HVAC with thermal energy storage: Optimal design and optimal scheduling

1997 ◽  
Vol 18 (1) ◽  
pp. 31-38 ◽  
Author(s):  
H. Arkin ◽  
R. Navon ◽  
I. Burg
Keyword(s):  
Energy Storage ◽  
Optimal Design ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Optimal Scheduling

Optimal design and operation of thermal energy storage systems in micro-cogeneration plants

2020 ◽  
Vol 265 ◽  
pp. 114769 ◽  
Author(s):  
E. Pérez-Iribarren ◽  
I. González-Pino ◽  
Z. Azkorra-Larrinaga ◽  
I. Gómez-Arriarán
Keyword(s):  
Energy Storage ◽  
Optimal Design ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Storage Systems ◽  
Energy Storage Systems ◽  
Thermal Energy Storage Systems

scholarly journals Optimal design and energy management of a renewable energy plant with seasonal energy storage

2021 ◽  
Vol 238 ◽  
pp. 02002
Author(s):  
Hilal Bahlawan ◽  
Enzo Losi ◽  
Lucrezia Manservigi ◽  
Mirko Morini ◽  
Michele Pinelli ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Optimal Design ◽  
Energy Management ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Fossil Fuels ◽  
Electrical Energy ◽  
Energy Plant ◽  
Energy Plants

The exploitation of fossil fuels is undoubtedly responsible of environmental problems such as global warming and sea level rise. Unlike energy plants based on fossil fuels, energy plants based on renewable energy sources may be sustainable and reduce greenhouse gas emissions. However, they are unpredictable because of the intermittent nature of environmental conditions. For this reason, energy storage technologies are needed to meet peak energy demands thanks to the stored energy. Moreover, the renewable energy systems composing the plant must be optimally designed and operated. Therefore, this paper investigates the challenge of the optimal design and energy management of a grid connected renewable energy plant composed of a solar thermal collector, photovoltaic system, ground source heat pump, battery, one short-term thermal energy storage and one seasonal thermal energy storage. To this aim, this paper develops a methodology based on a genetic algorithm that optimally designs a 100% renewable energy plant with the aim of minimizing the electrical energy taken from the grid. The load profiles of thermal, cooling and electrical energy during a whole year are taken into account for the case study of the Campus of the University of Parma (Italy).

Pareto optimal design of a finned latent heat thermal energy storage unit using a novel hybrid technique

2021 ◽  
Vol 44 ◽  
pp. 103310
Author(s):  
Hamid Maleki ◽  
Mehdi Ashrafi ◽  
Nastaran Zandy Ilghani ◽  
Marjan Goodarzi ◽  
Taseer Muhammad
Keyword(s):  
Energy Storage ◽  
Optimal Design ◽  
Latent Heat ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Pareto Optimal ◽  
Hybrid Technique ◽  
Storage Unit ◽  
Energy Storage Unit ◽  
Pareto Optimal Design

scholarly journals Integrated Optimal Design and Control of Fourth Generation District Heating Networks with Thermal Energy Storage

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2766 ◽  
Author(s):  
van der Heijde ◽  
Annelies Vandermeulen ◽  
Salenbien ◽  
Helsen
Keyword(s):  
Optimal Control ◽  
Energy Storage ◽  
Optimal Design ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
District Heating ◽  
Energy System ◽  
And Control ◽  
Heating Networks ◽  
Optimal Design And Control

In the quest to increase the share of renewable and residual energy sources in our energy system, and to reduce its greenhouse gas emissions, district heating networks and seasonal thermal energy storage have the potential to play a key role. Different studies prove the techno-economic potential of these technologies but, due to the added complexity, it is challenging to design and control such systems. This paper describes an integrated optimal design and control algorithm, which is applied to the design of a district heating network with solar thermal collectors, seasonal thermal energy storage and excess heat injection. The focus is mostly on the choice of the size and location of these technologies and less on the network layout optimisation. The algorithm uses a two-layer program, namely with a design optimisation layer implemented as a genetic algorithm and an optimal control evaluation layer implemented using the Python optimal control problem toolbox called modesto. This optimisation strategy is applied to the fictional district energy system case of the city of Genk in Belgium. We show that this algorithm can find optimal designs with respect to multiple objective functions and that even in the cheaper, less renewable solutions, seasonal thermal energy storage systems are installed in large quantities.

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