Reduced state space and cost function in reinforcement learning for demand response control of multiple EV charging stations

Electric vehicle (EV) charging stations represent a substantial load with significant flexibility. Balancing such load with model-free demand response (DR) based on reinforcement learning (RL) is an attractive approach. We build on previous RL research using a Markov decision process (MDP) to simultaneously coordinate multiple charging stations. The previously proposed approach is computationally expensive in terms of large training times, limiting its feasibility and practicality. We propose to a priori force the control policy to always fulfill any charging demand that does not offer any flexibility at a given point, and thus use an updated cost function. We compare the policy of the newly proposed approach with the original (costly) one, for the case of load flattening, in terms of (i) processing time to learn the RL-based charging policy, and (ii) overall performance of the policy decisions in terms of meeting the target load for unseen test data

Synthetic data generator for electric vehicle charging sessions : modeling and evaluation using real-world data

Electric vehicle (EV) charging stations have become prominent in electricity grids in the past few years. Their increased penetration introduces both challenges and opportunities; they contribute to increased load, but also offer flexibility potential, e.g., in deferring the load in time. To analyze such scenarios, realistic EV data are required, which are hard to come by. Therefore, in this article we define a synthetic data generator (SDG) for EV charging sessions based on a large real-world dataset. Arrival times of EVs are modeled assuming that the inter-arrival times of EVs follow an exponential distribution. Connection time for EVs is dependent on the arrival time of EV, and can be described using a conditional probability distribution. This distribution is estimated using Gaussian mixture models, and departure times can calculated by sampling connection times for EV arrivals from this distribution. Our SDG is based on a novel method for the temporal modeling of EV sessions, and jointly models the arrival and departure times of EVs for a large number of charging stations. Our SDG was trained using real-world EV sessions, and used to generate synthetic samples of session data, which were statistically indistinguishable from the real-world data. We provide both (i) source code to train SDG models from new data, and (ii) trained models that reflect real-world datasets

Designing user-centric transport strategies for urban road space redistribution

Cities worldwide are geared to promote economic growth, improve accessibility, address environmental issues, and enhance the quality of life. However, the processes that lead to the design of urban roads, particularly the space distribution, reflect the inequalities existing in the fabric of our society. Motorists often have shorter travel time and more space than passengers of other modes. Furthermore, the existing transport appraisal and planning tools that frame sustainable transport policies fall short of considering the dimension of social justice. Therefore, our urban transport systems are essential areas for advancing sustainability through a transport justice-based approach to planning that can pivot the distribution of infrastructure investments over different social groups and transport modes. This study proposes such an approach by which such suitable urban transport strategies can be identified, co-created with users and appraised while considering the commuters’ needs. Specifically, the interaction between the multidimensional characteristics of sustainability and the principles of transport justice are investigated. The proposed approach is applied to London and Birmingham. The results show that a transparent and holistic approach to integrating users within transport planning is an effective way to reflect diverse needs and local circumstances and thereby ensure a just transition to sustainable urban transport policies. The results from the case studies highlight a strong rationale for the centrality of justice in any urban transport planning and policy making efforts, particularly in the allocation of road space

Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households

Given its substantial contribution of 40\% to global power consumption, the built environment has received increasing attention to serve as a source of flexibility to assist the modern power grid. In that respect, previous research mainly focused on energy management of individual buildings. In contrast, in this paper, we focus on aggregated control of a set of residential buildings, to provide grid supporting services, that eventually should include ancillary services. In particular, we present a real-life pilot study that studies the effectiveness of reinforcement-learning (RL) in coordinating the power consumption of 8 residential buildings to jointly track a target power signal. Our RL approach relies solely on observed data from individual households and does not require any explicit building models or simulators, making it practical to implement and easy to scale. We show the feasibility of our proposed RL-based coordination strategy in a real-world setting. In a 4-week case study, we demonstrate a hierarchical control system, relying on an RL-based ranking system to select which households to activate flex assets from, and a real-time PI control-based power dispatch mechanism to control the selected assets. Our results demonstrate satisfactory power tracking, and the effectiveness of the RL-based ranks which are learnt in a purely data-driven manner.Comment: 8 pages, 2 figures, workshop article accepted at RLEM'23 (BuildSys'23

