329 research outputs found

    Enhancing grid reliability with coordination and control of distributed energy resources

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    The growing utilization of renewable energy resources (RES) within power systems has brought about new challenges due to the inherent uncertainty associated with RES, which makes it challenging to accurately forecast available generation. Further- more, the replacement of synchronous machines with inverter-based RES results in a reduction of power system inertia, complicating the task of maintaining a balance between generation and consumption. In this dissertation, coordinating Distributed Energy Resources (DER) is presented as a viable solution to these challenges.DERs have the potential to offer different ancillary services such as fast frequency response (FFR) when efficiently coordinated. However, the practical implementation of such services demands both effective local sensing and control at the device level and the ability to precisely estimate and predict the availability of synthetic damping from a fleet in real time. Additionally, the inherent trade-off between a fleet being available for fast frequency response while providing other ancillary services needs to be characterized. This dissertation introduces a fully decentralized, packet-based controller for a diverse range of flexible loads. This controller dynamically prioritizes and interrupts DERs to generate synthetic damping suitable for primary frequency control. Moreover, the packet-based control methodology is demonstrated to accu- rately assess the real-time availability of synthetic damping. Furthermore, spectral analysis of historical frequency regulation data is employed to establish a probabilis- tic bound on the expected synthetic damping available for primary frequency control from a fleet and the trade-off of concurrently offering secondary frequency control. It is noteworthy that coordinating a large number of DERs can potentially result in grid constraint violations. To tackle this challenge, this dissertation employs con- vex inner approximations (CIA) of the AC power flow to address the optimization problem of quantifying the capacity of a three-phase distribution feeder to accommo- date DERs. This capacity is often referred to as hosting capacity (HC). However, in this work, we consider separate limits for positive and negative DER injections at each node, ensuring that injections within these nodal limits adhere to feeder voltage and current constraints. The methodology dissects a three-phase feeder into individual phases and applies CIA-based techniques to each phase. Additionally, new approaches are introduced to modify the per-phase optimization problems to mitigate the inherent conservativeness associated with CIA methods and enhance HC. This includes selectively adjusting the per-phase impedances and proposing an iterative relaxation method for per-phase voltage bounds

    Load Shifting Versus Manual Frequency Reserve: Which One is More Appealing to Flexible Loads?

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    This paper investigates how a thermostatically controlled load can deliver flexibility either in form of manual frequency restoration reserves (mFRR) or load shifting, and which one is financially more appealing to such a load. A supermarket freezer is considered as a representative flexible load, and a grey-box model describing its temperature dynamics is developed using real data from a supermarket in Denmark. Taking into account price and activation uncertainties, a two-stage stochastic mixed-integer linear program is formulated to maximize the flexibility value from the freezer. For practical reasons, we propose a linear policy to determine regulating power bids, and then linearize the mFRR activation conditions through the McCormick relaxation approach. For computational ease, we develop a decomposition technique, splitting the problem to a set of smaller subproblems, one per scenario. Examined on an out-of-sample simulation based on real Danish spot and balancing market prices in 2022, load shifting shows to be more profitable than mFRR provision, but is also more consequential for temperature deviations in the freezer

    Bridging the Flexibility Concepts in the Buildings and Multi-energy Domains

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    paper aims to stimulate a discussion on how to create a bridge between the concept of flexibility used in power and energy systems and the flexibility that buildings can offer for providing services to the electrical system. The paper recalls the main concepts and approaches considered in the power systems and multi-energy systems, and summarises some aspects of flexibility in buildings. The overview shows that there is room to strengthen the contacts among the scientists operating in these fields. The common aim is to identify the complementary aspects and provide inputs to enhance the methodologies and models to enable and support an effective energy and ecologic transition

    Laxity-Aware Scalable Reinforcement Learning for HVAC Control

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    Demand flexibility plays a vital role in maintaining grid balance, reducing peak demand, and saving customers' energy bills. Given their highly shiftable load and significant contribution to a building's energy consumption, Heating, Ventilation, and Air Conditioning (HVAC) systems can provide valuable demand flexibility to the power systems by adjusting their energy consumption in response to electricity price and power system needs. To exploit this flexibility in both operation time and power, it is imperative to accurately model and aggregate the load flexibility of a large population of HVAC systems as well as designing effective control algorithms. In this paper, we tackle the curse of dimensionality issue in modeling and control by utilizing the concept of laxity to quantify the emergency level of each HVAC operation request. We further propose a two-level approach to address energy optimization for a large population of HVAC systems. The lower level involves an aggregator to aggregate HVAC load laxity information and use least-laxity-first (LLF) rule to allocate real-time power for individual HVAC systems based on the controller's total power. Due to the complex and uncertain nature of HVAC systems, we leverage a reinforcement learning (RL)-based controller to schedule the total power based on the aggregated laxity information and electricity price. We evaluate the temperature control and energy cost saving performance of a large-scale group of HVAC systems in both single-zone and multi-zone scenarios, under varying climate and electricity market conditions. The experiment results indicate that proposed approach outperforms the centralized methods in the majority of test scenarios, and performs comparably to model-based method in some scenarios.Comment: In Submissio

