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Article

Energy Scheduling Strategy for the Gas–Steam–Power System in Steel Enterprises Under the Influence of Time-Of-Use Tariff

1
Inner Mongolia Power (Group) Co., Ltd., Hohhot 010010, China
2
Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China
*
Author to whom correspondence should be addressed.
Energies 2025, 18(3), 721; https://doi.org/10.3390/en18030721
Submission received: 8 January 2025 / Revised: 20 January 2025 / Accepted: 24 January 2025 / Published: 4 February 2025
(This article belongs to the Section B: Energy and Environment)

Abstract

:
Fully harnessing the inherent flexible adjustment potential of steel enterprises and fostering their interaction with the power grid is a crucial pathway to advancing green transformation. However, traditional research usually takes reducing energy consumption as the optimization goal, which limits the adjustment response capability, or ignores the storage and conversion constraints of secondary energy sources such as gas, steam, and electricity, making it difficult to fully explore and reasonably utilize the potential of multi-energy coordination. This study considers the production constraints of the surplus energy recovery and utilization system, establishes a collaborative scheduling model for a gas–steam–power system (GSPS) in an iron and steel enterprise, and proposes a demand response strategy that considers internal production constraints. Considering the time-of-use (TOU) tariff, iron and steel enterprises achieve a dynamic optimization adjustment range of electricity demand response through the conversion and storage process of gas, steam, and power. The adjustment capability of the GSPS reaches 26.94% of the initial electricity load, while reducing the total system energy cost by 2.24%. There is vast development potential of iron and steel enterprises participating in electricity demand response for promoting cost reduction and efficiency improvement, as well as enhancing the power grid flexibility.

