Experimental implementation of an emission-aware prosumer with online flexibility quantification and provision

Emission-aware and flexible building operation can play a crucial role in the energy transition. On the one hand, building operation accounts for a significant portion of global energy-related emissions. On the other hand, they may provide the future low-carbon energy system with flexibility to achieve secure, stable, and efficient operation. This paper reports an experimental implementation of an emission-aware flexible prosumer considering all behind-the-meter assets of an actual occupied building by incorporating a model predictive control strategy into an existing building energy management system. The resultant can minimize the equivalent carbon emission due to electricity imports and provide flexibility to the energy system. The experimental results indicate an emission reduction of 12.5% compared to a benchmark that maximizes PV self-consumption. In addition, flexibility provision is demonstrated with an emulated distribution system operator. The results suggest that flexibility can be provided without the risk of rebound effects due to the flexibility envelope self-reported in advance

Experimental Validation for Distributed Control of Energy Hubs

As future energy systems become more decentralised due to the integration of renewable energy resources and storage technologies, several autonomous energy management and peer-to-peer trading mechanisms have been recently proposed for the operation of energy hub networks based on optimization and game theory. However, most of these strategies have been tested either only in simulated environments or small prosumer units as opposed to larger energy hubs. This simulation reality gap has hindered large-scale implementation and practical application of these method. In this paper, we aim to experimentally validate the performance of a novel multi-horizon distributed model predictive controller for an energy hub network by implementing the controller on a complete network of hubs comprising of a real energy hub inter-faced with multiple virtual hubs. The experiments are done using two different network topologies and the controller shows promising results in both setups.Comment: 6 pages, 2 figures, CISBAT conference 202

Data-Driven Demand-Side Flexibility Quantification: Prediction and Approximation of Flexibility Envelopes

Real-time quantification of residential building energy flexibility is needed to enable a cost-efficient operation of active distribution grids. A promising means is to use the so-called flexibility envelope concept to represent the time-dependent and inter-temporally coupled flexibility potential. However, existing optimization-based quantification entails high computational burdens limiting flexibility utilization in real-time applications, and a more computationally efficient quantification approach is desired. Additionally, the communication of a flexibility envelope to system operators in its original form is data-intensive. In order to address the computational burdens, this paper first trains several machine learning models based on historical quantification results for online use. Subsequently, probability distribution functions are proposed to approximate the flexibility envelopes with significantly fewer parameters, which can be communicated to system operators instead of the original flexibility envelope. The results show that the most promising prediction and approximation approaches allow for a minimum reduction of the computational burden by a factor of 9 and of the communication load by a factor of 6.6, respectively

Stochastic MPC for energy hubs using data driven demand forecasting

Energy hubs convert and distribute energy resources by combining different energy inputs through multiple conversion and storage components. The optimal operation of the energy hub exploits its flexibility to increase the energy efficiency and reduce the operational costs. However, uncertainties in the demand present challenges to energy hub optimization. In this paper, we propose a stochastic MPC controller to minimize energy costs using chance constraints for the uncertain electricity and thermal demands. Historical data is used to build a demand prediction model based on Gaussian processes to generate a forecast of the future electricity and heat demands. The stochastic optimization problem is solved via the Scenario Approach by sampling multi-step demand trajectories from the derived prediction model. The performance of the proposed predictor and of the stochastic controller is verified on a simulated energy hub model and demand data from a real building.Comment: 6 pages, 5 figures. Submitted to IFAC World Congress 202

Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging simple Rules

Reinforcement Learning (RL) generally suffers from poor sample complexity, mostly due to the need to exhaustively explore the state-action space to find well-performing policies. On the other hand, we postulate that expert knowledge of the system often allows us to design simple rules we expect good policies to follow at all times. In this work, we hence propose a simple yet effective modification of continuous actor-critic frameworks to incorporate such rules and avoid regions of the state-action space that are known to be suboptimal, thereby significantly accelerating the convergence of RL agents. Concretely, we saturate the actions chosen by the agent if they do not comply with our intuition and, critically, modify the gradient update step of the policy to ensure the learning process is not affected by the saturation step. On a room temperature control case study, it allows agents to converge to well-performing policies up to 6-7x faster than classical agents without computational overhead and while retaining good final performance.Comment: Submitted to CDC 202

Machine learning and robust MPC for frequency regulation with heat pumps

With the increased amount of volatile renewable energy sources connected to the electricity grid, there is an increased need for frequency regulation. On the demand side, frequency regulation services can be offered by buildings that are equipped with electric heating or cooling systems, by exploiting the thermal inertia of the building. Existing approaches for tapping into this potential typically rely on a first-principles building model, which in practice can be expensive to obtain and maintain. Here, we use the thermal inertia of a buffer storage instead, reducing the model of the building to a demand forecast. By combining a control scheme based on robust Model Predictive Control, with heating demand forecasting based on Artificial Neural Networks and online correction methods, we offer frequency regulation reserves and maintain user comfort with a system comprising a heat pump and a storage tank. We improve the exploitation of the small thermal capacity of buffer storage by using affine policies on uncertain variables. These are chosen optimally in advance, and modify the planned control sequence as the values of uncertain variables are discovered. In a three day experiment with a real multi-use building we show that the scheme is able to offer reserves and track a regulation signal while meeting the heating demand of the building. In additional numerical studies, we demonstrate that using affine policies significantly decreases the cost function and increases the amount of offered reserves and we investigate the suboptimality in comparison to an omniscient control system.Comment: 13 pages, 12 figures, 1 table, submitted to IEEE Transactions on Control Systems Technolog

