of the linear segments are sufficient to exactly describe the optimal bidding curve. This not only guarantees the optimality of bidding decisions, but also greatly reduces the computation and communication overhead compared with existing sampling-based approaches. By leveraging the proposed systematic bidding strategy, we propose a novel market clearing scheme for the distribution system operator (DSO) to coordinate the energy transaction among prosumers in a local distribution area (LDA). Specifically, when all prosumers, either grid-connected or isolated, follow the proposed optimal bidding strategy, the DSO is able to achieve a global optimal distributed energy resource (DER) dispatch in a decentralized manner. We verify the proposed bidding strategy and market clearing scheme in two practical scenarios, i.e., a prosumer with 34 buses in a distribution-level energy market and an LDA with four 13-bus microgrids. Numerical results demonstrate the superior performance of our proposed approach compared with other benchmarks.
Distribution grid agents are obliged to exchange and disclose their states explicitly to neighboring regions to enable distributed optimal power flow dispatch. However, the states contain sensitive ...information of individual agents, such as voltage and current measurements. These measurements can be inferred by adversaries, such as other participating agents or eavesdroppers, leading to the privacy leakage problem. To address the issue, we propose a privacy-preserving distributed optimal power flow (OPF) algorithm based on partially homomorphic encryption (PHE). First of all, we exploit the alternating direction method of multipliers (ADMM) to solve the OPF in a distributed fashion. In this way, the dual update of ADMM can be encrypted by PHE. We further relax the augmented term of the primal update of ADMM with the <inline-formula> <tex-math notation="LaTeX">\ell _{1} </tex-math></inline-formula>-norm regularization. In addition, we transform the relaxed ADMM with the <inline-formula> <tex-math notation="LaTeX">\ell _{1} </tex-math></inline-formula>-norm regularization to a semidefinite program (SDP), and prove that this transformation is exact. The SDP can be solved locally with only the sign messages from neighboring agents, which preserves the privacy of the primal update. At last, we strictly prove the privacy preservation guarantee of the proposed algorithm. Numerical case studies validate the effectiveness and exactness of the proposed approach. In particular, the case studies show that the encrypted messages cannot be inferred by adversaries. Besides, the proposed algorithm obtains the solutions that are very close to the global optimum, and converges much faster compared to competing alternatives.
Iron alginate is chosen as an eco‐friendly synergist to improve the flame retardancy, smoke suppression, and mechanical properties of epoxy resin/ammonium polyphosphate composites (EP/APP). The ...suitable additive amount of iron alginate further enhances the char‐forming ability in the higher‐temperature range and flame retardancy of EP/APP. EP/APP9.0‐iron alginate1.0 retains a char residue of 33.3% at 700 °C and obtains a limiting oxygen index value of 28.4% and vertical burning test (UL‐94) V‐0 rating, while EP/APP10 has no UL‐94 rating. The burning behaviors of EP/APP9.0‐iron alginate1.0 are also suppressed; and the total smoke production value is much lower than that of EP/APP10. EP/APP9.0‐iron alginate1.0 releases less smoke and flammable fragments. The suitable additive amount of iron alginate boosts the mechanical properties of EP/APP, while APP destroys the mechanical properties of EP. Therefore, the addition of suitable amount of iron alginates can further reduce the fire hazard, and improve the mechanical properties of EP/APP composites.
Bio‐based iron alginate is chosen as an eco‐friendly synergist and mixed with ammonium polyphosphate (APP) into epoxy resin (EP) aiming to enhance the flame retardancy and smoke suppression of EP/APP composites.
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