1. Introduction

The steel industry plays a foundational role in the global economy, supporting key sectors such as construction, manufacturing, and transportation. At the same time, it has a significant impact on energy consumption and carbon emissions. According to data from 2022, five major energy-intensive industries, including steel and non-ferrous metal smelting, account for 45% of China’s total energy consumption [1], with the steel industry alone contributing 14% of energy consumption [2] and 15.9% of carbon emissions [3]. This substantial environmental footprint makes the steel industry a critical area for achieving China’s “dual carbon” goals, highlighting the urgent need for energy-saving, emission-reduction, and industrial transformation [4], and further emphasizing the industry’s central role in future sustainable development [5]. Reducing energy consumption and carbon emissions while maintaining production efficiency and quality is one of the most pressing challenges facing the steel industry [6]. Studies have pointed out that during the industry’s transformation, a balance must be struck between environmental goals and production capacity [7].
Although steel production relies heavily on fossil fuels such as coal and natural gas, it also generates significant amounts of secondary energy, such as gas, steam, and electricity [8]. However, traditionally, these by-products have not been effectively recovered or utilized in steel production [9]. Research indicates that the recovery and reuse of secondary energy is key to improving overall energy efficiency in the steel industry [10]. Moreover, using these by-products can reduce dependence on external energy sources [11] and significantly enhance self-sufficiency in energy [12]. By strategically allocating and dynamically scheduling these energies, steel enterprises can enhance their self-generation capacity [13], reduce the burden on the power grid [14], and increase the grid’s ability to integrate renewable energy [15].
With the growing maturity of demand response (DR) mechanisms, steel enterprises, as flexible load resources, can adjust their energy consumption to support the grid during peak demand periods [16]. This flexibility not only stabilizes the grid [17] but also facilitates the large-scale integration of renewable energy. Studies have shown that by optimizing the dynamic conversion, storage, and distribution of gas, steam, and electricity, steel enterprises can reduce their reliance on traditional fossil fuels without compromising production, thereby driving green transformation. These measures also contribute to achieving China’s “dual carbon” goals, further pushing the industry toward sustainable development. As Yuan et al. have pointed out, methods for improving energy efficiency in steel production face multiple challenges, yet they also offer a variety of optimization pathways and promising prospects for future development [18].
In recent years, extensive research has focused on optimizing the allocation of secondary energy in steel production. Early studies concentrated on optimizing individual energy resources, such as gas [19,20], steam [21,22], and electricity [23,24]. Akimoto et al. [25] proposed a mixed-integer linear programming (MILP) model for optimizing gas resource allocation in steel enterprises, while Zhao et al. [26] developed an MILP model that includes penalty factors for boilers and gas storage to optimize gas distribution and reduce operational costs [27]. However, these studies generally focused on optimizing single energy types [28], overlooking the synergistic effects between gas, steam, and electricity and their impact on system flexibility [29].
Building on this, Shu et al. [30] introduced a collaborative scheduling model for gas, steam, and electricity, aimed at minimizing the overall operational cost by optimizing the integration of these energy types. While this model lays the theoretical groundwork for energy optimization, it still faces limitations, such as failing to account for dynamic changes in the supply and demand of gas, steam, and electricity, as well as production constraints like burner switching and gas storage safety. Therefore, a key challenge remains: how to optimize energy allocation while considering various production constraints.
To address these issues, He et al. [31] proposed a linear programming model that considers the dynamic variations in the demand for gas, steam, and electricity, along with the impacts of rolling schedules on energy dispatch [32]. Similarly, Zhao et al. [33] suggested an MILP model for optimizing secondary energy allocation in steel enterprises, based on time-of-use (TOU) electricity pricing and dynamic boiler efficiency. However, these studies largely overlook the collaborative role of critical internal energy systems, particularly the synergy between combined heat and power (CHP) units and waste heat recovery systems. As a result, these models have not fully enhanced system flexibility and efficiency.
In summary, the dynamic coordination and optimization of gas, steam, and electricity in steel enterprises is an effective approach to improving operational efficiency and enhancing system flexibility. While much of the existing research has made progress in optimizing individual energy types, it has not fully considered the coupling effects between different energy forms or properly accounted for the internal constraints of the production process. This limits the precise assessment and effective utilization of system flexibility. Moreover, most studies focus primarily on reducing operational costs under fixed electricity prices [34], neglecting the potential of steel enterprises to act as flexible load resources interacting with the grid [35].
To solve the above problems, this study selects a steel enterprise in Northeast China as a case study and first establishes an integrated gas–steam–electricity coupling optimization scheduling model. This model incorporates the internal production processes of the waste energy recovery system, focusing on the time-series coupling, conversion, and storage of gas, steam, and electricity. Based on this, the model considers production constraints such as the level changes in gas storage and boiler burner switching. Additionally, the study proposes a dynamic electricity demand response strategy based on peak, valley, and standard time periods, optimizing the electricity demand response strategy by leveraging the complementary characteristics of multi-energy conversion and the convenience of gas storage, thereby reducing dependence on external energy sources. Finally, this study quantitatively evaluates the load adjustment potential of the steel enterprise under different electricity price conditions and analyzes how the enterprise can provide flexible regulation to the grid through load response, thus promoting the green transformation of the steel enterprise and the stability of the power grid.

2. Problem Statement

A typical long flow steelmaking process includes sintering, pelletizing, coking, ironmaking, steelmaking, and hot rolling. During the coking process, washed coal is transformed into coke in coke ovens, and coke oven gas (COG) is recovered. Waste heat from the coke ovens is also captured through dry quenching (CDQ) technology, which generates electricity and steam. In the ironmaking process, raw materials such as sintered ore and coke are fed into the blast furnace to reduce iron ore to molten iron, and blast furnace gas (BFG) is recovered from the top of the blast furnace as a by-product. In the steel-making process, the carbon in the molten iron undergoes an oxidation reaction with oxygen to produce qualified molten steel, and the flue gas is recovered and purified to be converter gas (LDG) that can be used. In addition to the three types of gas in the production process to meet the needs of steel production, the surplus gas can be converted into steam, electricity and other energy sources to further meet the production needs. Figure 1 illustrates the main structure of the steel production process and the associated energy recovery and utilization system. COG and BFG are used to supply energy to the steam boilers (B1-B4) and the CHP unit, and are also utilized in the main process coke ovens for coke production and hot rolling. The high-pressure steam S1 (3.5 MPa), medium-pressure steam S2 (1.0 MPa), and low-pressure steam S3 (0.4 MPa) produced by the steam boilers are used in the main process. Additionally, high-pressure steam S1 drives steam turbines (TB1, TB2) for electricity generation. Steam S2, generated from the dry quenching power generation process, is also used in the main process. The steam turbine, CHP unit and CDQ unit together with the purchased power from the grid supply the production process.
In steel plants, boiler loads are regulated by controlling the burners, with most boilers being equipped with multiple burners to accommodate different types of by-product gas. To maintain safe boiler operation and avoid issues such as tempering or incomplete combustion, it is essential to minimize the frequent opening and closing of burners.
Additionally, gas cabinets act as a buffer between gas production and utilization, enabling gas storage for backup purposes, coordinating upstream and downstream production processes, and ensuring production safety. Different types of gas cabinets are limited by capacity constraints, and there are normal inventory levels and safe operating intervals. Taking the industrial production of an iron and steel plant in Northeast China as an example, the inventory levels of COG, BFG, and LDG in the gas cabinet should be kept near 100 km3, 180 km3, and 40 km3, respectively, and can fluctuate within 80–120 km3, 140–220 km3, and 25–55 km3. Once the gas cabinet reaches its maximum storage capacity, any excess by-product gas must be vented.

