Configuration and Efficiency Mechanism Analysis of Ultra-High Temperature Heat Pump Energy Storage Systems for New Power Systems

With the large-scale integration of renewable energy into the power grid, the power system needs to achieve energy supply and demand balance between the randomly fluctuating load demand and the power sources. This leads to fundamental changes in its structural form, operation control methods, as well as planning, construction, and management, forming a new generation power system dominated by renewable energy production, transmission, and consumption, i.e., the renewable energy power system [1-3]. Although the prospects of new power systems are promising, their development still faces many technical challenges, which mainly manifest in the three dimensions of sufficiency, safety, and economy [4, 5]. Sufficiency is one of the challenges for the development of new power systems, which comes from the sufficiency of wind and solar power generation relative to system load, i.e., how to reserve sufficient flexibility resources in different time and space to ensure the power balance on both the generation and load sides. Safety is the second challenge for the development of new power systems, arising from the "dual-high" characteristics of high renewable energy proportion and high-power electronics devices, and the safety challenges induced by these. Economy is the third challenge for the development of new power systems, which comes from the economic factors, i.e., the resource optimization allocation efficiency determined by the power market mechanism.

Energy storage technology is rapidly developing, and research on heat pump energy storage systems has also received widespread attention [14]. At present, there are two main technical routes for heat pump energy storage systems: one is based on the Brayton cycle, and the other is based on the Rankine cycle. The specific details of each technical route are as follows:

(1) Heat pump energy storage system based on the Brayton cycle.

A typical Brayton cycle system consists of a cold storage unit, a heat storage unit, a compressor, an expander, and a heat exchanger. The energy storage/release process of the Brayton cycle-based heat pump energy storage system is shown in Figure 1, where the power generation process is reversed. Scholars in France have studied the Brayton cycle-based heat pump energy storage system and conducted thermodynamic analysis to examine the effects of various irreversible losses on system efficiency and energy density [15]. In a heat pump energy storage system, the cycle efficiency is a function of the storage temperature. For the Brayton system, high storage temperatures are beneficial for achieving high round-trip efficiency and high storage density, but in the system, a high temperature ratio is achieved through a high-pressure ratio [16-19]. To increase the pressure ratio, higher performance requirements are needed for the compressor and expander, and this also increases the construction costs of the thermal storage/cooling units.

Some scholars have proposed a combined heat and power (CHP) system based on the Brayton cycle, suggesting three operating modes: energy storage, CHP, and combined cooling, heating, and power. The results show that when the system operates only in energy storage mode, the cycle efficiency is 63.5%; in the CHP mode, the maximum COP can reach 137.9%; in the combined cooling, heating, and power mode, the maximum COP achieved is 188.1%, and the maximum thermal efficiency is 63.9%, which is 1.4% higher than in energy storage mode, effectively recovering the system's irreversible losses [20].

(2) Heat pump energy storage system based on the Rankine cycle.

The heat pump energy storage system based on the Rankine cycle was first proposed and analyzed by German researchers [21]. Since the Brayton cycle-based heat pump energy storage system requires large high-pressure storage tanks, leading to higher system costs, German scholars proposed a solution based on the transcritical CO₂ Rankine cycle. The system principle is shown in Figure 2 [22]. This system has a maximum heat storage temperature of 123℃, and the system cycle efficiency reaches 53%. Building on this research, other scholars have constructed a trans-critical CO₂ system with both hot water and cold-water storage. In the energy storage phase, the heat pump cycle heats the water in the storage tank, and the heated water provides power for the heat engine cycle during the energy release phase [23]. The latent heat on the cold side is stored through low-temperature brine, ultimately achieving a 60% round-trip efficiency and a heat storage temperature of 177℃. Research on Rankine cycle-based heat pump energy storage systems has focused more on optimizing the system configuration [24]. Some scholars have added two heat exchangers to the conventional system, as shown in Figure 3 [25]. The addition of heat exchangers improves the system's performance. From an economic perspective, the system configuration with heat transfer oil as the thermal storage medium and heat recovery is considered the best solution [26]. To improve the energy storage density of the heat pump energy storage system, scholars in China have proposed a heat pump energy storage system with supplementary heating, and on this basis, coupled it with an organic Rankine cycle system [27].

