3,938 research outputs found

    Economic assessment of flexibility offered by an optimally controlled hybrid heat pump generator: a case study for residential building

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    Abstract The ongoing decarbonisation process of the current energy system, driven by the EU directives, requires that more renewable energy sources are integrated in the global energy mix, as well as policies promoting investments in new low-carbon technologies, energy efficiency and grid infrastructure. The technical integration of renewable energy sources into the existing power system is not straightforward, due to the intrinsic aleatory characteristics of renewable production, which make the power grid balance harder. To handle this issue, beside the traditional supply-side management, grid flexibility can also be provided by enabling the active participation of the demand-side in power system operational procedures, by means of the so-called demand-side management (DSM). The present paper is aimed at assessing the ability of a cost-optimal control strategy, based on model predictive control, to activate demand-response (DR) actions in a residential building equipped with a hybrid heat pump generator coupled with a water thermal storage. Hourly electricity prices are considered as external signals from the grid driving the demand response actions. It is shown that the thermal energy storage turns out to be an effective way to improve the controller performances and make the system more flexible and able to provide services to the power grid. A daily cost-saving up to 35% and 15% have been highlighted with a 1 m3 0.5.m3 tanks, respectively. Finally, the achievable flexibility is shown to be strictly dependent on the storage capacity and operations, which in turn are affected by the generators sizing

    Optimization approaches for exploiting the load flexibility of electric heating devices in smart grids

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    Energy systems all over the world are undergoing a fundamental transition to tackle climate change and other environmental challenges. The share of electricity generated by renewable energy sources has been steadily increasing. In order to cope with the intermittent nature of renewable energy sources, like photovoltaic systems and wind turbines, the electrical demand has to be adjusted to their power generation. To this end, flexible electrical loads are necessary. Moreover, optimization approaches and advanced information and communication technology can help to transform the traditional electricity grid into a smart grid. To shift the electricity consumption in time, electric heating devices, such as heat pumps or electric water heaters, provide significant flexibility. In order to exploit this flexibility, optimization approaches for controlling flexible devices are essential. Most studies in the literature use centralized optimization or uncoordinated decentralized optimization. Centralized optimization has crucial drawbacks regarding computational complexity, privacy, and robustness, but uncoordinated decentralized optimization leads to suboptimal results. In this thesis, coordinated decentralized and hybrid optimization approaches with low computational requirements are developed for exploiting the flexibility of electric heating devices. An essential feature of all developed methods is that they preserve the privacy of the residents. This cumulative thesis comprises four papers that introduce different types of optimization approaches. In Paper A, rule-based heuristic control algorithms for modulating electric heating devices are developed that minimize the heating costs of a residential area. Moreover, control algorithms for minimizing surplus energy that otherwise could be curtailed are introduced. They increase the self-consumption rate of locally generated electricity from photovoltaics. The heuristic control algorithms use a privacy-preserving control and communication architecture that combines centralized and decentralized control approaches. Compared to a conventional control strategy, the results of simulations show cost reductions of between 4.1% and 13.3% and reductions of between 38.3% and 52.6% regarding the surplus energy. Paper B introduces two novel coordinating decentralized optimization approaches for scheduling-based optimization. A comparison with different decentralized optimization approaches from the literature shows that the developed methods, on average, lead to 10% less surplus energy. Further, an optimization procedure is defined that generates a diverse solution pool for the problem of maximizing the self-consumption rate of locally generated renewable energy. This solution pool is needed for the coordination mechanisms of several decentralized optimization approaches. Combining the decentralized optimization approaches with the defined procedure to generate diverse solution pools, on average, leads to 100 kWh (16.5%) less surplus energy per day for a simulated residential area with 90 buildings. In Paper C, another decentralized optimization approach that aims to minimize surplus energy and reduce the peak load in a local grid is developed. Moreover, two methods that distribute a central wind power profile to the different buildings of a residential area are introduced. Compared to the approaches from the literature, the novel decentralized optimization approach leads to improvements of between 0.8% and 13.3% regarding the surplus energy and the peak load. Paper D introduces uncertainty handling control algorithms for modulating electricheating devices. The algorithms can help centralized and decentralized scheduling-based optimization approaches to react to erroneous predictions of demand and generation. The analysis shows that the developed methods avoid violations of the residents\u27 comfort limits and increase the self-consumption rate of electricity generated by photovoltaic systems. All introduced optimization approaches yield a good trade-off between runtime and the quality of the results. Further, they respect the privacy of residents, lead to better utilization of renewable energy, and stabilize the grid. Hence, the developed optimization approaches can help future energy systems to cope with the high share of intermittent renewable energy sources

    Demand response within the energy-for-water-nexus - A review. ESRI WP637, October 2019

