A Strategic Day-ahead Bidding Strategy and Operation for Battery Energy Storage System by Reinforcement Learning

The Battery Energy Storage System (BESS) plays an essential role in the smart grid, and the ancillary market offers a high revenue. It is important for BESS owners to maximise their profit by deciding how to balance between the different offers and bidding with the rivals. Therefore, this paper formulates the BESS bidding problem as a Markov Decision Process(MDP) to maximise the total profit from the e Automation Generation Control (AGC) market and the energy market, considering the factors such as charging/discharging losses and the lifetime of the BESS. In the proposed algorithm, function approximation technology is introduced to handle the continuous massive bidding scales and avoid the dimension curse. As a model-free approach, the proposed algorithm can learn from the stochastic and dynamic environment of a power market, so as to help the BESS owners to decide their bidding and operational schedules profitably. Several case studies illustrate the effectiveness and validity of the proposed algorithm.</p

Short term load forecasting with Markovian switching distributed deep belief networks

In modern power systems, centralised short term load forecasting (STLF) methods raise concern on high communication requirements and reliability when a central controller undertakes the processing of massive load data solely. As an alternative, distributed methods avoid the problems mentioned above, whilst the possible issues of cyberattacks and uncertain forecasting accuracy still exist. To address the two issues, a novel distributed deep belief networks (DDBN) with Markovian switching topology is proposed for an accurate STLF, based on a completely distributed framework. Without the central governor, the load dataset is separated and the model is trained locally, while obtaining the updates through communication with stochastic neighbours under a designed consensus procedure, and therefore significantly reduced the training time. The overall network reliability against cyberattacks is enhanced by continually switching communication topologies. In the meanwhile, to ensure that the distributed structure is still stable under such a varying topology, the consensus controller gain is delicately designed, and the convergence of the proposed algorithm is theoretically analysed via the Lyapunov function. Besides, restricted Boltzmann machines (RBM) based unsupervised learning is employed for DDBN initialisation and thereby guaranteeing the success rate of STLF model training. GEFCom 2017 competition and ISO New England load datasets are applied to validate the accuracy and effectiveness of the proposed method. Experiment results demonstrate that the proposed DDBN algorithm can enhance around 19% better forecasting accuracy than centralised DBN algorithm.</p

Decoy State Quantum Key Distribution With Modified Coherent State

To beat PNS attack, decoy state quantum key distribution (QKD) based on coherent state has been studied widely. We present a decoy state QKD protocol with modified coherent state (MCS). By destruction quantum interference, MCS with fewer multi-photon events can be get, which may improve key bit rate and security distance of QKD. Through numerical simulation, we show about 2-dB increment on security distance for BB84 protocol.Comment: 4 pages, 4 figure

Data Service Outsourcing and Privacy Protection in Mobile Internet

Mobile Internet data have the characteristics of large scale, variety of patterns, and complex association. On the one hand, it needs efficient data processing model to provide support for data services, and on the other hand, it needs certain computing resources to provide data security services. Due to the limited resources of mobile terminals, it is impossible to complete large-scale data computation and storage. However, outsourcing to third parties may cause some risks in user privacy protection. This monography focuses on key technologies of data service outsourcing and privacy protection, including the existing methods of data analysis and processing, the fine-grained data access control through effective user privacy protection mechanism, and the data sharing in the mobile Internet

Quantum repeaters free of polarization disturbance and phase noise

Original quantum repeater protocols based on single-photon interference suffer from phase noise of the channel, which makes the long-distance quantum communication infeasible. Fortunately, two-photon interference type quantum repeaters can be immune to phase noise of the channel. However, this type quantum repeaters may still suffer from polarization disturbance of the channel. Here we propose a quantum repeaters protocol which is free of polarization disturbance of the channel based on the invariance of the anti-symmetric Bell state $|\psi^->=(|H>|V>-|V>|H>)/\sqrt{2}$ under collective noise. Our protocol is also immune to phase noise with the Sagnac interferometer configuration. Through single-atom cavity-QED technology and linear optics, this scheme can be implemented easily.Comment: 5 pages, 2 figure

Decoy states for quantum key distribution based on decoherence-free subspaces

Quantum key distribution with decoherence-free subspaces has been proposed to overcome the collective noise to the polarization modes of photons flying in quantum channel. Prototype of this scheme have also been achieved with parametric-down conversion source. However, a novel type of photon-number-splitting attack we proposed in this paper will make the practical implementations of this scheme insecure since the parametric-down conversion source may emit multi-photon pairs occasionally. We propose decoy states method to make these implementations immune to this attack. And with this decoy states method, both the security distance and key bit rate will be increased.Comment: 6 pages,1 figur