Industrial Application of High-Temperature Heat and Electricity Storage for Process Efficiency and Power-to-Heat-to-Power Grid Integration

Abstract

This chapter explores the potential of thermal energy storage (TES) systems towards the decarbonization of industry and energy networks, considering its coordinated management with electrochemical energy storage and renewable energy sources (RES). It covers various TES technologies, including sensible heat storage (SHS), latent heat storage (LHS), and thermochemical energy storage (TCS), each offering unique benefits and facing specific challenges. The integration of TES into industrial parks is highlighted, showing how these systems can optimize energy manage-ment and reduce reliance on external sources. A district heating use case also demonstrates the economic and environmental advantages of a multi-energy management strategy over single-energy approaches. Overall, TES technologies are presented as a promising pathway to greater energy effi-ciency and sustainability in industrial processes.

1 Introduction

The industrial sector plays a major role in global greenhouse gas emissions, highlighting the urgent need for its decarbonization to halt climate change. In the first quarter of 2024, industrial emissions in the EU reached 669 thousand tonnes of CO₂ equivalents, nearly three times the emissions produced by households during the same period (Eurostat 2025). One promising approach to address this challenge is the integration of high-temperature heat and electricity storage systems, which can enhance process efficiency and support the transition to RES. These systems allow for the storage of excess renewable energy, which can be converted back into electricity or used directly for industrial heating processes, thereby improving process efficiency and supporting grid stability by balancing supply and demand (SETIS 2023; Pompei et al. 2023; Akar et al. 2021). This chapter explores the mid- to long-duration applications of these storage systems, focusing on their role in power-to-heat-to-power (P2H2P) grid integration.

The motivation for this chapter stems from the need to address the dual challenges of energy efficiency and sustainability in industrial operations. By leveraging TES technologies, industries can optimize energy use, reduce emissions, and improve resilience against energy supply fluctuations. TES technologies are being utilized in various industrial applications to supply thermal loads and incorporate RES. For example, industries are using TES to store solar thermal energy, which can be used to meet heating demands during periods of low solar irradiance. Additionally, TES systems are being employed to capture and store waste heat from industrial processes, which can then be reused, thereby reducing overall energy consumption and emissions (IRENA 2020). This is particularly relevant as industries seek to balance the increasing demand for energy with the imperative to reduce their carbon footprint (IRENA 2020; Energy.gov 2023; Sun et al. 2023).

Achieving these objectives requires the optimal management of TES, which in industrial settings often involves the integration with Combined Heat and Power (CHP) systems. CHP systems can simultaneously generate electricity and useful heat, and when combined with TES, they can store excess heat for later use, enhancing overall system efficiency. This integration allows industries to better manage energy loads, reduce peak demand, and improve energy security (Benalcazar 2021).

On the other hand, the maturity of TES technologies varies, with some solutions being well-established and widely adopted, while others are still in the development or early deployment stages. Sensible heat storage, using materials like water or molten salts, is a mature technology with numerous industrial applications. On the other hand, advanced TES technologies, such as thermochemical storage, are still being researched and developed to improve their efficiency and cost-effectiveness (Ali et al. 2024).

Multi-energy management, which involves the coordinated use of various energy sources and storage systems, offers significant advantages over single-energy management. By integrating TES with other energy systems, such as electrical grids, gas networks, and RES, industries can achieve greater flexibility, resilience, and efficiency. This holistic approach allows for better optimization of energy flows, reduced operational costs, and enhanced sustainability (Coelho 2023; Glücker et al. 2024).

Following this introduction, the chapter focuses on specific applications of TES in industrial processes, including cogeneration, biomass gasification and hydrogen production. Subsequent sections present a methodology for optimizing P2H2P systems, discuss a practical use case, and consider the broader implications and future directions of TES integration in industrial settings.

2 Thermal Energy Storage: A Brief Overview

TES is an emerging technology that stores thermal energy from diverse sources, such as industrial waste heat, concentrated solar power, or electricity generated from RES, for future heat and/or power generation applications, as illustrated in Fig. 1. These systems can provide mid-to-long-term storage duration, from weeks to months, and can present remarkable low-cost energy storage and high round-trip efficiencies by employing suitable materials and proper insulation systems. These systems can be used in space and water heating, cooling, power generation, and peak shaving applications (SINTEF 2018).

