289 research outputs found
Demand Response Management and Control Strategies for Integrated Smart Electricity Networks
Demand Response (DR) programs are being introduced by some electricity grid operators as resource options for curtailing and reducing the demand of electricity during certain time periods for balancing supply and demand. DR is considered as a class of demand-side management programs, where utilities offer incentives to end-users to reduce their power consumption during peak periods. DR is, indeed, a promising opportunity for consumers to control their energy usage in response to electricity tariffs or other incentives from their energy suppliers. Thus, successful execution of a DR program requires the design of efficient algorithms and strategies to be used in the utility grid to motivate end-users to actively engage in residential DR.
This thesis studies DR management using machine learning techniques such as Reinforcement Learning (RL), Fuzzy Logic (FL) and Neural Networks (NN) to develop a Home Energy Management System (HEMS) for customers, construct an energy customer behaviour framework, investigate the integration of Electrical Vehicles (EVs) into DR management at the home level and the provision of ancillary services to the utility grid such as Frequency Regulation (FR), and build effective pricing strategies for Peer-to-Peer (P2P) energy trading.
In this thesis, we firstly proposed a new and effective algorithm for residential energy management system using Q-learning method to minimise the electricity bills and maximise the user’s satisfaction. The proposed DR algorithm aims to schedule household appliances considering dynamic electricity prices and different household power consumption patterns. Moreover, a human comfort-based control approach for HEMS has been developed to increase the user’s satisfaction as much as possible while responding to DR schemes. The simulation results presented in this Chapter showed that the proposed algorithm leads to minimising energy consumption, reducing household electricity bills, and maximising the user’s satisfaction.
Secondly, with the increasing electrification of vehicles, emerging technologies such as Vehicle-to-Grid (V2G) and Vehicle-to-Home (V2H) have the potential to offer a broad range of benefits and services to achieve more effective management of electricity demand. In this way, EVs become as distributed energy storage resources and can conceivably, in conjunction with other electricity storage solutions, contribute to DR and provide additional capacity to the grid when needed. Therefore, we proposed an effective DR approach for V2G and V2H energy management using Reinforcement Learning (RL) to make optimal decisions to charge
or delay the charging of the EV battery pack and/or dispatch the stored electricity back to the grid without compromising the driving needs. Simulations studies are presented to demonstrate how the proposed DR strategy can effectively manage the charging/discharging schedule of the EV battery and how V2H and V2G can contribute to smooth the household load profile, minimise electricity bills and maximise revenue. In addition, the potential benefits of EVs battery and V2G technology to provide grid frequency response services have also been investigated. We have designed an optimal real-time V2G control strategy for EVs to perform supplementary frequency regulation using Deep Deterministic Policy Gradient (DDPG). The main feature that distinguishes the proposed approach from previous related works is that the scheduled charging power of an individual EV is optimally tracked and adjusted in real-time to fulfil the charging demand of EV's battery at the plug-out time without using the forced charging technique to maximise the frequency regulation capacity.
Finally, a Peer-to-Peer (P2P) model for energy transaction in a community microgrid has been proposed. The concept of P2P energy trading can promote the implementation of DR by providing consumers with greater control over their energy usage, incentivising them to manage their energy consumption patterns in response to changes in energy supply and demand. It also stimulates the adoption of renewable energy sources. The proposed P2P energy-sharing mechanism for a residential microgrid with price-based DR is designed to engage individual customers to participate in energy trading and ensures that not a single household would be worse off. The proposed pricing mechanism is compared with three popular P2P energy sharing models in the literature namely the Supply and Demand Ratio (SDR), Mid-Market Rate (MMR) and Bill Sharing (BS) considering different types of peers equipped with solar Photovoltaic (PV) panels, EVs, and domestic energy storage systems. The proposed P2P framework has been applied to a community consisting of 100 households and the simulation results demonstrate fairness and substantial energy cost saving/revenue among peers. The P2P model has also been assessed under the physical constrains of the distribution network
Don't Treat the Symptom, Find the Cause! Efficient Artificial-Intelligence Methods for (Interactive) Debugging
In the modern world, we are permanently using, leveraging, interacting with,
and relying upon systems of ever higher sophistication, ranging from our cars,
recommender systems in e-commerce, and networks when we go online, to
integrated circuits when using our PCs and smartphones, the power grid to
ensure our energy supply, security-critical software when accessing our bank
accounts, and spreadsheets for financial planning and decision making. The
complexity of these systems coupled with our high dependency on them implies
both a non-negligible likelihood of system failures, and a high potential that
such failures have significant negative effects on our everyday life. For that
reason, it is a vital requirement to keep the harm of emerging failures to a
minimum, which means minimizing the system downtime as well as the cost of
system repair. This is where model-based diagnosis comes into play.
