Identification, Modeling and Analysis of Energy Losses in a SOFC/GT Hybrid Power Plant

At DLR (German Aerospace Center), a pilot hybrid power plant made by a Solid Oxide Fuel Cell and a Gas Turbine is being built. In the present thesis, the energy losses from the hybrid power plant have been identified and modeled, in order to integrate a global system model and enhance its accuracy while keeping a high computational speed. Firstly, the state of the scientific and technological knowledge and the existing model paradigms on the topic have been reviewed, together with the theoretical fundamentals on heat transfer and computational techniques for its modeling. Then, various multi-dimensional models for the evaluation of heat losses from the hybrid power plant have been built for both stationary and transient operation. The real system parameters have been accounted for by building a database for material properties and components geometries. The results obtained with the different models have been compared in order to choose the paradigm that gives the lowest computational time while maintaining a feasible accuracy

The Status of Research and Innovation on Heating and Cooling Networks as Smart Energy Systems within Horizon 2020

The European Union is funding scientific research through the Horizon 2020 Framework Programme. Since the key priorities for the next few decades are the reduction in carbon emissions and the enhancement of energy system conversion efficiency, a collection of the most recent research projects can be beneficial to researchers and stakeholders who want to easily access and identify recent innovation in the energy sector. This paper proposes an overview of the Horizon 2020 projects on smart distributed energy systems, with particular focus on heating and cooling networks and their efficient management and control. The characteristics of the selected projects are summarized, and the relevant features, including the energy vectors involved, main applications and expected outputs are reported and analyzed. The resulting framework fosters the deployment of digital technologies and software platforms to achieve smart and optimized energy systems

Predictive Controller for Refrigeration Systems Aimed to Electrical Load Shifting and Energy Storage

The need to reduce greenhouse gas emissions is leading to an increase in the use of renewable energy sources. Due to the aleatory nature of these sources, to prevent grid imbalances, smart management of the entire system is required. Industrial refrigeration systems represent a source of flexibility in this context: being large electricity consumers, they can allow large-load shifting by varying separator levels or storing surplus energy in the products and thus balancing renewable electricity production. The work aims to model and control an industrial refrigeration system used for freezing food by applying the Model Predictive Control technique. The controller was developed in Matlab® and implemented in a Model-in-the-Loop environment. Two control objectives are proposed: the first aims to minimize total energy consumption, while the second also focuses on utilizing the maximum amount of renewable energy. The results show that the innovative controller allows energy savings and better exploitation of the available renewable electricity, with a 4.5% increase in its use, compared to traditional control methods. Since the proposed software solution is rapidly applicable without the need to modify the plant with additional hardware, its uptake can contribute to grid stability and renewable energy exploitation

a model in the loop application of a predictive controller to a district heating system

Abstract The high weather variability due to climate change and the need to reduce carbon emissions require innovative solutions for energy systems and grids. In particular, improvements in control strategies allow to increase efficiency without changing the system configuration. Adaptive controllers, as currently proposed, base the management of the system on its past behavior. The main drawback of these methods is the lack of flexibility required to face the mentioned scenario. This paper presents a Model Predictive Control approach which, instead, is based on the prediction of the future evolution of the controlled system. Since it allows to consider the external conditions variability, a more resilient way to manage District Heating and Cooling networks can be achieved. The novel control strategy is developed and tested through a Model-in-the-Loop application to a thermal energy network. This latter is composed by combining physics-based dynamic models from a dedicated library of energy systems components developed by the authors in the Matlab®/Simulink® environment. The network model is controlled by the MPC controller model, which shows to be flexible and reliable in the optimization and management of energy systems

development and application of a predictive controller to a mini district heating network fed by a biomass boiler

