With the rapid development of economic society and technology, massive emissions of greenhouse gases such as carbon dioxide and methane caused by human activities have become the main cause of global climate change (Liu Z. et al., 2023). To achieve the goal of carbon neutrality, many countries propose to vigorously develop renewable energy sources represented by solar energy, wind energy, and so on (Li et al., 2021). This move can effectively improve the current situation of relying on fossil energy for energy supply and mitigate environmental problems taken from the energy supply processes (Wang S. et al., 2023; Lin and Zhang, 2023). According to a survey by the International Energy Agency, the scale of renewable energy will expand rapidly over the next few years, and be the largest source of electricity supply by early 2025 (Agency and International Energy, 2022). Since typical renewable energies, like solar and wind, have features of instability and intermittency, it brings a great challenge for its integration into the power system (Fu et al., 2021). Therefore, energy storage systems are seen as an effective way to address this challenge and an important part of the energy mix transition at this stage (Zhang et al., 2023).

Energy storage technologies contain mechanical energy storage, thermal energy storage, electrochemical energy storage, chemical energy storage, etc. (Olabi et al., 2021). Among them, pumped hydro energy storage is mainly limited by geographic location and low energy density (Amirante et al., 2017). Flywheel energy storage technology has higher power and energy density, but also higher relative costs and losses (Olabi et al., 2021). Electrochemical energy storage technologies have the advantages of high energy density and flexibility and can be integrated into electrical systems, but they are costly and may be harmful to the environment (Luo et al., 2015; Deguenon et al., 2023). Chemical energy storage, including hydrogen storage and fuel cells, fits well with net-zero emission standards, but its high cost and risk cannot be ignored (Olabi et al., 2021; Deguenon et al., 2023). Moreover, as energy storage technology continues to evolve, pumped thermal energy storage (PTES) has attracted much attention due to its small investment cost, high round-trip efficiency, long service life, and lack of geographical constraints, when compared to pumped hydro energy storage and compressed air energy storage (Benato and Stoppato, 2018; Frate et al., 2021).

PTES is a new energy storage technology that can convert electrical energy into heat and store it in a high-temperature heat storage device, and release the energy when it is needed (Zhang and Xie, 2022). Blanquiceth et al. (2023) evaluated the effectiveness of PTES systems integrated with large-scale thermal power plants and concluded that the RTE can be more than 50% and that it can be greater than 63% in modern supercritical Rankine cycles. Tafone et al. (2023) developed a composite dynamic numerical model to evaluate a proposed CHEST structure based on cascades of PMC-based energy storage, with significant improvement in round-trip efficiency. Zhao et al. (2023) conducted multi-objective economic optimization of PTES systems with a power capacity of 10 MW and a discharge time of 6 h and concluded that magnetite as a solid storage material and helium as a working fluid are the optimal choices for systems with STR.

In contrast to the PTES systems that rely on the trans-critical CO₂ cycle and the Brayton cycle, the PTES utilizing the organic Rankine cycle (ORC) as the discharge cycle demonstrates superiority when low-grade heat sources are utilized for energy storage (Wang et al., 2021; Wang P. et al., 2022; Wang S. et al., 2022; Tang et al., 2022; Liu L. et al., 2023; Wang P. et al., 2023; Tang et al., 2023). Tillmanns et al. (2022) investigated the thermo-economic potential of PTES systems based on the ORC, and C3 and alkenes are more promising as working fluids for the ORC through computational molecular-assisted design methods. Eppinger et al. (2020) investigated the effect of several different fluids, and R1233zd(E) was probably the best choice after taking into account efficiency and environmental and safety considerations, and cyclopentane achieved the highest efficiency for latent storage. Peterson (2011) analyzed the model for storing electrical power by latent heat using PTES. They investigated the working process of the expander/compressor under small temperature differentials, predicting power-to-power efficiencies in the range of 50%–60% when utilizing ordinary refrigerants.

