Application of Mobile Energy Storage to Facilitate Energy Transfer Between TSO and DSO Networks

A new feeding concept for electrical transportation systems is presented, based on supercapacitive energy storage. Supercapacitors are new and powerful components for energy storage. Compared with batteries, the amount of energy supercapacitors can store is low and does not allow large vehicle autonomy. Because supercapacitors have the property to be re-loadable in a few seconds, a sequential supply system has been developed, considering repetitive feeding at the stops. To solve the problem of the high power amount needed to reach short refill times, a solution is proposed, which consists of using an intermediary supercapacitive tank placed at fixed stations, which is refilled between the bus arrivals with a much lower power. In addition to the description of the needed power electronic converters, theoretical and experimental results are presented, defining the controlled profile of the instantaneous power level, in order to achieve a fast energy transfer between two supercapacitive tanks.

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

Energies ◽

10.3390/en13174339 ◽

2020 ◽

Vol 13 (17) ◽

pp. 4339 ◽

Cited By ~ 2

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.

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