• Energy Research

Abstract This paper proposes a real-time energy management strategy for pumped hydro storage systems in farmhouses to manage surplus renewable energy. The proposed system meets both electricity and water demand in a farm. The novelty of this paper is its combination of a scheduling method and a real-time controller to take into account both present and future conditions of the microgrid. The scheduling part determines irrigation times, required stored water, and pumped hydro storage schedule. The real-time controller receives the schedule and current condition of the microgrid in order to adjust the pump power and turbine flow rate efficiently. Two methods of fuzzy logic and artificial neural network are tested to investigate which can address the forecast error problem more economically. An innovative approach is presented to produce target data for artificial neural network training. The designed system is simulated for 365 days to investigate the effect of real-time management on the performance of the microgrid on both sunny and cloudy days. The proposed energy management system is applied in an experimental setup, tested with a real pump and turbine. Results show that a real-time management system could keep the stored water level the same as the scheduling method; however, the pump and turbine can be controlled more cost-effectively. Finally, an economic study is conducted to determine the payback period of the system.

Recent studies have shown how motion-based biometrics can be used as a form of user authentication and identification without requiring any human cooperation. This category of behavioural biometrics deals with the features we learn in our life as a result of our interaction with the environment and nature. This modality is related to changes in human behaviour over time. The developments in these methods aim to amplify continuous authentication such as biometrics to protect their privacy on user devices. Various Continuous Authentication (CA) systems have been proposed in the literature. They represent a new generation of security mechanisms that continuously monitor user behaviour and use this as the basis to re-authenticate them periodically throughout a login session. However, these methods usually constitute a single classification model which is used to identify or verify a user. This work proposes an algorithm to blend behavioural biometrics with multi-factor authentication (MFA) by introducing a two-step user verification algorithm that verifies the user’s identity using motion-based biometrics and complements the multi-factor authentication, thus making it more secure and flexible. This two-step user verification algorithm is also immune to adversarial attacks, based on our experimental results that show how the rate of misclassification drops while using this model with adversarial data.

The global market share of electric vehicles (EVs) is on the rise, resulting in a rapid increase in their charging demand in both spatial and temporal domains. A remedy to shift the extra charging loads at peak hours to off-peak hours, caused by charging EVs at public charging stations, is an online pricing strategy. This paper presents a novel combinatorial online pricing strategy that has been established upon a reward-based model to prevent network instability and power outages. In the proposed solution, the utility provides incentives to the charging stations for their contributions in the EVs charging load shifting. Then, a constraint optimization problem is developed to minimize the total charging demand of the EVs during peak hours. To control the EVs charging demands in supporting utility's stability and increasing the total revenue of the charging stations, treated as a multi-agent framework, an online reinforcement learning model is developed which is based on the combination of an adaptive heuristic critic and recursive least square algorithm. The effective performance of the proposed model is validated through extensive simulation studies such as qualitative, numerical, and robustness performance assessment tests. The simulation results indicate significant improvement in the robustness and effectiveness of the proposed solution in terms of utility's power saving and charging stations' profit.

High penetration of renewable generation in the electricity grid presents power system operators with challenges including voltage instability mainly due to fluctuating power generation. To cope with intermittent generation, community batteries introduce an elegant solution for storing excess generation of renewable resources and reverting to the grid in peak demand periods. The question of the right battery type and size coupled with the right investment is challenging. Furthermore, the growth in adapting EVs imposes additional demand challenges on the power system compared to traditional industrial and household demand. This paper introduces long-term planning for community batteries to capture the surplus generation of PV resources for a given area and redirect these resources to charge EVs, without direct injection to the grid. For long-term investment planning on batteries, we consider 15 years' worth of historical data associated with solar irradiance, temperature, EV demands, and household demands. A novel stochastic mathematical model is proposed for decision-making on battery specifications (the type and capacity per year) based on the four standard battery types provided by the CSIRO in Australia. Uncertainties related to the EVs and RESs are captured by a non-parametric robust technique, named information gap decision theory, from optimistic and pessimistic perspectives. The investment decision-making part is formulated as mixed-integer linear programming taking advantage of the powerful commercial solver -- GUROBI -- which leads to finding feasible global solutions with low computational burden. The outcomes of this investigation not only detect optimal battery installation strategies to improve the stability profile of the grid by capturing the excess generation of PV resources but also facilitate EV integration in the community toward reaching net-zero emissions targets.