Fig. 1

Schematic integration of a thermal energy storage system

TES systems can be classified into four groups according to the operating temperature:

• Cold TES—T < 20 °C

• Low-temperature TES—20 °C < T < 100 °C

• Mid-temperature TES—100 °C < T < 300 °C

• High/very high-temperature TES—T > 300 °C (high), T > 500 °C (very high).

Solutions for all temperature ranges are needed to meet industry needs. Although most global heat demand is for low-to-medium-temperature applications, several hard-to-decarbonize industry areas, like cement and steel production, require very high temperatures, typically above 1000 °C to operate (IRENA 2023).

Like any other energy storage system, TES systems can be described in terms of the following parameters:

• Capacity—presents the total energy stored in the system, generally in kWh.

• Power—defines the amount of heat transferred per second for the storage medium in charging or from the storage medium in discharging, generally in kW.

• Efficiency—quantifies the amount of energy retrieved compared with the energy given to the system, thus accounting for heat losses during storage, commonly in %.

• Storage period—defines the duration of storage, which can be given from seconds to months depending on the type of storage.

• Charge and discharge time—outlines how much time the system takes to charge or discharge, usually in hours.

• Cost—states the cost per unit of energy stored, typically in € ⋅ kWh⁻¹.

However, considering its applications the following specific characteristics are also relevant, its temperature range for different applications and the characteristics of its materials (e.g. density, specific heat and thermal conductivity. The next sections characterize the most relevant types of thermal energy storage technologies for industrial applications.

2.1 Sensible Heat Storage

Sensible Heat Storage (SHS) is extensively utilized due to the practicality and simplicity of its underlying principles. This method stores energy by varying the temperature of a storage medium without inducing a phase change. Figure 2 illustrates the typically linear relationship between the amount of heat stored and the system’s temperature. Heat is absorbed and released through radiation, conduction, and/or convection processes.

Fig. 2

Linear relationship between the amount of stored heat and the system’s temperature, demonstrating how temperature increases in direct proportion to heat stored

The storage capacity of a material is primarily determined by its specific heat capacity, which is the amount of heat required to raise the temperature of 1 kg by 1 °C. A material with a lower specific heat capacity must reach a higher temperature to store the same amount of energy as a material with a higher specific heat capacity. For example, 1 kg of water ($c_{p}$ = 4.18 kJ kg⁻¹ °C⁻¹) stores approximately 200 kJ when heated from 20 to 68 °C. In contrast, 1 kg of air ($c_{p}$ = 1.005 kJ kg⁻¹ °C⁻¹) must be heated to around 199 °C to store the same heat.

SHS systems utilize the specific heat capacity to store heat in a medium by raising its temperature. The quantity of heat stored is given in (1),

$$Q_{{{stored,\, SHS}}} = \mathop \int \limits_{{T_{i} }}^{{T_{f} }} m \cdot c_{p} \cdot {\text{d}}t$$

(1)

where $Q_{{\text{stored, SHS}}}$ is the heat stored, $m$ is the storage medium mass, $c_{p}$ is the specific heat capacity, and $T_{i}$ and $T_{f}$ the initial and final temperatures, respectively.

Table 1 provides an overview of the most used materials for this technology, highlighting water as the most effective SHS liquid-state material due to its low cost, abundancy, and high specific heat. The primary characteristics of widely utilized solid-state thermal storage materials, such as sand-rock minerals, concrete, fire bricks, and molten salts are also detailed.

Table 1 Liquid (highlighted in blue) and solid-state materials (in orange) utilized in TES systems (Alva et al. 2018; Bauer et al. 2021)

Molten salts are currently the state-of-the-art in energy storage for mid to high/very-high temperature applications such as concentrated solar power. With a storage cost ranging from 4 to 20 € kWh⁻¹, these systems can operate in the salt’s thermally stable liquid phase, between 250 and 600 °C, having a storage energy density of ca. 200 kWh m⁻³ (European Commission 2023). However, TES systems based on sensible heat face significant limitations compared to other available methods. These limitations stem from the materials used, which typically have lower energy density and poor thermal conductivity. This complicates the device design, particularly in terms of efficiently extracting stored energy, as well as in impractically large storage units. Additionally, the variable outlet temperature, in the case of electricity production, can result in unused thermal capacity when the outlet temperature falls below the turbine’s operational threshold.