Model-based diagnosis is a principled, domain-independent approach that can
be generally applied to troubleshoot systems of a wide variety of types,
including all the ones mentioned above, and many more. It exploits and
orchestrates i.a. techniques for knowledge representation, automated reasoning,
heuristic problem solving, intelligent search, optimization, stochastics,
statistics, decision making under uncertainty, machine learning, as well as
calculus, combinatorics and set theory to detect, localize, and fix faults in
abnormally behaving systems.
In this thesis, we will give an introduction to the topic of model-based
diagnosis, point out the major challenges in the field, and discuss a selection
of approaches from our research addressing these issues.Comment: Habilitation Thesi
A Comprehensive Survey on Orbital Edge Computing: Systems, Applications, and Algorithms
The number of satellites, especially those operating in low-earth orbit
(LEO), is exploding in recent years. Additionally, the use of COTS hardware
into those satellites enables a new paradigm of computing: orbital edge
computing (OEC). OEC entails more technically advanced steps compared to
single-satellite computing. This feature allows for vast design spaces with
multiple parameters, rendering several novel approaches feasible. The mobility
of LEO satellites in the network and limited resources of communication,
computation, and storage make it challenging to design an appropriate
scheduling algorithm for specific tasks in comparison to traditional
ground-based edge computing. This article comprehensively surveys the
significant areas of focus in orbital edge computing, which include protocol
optimization, mobility management, and resource allocation. This article
provides the first comprehensive survey of OEC. Previous survey papers have
only concentrated on ground-based edge computing or the integration of space
and ground technologies. This article presents a review of recent research from
2000 to 2023 on orbital edge computing that covers network design, computation
offloading, resource allocation, performance analysis, and optimization.
Moreover, having discussed several related works, both technological challenges
and future directions are highlighted in the field.Comment: 18 pages, 9 figures and 5 table
Low Power Memory/Memristor Devices and Systems
This reprint focusses on achieving low-power computation using memristive devices. The topic was designed as a convenient reference point: it contains a mix of techniques starting from the fundamental manufacturing of memristive devices all the way to applications such as physically unclonable functions, and also covers perspectives on, e.g., in-memory computing, which is inextricably linked with emerging memory devices such as memristors. Finally, the reprint contains a few articles representing how other communities (from typical CMOS design to photonics) are fighting on their own fronts in the quest towards low-power computation, as a comparison with the memristor literature. We hope that readers will enjoy discovering the articles within
Peer-to-Peer Trading for Enhancing Electric Vehicle Charging with Renewable Energy
Electric vehicles (EVs) are rapidly increasing in popularity as greater attention is paid to climate change and decarbonisation, however the environmental benefits that EVs offer can only be fully realised through the use of renewable energy for their charging. Smart charging solutions are essential for managing the impact of EVs and increasing the utilisation of renewable energy, however, questions remain over whether low-voltage distribution networks can accommodate the upcoming increases in EV charging demand.
This thesis addresses both the challenge of increasing the utilisation of renewable energy for EV charging and also the importance of ensuring safe operation of low-voltage distribution networks with the integration of EV charging, distributed renewable energy generation, battery storage and vehicle-to-grid technologies.
Chapter 3 examines a scenario where houses equipped with solar photovoltaic panels and EV charge points endeavour to sell surplus solar energy and the use of their EV charge point to visiting EVs that require charging. A peer-to-peer auction is proposed, with a novel matching mechanism presented to increase the amount of EV charging completed using solar energy without any knowledge about future EV arrivals.
Chapter 4 presents a full peer-to-peer trading model of Network Impact Tokens and Phase Impact Tokens between houses in a low-voltage network. The Impact Tokens guarantee that all EV charging and renewable energy generation does not cause the network to exceed its voltage, current or transformer loading limits, while ensuring each house retains control over its energy usage, requiring no real-time monitoring or sensors in the network, and no privacy issues are encountered.