Abstract Energy saving is actually recognized as one of the most significant ways to reduce primary energy consumption and pollutant emissions. Due to the remarkable importance of heating systems and heat distribution grids, Siram by Veolia and the University of Parma have developed an optimal control system for District Heating Networks. Usually building control systems are designed to manage plants relying on past experience: the optimal control system described in the paper defines plant management strategy on the basis of the future behavior of the systems and the external environment. The proposed control system has been applied to the heating system and the distribution network of a school complex in Podenzano (Emilia-Romagna region). The district heating supplies heat to three different buildings (primary school, secondary school and sports hall). The heating plant is composed of three generators (two fed by natural gas and one by wooden biomass), a Thermal Energy Storage, two main distribution manifolds (supply and return) and three secondary circuits, which distribute heat to the buildings. In the first part of the paper the control algorithm is described, split into plant simulation models and the optimization algorithm. In the second part, the real application and the new communication architecture applied on site are outlined and, finally, the obtained results are reported highlighting the management strategies of generators and pumps. The optimal control strategy application gave important results in terms of energy saving, in particular the energy supplied to the buildings dropped significantly, this is the result of knowing the building behavior in advance

Smart management of integrated energy systems through co-optimization with long and short horizons

The integration of all sectors of energy production, distribution and consumption in multi-source energy networks has lately gained attention as an attractive strategy to deal with the challenges raised by decarbonization roadmaps. For such a network to become a smart energy system, however, it needs to be managed and controlled in a smart way. While existing techniques mainly focus either on short-term unit commitment or on yearly scheduling separately, this work presents an original combined optimization algorithm which merges the two methods, in order to enhance system real-time control with long-term evaluations (e.g. incentives and yearly constraints). The control architecture comprises three coordinated optimization levels, each periodically updated through the receding time horizon strategy. A long-term supervisory module performs whole-year optimal scheduling accounting for long-term factors and determines the constraints for a short-term supervisory module which, in turn, optimizes the control action for the energy production system in real-time. In parallel, energy distribution modules minimize energy supply to the different portions of the distribution network downstream. Simulation results on a hospital case study demonstrate a 9.7% reduction in total operating cost over the whole year, as well as an increase in revenues deriving from incentives for high efficiency cogeneration

Development of a model-based Predictive Controller for a heat distribution network

Research studies concerning heating and cooling systems have increased in recent years, pledging great potential for energy saving, efficient thermal energy distribution and renewable energy source integration. Currently, heating systems are managed on the basis of operator experience or by using adaptive controllers, however these solutions are not suitable when there are remarkable variations in boundary conditions (e.g. weather changes). In this context, Model Predictive Control is a promising strategy as it optimizes the control action based on the prediction of the future behavior of both system dynamics and disturbances by means of simplified models. This paper presents the control of a building heating system through a Model-in-the-Loop Model Predictive Control approach. A detailed model that replicates the behavior of the real system is controlled with a predictive controller based on a novel Dynamic Programming optimization algorithm implemented in Matlab ® . The performance of the innovative controller is compared to the results obtained with a PID controller. Overall, the Model Predictive Control strategy is able to fulfill comfort requirements properly while minimizing energy consumption

Application of modelling approaches of twin-screw compressors: thermodynamic investigation and reduced-order model identification

Refrigeration is an essential part of the food chain. It is used in all stages of the chain, from industrial food processing to final consumption at home. In these processes, mechanical refrigeration technologies are employed, where compressors increase gas pressure from evaporation to condensation. In industrial refrigeration systems, twin-screw compressors represent the most widely used technology. A detailed mathematical model of a twin-screw compressor has been developed in Simulink® using differential equations for energy and mass balances to simulate the compression cycle that includes suction, compression and discharge phases. Gas pressure and enthalpy can be calculated as time functions during the cycle. However, the computational times obtained limit the possibility to extend the use of the model in the development of control strategies for the whole refrigeration plant in its real operating conditions. Therefore, the detailed model has been used to train a simplified model developed in Matlab®: the simulated mass flow rate, shaft power and the fluid discharge temperature have been employed to identify several geometrical and thermodynamic parameters of the simplified model. The latter relies on non-linear algebraic equations and, thus, requires a very short computational time. A limited performance dataset has been used to train the model, and a different dataset to test it: the results of the models have been compared, and small errors in mass flow rate, shaft power and fluid discharge temperature have been observed