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Figure 1. Schematic diagram of the Brayton cycle heat pump energy storage system process

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Figure 2. Schematic diagram of the Rankine cycle heat pump energy storage system

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(a) Energy storage process

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(b) Energy release process

Figure 3. Energy storage and release process of the high-temperature heat pump energy storage system

3.1 Comparison of different technical routes

3.1.1 Heat pump energy storage system based on the Brayton cycle

The energy storage/release process of the Brayton cycle-based heat pump energy storage system is the reverse of the power generation process. Early research on heat pump energy storage technology focused more on the Brayton cycle-based heat pump energy storage system [28-30]. This type of system uses helium, argon, CO₂, or air as the working fluid. The Brayton cycle-based heat pump energy storage system has a high heat storage temperature, typically exceeding 800 K, and even approaching 1300 K [31]. Such high storage temperatures, on one hand, increase thermal losses and pressure losses in the heat storage components, and on the other hand, pose greater challenges for the manufacturing of high-temperature, high-pressure compression components. It is generally believed that these factors are important barriers to the large-scale commercialization of Brayton cycle-based heat pump energy storage systems [32]. Scholars in France have studied the Brayton cycle-based heat pump energy storage system and conducted thermodynamic analysis, examining how various irreversible losses affect system efficiency and energy density, concluding that the system efficiency is approximately 66.7% [33]. Scholars in the UK analyzed and optimized the parameters of heat pump energy storage, and the results showed that the system efficiency mainly depends on the performance of the compressor and expander. When the variable efficiency of the compressor and expander reaches 99%, the energy storage efficiency of the system can reach 70% [34]. Both domestic and international scholars generally agree that the closed Brayton cycle heat pump energy storage system has advantages such as high energy storage density, high energy storage efficiency, and simple structure, which saves costs and is more suitable for large-scale engineering applications [35].

3.1.2 Heat pump energy storage system based on the Rankine cycle

The Rankine cycle-based heat pump energy storage system has a lower energy storage temperature and a simpler structure. Therefore, another popular research direction for Rankine cycle-based heat pump energy storage systems is using heat-integrated systems. Compared with conventional heat pump energy storage systems, heat-integrated systems offer better application prospects. They not only allow efficient energy storage by utilizing various forms of low-grade heat, but also can be connected to regional power grids and heating networks in smart energy systems. Common heat integration methods include industrial waste heat, district heating networks, solar thermal energy, and geothermal brine injection [36].

In summary, both domestic and international scholars generally believe that the Brayton cycle-based heat pump energy storage system has advantages such as high energy storage density, high energy storage efficiency, and simple structure, which saves costs and is more suitable for large-scale engineering applications. Research on Rankine cycle-based systems focuses more on optimizing the selection of working fluids and exploring system performance and economic benefits under different heat integration scenarios. In comparison, the Brayton cycle-based system is closer to actual engineering applications and is more suitable for large-scale energy storage applications [37]. Therefore, considering factors such as system simplicity, ease of operation, and low maintenance workload, the Brayton cycle technology route with air as the working fluid is preferred [38].

3.2 System model composition

The energy storage and release processes of the heat pump energy storage system are shown in Figure 4. When there is an excess of power, the system stores energy by running the reverse Brayton cycle heat pump mode [39]. The residual electricity in the grid drives an electric motor, which in turn drives the compressor. The compressor drives the working fluid to absorb heat from the low-temperature heat source and release heat to the high-temperature heat source, establishing a temperature difference between the thermal storage and cooling systems, thus converting electrical energy into thermal energy stored in the storage medium. When there is insufficient power or high electricity demand, the system releases energy by running the forward Brayton cycle heat engine mode [40]. The working fluid expands through the turbine, doing work to drive the generator and regenerate electricity. In this phase, the working fluid absorbs heat from the high-temperature heat source and releases heat to the low-temperature heat source, transferring heat from the thermal storage system to the cooling system [41].

3.2.1 Energy storage phase

The required power for the charging part can come from the abandoned electricity of renewable energy stations such as solar and wind energy or off-peak electricity from the grid. Through the heat pump cycle, composed of compressor C1, expander T1, cold-source heat exchanger Hc, regenerator Hr, and hot-source heat exchanger Hh, electrical energy is efficiently converted into thermal energy of the working fluid [42]. In the energy storage section, the low-temperature molten salt working fluid is heated into high-temperature molten salt through Hh and stored in the high-temperature molten salt tank HTh, achieving large-scale energy storage [43].