3. Mathematical Model

This study aims to explore gas, coal, steam, and electricity distribution and dispatch in the iron and steel enterprise over a 24-h period (t = 1, 2, …, 24). Each period starts at Ht−1 and ends at Ht, which gives
τ t = H t H t 1
A.
Steam and Power Generation Model
The by-product gas used as fuel in the boiler generates different temperature grates of steam, which is then applied in the main production process and waste heat power generation system. The steam and power generation model comprises steam boilers, turbines, CHP units, and CDQ units.
(1)
Boilers
Boilers in steel enterprises are capable of consuming multiple types of by-product gases, such as COG and BFG, to produce steam of varying energy grades (S1, S2, and S3). The energy balance and constraint equations for steam boilers are [35]
r = 1 R S i r t h r s S i t w h w = η i q = 1 Q V i q t
q = 1 Q V i q t h v q q = 1 Q V i q t h v i min
S i t w = r S i r t
0 V i q t V i q max
S i r min S i r t S i r m a x
where r (r = 1, 2, …, R) represents the different grades of steam, i (i = 1, 2, …, I) represents the different boilers, and t represents time (t = 1, 2, …, 24). Sirt represents the flow rate of steam of grade r produced by boiler i during time period t, S i t w represents the feedwater flow rate of boiler i during time period t. hs and hw represent the specific enthalpy of steam and feedwater, respectively. Viqt represents the feed flow rate of fuel q supplied to boiler i during time period t. In boilers, q indicates BFG and COG. ηi represents the thermal efficiency of boiler i. hvq is the calorific value of different gases, and h v i min is the minimum calorific value required for the normal operation of boiler i. Equation (4) ensures that the total amount of steam generated by a boiler equals its total feedwater input. V iq max indicates the maximum flow rate of the q type of fuel fed into the boiler. S i r min indicates the minimum flow rate of the r level steam generated from the boiler. S i r max indicates the maximum flow rate of the r level steam generated from the boiler.