2.2 Latent Heat Storage

Latent Heat Storage (LHS) involves phase change materials (PCMs) with high energy density, high thermal conductivity, and small volume change to provide a constant outlet temperature at the melting or vaporization point. The phase transition can be solid–solid, such as the crystalline form transitions in iron; solid–liquid, consisting of a melting-solidification process; or liquid–vapor, where vaporization occurs. Figure 3 illustrates the characteristic plateau in the temperature profile during phase change, representing the constant temperature at which latent heat is absorbed or released.

Fig. 3

Temperature profile showing the plateau during phase change, where latent heat is consistently absorbed or released

Although the liquid–vapor transition usually possesses higher energy, it involves a huge volume expansion, which precludes technological implementation. On the other hand, solid–liquid transitions encompass an average 10% volume expansion with a significantly higher energy density than sensible heat systems, making them the focus of study in latent-TES systems. The heat stored for a liquid-solid phase transition can be determined in (2),

$$Q_{{{stored,\, LHS}}} = \mathop \int \limits_{{T_{i} }}^{{T_{m} }} m \cdot c_{p,s} \cdot {\text{d}}t + m \cdot h_{f} + \mathop \int \limits_{{T_{m} }}^{{T_{f} }} m \cdot c_{p,l} \cdot {\text{d}}t$$

(2)

where $c_{p,s}$ and $c_{p,l}$ represent the specific heat capacity of the solid and liquid phase, $h_{f}$ the enthalpy of fusion and $T_{m}$ the melting temperature.

PCMs can be organic (sugar alcohols), inorganic (salts, metal alloys, etc.), and eutectic mixture materials. Sugar alcohols appear as promising PCM candidates for mid-temperature TES applications for their high specific heat storage capacity, non-toxicity, non-flammability, and absence of corrosion to the insulation materials. However, the low thermal conductivity (ca. 0.5 W m⁻¹ k⁻¹) and reduced thermal endurance with $T_{m}$ over 150 °C present significant limitations to their implementation.

On the other hand, inorganic materials can withstand high temperatures with greater thermal stability. Molten salts (e.g., nitrates, carbonates, and chlorides), which are especially used in the liquid state for sensible heat TES applications, can also function as PCMs for mid- to high-temperature TES systems. Besides a high specific heat storage capacity, they are generally low-cost, thus contributing to a high specific heat storage capacity per unit cost. Nevertheless, molten salts are largely affected by their low thermal conductivity (< 1 W m⁻¹ k⁻¹), high volume expansion ratio, and high levels of chemical corrosion to the shell materials.

Finally, metal alloys have been gaining attention for their superior energy density, given by metal’s high densities. Thus, more compact TES systems can be achieved. In addition, metallic PCMs present high thermal conductivities and very low volume expansion ratios during phase transition. The challenges here are related to high chemical corrosion exhibited by the liquid metal alloys, which force the use of ceramics over the structural materials of the storage units.

Tables 2 and 3 present the PCMs candidates considered for integration into the mid, high, and very high-temperature TES. Organic and salt PCMs were grouped together in Table 2, while metal alloys are presented in Table 3. Metal alloys containing lead (Pb) and cadmium (Cd) were excluded for their threats to human life and environment (EPA 2000, 2011).

Table 2 Organic and molten salts PCMs for mid and high temperature TES system (Takahashi et al. 1988; Nomura and Akiyama 2017; Liu et al. 2022; Tye et al. 1976)

Table 3 Metals and metallic alloys PCMs for mid, high, and very high- temperature TES systems (Costa Pereira and Kenisarin 2022; Morando et al. 2014; Manasijevic et al. 2021; The Engineering ToolBox 2005; King 1988; Bilek et al. 2006; Wang et al. 2006)

2.3 Thermochemical Heat Storage

Thermochemical Heat Storage (TCS) operates through two primary mechanisms: chemical reactions and sorption processes. In chemical reactions, energy is stored as the heat of reversible reactions, while sorption processes involve storing thermal energy either through adsorption (physical bonding) or absorption (dissolution of a material). TCS technologies can accommodate a wide range of temperatures, from as low as 0 °C to as high as 900 °C, and typically provide storage durations ranging from several hours to days and potentially up to several months (EASE 2023).