The Network and Phase Impact Token approach is further verified in Chapter 5, as it forms the basis of a novel approach for Distribution System Operators to evaluate the maximum EV hosting capacity of their networks in conjunction with renewable energy generation and battery storage. The maximum EV capacity results are verified by an alternate Optimisation approach and the maximum EV penetration is evaluated for a number of scenarios
Learning from safety science: A way forward for studying cybersecurity incidents in organizations
In the aftermath of cybersecurity incidents within organizations, explanations of their causes often revolve around isolated technical or human events such as an Advanced Persistent Threat or a “bad click by an employee.” These explanations serve to identify the responsible parties and inform efforts to improve security measures. However, safety science researchers have long been aware that explaining incidents in socio-technical systems and determining the role of humans and technology in incidents is not an objective procedure but rather an act of social constructivism: what you look for is what you find, and what you find is what you fix. For example, the search for a technical “root cause” of an incident might likely result in a technical fix, while from a sociological perspective, cultural issues might be blamed for the same incident and subsequently lead to the improvement of the security culture. Starting from the insights of safety science, this paper aims to extract lessons on what general explanations for cybersecurity incidents can be identified and what methods can be used to study causes of cybersecurity incidents in organizations. We provide a framework that allows researchers and practitioners to proactively select models and methods for the investigation of cybersecurity incidents
Impact of peer-to-peer trading and flexibility on local energy systems
To meet the 2050 net zero emission targets, energy systems around the globe are being revisited to achieve multi-vector decarbonisation in terms of electricity, transport, heating and cooling. As energy systems become more decentralised and digitised, local energy systems will have greater potential to self-sustain and hence, decrease reliance on fossil-fuelled central generation. While the uptake of electric vehicles, heat pumps, solar and battery systems offer a solution, the increase in electricity demand poses challenges in terms of higher peak demand, imbalance and overloading. Additionally, the current energy market structure prevents these assets in the distribution network from reaching their true techno-economic potential in flexibility services and energy trading. Peer-to-peer energy trading and community-level control algorithms achieve better matching of local demand and supply through the use of transactive energy markets, load shifting and peak shaving techniques. Existing research addresses the challenges of local energy markets and others investigate the effect of increased distributed assets on the network. However, the combined techno-economic effect requires the co-simulation of both market and network levels, coupled with simultaneous system balance, cost and carbon intensity considerations.
Using bottom-up coordination and user-centric optimisation, this project investigated the potential of network-aware peer-to-peer trading and community-level control to increase self-sufficiency and self-consumption in energy communities. The techno-economic effects of these strategies are modelled while maintaining user comfort levels and healthy operation of the network and assets. The proposed strategies are evaluated according to their economic benefit, environmental impact and network stress. A case study in Scotland was employed to demonstrate the benefits of peer-to-peer trading and community self-consumption using future projections of demand, generation and storage. Additionally, the concept of energy smart contracts, embedded in blockchains, are proposed and demonstrated to overcome the major challenges of monitoring and contracting.
The results indicate benefits for various energy systems stakeholders. Distribution system end-users benefit from lower energy costs while system operators obtain better visibility of the local-level flexibility along with the associated technical challenges in terms of losses, imbalance and loading. From a commercial perspective, community energy companies may utilise this study to inform investment decisions regarding storage, distributed generation and transactive market solutions. Additionally, the insights about the energy smart contracts allow blockchain and relevant technology sectors to recognise the opportunities and challenges of smart contracts and distributed ledger technologies that are specific to the energy sector. On the broader scale, energy system operators, regulators and high-level decision-makers can compare the simulated impact of community-led energy transition on the net zero goals with large-scale top-down initiatives
A comprehensive survey of V2X cybersecurity mechanisms and future research paths
Recent advancements in vehicle-to-everything (V2X) communication have notably improved existing transport systems by enabling increased connectivity and driving autonomy levels. The remarkable benefits of V2X connectivity come inadvertently with challenges which involve security vulnerabilities and breaches. Addressing security concerns is essential for seamless and safe operation of mission-critical V2X use cases. This paper surveys current literature on V2X security and provides a systematic and comprehensive review of the most relevant security enhancements to date. An in-depth classification of V2X attacks is first performed according to key security and privacy requirements. Our methodology resumes with a taxonomy of security mechanisms based on their proactive/reactive defensive approach, which helps identify strengths and limitations of state-of-the-art countermeasures for V2X attacks. In addition, this paper delves into the potential of emerging security approaches leveraging artificial intelligence tools to meet security objectives. Promising data-driven solutions tailored to tackle security, privacy and trust issues are thoroughly discussed along with new threat vectors introduced inevitably by these enablers. The lessons learned from the detailed review of existing works are also compiled and highlighted. We conclude this survey with a structured synthesis of open challenges and future research directions to foster contributions in this prominent field.This work is supported by the H2020-INSPIRE-5Gplus project (under Grant agreement No. 871808), the ”Ministerio de Asuntos Económicos y Transformacion Digital” and the European Union-NextGenerationEU in the frameworks of the ”Plan de Recuperación, Transformación y Resiliencia” and of the ”Mecanismo de Recuperación y Resiliencia” under references TSI-063000-2021-39/40/41, and the CHIST-ERA-17-BDSI-003 FIREMAN project funded by the Spanish National Foundation (Grant PCI2019-103780).Peer ReviewedPostprint (published version
- …