Effective energy management of electric vehicle (EV) charging stations is critical to supporting the transport sector's sustainable energy transition. This paper addresses the EV charging coordination by considering vehicle- to- vehicle (V2V) energy exchange as the flexibility to harness in EV charging stations. Moreover, this paper takes into account EV user experiences, such as charging satisfaction and fairness. We propose a Multi-Agent Reinforcement Learning (MARL) approach to coordinate EV charging with V2V energy exchange while considering uncertainties in the EV arrival time, energy price, and solar energy generation. The exploration capability of MARL is enhanced by introducing parameter noise into MARL's neural network models. Experimental results demonstrate the superior performance and scalability of our proposed method compared to traditional optimization baselines. The decentralized execution of the algorithm enables it to effectively deal with partial system faults in the charging station.

Distributed demand response (DR) schemes for smart energy networks rely on data from various sources, many of them outside the network operator’s perimeter. Therefore, compromised inputs from false data injection attacks (FDIAs) can be detrimental to the expectations of stakeholders, pro-vide financial benefits to malicious actors, compromise the commercial viability of the scheme and have the potential to disrupt the energy supply. Due to the heterogeneity of data sources, FDIAs are arduous to prevent with standard security controls. Thus, detecting FDIAs is necessary to facilitate impact mitigations. However, FDIA detection in the residential DR context is arduous, given the inherent challenges such as the noisiness of residential demand, lack of labelled data in real-life settings, and variety and dynamicity of demand forecasts (e.g., weekdays vs weekend, different months/seasons). Addressing mentioned challenges, in this paper, we propose a data-driven unsupervised anomaly detection approach, named Clustering-based Spectral Residual (CSR), to detect false data injection attacks in smart grids’ DR. The CSR model is based on the popular k-means clustering and Spectral Residual method. The combination highlights the attack time slots, which increases the detection accuracy in our model. A supervised model is also proposed based on Convolutional Neural Network (CNN) to increase the detection accuracy in scenarios where label information is available. Using an energy consumption dataset from Austin, Texas, as a case study and through extensive experimental results, we show that our proposed CSR and CNN models outperform 25 widely used anomaly detection benchmarks.

Advanced metering infrastructure, along with home automation processes, is enabling more efficient and effective demand-side management opportunities for both consumers and utility companies. However, tight cyber-physical integration also enables novel attack vectors for false data injection attacks (FDIA) as home automation/ home energy management systems reside outside the utilities' control perimeter. Authentic users themselves can manipulate these systems without causing significant security breaches compared to traditional FDIAs. This work depicts a novel FDIA that exploits one of the commonly utilised distributed device scheduling architectures. We evaluate the attack impact using a realistic dataset to demonstrate that adversaries gain significant benefits, independently from the actual algorithm used for optimisation, as long as they have control over a sufficient amount of demand. Compared to traditional FDIAs, reliable security mechanisms such as proper authentication, security protocols, security controls or, sealed/controlled devices cannot prevent this new type of FDIA. Thus, we propose a set of possible impact alleviation solutions to thwart this type of attack.

The transition of energy networks to so-called smart grids benefits from advancements in Internet of Things technology. Energy management systems enable efficient and effective demand response (DR) schemes optimising load distribution. The increased user involvements through such DR schemes creates a new vector for false data injection attacks (FDIA), where authentic users themselves inject false data. Unlike in most existing FDIAs, no breaches to communication or devices are needed to execute this type of FDIA. In this work, we depict that this new FDIA can impact any optimisation-based DR scheme. Further, we show that adversaries achieve financial benefits independently from the actual algorithm used for optimisation, as long as they are able to inject false demand predictions. Compared to traditional FDIAs, reliable security mechanisms such as proper authentication, security protocols, security controls or sealed/controlled devices cannot prevent this new type of FDIA. Additionally, we show that there is no straightforward solution and we highlight the need for highly reliable FDIA detection mechanisms to thwart this type of attacks.

Battery swapping stations (BSSs) present an alternative way of charging electric vehicles (EVs) that can lead toward a sustainable EV ecosystem. Although research focusing on the BSS strategies has been ongoing, the results are fragmented. Currently, an integrated way of considering stochastic EV station visits through planning and operations has not been fully investigated. To create comprehensive and resilient battery swapping stations, a two-stage optimization with recourse is proposed. In the planning stage, the investment for battery purchases is recommended even before the EV station visit uncertainties are made known. In the operation stage, the battery allocation decisions, such as charging, discharging, and swapping are then coordinated. To apply the recourse strategy in creating representative scenarios, the EV station visit distribution techniques are also proposed using a modified K-means clustering method. Aside from the sensitivity analysis made with swapping prices and charging intervals, the strategy comparisons with conventional strategies have also demonstrated the practicality of the BSS coordination to future electricity and transportation networks.

The introduction of smart meters has motivated the electricity industry to manage electrical demand, using dynamic pricing schemes such as real-time pricing. The overall aim of demand management is to minimize electricity generation and distribution costs while meeting the demands and preferences of consumers. However, rapidly scheduling consumption of large groups of households is a challenge. In this paper, we present a highly scalable approach to find the optimal consumption levels for households in an iterative and distributed manner. The complexity of this approach is independent of the number of households, which allows it to be applied to problems with large groups of households. Moreover, the intermediate results of this approach can be used by smart meters to schedule tasks with a simple randomized method.

We present an efficient technique for topology-preserving map deformation and apply it to the visualization of dissimilarity data in a geographic context. Map deformation techniques such as value-by-area cartograms are well studied. However, using deformation to highlight (dis)similarity between locations on a map in terms of their underlying data attributes is novel. We also identify an alternative way to represent dissimilarities on a map through the use of visual overlays. These overlays are complementary to deformation techniques and enable us to assess the quality of the deformation as well as to explore the design space of blending the two methods. Finally, we demonstrate how these techniques can be useful in several-quite different-applied contexts: travel-time visualization, social demographics research and understanding energy flowing in a wide-area power-grid.