This paper proposes a novel graph-based approach with automorphic grouping for the modelling, synthesis, and analysis of electric vehicle (EV) networks with charging stations (CSs) that considers the impacts of traffic. The EV charge demands are modeled by a graph where nodes are positioned at potential locations for CSs, and edges represent traffic flow between the nodes. A synchronization protocol is assumed for the network where the system states correspond to the waiting time at each node. These models are then utilized for the placement and sizing of CSs in order to limit vehicle waiting times at all stations below a desirable threshold level. The main idea is to reformulate the CS placement and sizing problems in a control framework. Moreover, a strategy for the deployment of portable charging stations (PCSs) in selected areas is introduced to further improve the quality of solutions by reducing the overshooting of waiting times during peak traffic hours. Further, the inherent symmetry of the graph, described by graph automorphisms, are leveraged to investigate the number and positions of CSs. Detailed simulations are performed for the EV network of Perth Metropolitan in Western Australia to verify the effectiveness of the proposed approach.

The charging loads of plug-in electric vehicles (PEVs) within a network of charging stations (CSs) are not uniformly distributed. The load distribution is skewed toward the stations located in the hotspot areas, instigating longer queues and waiting times, particularly during afternoon peak traffic hours. This can lead to a major challenge for the utilities in the form of an extended PEV load period, which could overlap with the residential evening peak load hours, increase peak demand, and cause serious issues, such as network instability and power outages. This paper presents a new coordinated dynamic pricing model to reduce the overlaps between residential and CS loads by inspiring the temporal PEV load shifting during evening peak load hours. The new idea is to dynamically adjust the price incentives to drift PEVs toward less popular/underutilized CSs. We formulate a constraint optimization problem and introduce a heuristic solution to minimize the overlap between the PEV and residential peak load periods. Our extensive simulation results indicate that the proposed model significantly reduces the overlap and the PEV load during evening peak hours.

Although the concept of transportation electrification holds enormous prospects in addressing the global environmental pollution problem, in reality the market penetration of plug-in electric vehicles (PEVs) has been very low. Consumer concerns over the limited availability of charging facilities and unacceptably long charging periods are major factors behind this low penetration rate. From the perspective of the electricity grid, a longer PEV peak load period can potentially overlap with the residential peak load period, making energy management more challenging. A suitably designed charging strategy can help to address these concerns, which motivated us to conduct this research. In this paper, we present a smart charging strategy for a PEV network that offers multiple charging options, including ac level 2 charging, dc fast charging, and battery swapping facilities at charging stations. For a PEV requiring charging facilities, we model the issue of finding the optimal charging station as a multiobjective optimization problem, where the goal is to find a station that ensures the minimum charging time, travel time, and charging cost. We extend the model to a metaheuristic solution in the form of an ant colony optimization. Simulation results show that the proposed solution significantly reduces waiting time and charging cost.

Abstract The global market share of plug-in electric vehicles (PEVs) is on the rise, resulting in a rapid increase in charging demand in both spatial and temporal domains. The network and coverage of public fixed charging stations (FCSs) are currently constrained by infrastructure costs. As a result, FCSs are not as ubiquitous as traditional gas stations. In addition, as PEVs require a reasonably long time to recharge, waiting times at public charging stations can easily become excessive particularly during busy traffic hours. This paper introduces the new idea of allocating and dispatching portable charging stations (PCSs) in hotspot areas of EV network during busy traffic hours to: i) relieve the burden on FCSs, ii) minimize vehicle waiting times at charging stations, and iii) reduce the overlaps between total PEV demand and peak residential load. We formulate the research challenge for the smart management of PCSs as a constrained optimization problem and introduce a heuristic solution. Detailed simulation results for the Washington green highway EV network show that the proposed approach can significantly reduce the average PEV waiting times and decrease the average PEV loads at FCSs during peak hours by up to 64.7% and 67%, respectively.