    Scaling energy management in buildings with artificial intelligence

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    L'abstract è presente nell'allegato / the abstract is in the attachmen

    Estudio del rol de las Plantas Virtuales de Producción en la gestión de las redes de generación y distribución eléctrica

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    141 p.El objetivo principal de las Planta Virtuales de Producción Eléctrica o Virtual Power Plants (VPPs) es darle cumplimiento a los requerimientos de sus Stakeholders dentro de las redes de generación y distribución eléctrica que gestionan, así como aprovechar eficientemente los elementos y recursos que tienen a su disposición. Uno de los desafíos más importantes de las VPPs es satisfacer los requerimientos de sus Stakeholders optimizando el uso de la electricidad generada en sus sistemas de manera sostenible, minimizando los costes de operación y maximizando los beneficios comerciales. En esta tesis se presenta la información clave, recopilada a partir de más de 160 artículos científicos, para dar apoyo en el conocimiento de conceptos infraestructurales, tecnológicos y de desarrollo sostenible que involucran la gestión de las VPPs en las redes de generación y distribución eléctrica. De esta manera, se analiza, selecciona y organiza la información suministrada por los investigadores a través de sus artículos científicos, identificándose las distintas interacciones de las VPPs relacionadas con la arquitectura infraestructural y tecnología de los sistemas, el mercado eléctrico, modelos de optimización para la toma de decisiones, y las tecnologías de la información y de la comunicación; y dar así respuesta a la pregunta clave: ¿Cómo el rol de las VPPs puede utilizarse para minimizar los costes de operación y maximizar los beneficios comerciales de las redes de generación y distribución eléctrica, optimizando el uso de la electricidad de manera sostenible?. Finalmente, se indican esquemas de sistemas centralizados, descentralizados e híbridos, en los cuales las VPPs desempeñan su gestión mediante la aplicación de su rol y funciones para cumplir con los requerimientos de sus Stakeholders, mencionando las ventajas y dificultades según los escenarios en los que se desempeñe

    Opening of Ancillary Service Markets to Distributed Energy Resources: A Review

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    Electric power systems are moving toward more decentralized models, where energy generation is performed by small and distributed power plants, often from renewables. With the gradual phase out from fossil fuels, however, Distribution Energy Resources (DERs) are expected to take over in the provision of all regulation services required to operate the grid. To this purpose, the opening of national Ancillary Service Markets (ASMs) to DERs is considered an essential passage. In order to allow this transition to happen, current opportunities and barriers to market participation of DERs must be clearly identified. In this work, a comprehensive review is provided of the state-of-the-art of research on DER integration into ASMs. The topic at hand is analyzed from different perspectives. First, the current situation and main trends regarding the reformation processes of national ASMs are analyzed to get a clear picture of the evolutions expected and adjustment required in the future, according to the scientific community. Then, the focus is moved to the strategies to be adopted by aggregators for the effective control and coordination of DERs, exploring the challenges posed by the uncertainties affecting the problem. Coordination schemes between transmission and distribution system operators, and the implications on the grid infrastructure operation and planning, are also investigated. Finally, the review deepens the control capabilities required for DER technologies to perform the needed control actions

    Quantifying non-exhaust emissions and the impact of hybrid and electric vehicles using combined measurement and modelling approaches