Design and optimal integration of seasonal borehole thermal energy storage in district heating and cooling networks

Conference Proceedings available at: https://proceedings.open.tudelft.nl/index.php/clima2022Technologies that can close the seasonal gap between summer renewable generation and winter heating demand are crucial in reducing CO2 emissions of energy systems. Borehole thermal energy storage (BTES) systems offer an attractive solution, and their correct sizing is important for their techno-economic success. Most of the BTES design studies either employ detailed modelling and simulation techniques, which are not suitable for numerical optimization, or use significantly simplified models that do not consider the effects of operational variables. This paper proposes a BTES modelling approach and a mixed-integer bilinear programming formulation that can consider the influence of the seasonal BTES temperature swing on its capacity, thermal losses, maximum heat transfer rate and on the efficiency of connected heat pumps or chillers. This enables an accurate assessment of its integration performance in different district heating and cooling networks operated at different temperatures and with different operating modes (e.g. direct discharge of the BTES or via a heat pump). Considering a case study utilizing air sourced heat pumps under seasonally varying CO2 intensity of the electricity, the optimal design and operation of an energy system integrating a BTES and solar thermal collectors were studied. The optimization, aiming at minimizing the annual cost and CO2 emissions of the energy system, was applied to two heating network temperatures and five representative carbon prices. Results show that the optimal BTES design changed in terms of both size and operational conditions, and reductions in emissions up to 43% could be achieved compared to a standard air-source heat pumps based system

Colloquium: Quantum interference of clusters and molecules

We review recent progress and future prospects of matter wave interferometry with complex organic molecules and inorganic clusters. Three variants of a near-field interference effect, based on diffraction by material nanostructures, at optical phase gratings, and at ionizing laser fields are considered. We discuss the theoretical concepts underlying these experiments and the experimental challenges. This includes optimizing interferometer designs as well as understanding the role of decoherence. The high sensitivity of matter wave interference experiments to external perturbations is demonstrated to be useful for accurately measuring internal properties of delocalized nanoparticles. We conclude by investigating the prospects for probing the quantum superposition principle in the limit of high particle mass and complexity.Comment: 19 pages, 13 figures; v2: corresponds to published versio

A core outcome set for localised prostate cancer effectiveness trials

Objective: To develop a core outcome set (COS) applicable for effectiveness trials of all interventions for localised prostate cancer. Background: Many treatments exist for localised prostate cancer, although it is unclear which offers the optimal therapeutic ratio. This is confounded by inconsistencies in the selection, definition, measurement and reporting of outcomes in clinical trials. Subjects and methods: A list of 79 outcomes was derived from a systematic review of published localised prostate cancer effectiveness studies and semi-structured interviews with 15 prostate cancer patients. A two-stage consensus process involving 118 patients and 56 international healthcare professionals (HCPs) (cancer specialist nurses, urological surgeons and oncologists) was undertaken, consisting of a three-round Delphi survey followed by a face-to-face consensus panel meeting of 13 HCPs and 8 patients. Results: The final COS included 19 outcomes. Twelve apply to all interventions: death from prostate cancer, death from any cause, local disease recurrence, distant disease recurrence/metastases, disease progression, need for salvage therapy, overall quality of life, stress urinary incontinence, urinary function, bowel function, faecal incontinence, sexual function. Seven were intervention-specific: perioperative deaths (surgery), positive surgical margin (surgery), thromboembolic disease (surgery), bothersome or symptomatic urethral or anastomotic stricture (surgery), need for curative treatment (active surveillance), treatment failure (ablative therapy), and side effects of hormonal therapy (hormone therapy). The UK-centric participants may limit the generalisability to other countries, but trialists should reason why the COS would not be applicable. The default position should not be that a COS developed in one country will automatically not be applicable elsewhere. Conclusion: We have established a COS for trials of effectiveness in localised prostate cancer, applicable across all interventions which should be measured in all localised prostate cancer effectiveness trials

Frequency of serological non-responders and false-negative RT-PCR results in SARS-CoV-2 testing: a population-based study.

Objectives The sensitivity of molecular and serological methods for COVID-19 testing in an epidemiological setting is not well described. The aim of the study was to determine the frequency of negative RT-PCR results at first clinical presentation as well as negative serological results after a follow-up of at least 3 weeks. Methods Among all patients seen for suspected COVID-19 in Liechtenstein (n=1921), we included initially RT-PCR positive index patients (n=85) as well as initially RT-PCR negative (n=66) for follow-up with SARS-CoV-2 antibody testing. Antibodies were detected with seven different commercially available immunoassays. Frequencies of negative RT-PCR and serology results in individuals with COVID-19 were determined and compared to those observed in a validation cohort of Swiss patients (n=211). Results Among COVID-19 patients in Liechtenstein, false-negative RT-PCR at initial presentation was seen in 18% (12/66), whereas negative serology in COVID-19 patients was 4% (3/85). The validation cohort showed similar frequencies: 2/66 (3%) for negative serology, and 16/155 (10%) for false negative RT-PCR. COVID-19 patients with negative follow-up serology tended to have a longer disease duration (p=0.05) and more clinical symptoms than other patients with COVID-19 (p<0.05). The antibody titer from quantitative immunoassays was positively associated with the number of disease symptoms and disease duration (p<0.001). Conclusions RT-PCR at initial presentation in patients with suspected COVID-19 can miss infected patients. Antibody titers of SARS-CoV-2 assays are linked to the number of disease symptoms and the duration of disease. One in 25 patients with RT-PCR-positive COVID-19 does not develop antibodies detectable with frequently employed and commercially available immunoassays