Compared to ordinary ORC-PTES, the thermally integrated PTES (TIPTES) can make better use of low-grade thermal energy and obtain higher thermal efficiency (Frate et al., 2017; Ökten and Kurşun, 2022). Wang P. et al. (2022) investigated the effect of five different working fluids on the thermodynamic and thermo-economic performances of different systems and found that the best choices for ORC-TIPTES and OFC-TIPTES were cyclohexane and butane, respectively. Ökten and Kurşun (2022) integrated an absorption refrigeration cycle (ARC) into the TIPTES system. A thermodynamic analysis of the system revealed a performance improvement ranging from 15.3% to 41.5%. Hu et al. (2021) evaluated the thermal economy of the TI-PTES system with different typical heat source scenarios and found that an increased heat supply rate/temperature results in improved component efficiency, and that a reduced temperature difference at the pinch point leads to increased efficiency or decreased expenses. Jockenhöfer et al. (2018) proposed a PTES system based on subcritical total heat integration using butene as the working fluid, and it was found that the maximum round-trip efficiency was 1.25 at a heat source temperature of 100°C and a heat sink temperature of 15°C, while 0.59 was the maximum exergy efficiency.

Based on the above studies, the current research on TIPTES mainly focuses on the charging cycle and discharging cycle with the same working fluid, but the research on the charging and discharging cycle with different working fluids is relatively insufficient. Fan and Xi (2022a) established a Carnot cell model with two different working fluid pairs and showed that the system has the best economy when the R1336mzz(Z)+R245fa working fluid pair is employed in the HP and ORC, respectively, and the system has the highest exergy efficiency when the R245fa + HFO-1336mzz(Z) working fluid pair is used. However, they only used two types of working fluids in the study. Xue et al. (2022) proposed a PTES system integrated with waste heat, simultaneously screening 22 organic working fluids. Ultimately, the optimal working fluid suitable for the system was selected, and a performance analysis, as well as multi-objective optimization, were conducted. However, this paper exclusively investigates scenarios where the charging and discharging cycles use the same working fluid, neglecting situations where the two cycles use different working fluids.

In the current study, most researchers typically use the same fluid as the working fluid for charge and discharge cycles (Wang et al., 2020; Wang S. et al., 2022; Yu et al., 2023). In addition, the researchers found that mixing several pure working fluids to form a hybrid working fluid with complementary advantages. In particular, utilizing the temperature slip characteristics of the phase transition process of non-eutectic working fluid can effectively reduce the irreversible losses in the heat transfer process and improve the efficiency of the thermal cycle (Sun et al., 2019; Wang et al., 2019; Blondel et al., 2023). However, in current research on the TIPTES system using different working fluids, there is a limitation in the variety of employed fluids or a tendency to use the same working fluid in both the charging and discharging cycles. The impact of different fluid combinations on system performance needs further investigation. In this paper, four different working fluids of R600, R245fa, R601a, and R1336mzz(Z) are combined in pairs and applied to the charging and discharging cycles respectively, and the system performance and economy are specifically analyzed through numerical simulation and single-objective optimization. The contributions of this article are as follows:

1) The system involves a selection and pairing of four distinct working fluids to optimize charging and discharging cycles.

In this paper, Section 2 constructs an ORC-TIPTES system and picks the working fluids. In Section 3, the article presents the mathematical models of energy and exergy functions for the system accordingly. Section 4 introduces genetic algorithms and describes the implementation of single-objective optimization. The fifth section displays the model verification of the ORC-TIPTES system. Section 6 then analyses and discusses the parametric analysis of the system and the results of the single-objective optimization.

The waste flue gas was taken as a low-temperature heat source for the ORC-TIPTES. Figure 1 and Figure 2 respectively show the system form of the ORC-TIPTES and the related T-s diagram. The ORC-TIPTES consists of a heat source (flue gas), an HP subsystem, an ORC subsystem, and a thermal energy storage subsystem (TES), which includes two evaporators, two condensers, a compressor, throttle valves, a turbine, a pump, and two storage tank. Water was chosen as the energy storage medium in this system due to its high heat capacity, stability, availability, and low cost.

