Evaluation of SOFC-CHP’s ability to integrate thermal and electrical energy system decentrally in a single-family house with model predictive controller

2021 ◽  
Vol 48 ◽  
pp. 101643
Author(s):  
Muthalagappan Narayanan ◽  
Gerhard Mengedoht ◽  
Walter Commerell
Keyword(s):  
Electrical Energy ◽  
Energy System ◽  
Single Family ◽  
Model Predictive Controller ◽  
Predictive Controller ◽  
Family House

scholarly journals Configuring the Objective Function of A Model Predictive Controller for An Integrated Thermal-Electrical Decentral Renewable Energy System

2021 ◽  
Vol 10 (2) ◽  
pp. 317-331
Author(s):  
Muthalagappan Narayanan
Keyword(s):  
Renewable Energy ◽  
Fuel Cell ◽  
Objective Function ◽  
Energy System ◽  
Thermal System ◽  
Single Family ◽  
Model Predictive Controller ◽  
Renewable Energy System ◽  
Predictive Controller ◽  
Family House

With the increasing integration of decentral renewable energy systems in the residential sector, the opportunity to enhance the control via model predictive control is available. In this article, the main focus is to investigate the objective function of the model predictive controller (MPC) of an integrated thermal-electrical renewable energy system consisting of photovoltaics, solar thermal collectors, fuel cell along with auxiliary gas boiler and electricity grid using electrical and thermal storage in a single-family house. The mathematical definition of the objective function and the depth of detailing the objectives are the prime focus of this particular article. Four different objective functions are defined and are investigated on a day-to-day basis in the selected six representative days of the whole year for the single-family house in Ehingen, Germany with a white-box simulation model simulated using TRNSYS and MATLAB. Using the clustering technique then the six representative days are weighted extrapolated to a whole year and the outcomes of the whole year MPC implementation are estimated. The results show that the detailing of the mathematical model, even though is time and personnel consuming, does have its advantages. With the detailed objective function, 9% more solar thermal fraction; 32% less power-to-heat at an expense of 32% more gas boiler usage; 6% more thermal system effectiveness along with 10% increased total self-consumption fraction with 16% decrease in space heating demand, 492 kWh more battery usage and 66% reduced fuel cell production is achieved by the MPC in comparison to the status quo controller. Except for the effectiveness of the thermal system with increased gas boiler usage, which occurs due to less power-to-heat, the detailed objective function in comparison to the simple mathematical definition does evidently increase the smartness of the MPC.

Air-water heat pump applied as water boiler in single-family house, coefficient of performance analysis under the real conditions

2018 ◽  
Vol 3 (2) ◽  
pp. 58-61
Author(s):  
Agnieszka Lisowska-Lis ◽  
Robert Leszczyński
Keyword(s):  
Heat Source ◽  
Heat Pump ◽  
Air Temperature ◽  
Electrical Energy ◽  
Coefficient Of Performance ◽  
Single Family ◽  
Pump System ◽  
Heat Pump System ◽  
System Temperature ◽  
Family House

The subject of the research was an air-water heat pump, model PCUW 2.5kW from HEWALEX, installed in a single-family house. The pump is only used for heating water. The research was carried out from 25-08-2017 to 18-09-2017 in the village of Zborowice, in Malopolska region, Poland. The data were recorded from the heat pump system: temperature of the lower heat source (external air), temperature of the upper heat source (water temperature in the tank), time of heat pump was calculated during the analysed cycle of work and electrical energy consumption. The Coefficient Of Performance (COP) of the analysed air-water heat pump was determined. The analysis of the results was carried out using the MATLAB and EXCEL statistical tools. The correlation between COP coefficient and external air temperature is strong: 0.67.

scholarly journals Combinations of heat pump and photovoltaics for renovated buildings

2019 ◽  
Vol 111 ◽  
pp. 01003
Author(s):  
Andreas Heinz ◽  
Christian Gaber
Keyword(s):  
Heat Pump ◽  
Residential Buildings ◽  
Electrical Energy ◽  
Heating System ◽  
Heat Emission ◽  
Single Family ◽  
Pv System ◽  
Air Source Heat Pump ◽  
Emission System ◽  
Family House

The aim of this work is the analysis of hybrid heating systems consisting of an air source heat pump, a storage tank and a photovoltaic (PV) system for the use in renovated residential buildings. The potential for decreasing the electrical energy consumption of the heat pump from the grid by targeted operation of the speed controlled compressor with electricity from PV is determined by means of dynamic system simulations in TRNSYS for a renovated single family house under the assumption that the existing radiator heating system is not replaced, and that therefore relatively high supply temperatures are necessary. Different variants were considered with regard to the size of the PV system, the storage volume and the influence of the heat emission system.

