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

Double threshold authentication using body area radio channel characteristics

The demand of portable and body-worn devices for remote health monitoring is ever increasing. One of the major challenges caused by this influx of wireless body area network (WBAN) devices is security of user's extremely vital and personal information. Conventional authentication techniques implemented at upper layers of the Open System Interconnection (OSI) model usually consumes huge amount of power. They also require significant changes at hardware and software levels. It makes them unsuitable for inherently low powered WBAN devices. This letter investigates the usability of a double threshold algorithm as a physical layer security measure in these scenarios. The algorithm is based on the user's behavioral fingerprint extracted from the radio channel characteristics. Effectiveness of this technique is established through experimental measurements considering a variety of common usage scenarios. The results show that this method provides high level of security against false authentication attacks and has great potential in WBANs

Patterns-of-Life Aided Authentication

Wireless Body Area Network (WBAN) applications have grown immensely in the past few years. However, security and privacy of the user are two major obstacles in their development. The complex and very sensitive nature of the body-mounted sensors means the traditional network layer security arrangements are not sufficient to employ their full potential, and novel solutions are necessary. In contrast, security methods based on physical layers tend to be more suitable and have simple requirements. The problem of initial trust needs to be addressed as a prelude to the physical layer security key arrangement. This paper proposes a patterns-of-life aided authentication model to solve this issue. The model employs the wireless channel fingerprint created by the user’s behavior characterization. The performance of the proposed model is established through experimental measurements at 2.45 GHz. Experimental results show that high correlation values of 0.852 to 0.959 with the habitual action of the user in different scenarios can be used for auxiliary identity authentication, which is a scalable result for future studies

A potato late blight resistance gene protects against multiple Phytophthora species by recognizing a broadly conserved RXLR-WY effector

Species of the genus Phytophthora, the plant killer, cause disease and reduce yields in many crop plants. Although many Resistance to Phytophthora infestans (Rpi) genes effective against potato late blight have been cloned, few have been cloned against other Phytophthora species. Most Rpi genes encode nucleotide-binding domain, leucine-rich repeat-containing (NLR) immune receptor proteins that recognize RXLR (Arg-X-Leu-Arg) effectors. However, whether NLR proteins can recognize RXLR effectors from multiple Phytophthora species has rarely been investigated. Here, we identified a new RXLR-WY effector AVRamr3 from P. infestans that is recognized by Rpi-amr3 from a wild Solanaceae species Solanum americanum. Rpi-amr3 associates with AVRamr3 in planta. AVRamr3 is broadly conserved in many different Phytophthora species, and the recognition of AVRamr3 homologs by Rpi-amr3 activates resistance against multiple Phytophthora pathogens, including the tobacco black shank disease and cacao black pod disease pathogens P. parasitica and P. palmivora. Rpi-amr3 is thus the first characterized resistance gene that acts against P. parasitica or P. palmivora. These findings suggest a novel path to redeploy known R genes against different important plant pathogens

Resilient Distributed Control Approach for Online Voltage Regulation in Distribution Networks under Adversaries

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