This technology is recognized for its high energy density, as it stores large amounts of energy within compact volumes by converting heat into reversible chemical bonds. This capability makes it well-suited for long-term storage, with minimal energy losses over extended periods and reduced heat dissipation compared to other storage methods.

Despite its potential, thermochemical storage faces several significant challenges compared to more established technologies, namely the high cost of materials necessary for chemical reactions, which impacts the economic competitiveness of these systems. Also, the slow reaction kinetics observed in some thermochemical processes does not allow for rapid absorption and release of energy, affecting the dynamic response of the TES. Finally, the complexity of system design—requiring precise control over temperature, pressure, and chemical conditions—contributes to higher operational and maintenance costs. Integration with existing energy infrastructure is also demanding due to the need for high-pressure vessels and sophisticated heat exchangers.

3 Integration in Industrial Parks

Integrating TES systems into industrial parks plays a pivotal role in enhancing energy efficiency, reducing emissions, and facilitating the transition to RES. TES enables the storage of excess energy—such as waste heat from industrial processes or surplus electrical generation from renewable sources—for later use in heating, cooling, or power generation. This allows industrial parks to optimize energy management by balancing supply and demand, reducing reliance on external energy sources, and improving overall system flexibility.

Below are key applications where TES can be integrated into industrial processes to maximize energy use efficiency:

• Concentrated Solar Plants (CSP). As already implemented with molten salts, TES systems in CSP allow the storage of the harvested solar energy during the sunlight hours. This capability enables CSP facilities to maintain continuous electricity generation and dispatchability, enhancing grid stability and reliability.

• Cogeneration Plant Integration, where TES allows excess electricity generation or heat to be stored for later use, enabling the dispatch of thermal energy when the cogeneration plant is offline. This approach enhances energy reliability and ensures that industrial processes continue to receive power and heat as needed, even during periods of reduced generation.

• Biomass Gasification process depends on the supply of superheated steam at temperatures as high as 800 °C, which is crucial for converting biomass into syngas. However, achieving such high temperatures presents considerable challenges, including increased operational costs and safety concerns related to high-temperature operations and equipment durability.

• Peak Shaving Applications for Electrolyzers. Alkaline (AE) and proton exchange membrane (PEM) electrolizers require a stable direct current power supply to ensure high efficiency and purity of the H₂ produced. TES can be used in peak shaving applications to ensure the constant power supply of this equipment. In this case, thermal energy in the form of superheated steam can be converted to electrical energy using steam turbines and generators, thereby increasing the operational efficiency of the electrolyzers during high-demand periods.

• Integration with Renewable Methanol Plants. In renewable methanol production, integration with industrial park energy systems can occur in multiple ways. For instance, thermal energy can be supplied to both gasification and distillation units to improve the efficiency of methanol production. Additionally, steam turbines may be utilized to power the entire methanol plant or specific units within the facility, reducing the reliance on external energy sources.

• Hydrogen and Oxygen Purification via Temperature Swing Adsorption. In hydrogen and oxygen production from electrolyzers, purification can occur through temperature swing adsorption (TSA). This technique involves the adsorption of water from the gas streams using an adsorbent bed. To regenerate the adsorbent, heat is applied at a constant temperature, creating a significant thermal demand. The required temperature for regeneration ranges from 100 to 300 °C, depending on the adsorbent material employed.

4 Power-to-Heat-to-Power Grid Integration—A Methodology to Optimize P2H2P Systems

Industrial parks typically integrate multi-energy resources and multi-energy networks, as represented in Fig. 4. Decarbonizing industrial parks needs to consider both electric resources (like photovoltaic panels (PVs), batteries, and heat pumps) and thermal resources (like CHP systems and TES). Two energy networks may also need to be considered, namely an electricity network and district heating. The industrial loads (to consume heat), the CHPs (to provide heat), and thermal storage systems (to provide heat) are connected to the district heating. The CHPs are also connected to the gas network (to consume gas) and to the electricity network (to provide electricity), while the thermal storage is connected to the electricity network (to consume electricity). The PVs, batteries, and heat pumps are only connected with the electricity network.