Annual evaluation of a model predictive controller in an integrated thermal-electrical renewable energy system using clustering technique

2021 ◽  
pp. 1-43
Author(s):  
Muthalagappan Narayanan
Keyword(s):  
Renewable Energy ◽  
Fuel Cell ◽  
Thermal Energy ◽  
Energy Supply ◽  
Energy System ◽  
Status Quo ◽  
Solar Thermal ◽  
Model Predictive Controller ◽  
Renewable Energy System ◽  
Predictive Controller

Abstract Whitebox model in a model predictive controller (MPC) for energy systems though does help in developing accurate system model, requires a long time for optimization. In this article, an adaptation of the clustering technique used in hardware-in-the-loop is proposed for evaluation of the MPC on an annual scale with selected six representative days. Initially, the various input parameters for clustering (algorithm, distance metric, and datapoint input dimensions) are studied for the selected thermal-electrical integrated renewable energy system (with solar thermal collectors, auxiliary gas boiler, stratified thermal storage, micro fuel cell combined heat and power (FC-CHP), photovoltaic system, a lithium-ion battery) for a Sonnenhaus standard single-family residential building. Finally, the proposed methodology is used to compare the annual derived energy values and key performance indicators for an MPC implementation with a status quo controller as a reference. Also, extreme exemplary weather days are investigated as the selected representative days were only average days in each season. Despite the conflict of using the FC-CHP on cold-sunny days, instead of utilizing the battery and increased gas boiler energy input, 9% increase in decentral system fraction is reported. Via the use of MPC instead of status quo controllers, the results indicate -18% space heating demand; +30% solar thermal energy production; -29% gas boiler energy supply; -52% power-to-heat thermal energy supply; -52% electrical fuel cell production; +240 kWh battery utilization; and -52% reduced grid import at the expense of 1.2% grid import.

scholarly journals THE OPTIMISATION OF ENERGY SYSTEMS BY USING 3E FACTOR: THE CASE STUDIES

2006 ◽  
Vol 12 (1) ◽  
pp. 63-68 ◽  
Author(s):  
Artur Rogoža ◽  
Kestutis Čiuprinskas ◽  
Giedrius Šiupšinskas
Keyword(s):  
Sustainable Development ◽  
District Heating ◽  
Energy Systems ◽  
Energy System ◽  
System Evaluation ◽  
Single Family ◽  
Energy Economy ◽  
Technical Systems ◽  
System Life ◽  
Family House

Energy systems should be analysed according to principles of sustainable development. The optimality of technical systems usually is evaluated by only technical and economical criteria. But the estimation of these criteria is not comprehensive enough in the case when system life cycle is much longer than the period of reliable economical prognosis. In this paper the criteria set of energy system evaluation and optimisation was expanded by energy and ecological standpoints and a new multiple criteria indicator (3E factor ‐ Energy, Economy, Ecology) was introduced. The application of this factor was demonstrated by two examples: optimisation of the district heating network pipeline replacement periodicity and optimisation of the thermal insulation of the single family house.

Study of the operation of an artificial neural network that works in the electric network in order to prevent emergency conditions.

2020 ◽  
Vol 14 (1) ◽  
pp. 48-54
Author(s):  
D. Ostrenko ◽  
Keyword(s):  
Neural Networks ◽  
Learning Algorithm ◽  
Electricity Consumption ◽  
Electrical Energy ◽  
Energy System ◽  
Electrical Networks ◽  
Electric Networks ◽  
Electric Network ◽  
Time Dynamics ◽  
Emergency Situations

Emergency modes in electrical networks, arising for various reasons, lead to a break in the transmission of electrical energy on the way from the generating facility to the consumer. In most cases, such time breaks are unacceptable (the degree depends on the class of the consumer). Therefore, an effective solution is to both deal with the consequences, use emergency input of the reserve, and prevent these emergency situations by predicting events in the electric network. After analyzing the source [1], it was concluded that there are several methods for performing the forecast of emergency situations in electric networks. It can be: technical analysis, operational data processing (or online analytical processing), nonlinear regression methods. However, it is neural networks that have received the greatest application for solving these tasks. In this paper, we analyze existing neural networks used to predict processes in electrical systems, analyze the learning algorithm, and propose a new method for using neural networks to predict in electrical networks. Prognostication in electrical engineering plays a key role in shaping the balance of electricity in the grid, influencing the choice of mode parameters and estimated electrical loads. The balance of generation of electricity is the basis of technological stability of the energy system, its violation affects the quality of electricity (there are frequency and voltage jumps in the network), which reduces the efficiency of the equipment. Also, the correct forecast allows to ensure the optimal load distribution between the objects of the grid. According to the experience of [2], different methods are usually used for forecasting electricity consumption and building customer profiles, usually based on the analysis of the time dynamics of electricity consumption and its factors, the identification of statistical relationships between features and the construction of models.

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