Partial discharge measurements in a high voltage gas insulated transmission line insulated with CO2

This paper uses practical experimentation to analyse the effect of replacing SF6 with pure CO2 in conventional gas insulated transmission line sections by studying partial discharge measurements taken with applied voltages up to 242 kV (rms). The results can also help in understanding the properties of new alternative gas mixtures which can be utilised with a ratio of up to and over 95% CO2. The experiments undertaken involved filling a gas insulated line demonstrator with 3 bars of CO2 and applying voltages up to 242 kV in both clean conditions and particle-contaminated enclosure conditions. The results demonstrate that CO2 can be used to insulate gas equipment without breakdown at high voltage, however, a higher gas-filling pressure may be needed to reduce the partial discharge found in the tests presented in this paper. Another aspect of the work showed that partial discharge (PD) measurements from internal ultra-high frequency (UHF) sensors compared with a direct measurement from a capacitive divider both clearly showed the effect of contaminating particles in CO2. However, the PD divider measurements also showed considerable external PD on the outside of the gas compartment, leading to the conclusion that UHF sensors are still regarded as having the highest sensitivity and noise immunity for gas insulated switchgear (GIS) or gas insulated transmission line (GIL) systems including when the equipment is insulated with CO2

Lightning strike damage resistance of carbon‐fiber composites with nanocarbon‐modified epoxy matrices

Carbon‐fiber reinforced polymer (CFRP) composites are replacing metal alloys in aerospace structures, but they can be vulnerable to lightning strike damage if not adequately protected due to the poor electrical conductivity of the polymeric matrix. In the present work, to improve the conductivity of the CFRP, two electrically conductive epoxy formulations were developed via the addition of 0.5 wt% of graphene nanoplatelets (GNPs) and a hybrid of 0.5 wt% of GNPs/carbon nanotubes (CNTs) at an 8:2 mass ratio. Unidirectional CFRP laminates were manufactured using resin‐infusion under flexible tooling (RIFT) and wet lay‐up (WL) processes, and subjected to simulated lightning strike tests. The electrical performance of the RIFT plates was far superior to that of the WL plates, independent of matrix modification, due to their greater carbon‐fiber volume fraction. The GNP‐modified panel made using RIFT demonstrated an electrical conductivity value of 8 S/cm. After the lightning strike test, the CFRP panel remains largely unaffected as no perforation occurs. Damage is limited to matrix degradation within the top ply at the point of impact and localized charring of the surface. The GNP‐modified panel showed a comparable level of resistance against lightning damage with the existing copper mesh technology, offering at the same time a 20% reduction in the structural weight. This indicates a feasible route to improve the lightning strike damage resistance of carbon‐fiber composites without the addition of extra weight, hence reducing fuel consumption but not safety

Post Occupancy Evaluation of School Refurbishment Projects: Multiple Case Study in the UK

Buildings inevitably deteriorate with time. Schools buildings are no exception and require refurbishment at times. Despite the UK Government announcing the £1 billion funding for rebuilding 50 schools over 10 years starting 2010–2021, it is common practice for builders and designers to, upon completion of a building project, move on to the next development without considering how the completed building performs. This research undertakes a post occupancy evaluation (POE) of three schools in the West Midlands, UK with specific focus on building services, viz., heating, lighting, and air conditioning and ventilation. The research adopted a mixed philosophical approach of interpretivism and post-positivism to conduct inductive reasoning. A questionnaire that collected both quantitative and qualitative primary data was distributed to the end-users of the schools. Data was analysed using the Cronbach’s alpha, one sample t-test and Kruskal–Wallis test to identify any differences between the questionnaire responses. Findings revealed that building users demanded greater control of the internal environment thus contradicting the current trend for automated ‘intelligent systems’ approaches. This research represents the first work to consider the contractor’s perspective towards developing a better understanding of client satisfaction with the school buildings. Moreover, the POE result represents a notable pragmatic advancement to knowledge that will influence the contractor’s knowledge and understanding of client satisfaction, and where to improve upon these