3.2.2 Energy release phase

The working fluid flows through the cooling system to complete pre-cooling. Compressor C1 compresses the low-temperature, low-pressure gaseous working fluid to a medium-low-temperature, high-pressure state. After passing through the regenerator Hr, the fluid becomes a medium-temperature, high-pressure state. The thermal storage system releases heat through the high-temperature heat exchanger, and the working fluid absorbs heat to become a high-temperature, high-pressure state. The expander expands the working fluid to a medium-temperature, low-pressure state. The fluid then passes through the regenerator and becomes a medium-low-temperature, low-pressure state. The cooling system releases cold energy through the low-temperature heat exchanger, and the working fluid, after releasing heat, reaches a low-temperature, low-pressure state, returning to the compressor inlet, completing the discharge cycle [30, 35].

3.3 System modeling

In this paper, Aspen Plus simulation software is used to build a simulation analysis model of the high-temperature heat pump energy storage system. The main components include the compressor, expander, low-temperature heat exchanger, and high-temperature heat exchanger. In the Aspen Plus V12 version modeling, both the compressor and expander use the compressor/turbine Comor model. The high-temperature and low-temperature heat exchangers use the Heater model for heat exchangers. The physical property method of the working fluid uses the PENG-ROB method. The process flow diagram of the cycle is shown in Figure 4.

Based on the operating conditions of the heat pump energy storage system, the design parameters of the compressor and expander, as well as the performance parameters of the heat exchangers, are determined. The system parameters selected are shown in the Table 1 below. The influence of each parameter on the cycle efficiency is analyzed using the control variable method [13].

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Figure 4. Simulation process flow of the high-temperature heat pump system

Table 1. Parameter values for the heat pump energy storage system design

3.4 Economic modeling

(1) System economic analysis model [29].

Let the net profit Y=Revenue E−Cost C

Revenue E=Peak-shaving Subsidy E1+Carbon Reduction Benefit E2+Electricity Sales Revenue E3

Cost C=Electricity Cost C1+Operation and Maintenance Cost C2

System Efficiency $\eta_p$ = Power Generation Pg/Electricity Consumption Pu

Thus, it can be written as:

$\begin{align}  & Y=E-C={{\text{p}}_{\text{u}}}  \left( {{\text{e}}_{1}}\times {{\eta }_{P}}\times \text{a}+{{\text{e}}_{2}}\times {{\eta }_{P}}\times \text{a}+{{\text{e}}_{3}}\times {{\eta }_{P}}\times \text{a}-{{\text{c}}_{1}}\times \text{a}-{{\text{c}}_{2}} \right) \\\end{align}$     (1)

where, $e_1$ represents the unit peak-shaving subsidy, in yuan/kWh; $e_2$ represents the unit carbon reduction benefit, in yuan/kWh; $e_3$ represents the unit electricity sales price, in yuan/kWh; $c_1$ represents the unit electricity cost, in yuan/kWh.

(2) Sensitivity concept and calculation.

The purpose of sensitivity analysis is to identify the sensitive factors, which can be determined by calculating the sensitivity coefficient and critical points.

3.4.1 Sensitivity coefficient (SAF)

The sensitivity coefficient represents the degree of sensitivity of the economic effect evaluation index of the technical solution to uncertain factors. The calculation formula is:

${{\chi }_{EF}}=\frac{\Delta E/E}{\Delta F/F}$     (2)

where, $\chi_{E F}$ is the sensitivity coefficient; $\frac{\Delta F}{F}$ is the rate of change of the uncertain factor F (%); $\frac{\Delta E}{E}$ is the relative rate of change of the evaluation index $E$ when the uncertain factor F changes (%).

The method of calculating the sensitivity coefficient to identify sensitive factors is a relative measurement approach, which determines the ranking of the sensitivity of each factor based on its relative change's effect on the economic effect index of the technical solution.