Fig. 4

Multi-energy resources

The industrial park energy resources (see Fig. 4) can be operated in a coordinated way as a multi-energy microgrid (MES), in order to minimize the energy-related costs, considering participation in the energy markets (electricity, gas, and carbon). Figure 5 presents the main steps involved in the optimization of the day-ahead bidding process, minimizing the energy costs of the industrial microgrid.

Fig. 5

Scheme of the methodology with the sequential steps, energy markets, resources, and networks considered

In this chapter, the Iberian Energy market operation was considered. It was assumed that the industrial park operator (IO) will act as a price-taker. This way, the IO submits demand bids (electricity, natural gas, and carbon) at cap-prices, and submits supply bids (electricity and secondary reserve) at floor prices during the day-ahead phase of the markets. The day-ahead phase occurs in the day before (d-1) of the trading day (d). During the trading day (d), the IO needs to deliver the market services that were bought/sold during the day-ahead session.

The objective function of the optimization problem is to minimize the net cost of trading energy services in the multi-energy markets (electricity energy, secondary reserves, natural gas, and carbon), as in (3). The system cost can be divided into four terms: the first term is the net cost of buying and selling energy and secondary reserves,, $f_{t}^{E}$, the second term is the cost of buying natural gas, $f_{t}^{G}$, and the third cost are the costs of buying carbon allowances $f_{t}^{C} + f^{CFA}$.

$$\min \sum\limits_{t \in T} {\underbrace {{f_{t}^{E} }}_{\begin{subarray}{l} {\text{Buying energy}} \\ {\text{Selling energy}} \\ {\text{Selling secondary reserves}} \end{subarray} } + \underbrace {{f_{t}^{G} }}_{{\text{Buying natural gas}}} + \underbrace {{f_{t}^{C} + f^{CFA} }}_{{\text{Buying carbon allowances}}}}$$

(3)

$$f_{t}^{E} = \lambda_{t}^{E} E_{t}^{E} \Delta t - \lambda_{t}^{B} (U_{t}^{E} + D_{t}^{E} ) + \left( {\lambda_{t}^{D,E} \phi_{t}^{D} D_{t}^{E} - \lambda_{t}^{U,E} \phi_{t}^{U} U_{t}^{E} } \right)\Delta t$$

(4)

$$f_{t}^{G} = \lambda_{t}^{G} E_{t}^{G} \Delta t + \left( {\lambda_{t}^{U,G} \phi_{t}^{U} U_{t}^{G} - \lambda_{t}^{D,G} \phi_{t}^{D} D_{t}^{G} } \right)\Delta t$$

(5)

$$f_{t}^{C} = \lambda^{\text{CO}2} \mathop \sum \limits_{{j \in J_{n} }} \left( {P_{j,t}^{CHP,E} + U_{j,t}^{CHP,E} \,.\,\phi_{t}^{U} - D_{j,t}^{CHP,E} \,.\,\phi_{t}^{D} } \right)\alpha^{\text{CO}2,G} \Delta t$$

(6)

$$f^{CFA} = \lambda^{\text{CO}2} \,.\,{\text{A}}^{ +, \text{CO}2}$$

(7)

According to the rules of secondary reserve market, the upward secondary reserve band must twice as much as the downward secondary reserve band offered (Iria et al. 2019), as described in (8).

$$U_{t}^{DA} = 2.D_{t}^{DA},\quad\forall t \in T$$

(8)

Constraint (9) and (10) define the carbon allowances that the IO needs to buy from the heat produced by the CHPs.

$$\begin{aligned} {\text{A}}^{ +, \text{CO}2} - {\text{A}}^{ -, \text{CO}2} &= \mathop \sum \limits_{t \in T} \mathop \sum \limits_{{j \in J_{n} }} \left[ \left(P_{j,t}^{CHP,H} + U_{j,t}^{CHP,H}.\phi_{t}^{U} - D_{j,t}^{CHP,H}.\phi_{t}^{D} \right).\alpha^{\text{CO}2,G}.\Delta t\right]\\ &\quad -\, FA^{\text{CO}2} \end{aligned}$$

(9)

$${\text{A}}^{ +, \text{CO}2},{\text{A}}^{ -, \text{CO}2} \ge 0$$

(10)

To facilitate the readability of the chapter, the rest of the formulation will not be fully presented. Instead, we provide an overview of how the bids, resources, and networks were modeled.

