scholarly journals Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4339 ◽  
Author(s):  
Simone Buffa ◽  
Anton Soppelsa ◽  
Mauro Pipiciello ◽  
Gregor Henze ◽  
Roberto Fedrizzi
Keyword(s):  
Energy Transfer ◽  
Energy Storage ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
District Heating ◽  
Heating And Cooling ◽  
Fifth Generation ◽  
Artificial Neural

District heating and cooling (DHC) is considered one of the most sustainable technologies to meet the heating and cooling demands of buildings in urban areas. The fifth-generation district heating and cooling (5GDHC) concept, often referred to as ambient loops, is a novel solution emerging in Europe and has become a widely discussed topic in current energy system research. 5GDHC systems operate at a temperature close to the ground and include electrically driven heat pumps and associated thermal energy storage in a building-sited energy transfer station (ETS) to satisfy user comfort. This work presents new strategies for improving the operation of these energy transfer stations by means of a model predictive control (MPC) method based on recurrent artificial neural networks. The results show that, under simple time-of-use utility rates, the advanced controller outperforms a rule-based controller for smart charging of the domestic hot water (DHW) thermal energy storage under specific boundary conditions. By exploiting the available thermal energy storage capacity, the MPC controller is capable of shifting up to 14% of the electricity consumption of the ETS from on-peak to off-peak hours. Therefore, the advanced control implemented in 5GDHC networks promotes coupling between the thermal and the electric sector, producing flexibility on the electric grid.

Analysis of thermal energy storage tanks and PV panels combinations in different buildings controlled through model predictive control

Energy ◽  
2021 ◽  
pp. 122201
Author(s):  
Joan Tarragona ◽  
Anna Laura Pisello ◽  
Cèsar Fernández ◽  
Luisa F. Cabeza ◽  
Jorge Payá ◽  
...  
Keyword(s):  
Energy Storage ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Storage Tanks

Near Optimal Model Predictive Control of Thermal Energy Storage

2020 ◽  
Author(s):  
O. A. Qureshi ◽  
P. R. Armstrong
Keyword(s):  
Energy Storage ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
High Reliability ◽  
Operating Cost ◽  
Weather Conditions ◽  
Preliminary Evaluation ◽  
Novel Algorithm

Abstract Efficient plant operation can be achieved by properly loading and sequencing available chillers to charge a thermal energy storage (TES) reservoir. TES charging sequences are often determined by heuristic rules that typically aim to reduce utility costs under time of use rates. However, such rules of thumb are in most cases far from optimal even for this task. Rigorous optimization, on the other hand, is computationally expensive and can be unreliable as well if not carefully implemented. Model-predictive control (MPC) that is reliable, as well as effective, in TES application must be developed. The goal is to develop an algorithm that can reach ∼80% of achievable energy efficiency and peak shifting capacity with very high reliability. A novel algorithm is developed to reliably achieve near optimal control for charging cool storage in chiller plants. Algorithm provides a constant COP (or cost per ton-hour) for 24-hr dispatch plan at which plant operates during most favorable weather conditions. Preliminary evaluation of this novel algorithm has indicated up to 6% improvement in plant annual operating cost relative to the same plant operating without TES. TOU rate used in both cases charges 7.4cents/kWh during off peak hours and 9.8cents/kWh during peak hours (Peak hours are 10 am to 10 pm).

A Distributed Predictive Control of Energy Resources in Radiant Floor Buildings

2019 ◽  
Vol 141 (10) ◽  
Author(s):  
Soroush Rastegarpour ◽  
Luca Ferrarini ◽  
Foivos Palaiogiannis
Keyword(s):  
Energy Storage ◽  
Model Predictive Control ◽  
Water Flow ◽  
Predictive Control ◽  
Flow Rate ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Water Flow Rate ◽  
Heating System ◽  
Energy Resources

This paper studies the impact of using different types of energy storages integrated with a heat pump for energy efficiency in radiant-floor buildings. In particular, the performance of the building energy resources management system is improved through the application of distributed model predictive control (DMPC) to better anticipate the effects of disturbances and real-time pricing together with following the modular structure of the system under control. To this end, the load side and heating system are decoupled through a three-element mixing valve, which enforces a fixed water flow rate in the building pipelines. Hence, the building temperature control is executed by a linear model predictive control, which in turn is able to exchange the building information with the heating system controller. On the contrary, there is a variable action of the mixing valve, which enforces a variable circulated water flow rate within the tank. In this case, the optimization problem is more complex than in literature due to the variable circulation water flow rate within the tank layers, which gives rise to a nonlinear model. Therefore, an adaptive linear model predictive control is designed for the heating system to deal with the system nonlinearity trough a successive linearization method around the current operating point. A battery is also installed as a further storage, in addition to the thermal energy storage, in order to have the option between the charging and discharging of both storages based on the electricity price tariff and the building and thermal energy storage inertia. A qualitative comparative analysis has been also carried out with a rule-based heuristic logic and a centralized model predictive control (CMPC) algorithm. Finally, the proposed control algorithm has been experimentally validated in a well-equipped smart grid research laboratory belonging to the ERIGrid Research Infrastructure, funded by European Union's Horizon 2020 Research and Innovation Programme.

Sign in / Sign up

Export Citation Format

Share Document