Abstract

A multi-objective and multi-parametric optimization of a Pumped Thermal Electricity Storage system based on Brayton cycles is presented by the calculation of different Pareto fronts and the associated Pareto optimal sets for energetic and design analysis, respectively. A large range of internal and external irreversibilities and the thermodynamic properties of the storage media are taken into account. The analysis shows that the heat capacity of the working fluid and the heat capacity of the storage media should be the same in the contact with the hot reservoirs and in the contact with the cold reservoir in the heat pump, but in the contact with the cold reservoir for the heat engine the ratio should be 0.33, this offers information regarding the mass flow increasing significantly the achievable values for the round-trip efficiency, power output and the heat engine efficiency in the discharge process. Optimal values are given in terms of the degree of irreversibilities in the system and a comparison is made with extreme cases of infinite and minimum sizes for the storage system. Round-trip efficiencies in the so-called optimum scale/mass-flow-ratio design point exhibits noticeably larger values compared to previously reported results including the so-called endoreversible limit, where no internal irreversibilities are considered and where the improvement can achieve 49% over the endoreversible case in the most ideal scenario. Explicit numerical values of the maximum round trip efficiency, power output, and efficiency are given for a broad range of both internal and external irreversibilities.

Highlights

  • Electric energy storage technology based on the joint use of a heat pump and a heat engine cycles is nowadays a real alternative to most conventional technologies as compressed air energy storage (CAES) or pumped hydro storage (PHS) [1]

  • Compared with solid media storage [6], two clear additional advantages of the liquid storage are: (a) the pressure inside the tanks is independent of the pressure of the cyclic working fluid and more compact heat exchangers can be used; and (b) the temperature inside each tank remains almost constant, avoiding the problems associated to the propagation of the hot front in solid storage [7,8]

  • The liquid media is stored in four tanks at different constrained temperatures, in such a way that adequate integration profiles for the charge/discharge processes holds by counter-flow heat exchangers, whose temperatures are affected by a small heat-leak that is taken into account

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Summary

Introduction

Electric energy storage technology based on the joint use of a heat pump and a heat engine cycles (pumped thermal electricity storage, PTES) is nowadays a real alternative to most conventional technologies as compressed air energy storage (CAES) or pumped hydro storage (PHS) [1]. Coefficient of performance Heat engine Heat pump Pumped thermal electricity storage Round trip efficiency Thermal energy storage based on finite-time thermodynamic frameworks [20] assuming Carnotlike models [21], weakly-dissipative models [22], and/or Braytonlike cycles [23] All these thermodynamic models assume constant temperatures for TES systems so that they are amenable to analytical expressions for the main involved energetic magnitudes (as maximum power, maximum round trip efficiency, maximum COP) and/or trade-off figures of merit. Theoretical parametric studies provide physical basis for guiding pre-design of main energetic magnitudes, a full optimization of the overall system for a selected salt and cryogenic medium remains a complex task due to three main points [27]: (a) the large number of operational and design parameters; (b) the trade-off objectives (e.g. round trip efficiency, power output, heat pump COP, heat engine efficiency); and (c) uncertainties in regards with both internal and external losses and heat leaks models.

Thermodynamic model: background
Background
Performance parameters and constraints
Multiobjective and multiparametric optimization
Technical background
Determining an optimum design point
Multiobjective optimization in the design point
Φ at maximum power: endoreversible limit
Summary and conclusions
Findings
Pareto front using η and Pout as objective functions
Full Text
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