The electricity energy bids were calculated considering the consumption from heat pumps, batteries, thermal storage systems, and inflexible loads, and the injection from PVs, batteries, and CHPs. The natural gas bids were calculated considering the consumption from CHPs. The carbon bids were calculated considering the emissions of CO₂ from CHPs. The upward and downward bids were calculated coidering the flexibility provided by PVs, batteries, heat pumps, and CHPs.

A large set of constraints have been considered for the energy resources, which are related to their operational limits and technical characteristics of the following:

• PV—limits related with maximum and minimum power and available reserves.

• Heat pumps—limits related with maximum and minimum power, thermal characteristics of the building’s thermal loads, and available reserves.

• Batteries—limits related with maximum and minimum power and capacity, and available reserves.

• Combined heat and power—limits related with maximum and minimum power and available reserves.

• Thermal storage—limits related to maximum and minimum power and capacity.

See references (Coelho 2023; Coelho et al. 2021) for a detailed description of the constraints considered.

In relation to the electricity and district heating networks, they each have their own formulation. The electricity network is modelled using a non-convex formulation of the branch flow model (Baran and Wu 1989). This model considers the branch power flows, and the limits of voltage and current. The district heating is modelled using a hydraulic and a thermal model (Cao et al. 2019). With these models it is possible to calculate the mass flows and temperatures of the pipes and nodes of the network.

To optimize the mathematical problem considering the restrictions of the multi-energy resources, markets, and networks, we decompose the problem into sub-problems using a distributed algorithm, the alternating direction method of multipliers. By doing this, we will have three main sub-problems: one considering the restrictions of the markets and resources, another one considering the restrictions of the electricity network, and another one considering the restrictions of the district heating. This way, the problem becomes easier to solve as the sub-problems are smaller.

4.1 Day-Ahead Dispatch of a MES: The Case of the Manchester Microgrid

In order to illustrate the benefits of an integrated energy dispatch in a MES, the Manchester microgrid case is describe in this section, considering real data from a university campus’s multi-energy microgrid, which features both electricity and heat networks, as shown in Fig. 6.

Fig. 6

Adapted from Martínez Ceseña et al. (2020)

Electricity and heat networks of the study case.

The data for the networks is available in (Martínez Ceseña et al. 2020) and includes network parameters and inflexible load profiles for electricity, gas, and heat in the buildings. In the electricity network, the voltage limits were set between 0.9 and 1.1 p.u., with the slack bus voltage fixed at 1 p.u.. For the heat network, the mass flow limit was set at 40 kg/s, the generator supply temperature was fixed at 85 °C, each load’s outlet temperature was set to 70 °C, and the ambient ground temperature was defined as 7 °C.

The Distributed Multi-Energy Resources (DMERs) can be connected to the electricity and heat networks. The DMERs connected to the electricity network are PV systems, energy storage systems (ESSs), heat pumps (HPs), and CHPs. The CHPs are also connected to the heat network. The district heating flexible loads are connected to the heat network. Table 4 presents the resources’ parameters.

Table 4 Resources’ parameters

The buildings connected to the HPs and district heating system are maintained within a comfort temperature range of 19–23°C between 7 and 18 h, and 16–26 °C for the remaining hours of the day. Real outdoor temperature profiles were used in the simulations performed.

The electricity market data considers the energy prices, secondary reserve prices, upward and downward tertiary reserve prices, and ratios of upward and downward mobilizations (Fig. 7) (ENTSO-E 2025; REN 2025). The gas market data considers the gas prices (63.07 €/MWh) (MIBGAS 2025). The carbon market data considers the price of CO₂ emissions (82.3 €/permit) and number of free allowances (2.8). The data chosen is from 15 of January of 2023.