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    Road traffic is a significant emission source of urban particulate matter (PM). Due to the implementation of exhaust regulatory standards in the UK, PM emissions which arise from the wear of brakes, tyres and the road surface, together with the resuspension of road dust are now predicted to exceed tailpipe emissions. While a growing body of academic literature has developed in recent years, non-exhaust emissions (NEE) remain unregulated and largely understudied, and the impact of powertrain electrification on the vehicle fleet has not been quantified. Thus, the aim of this thesis is to improve our understanding of these important emission sources and to determine the impact of NEE on urban air pollution - both now, and in the future. A series of highly time-resolved atmospheric measurement campaigns has been undertaken at roadside and background locations to determine roadside traffic increments. These measurements provide a comprehensive dataset of traffic emissions in London, Birmingham and Manchester, incorporating locations with different vehicle mix and speed, during summer and winter periods. PM mass and elemental tracers have been used to estimate the contribution of NEE concentrations using a scaling factor approach. A novel CO2 dilution approach has been undertaken to determine average fleet emission factors (EFs), whilst the impact of electric vehicle regenerative braking has also been simulated. The results indicate that NEE concentrations and EFs are highly dependent upon meteorological conditions, traffic speed, traffic volume and vehicle class. Brake wear is the dominant source of road traffic PM emissions in congested environments, whilst for each emission source, heavy duty vehicles (HDVs) contribute an order of magnitude greater than light duty vehicles (LDVs). On the other hand, despite the predicted increase in mass, the regenerative braking simulations suggest that passenger vehicles under electric powertrains will reduce brake wear emissions by 65 – 95%. This reduction depends on the assessed drive cycle and vehicle class, highlighting the importance of driving style on future brake wear emissions. The EFs developed in this thesis have been combined with traffic forecasts to project total national emissions in the UK up to 2035 – and can be used to validate the national atmospheric emission inventory. To conclude, a number of recommendations have been made with respect to air quality measurement strategies and emission policies which are needed to further our understanding of NEE, and to reduce these traffic-related emissions. It is proposed that a multi-disciplinary study should be undertaken encompassing laboratory dynamometer testing, on-vehicle measurements and environmental atmospheric measurements.Open Acces

    Sustainability Matchmaking: Exploration into using excess renewable energy to deliver ‘free’ energy to fuel poor homes – a preliminary case study in Ireland

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    The aggregated fuel cost of domestic hot water (DHW) generation in Ireland, in 2022, was €529M with associated emissions/load of 1.3MtCO2/289GWh. The shadow price of carbon monetises the negative impact of emissions, rising with time; DHW generation has an associated shadow carbon cost of €13M in 2022, rising to €42M in 2030 and €335M in 2050. In 2020, c12%/€441M of wind was curtailed or wasted as inter alia, there was no demand at times of high wind. Meanwhile, a ‘silent crisis’ is occurring in Ireland wherein one-in-two dwellings were considered in fuel poverty in 2022. Households in fuel poverty are known to limit DHW generation, impacting hygiene and well-being. As most Irish households have an electrical immersion already installed in DHW tanks, this research develops a preliminary (first round) wind allocation model to assess the potentials and economics of redeploying excess wind to heat DHW and, in the interest of a just-transition, focuses on households at risk of fuel poverty. It is found that fuel-poor households in Ireland could be theoretically provided with a ‘free’ full tank of hot water, once in every 3 weeks, redeploying 89% of overnight curtailed wind energy in 2019, realising a potential carbon cost saving to the Irish state of c€4M in 2030, rising to c€11M in 2050 along with a better quality of life for fuel-poor citizens. This research concludes this massive, readily deployable, shared, citizen-owned dispatch-down resource should be utilised and further research into redeployment of dispatch-down as a service is merited

    Social Shaping for Multi-Agent Systems

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    Multi-agent systems have gained attention due to advances in automation, technology, and AI. In these systems, intelligent agents collaborate through networks to achieve goals. Despite successes, multi-agent systems pose social challenges. Problems include agents finding resource prices unacceptable due to efficient allocation, interactions being cooperative/competitive, leading to varying outcomes, and sensitive data being at risk due to sharing. Problems are: 1. Price Acceptance; 2. Agent Cooperation and Competition; 3. Privacy Risks. For Price Acceptance, we address decentralized resource allocation systems as markets. We solve price acceptance in static systems with quadratic utility functions by defining allowed quadratic ranges. For dynamic systems, we present dynamic competitive equilibrium computation and propose a horizon strategy for smoothing dynamic pricing. Concerning Agent Cooperation and Competition, we study the well-known Regional Integrated Climate-Economy model (RICE). It's a dynamic game. We analyze cooperative and competitive solutions, showing impact on negotiations and consensus for regional climate action. Regarding Privacy Risks, we infer network structures from linear-quadratic game best-response dynamics to reveal agent vulnerabilities. We prove network identifiability tied to controllability conditions. A stable, sparse system identification algorithm learns network structures despite noise. Lastly, we contribute privacy-aware algorithms. We address network games where agents aggregate under differential privacy. Extending to network games, we propose a Laplace linear-quadratic functional perturbation algorithm. A tutorial example demonstrates meeting privacy needs through tuning. In summary, this thesis solves social challenges in multi-agent systems: Price Acceptance, Agent Cooperation and Competition, and Privacy Risks
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