The working process of the ORC-TIPTES mainly contains charging, discharging, and energy storage processes. For the charging process of the system, the working fluid at low temperature and low pressure absorbs waste heat from the flue gas through the heat pump evaporator and is vaporized into superheated steam (1–2). Then, the working fluid is compressed by the compressor driven by excess electricity from the grid (2–3). Afterward, the high-temperature and high-pressure working fluid releases heat through the heat pump condenser to transfer heat to the thermal storage medium, which is stored in the thermal storage tank (three to four and 5–6). The condensed medium is throttled through a throttle valve to become a low-temperature and low-pressure liquid and then reenters the evaporator (4–1) to complete the cycle. During the discharging process of the system, the refrigerant absorbs heat in the thermal storage tank through the ORC evaporator, which becomes superheated steam (12–9). As the high-temperature and high-pressure working fluid enters the turbine, it expands and converts its thermal energy into mechanical energy (9–10). Subsequently, the turbine generates electricity by utilizing the low-pressure steam produced during the expansion process. Then, the exhaust gas from the turbine is cooled by air in the ORC condenser (10–11), and the saturated liquid flowing out of the condenser is pressurized by a pump (11–12) which reenters the evaporator to complete the process again.

The basic input parameters of the ORC-TIPTES system are displayed in Table 1. To make the calculation easier, the simplifying assumptions were made as follows:

1) The system operated in a stable state.

The choice of the working fluid as the energy carrier in a thermodynamic cycle plays a decisive role in the performance of the system (Liang et al., 2022). An appropriate working fluid can maximize the efficiency of the system and improve its economy and reliability. When selecting a working fluid, it is necessary to consider not only its physical and chemical properties but also its environmental friendliness, safety, price, and other aspects. Currently, there is no perfect working fluid candidate that is suitable for all operating conditions. Therefore, considering the thermophysical properties and environmental friendliness of the working fluid, the main parameters of the four selected working fluids are listed in Table 2, which include R600, R245fa, R601a, and R1336mzz(Z). They are all dry fluids, which could improve the performance of the system, also reduce the system investment and operating costs (Bao and Zhao, 2013). For this research, the ozone depletion potential of the R600 and R601a is 0, and the global warming potential is very low, which is environmental friendliness. However, the R245fa and R1336mzz(Z) with zero ODP but relatively higher GWP are also acceptable due to their wide range of engineering applications (Wang Q. et al., 2022).

For the heat exchangers of the system, the thermal transfer can be calculated as: Q he = m w h out − h in (1) where Q _he is the heat transfer amount of the heat exchangers evaporator and condenser; m _w is the mass flow rate of the fluid; and h is the specific enthalpy. The subscripts of ‘in’ and ‘out’, respectively, indicate heat exchanger import and export.

The consumed power of the compressor can be expressed as: W com = m w h com , out − h com , in = m w h com , out a − h com , in / η com (2)

The power consumption of the pump and the output power of the turbine can be expressed as: W pum = m w h pum , out − h pum , in = m w h pum , out a − h pum , in / η pum (3) W tur = m w h tur , out − h tur , in = m w h tur , out a − h tur , in / η tur (4) where h _out(a) is the isentropic enthalpy; η _com, η _pum, and η _tur are respectively the isentropic efficiency of the compressor, pump, and turbine which are defined as follows: η com = h com , out a − h com , in / h com , out − h com , in (5) η pum = h pum , out a − h pum , in / h pum , out − h pum , in (6) η tur = h tur , out a − h tur , in / h tur , out − h tur , in (7)

The net power output W _np of the system is: W np = W tur − W pum (8)

In the entire system, the energy balance equation can be expressed as. Σ m in h in + Q = Σ m out h out + W (9) where h represents the specific enthalpy; m represents the mass flow rate; W represents the output power of the system and Q represents the heat absorption amount.