Fig. 7

Electricity prices for energy, secondary band, and tertiary reserves and mobilization ratios

4.1.1 Results Analysis

For the analysis performed two bidding strategies were considered:

• Single-energy (S) strategy: under this strategy, separate dispatch problems are considered for the electricity network (S-ELE) and for the heat network (S-HEAT);

• Multi-energy (MULTI) strategy: where according to the optimal dispatch problem described in Sect. 1.4.1, all DMERs and both the electricity and heat networks are operated with a coordinated strategy.

Figure 8 presents electricity (energy), gas, and upward and downward secondary reserve band bids. The two bidding strategies exhibit similar patterns in the day-ahead energy market (electricity). Both demand (positive values) and supply (negative values) bids are primarily influenced by PV production and market prices. The aggregator predominantly places supply bids during forecasted PV generation periods. Additionally, higher demand bids are placed around 4 and 23 h when prices are lower.

Fig. 8

Aggregator’s bids

In the day-ahead gas market, the two bidding strategies also show comparable bid placement patterns. Gas bids are mainly driven by prices and heat requirements. Most of the gas is procured to fuel the CHPs, which provide heat to meet the needs of prosumers connected to the heat network. Heat load requirements are more stringent between 8 and 10 h, leading the aggregator to purchase more gas during these hours. Conversely, from midnight to 7 h and from 23 h onwards, gas bids remain relatively stable, indicating that the aggregator is primarily meeting the gas and heat load requirements expected from the CHPs during these times. This pattern is clearly seen in the SINGLE strategy, while in the MULTI strategy it diverges slightly.

The two bidding strategies also present a very similar placement of upward and downward secondary reserve band bids. It is possible to observe that they have higher values in the middle of the day, as it is the time the PV systems can provide more reserves.

Figure 9 presents the result of the optimal dispatch of PVs, batteries, CHPs, and thermal storage systems for the SINGLE and MULTI strategies. Analyzing the PV and CHP generation, we can observe that they both have a similar profile in the two strategies. Nonetheless, in the MULTI strategy the PVs produce more electricity while the CHPs produce less. In relation to the batteries, they have different profiles, but they are used throughout the day in both cases. The MULTI strategy presents higher values of charging/discharging while in the SINGLE strategy their behavior exhibits a more flatten curve (up until the end of the day). Finally, the profiles of the thermal storage system charging/discharging have some differences in each strategy. In the SINGLE strategy, they are in a cycle of charging/discharging throughout the 24 h period. On the other hand, in the MULTI strategy, they are used right at the beginning of the day to charge (as prices are lower), in the middle of the day and at the end of the day. As we can see, the thermal storage systems, which are charged by electric resistance, end up using the overproduction of electricity by the PVs.

Fig. 9

Disaggregation of the electricity bids by DMER

Figure 10 presents the upward and downward reserve bids offered by each resource. In both SINGLE and MULTI strategies, the resources that provided more upward and downward reserve bids were the PVs, followed by the CHPs and batteries. It is also possible to observe that in the MULTI strategy, the PVs and CHPs provided more upward reserves than the SINGLE strategy, while batteries provided less upward reserves. In relation to the downward reserves, the PVs and batteries provided more downward reserves while the CHPs almost did not provide any reserves. This way, in the MULTI strategy, it is possible to observe a better use of flexibility available in the system and integrated between the electric (PVs and batteries) and thermal (CHPs) resources. This is because in the SINGLE strategy, the ratio between upward secondary reserves and downward secondary reserves of each resource is clearly influenced by the secondary reserve market rule stating that the upward reserve must be twice as much as the downward reserve. This shows a limit imposed on the resources which are not able to provide full potential for their flexibility. On the other hand, in the MULTI strategy, it is possible to observe a different distribution of the upward and downward reserves offered by each resource, indicating better use of the flexibility available in the system. This ends up increasing the reserve bids offered to the market and thus, the profitability of the system.

Fig. 10

Disaggregation of the secondary reserve band bids by DMER

Table 5 presents the cumulative costs obtained for the two bidding strategies (SINGLE and MULTI). The costs of the SINGLE strategy are the sum of the costs of strategy S-HEAT and S-ELE. Positive values represent costs and negative values represent income.