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    A promising tool to achieve more flexibility within power systems is demand re-sponse (DR). End-users in many strands of industry have been subject to research up to now regarding the opportunities for implementing DR programmes. One sector that has received little attention from the literature so far, is wastewater treatment. However, case studies indicate that the potential for wastewater treatment plants to provide DR services might be significant. This review presents and categorises recent modelling approaches for industrial demand response as well as for the wastewater treatment plant operation. Furthermore, the main sources of flexibility from wastewater treatment plants are presented: a potential for variable electricity use in aeration, the time-shifting operation of pumps, the exploitation of built-in redundan-cy in the system and flexibility in the sludge processing. Although case studies con-note the potential for DR from individual WWTPs, no study acknowledges the en-dogeneity of energy prices which arises from a large-scale utilisation of DR. There-fore, an integrated energy systems approach is required to quantify system and market effects effectively

    Experimental Investigation of a Capacity-Based Demand Response Mechanism for District-Scale Applications

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    District heating and cooling systems incorporating heat recovery and large-scale thermal storage dramatically reduce energy waste and greenhouse gas emissions. Electrifying district energy systems also has the effect of introducing city-scale controllable loads at the level of the electrical substation. Here we explore the opportunity for these systems to provide energy services to the grid through capacity-based demand response mechanisms. We present both a planning approach to estimate available demand-side capacity and a control framework to guide real-time scheduling when the program is active. These tools are used to assess the technical feasibility and the economic viability of participating in capacity-based demand response in the context of a real-world, megawatt-scale pilot during the summer of 2018 on the Stanford University campus

    Ten questions concerning energy flexibility in buildings

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    Funding Information: The authors are key collaborators in the IEA EBC Annex 82 project. Dr. Li leads IEA EBC Annex 82 “Energy Flexible Buildings Towards Resilient Low Carbon Energy Systems.” Mr. Satchwell researches utility regulatory and business models that achieve greater deployment of energy efficiency, demand flexibility, and other distributed energy resources. Prof. Finn investigates demand response measures in the residential and commercial building sectors. Senior researcher Christensen researches the role of users in smart energy solutions and low-carbon energy transitions. Prof. Michaël Kummert's research focuses on modeling and control of building-scale and community-scale energy systems to optimize energy flexibility and resilience. Dr. Le Dréau researches energy flexibility of buildings both at building and district scales, develops occupant behavior models and prediction techniques related to flexibility. Dr. Lopes is involved in two international projects funded by the European Union's H2020 programme where he is developing and applying energy flexibility characterization methodologies and optimization algorithms in several demonstration activities. Prof. Madsen leads a national research project ‘Energy Flexible Denmark’ and he focuses on grey-box modeling, digital twins, forecasting and control for smart buildings in smart grids. Dr. Salom research works focus on zero/positive energy buildings and districts and their interaction with energy infrastructures being involved in several international projects. Prof. Henze researches model predictive and reinforcement learning control and data analytics for the integration of building and district energy systems with the electric grid. Mr. Wittchen research works focus on zero/positive energy buildings and districts and implementation of European legislation on building's energy performance. Funding Information: The authors acknowledge the many organizations that directly or indirectly supported the completion of this article. We acknowledge the European Commission for the ARV (grant number 101036723 ), Syn.ikia (grant number 869918 ), Hestia (grant number 957823 ) projects; the Danish Energy Agency for supporting the Danish delegates participating IEA EBC Annex 82 through EUDP (grant number 64020-2131 ); Innovation Fund Denmark in relation to SEM4Cities ( IFD 0143–0004 ) and Flexible Energy Denmark ( IFD 8090-00069B ); the Building Technologies Office, Office of Energy Efficiency and Renewable Energy, at the US Department of Energy , under Lawrence Berkeley National Laboratory (contract number DE-AC02-05CH11231 ); the Center of Technology and Systems (CTS UNINOVA) and the Portuguese Foundation for Science and Technology (FCT) through the Strategic Program UIDB/00066/2020 ; Research Council of Norway in relation to Research Centre on Zero Emission Neighborhoods in Smart Cities - FME-ZEN (No. 2576609 ) and FlexBuild (No. 294920 ); the AGAUR Agency from the Generalitat de Catalunya through the project ComMit-20 ( 2020PANDE00116 ); the National Science and Engineering Research Council of Canada (NSERC Discovery Grant RGPIN 2016-06643 ). Publisher Copyright: © 2022 The AuthorsDemand side energy flexibility is increasingly being viewed as an essential enabler for the swift transition to a low-carbon energy system that displaces conventional fossil fuels with renewable energy sources while maintaining, if not improving, the operation of the energy system. Building energy flexibility may address several challenges facing energy systems and electricity consumers as society transitions to a low-carbon energy system characterized by distributed and intermittent energy resources. For example, by changing the timing and amount of building energy consumption through advanced building technologies, electricity demand and supply balance can be improved to enable greater integration of variable renewable energy. Although the benefits of utilizing energy flexibility from the built environment are generally recognized, solutions that reflect diversity in building stocks, customer behavior, and market rules and regulations need to be developed for successful implementation. In this paper, we pose and answer ten questions covering technological, social, commercial, and regulatory aspects to enable the utilization of energy flexibility of buildings in practice. In particular, we provide a critical overview of techniques and methods for quantifying and harnessing energy flexibility. We discuss the concepts of resilience and multi-carrier energy systems and their relation to energy flexibility. We argue the importance of balancing stakeholder engagement and technology deployment. Finally, we highlight the crucial roles of standardization, regulation, and policy in advancing the deployment of energy flexible buildings.publishersversionpublishe