If $\chi_{E F}>0$, it indicates that the evaluation index $E$ changes in the same direction as the uncertain factor $F$. If $\chi_{E F}<0$, it indicates that the evaluation index $E$ changes in the opposite direction to the uncertain factor $F$. The larger the sensitivity coefficient $\left|\chi_{E F}\right|$, the more sensitive the evaluation index $E$ is to the uncertain factor $F$; conversely, the smaller the sensitivity coefficient, the less sensitive the evaluation index $E$ is to the uncertain factor $F$. Based on this, we can identify the factors that have the greatest impact on the evaluation index $E$.

3.4.2 Determination of sensitivity coefficients

By rearranging Eq. (2), we obtain:

${{\chi }_{EF}}=\frac{F}{E}\cdot \frac{\Delta E}{\Delta F}=\frac{F}{E}\cdot \frac{\partial E}{\partial F}$     (3)

Thus, the sensitivity coefficients of heat storage power Pu, unit peak-shaving subsidy $e_1$, system efficiency $\eta_p$, unit carbon reduction benefit $e_2$, unit electricity sales price $e_3$, and unit electricity cost $c_1$ to the net profit can be obtained by calculating the above Eqs. (1)-(3).

$\begin{align}  & \frac{\partial Y}{\partial {{P}_{\text{u}}}}={{\text{e}}_{1}}\times {{\eta }_{P}}\times \mathbf{a}+{{\text{e}}_{2}}\times {{\eta }_{P}}\times \mathbf{a}  +{{\text{e}}_{3}}\times {{\eta }_{P}}\times \mathbf{a}-{{\text{c}}_{1}}\times \mathbf{a}-{{\mathbf{c}}_{2}} \\\end{align}$     (4)

$\frac{\partial Y}{\partial {{\eta }_{\text{p}}}}={{\text{p}}_{\text{u}}}\left( {{\text{e}}_{1}}\times \text{a}+{{\text{e}}_{2}}\times \text{a}+{{\text{e}}_{3}}\times \text{a} \right)$     (5)

$\frac{\partial Y}{\partial \text{a}}={{\text{p}}_{\text{u}}}\left( {{\text{e}}_{1}}\times {{\eta }_{P}}+{{\text{e}}_{2}}\times {{\eta }_{P}}+{{\text{e}}_{3}}\times {{\eta }_{P}}-{{\text{c}}_{1}} \right)$     (6)

$\frac{\partial Y}{\partial {{\text{c}}_{1}}}=-{{\text{p}}_{\text{u}}}\times \text{a}$     (7)

$\frac{\partial Y}{\partial {{\text{e}}_{1}}}={{\text{p}}_{\text{u}}}\times {{\eta }_{P}}\times \text{a}$     (8)

$\frac{\partial Y}{\partial {{\text{e}}_{2}}}={{\text{p}}_{\text{u}}}\times {{\eta }_{P}}\times \text{a}$     (9)

$\frac{\partial Y}{\partial {{\text{e}}_{3}}}={{\text{p}}_{\text{u}}}\times {{\eta }_{P}}\times \text{a}$     (10)

${{\chi }_{I{{P}_{\text{u}}}}}=\frac{{{P}_{\text{u}}}}{Y}\cdot \frac{\partial Y}{\partial {{P}_{\text{u}}}}$     (11)

${{\chi }_{Y{{\eta }_{P}}}}=\frac{{{\eta }_{P}}}{Y}\cdot \frac{\partial Y}{\partial {{\eta }_{P}}}$     (12)

${{\chi }_{Y\text{a}}}=\frac{\text{a}}{Y}\cdot \frac{\partial Y}{\partial \text{a}}$     (13)

${{\chi }_{Y{{\eta }_{P}}}}=\frac{{{\eta }_{P}}}{Y}\cdot \frac{\partial Y}{\partial {{\eta }_{P}}}$     (14)

${{\chi }_{\text{Y}{{\text{e}}_{1}}}}=\frac{{{\text{e}}_{1}}}{Y}\cdot \frac{\partial Y}{\partial {{\text{e}}_{1}}}$     (15)

${{\chi }_{\text{Y}{{\text{e}}_{2}}}}=\frac{{{\text{e}}_{2}}}{Y}\cdot \frac{\partial Y}{\partial {{\text{e}}_{2}}}$     (16)

${{\chi }_{\text{Y}{{\text{e}}_{3}}}}=\frac{{{\text{e}}_{3}}}{Y}\cdot \frac{\partial Y}{\partial {{\text{e}}_{3}}}$     (17)