Energy Management in Buildings: Lessons Learnt for Modeling and Advanced Control Design

This paper presents a comparative analysis of different modeling and control techniques that can be used to tackle the energy efficiency and management problems in buildings. Multiple resources are considered, from generation to storage, distribution and delivery. In particular, it is shown what are the real needs and advantages of adopting different techniques, based on different applications, type of buildings, boundary conditions. This contribution is based widely on the experience performed by the authors in the recent years in dealing with existing residential, commercial and tertiary filed buildings, with application ranging from local temperature control up to smart grids where buildings are seen as an active node of the grid thanks to their ability to shape the thermal and electrical profile in real time. As for control models, a wide range of modeling techniques are here investigated and compared, from linear time-invariant models, to time-varying, to nonlinear ones. Similarly, control techniques include adaptive ones and real-time predictive ones

Uncertainty-based optimal energy retrofit methodology for building heat electrification with enhanced energy flexibility and climate adaptability

To reach net zero emissions by 2050, the UK government relies heavily on heat degasification in buildings by using heat pump technology. However, existing buildings may have terminal radiators that require a higher operating temperature than what heat pumps typically provide. Increasing the size of radiators and thermally insulating building envelopes could be a potential solution, but the feasibility of these practices is uncertain due to space constraints and high retrofit costs. This study investigates the feasibility and potential benefits of incorporating air-source heat pumps into existing gas boiler heating systems to meet heating demands. The proposed probabilistic optimal air-source heat pump design method enhances energy flexibility and climate adaptability, taking into account a wide range of uncertainty sources and multiple flexibility services (e.g., energy and ancillary services). Heating systems of three educational buildings at the University of Cambridge are used as a testbed to assess and validate the effectiveness of the proposed method, under future climate scenarios and projected decreases in heating demand due to climate change. Results indicate that the best retrofit alternative of the hybrid heating system reduces carbon emissions by 88%, total costs by 54% over its lifespan, and has an average payback period of around 3 years. Air-source heat pumps can meet the majority of the heating demand (around 80%) with gas boilers used for “top-up” heating during high demand. Furthermore, air-source heat pumps' design capacity can fulfil future cooling demand even if retrofit optimization is initially focused on meeting heating needs

Providing Grid Services With Heat Pumps: A Review

Abstract The integration of variable and intermittent renewable energy generation into the power system is a grand challenge to our efforts to achieve a sustainable future. Flexible demand is one solution to this challenge, where the demand can be controlled to follow energy supply, rather than the conventional way of controlling energy supply to follow demand. Recent research has shown that electric building climate control systems like heat pumps can provide this demand flexibility by effectively storing energy as heat in the thermal mass of the building. While some forms of heat pump demand flexibility have been implemented in the form of peak pricing and utility demand response programs, controlling heat pumps to provide ancillary services like frequency regulation, load following, and reserve have yet to be widely implemented. In this paper, we review the recent advances and remaining challenges in controlling heat pumps to provide these grid services. This analysis includes heat pump and building modeling, control methods both for isolated heat pumps and heat pumps in aggregate, and the potential implications that this concept has on the power system

Long-term experimental study of price responsive predictive control in a real occupied single-family house with heat pump

The continuous introduction of renewable electricity and increased consumption through electrification of the transport and heating sector challenges grid stability. This study investigates load shifting through demand side management as a solution. We present a four-month experimental study of a low-complexity, hierarchical Model Predictive Control approach for demand side management in a near-zero emission occupied single-family house in Denmark. The control algorithm uses a price signal, weather forecast, a single-zone building model, and a non-linear heat pump efficiency model to generate a space-heating schedule. The weather-compensated, commercial heat pump is made to act smart grid-ready through outdoor temperature input override to enable heat boosting and forced stops to accommodate the heating schedule. The cost reduction from the controller ranged from 2-33% depending on the chosen comfort level. The experiment demonstrates that load shifting is feasible and cost-effective, even without energy storage, and that the current price scheme provides an incentive for Danish end-consumers to shift heating loads. However, issues related to controlling the heat pump through input-manipulation were identified, and the authors propose a more promising path forward involving coordination with manufacturers and regulators to make commercial heat pumps truly smart grid-ready

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

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

Optimization and multivariable control of refrigeration systems

Los ciclos de refrigeración por compresión de vapor constituyen el método más extendido a nivel mundial para la generación de frío. Estos sistemas se utilizan en áreas tan diversas como regulación de la temperatura en estancias habitadas, almacenamiento y transporte de alimentos y múltiples procesos industriales. Dado el considerable impacto causado por el consumo energético de estos sistemas en los balances económicos y medioambientales de los países desarrollados y en vías de desarrollo, y teniendo en cuenta la escasez creciente de fuentes de energía fósiles y el desarrollo todavía lento de las diferentes tecnologías de energía renovable, la operación óptima en términos de eficiencia energética de los sistemas de refrigeración por compresión de vapor existentes se presenta como un problema clave que abordar. Esta Tesis aborda la operación óptima de los ciclos de refrigeración desde el punto de vista de la eficiencia energética. Aunque el trabajo se centra principalmente en sistemas de una etapa de compresión y un recinto a refrigerar, se analizan también otras configuraciones con varias etapas y varios recintos. Existen varios factores clave para alcanzar la operación óptima de un sistema de refrigeración en el campo del Control Automático: el modelado, la optimización y el control propiamente dicho. En primer lugar, se estudia ampliamente el modelado estático y dinámico de los sistemas de refrigeración. En cuanto al segundo, se desarrolla un modelo dinámico simplificado y orientado al control de un ciclo de una etapa de compresión y un recinto a refrigerar. El objetivo es que pueda ser incorporado en estrategias de control basado en modelo, donde se requieren tanto una baja carga computacional como una descripción suficientemente precisa de la dinámica dominante del sistema, de acuerdo con los objetivos de control. En segundo lugar, se analiza la operación óptima en régimen permanente de un ciclo de una etapa de compresión y un recinto a refrigerar. Dada una cierta demanda de frío, el objetivo de la fase de optimización es calcular el ciclo en régimen permanente que alcanza la máxima eficiencia energética posible asegurando la satisfacción de la demanda de frío y a la vez respetando las restricciones de operación. Una vez calculado, se pretende que este ciclo óptimo constituya la referencia a seguir por parte del controlador. Finalmente, se estudia asimismo el problema de control. En la literatura sobre sistemas de refrigeración se encuentran principalmente dos esquemas: el control convencional y el control centrado en la eficiencia energética. En el primer esquema, además de la referencia impuesta por la demanda de frío, se impone un valor bajo pero constante como referencia para el grado de sobrecalentamiento del refrigerante a la salida del evaporador, to achieve the cycle defined by the optimization stage by manipulating the available control actions. Therefore, the controllability of the one-stage, one-load-demand cycle is analysed using linear theory and a nonlinear pointwise analysis based on the phase portrait method. Given the conclusions of the controllability analysis, a suboptimal hierarchical control strategy is proposed to achieve the highest possible efficiency while satisfying the cooling load. Most contributions of this Thesis are of theoretical nature. Notwithstanding, the application of the proposed control strategy to a multi-compression-stage, multi-loaddemand experimental plant is intended. Then, steady-state identification of the plant is performed from experimental data, whereas validation of the models considering different plant configurations is also carried out.Premio Extraordinario de Doctorado U

Scaling energy management in buildings with artificial intelligence

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