    Model predictive control for microgrid functionalities: review and future challenges

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    ABSTRACT: Renewable generation and energy storage systems are technologies which evoke the future energy paradigm. While these technologies have reached their technological maturity, the way they are integrated and operated in the future smart grids still presents several challenges. Microgrids appear as a key technology to pave the path towards the integration and optimized operation in smart grids. However, the optimization of microgrids considered as a set of subsystems introduces a high degree of complexity in the associated control problem. Model Predictive Control (MPC) is a control methodology which has been satisfactorily applied to solve complex control problems in the industry and also currently it is widely researched and adopted in the research community. This paper reviews the application of MPC to microgrids from the point of view of their main functionalities, describing the design methodology and the main current advances. Finally, challenges and future perspectives of MPC and its applications in microgrids are described and summarized.info:eu-repo/semantics/publishedVersio

    Optimierungsrahmen für die Verbesserung der Energieflexibilität in Wohngebäuden

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    Energy flexibility is balancing the supply and demand of a building according to climate conditions, user preferences, and grid constraints. Energy flexibility in households is a practical approach to achieving sustainability in the building sector. However, the diversity in flexibility potential of energy systems and climatic variability complicate the selection of envelope parameters and building energy systems (BESs). This study aimed to design a framework to improve the energy flexibility of the building. For this purpose, a single-family house and diversified BESs were simulated in a TRNSYS-Python co-simulation platform. Initially, the bi-objective optimization identified flexible building envelopes in twenty-four locations. Then, the multi-criteria assessment of BESs was conducted using life-cycle energy flexibility indicators. Lastly, the energy flexibility potential of the BES was evaluated by employing steady-state optimization and model predictive control (MPC). The findings of this work set a benchmark for flexible household envelopes. The systematic approach for selecting BES could guide the energy system design, providing insight into energy flexibility. Further, this investigation has established that the dataset of building thermal load, boundary conditions, and control disturbances can be used to develop an MPC-based dynamic control. That controller could be employed on different BESs to achieve energy flexibility.Energieflexibilität ist der Ausgleich von Versorgung und Bedarf eines Gebäudes je nach Klima, Nutzerpräferenzen und Netzbeschränkungen. Energieflexibilität ist damit ein praktischer Ansatz für Nachhaltigkeit in Gebäuden. Die Vielfalt des Flexibilitätspotenzials von Energiesystemen und die klimatischen Unterschiede erschweren jedoch die Auswahl von Hüllparametern und Gebäudeenergiesystemen (BESs). Diese Studie zielte darauf ab, einen Rahmen zur Verbesserung der energetischen Flexibilität von Gebäuden zu entwickeln. Hierzu wurden ein Einfamilienhaus und verschiedene BES in einer TRNSYS-Python Co-Simulationsplattform simuliert. Zunächst wurden über eine bi-objektive Optimierung flexible Gebäudehüllen an vierundzwanzig Standorten ermittelt. Danach erfolgte eine multikriterielle Bewertung der BES anhand von Energieflexibilitätsindikatoren über den gesamten Lebenszyklus. Schließlich wurde das Energieflexibilitätspotenzial der BES durch den Einsatz statischer Optimierung und modellprädiktiver Regelung (MPC) bewertet. Die Ergebnisse dieser Arbeit setzen einen Maßstab für flexible Gebäudehüllen. Der systematische Ansatz zur Auswahl von BES könnte als Leitfaden für die Auslegung zukünftiger Systeme dienen. Darüber hinaus hat die Untersuchung ergeben, dass Daten zu thermischer Belastung des Gebäudes, Randbedingungen und Regelungsstörungen zur Entwicklung eines MPC verwendet werden können. Dieser Regler könnte bei verschiedenen BES eingesetzt werden, um Energieflexibilität zu erreichen
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