Table 5 Costs of each strategy

The results in Table 5 show that the MULTI strategy produced the most profitable outcome. The MULTI strategy outperformed the SINGLE strategy with 28% lower costs, which allows concluding that a multi-energy aggregator exploits better the flexibility of DMERs than single-energy aggregators.

The electricity energy cost is negative for S-HEAT, suggesting it might be benefiting from some form of revenue or cost reduction in electricity. Conversely, S-ELE has a positive cost, indicating higher expenses related to electricity. The SINGLE strategy has a higher electricity cost than the MULTI strategy. Gas energy costs are highest for S-HEAT, reflecting higher gas consumption from the CHPs. The gas cost for S-ELE is significantly lower, due to reduced gas use. In this case, the SINGLE strategy has higher costs than the MULTI strategy. Regarding the costs associated with secondary reserve bands and activation, the S-HEAT strategy shows lower revenues than the S-ELE strategy as it has less resources participating in secondary reserves. Comparing the SINGLE and MULTI strategies, we can observe that the MULTI strategy has higher revenues from participating in secondary reserve markets. Carbon costs are incurred for all strategies except S-ELE, as it does not consider the CHPs. The MULTI strategy has slightly lower costs than the SINGLE strategy.

In conclusion, the MULTI strategy ends up benefiting from lower costs/higher revenues of gas energy, secondary reserve participation and carbon allowances. Nonetheless, the main factor is the provision of secondary reserves which are higher in the MULTI strategy. As previously stated, this occurs due to a better use of the multi-energy flexibility available in the energy system, benefiting the aggregator.

Regarding the use of TES, the MULTI strategy demonstrated a more strategic use of thermal storage systems by charging during low-price periods and discharging during high-demand times, effectively leveraging PV overproduction. Economically, the MULTI strategy was more profitable, with 28% lower costs than the SINGLE strategy. This was achieved through better exploitation of multi-energy flexibility, leading to lower electricity and gas costs, higher revenues from secondary reserve markets, and reduced carbon costs. Overall, the MULTI strategy’s superior performance was driven by more effective use of available flexibility, enhancing reserve bids and overall profitability.

This case clearly demonstrates the significant advantages of implementing an optimal MES management strategy. It not only enhances the integration of RES, reducing reliance on carbon-intensive alternatives, but also delivers substantial economic benefits.

5 Conclusion

In this chapter, TES systems were first analyzed to highlight their potential to enhance energy efficiency, reduce greenhouse gas emissions, and support the transition to RES. The analysis covered various TES technologies, including SHS, LHS, and TCS, to assess the advantages and disadvantages of each solution for storing thermal energy from sources like industrial waste heat, concentrated solar power, and renewable electricity.

The classification of TES systems based on operating temperatures was discussed, addressing the demands of different industrial applications. Despite its simplicity and practicality, SHS faces limitations such as lower energy density and poor thermal conductivity. On the other hand, using PCMs, LHS offers higher energy density with constant outlet temperature discharge, which is crucial for power generation and heat applications requiring precise temperature control. Finally, although TCS presents the highest energy density and long-term storage capabilities, it faces issues due to high material costs, slow reaction kinetics, and low TRL state of the technology.

Furthermore, the integration of TES systems into industrial parks was examined, demonstrating their role in enhancing energy efficiency by storing excess thermal energy for later use, optimizing energy management, and reducing reliance on external energy sources. Key applications include concentrated solar plants, cogeneration plants, biomass gasification, electrolyzers for hydrogen production, renewable methanol plants, and TSA for gas purification.

The chapter also highlighted the economic and environmental benefits of a multi-energy management strategy, including TES, as demonstrated in a multi-energy microgrid use case. This strategy showed significant cost savings and increased revenues compared to single-energy strategies by leveraging the flexibility of diverse energy resources and enhancing the integration of RES.

Future research directions were identified, including exploring the role of aggregators as price makers in energy markets, incorporating stochastic approaches to model uncertainty, and integrating optimization frameworks into energy system planning tools. These advancements could further improve the efficiency, sustainability, and economic performance of TES systems in industrial applications.

Overall, TES technologies offer a promising pathway to achieving greater energy efficiency and sustainability in industrial processes, supporting the broader goals of decarbonization and renewable energy integration.