In the research, power-to-power efficiency is an important indicator to evaluate the performance of PTES systems, which is defined as: η ptp = η ts η dt C O P (10) where η _ts refers to the TES system efficiency, η _dt refers to the discharging thermal efficiency which is set to be 1 (Eppinger et al., 2021), and COP refers to the HP cycle coefficient of performance which is defined as follows: C O P = Q t s W com (11) where Q _ts expresses the storage tank’s heat storage amount. The discharging part’s thermal efficiency can be defined as: η dt = W np Q ts (12)

Generally, the temperature sequence of each part in the system is as follows: T ts > T hs > T 0 (13)

Based on the concept of the Carnot cycle, the maximum COP of the HP and η _dt at the same temperature condition can be calculated as: C O P max = T ts T ts − T hs (14) η dt , max = T ts − T 0 T ts (15)

Therefore, η _{ptp, max} can be obtained from the above formulas as follows: η ptp , max = T ts T ts − T hs ⋅ T ts − T 0 T ts = 1 + T hs − T 0 T ts − T hs > 1 (16) where the maximum η _ptp of the TIPTES system may exceed 100% if adding low-grade heat sources (Bellos et al., 2021).

For exergy analysis, the physical exergy of each stream in the whole system be calculated as: E = m h − h 0 − T 0 s − s 0 (17) where s ₀ refers to the specific entropy and h ₀ refers to the specific enthalpy in T ₀.

The components’ exergy destruction of the system is as follows. Σ E d = Σ E in − Σ E out (18)

The exergy efficiency η _ex of the system is as follows: η ex = E dt E hs + E HP = E HP η PTP E hs + E HP (19) E HP = Q hs C O P − 1 τ (20) E hs = m hs τ h hs , max − h hs , min − T 0 s hs , max − s hs , min (21) where E _hs refers to the additional heat source exergy, E _hp refers to the electric energy exergy, s _hs refers to additional heat source entropy, h _hs refers to the additional heat source enthalpy, and τ denotes thermal energy storage duration.

To solve the single-objective optimization problem of ORC-TIPTES, a genetic algorithm (GA) is chosen in this study. The GA generates a variety of new populations through the selection, crossover, and mutation of a series of individuals in the current population, and gradually makes the population evolve to the desired optimal state. Because of its adaptability and parallel processing ability, genetic algorithm has been widely used in many fields, such as engineering optimization, machine learning, scheduling problems, and so on.

System performance evaluation needs to consider the different parameters affecting thermodynamic performance simultaneously. In this paper, system power efficiency and exergy efficiency are selected as optimization objective functions. For the ORC-TIPTES, T _{eva, hp}, T _cs, and T _ts are the main parameters of system performance taken for the decision variables. In this study, T _{eva, hp} refers to the HP system evaporation temperature, T _cs refers to the cold storage tank temperature, and T _ts refers to the hot storage tank temperature. Table 3 shows the range of the various decision variables, and the optimization results of the system are shown in Table 4. The population size set in the genetic algorithm is 50, the generation set is 15, and the Pareto fraction is 0.9.

Model verification is a key step to verify the accuracy of research. Therefore, the feasibility of the HP system and ORC system is verified by comparing them with the research data in the literature (Hu et al., 2021; Fan and Xi, 2022b). The results of specific data comparisons between this paper and the literature are listed in Table 3 and Table 4. It is found that the error between the results in this paper and those in the literature is very small by the model verification results. Therefore, the model is feasible in this paper.

Based on the physical model and mathematical model of the ORC-TIPTES established above, the influence of different working fluid pairs on the key parameters T ₁ and T ₆ on the system performance should be analyzed. Meanwhile, in this paper, the term “working fluid pairs” refers to the use of the same or different pure working fluids in the charging and discharging cycles. The way (working fluid 1 + working fluid 2) is adopted to express the working fluid pair, and the former represents the fluid in the HP subsystem during the charging process while the latter denotes the fluid in the ORC subsystem during the discharging process.

In this study, a system of ORC-TIPTES is built to test the effect of different combinations of working fluid pairs on the system performance. Further, the working fluid pairs of R245fa + R245fa and R601a + R601a are selected for single-objective optimization and the results based on the optimization were analyzed and discussed. The primary conclusions are as follows:

1) The effects of T ₆ and T ₁ on the system’s η _ptp and η _ex are opposite. An increase in T ₆ results in a decrease in both η _ptp and η _ex, while an increase in T ₁ leads to an increase in both η _ptp and η _ex.