Steam Turbines

The relationship between the inlet and outlet steam flows for the turbine during each time period can be expressed by [36]
S j t in = S j t o u t
S j t in = r S j r t in
S j t o u t = r S j r t o u t
f j t = η i r S j r t i n h r s r S j r t o u t h r s
S j r i n , min S j r t S j r i n , max
S j r o u t , min S j r S j r o u t , max
f j min f j t f j max
D R j f j t f j t 1 U R j
where j (j = 1, 2, …, J) represents different turbines. S j t in and S j t out denote the flow rate of steam into and out of the turbine j, respectively, during time period t. S j r t in and S j r t out represent the flow rate of grade r steam into and out of the turbine j, respectively, during time period t. fj,t denotes the power generation of turbine j during time period t and ηi represents the thermal efficiency of turbine i. S j r in , min and S j r in , max represent the minimum and maximum flow rate of the r level steam into the turbine, respectively. S j r out , min and S j r out , max represent the minimum and maximum flow rate of the r level steam out of the turbine, respectively. f j min and f j max represent the minimum and maximum electricity generation rate from the turbine, respectively. DRj and URj represent the maximum ramp down and up rate of the turbine, respectively.
(2)
CHP
CHP units commonly found in iron and steel enterprise generally comprise fuel boilers, steam turbines, and gas turbines. The input energy sources include gas and coal, and the output includes electricity and steam. The CHP model is as follows [37].
f k t + r = 1 R S k r t h r s S k r t h w = η k i I k q = 1 Q V i q t h v q
i I k q = 1 Q V i q t h v q h v k min ( i I k q = 1 Q V i q t ) i I k q = 1 Q V i q t
0 V i q t V i q max
S k r min S k r t S k r max
f k min f k t f k max
D R k f k t f k t 1 U R k
where k (k = 1, 2, …, K) represents the CHP units and i (i = 1,2, …,Ik) represents the boilers within the CHP units. q indicates BFG, COG, and coal. fkt denotes the power output of the CHP unit k during time period t, and Skrt represents the flow rate of grade r steam produced by CHP unit k during time period t. S k r min and S k r max represent minimum and maximum allowable generation rate of the r level of steam from the CHP unit, respectively. f k min and f k max represent minimum and maximum allowable electricity generation rate from the CHP unit, respectively. DRk and URk represent the maximum ramp down and up rate of the CHP unit, respectively.
(3)
CDQ
The iron and steel enterprise in this study use CDQ technology to recycle waste heat in the production process. The model for the CDQ is as follows [38].
f m t τ t + r = 1 R S m r t τ t h r s S m r t τ t h w = η m E m t i n
S m r min S m r t S m r max
f m min f m t f m max
D R m f m t f m t 1 U R m
where m (m = 1, 2, …, M) represents the CDQ units. fmt denotes the power generation of the CDQ units in time period t. Smrt represents the steam generation rate of the CDQ units in time period t and at level r, and E m t in represents the total waste heat recovered by the CDQ units in time period t. S m r min and S m r max represent the minimum and maximum allowable generation rate of the r level of steam from the waste heat unit, respectively. f m min and f m max represent the minimum and electricity generation rate from the waste heat unit, respectively. DRm and URm represent the maximum ramp down and up rate of the waste heat unit, respectively.
B.
Gas, Steam, and Power Balances
In terms of power balance, the generator units comprise steam turbine units, CHP units, and waste heat recovery units. The total power generation of the generator units must meet the electricity consumption of the production process in each time period. The power balance equation is [31]
j = 1 j f j t + k = 1 k f k t + m = 1 M f m t + f t i m p = f t d e m + f t exp
y t imp + y t exp = 1
0 f t imp f t imp , max y t imp t
0 f t exp f t exp , max y t exp t
where f t dem is the demand for power, f t imp is the electricity purchase from the grid in each period, and f t exp is the electricity sales of the grid during each period. y t imp and y t exp are 0–1 variables indicating the purchase and sale states, respectively. f t imp , max and f t exp , max are the maximum power purchase and sale limits during t, respectively.
The steam is generated by boilers, CHP units, and CDQ units. The steam balance equation is
i I S i r t + k = 1 K S k r t + m = 1 M S m r t + j = 1 j S j r t o u t j = 1 j S j r t in = S r t dem
where S r t dem represents the steam demand for grade r during time period t from the production process.
The gas storage of the gasholders at the end of each optimization cycle is
i n v q t = i n v q t 1 + V q t gen τ t u = 1 U V q t u τ t i = 1 I V i q t τ t Q emission
where q (q = 1, 2, …, Qg) represents different types of gases, invqt denotes the storage level of gas type q in the gas holder at the end of time period t, V q t g e n represents the generation rate of gas type q, Vqtu indicates the consumption rate of gas type q in the steel production process u, Viqt denotes the consumption rate of gas type q in equipment i of the energy recovery system during time period t, and Qemission represents the amount of gas dissipated during time period t.
C.
Gas Holder Operational Model
The gas holder, as a buffering device, can store excess gas or supply the required amount of gas to the production process, serving as a buffer. The gas storage amount in the gas holder must remain within its capacity limits, which can be expressed [39]
I n v q min I n v q t I n v q max q Q x , t
where I n v q min and I n v q max represent the minimum and maximum capacity of gas holders, respectively.
In addition to the capacity limits, the gas storage volume of the gas holders should remain within a safe operating range to provide standby gas for the production process. Otherwise, operational risks may arise. This constraint can be expressed as
I n v q L S I n v q t L i n v q t I n v q H + S I n v q t H q Q x , t
where I n v q H and I n v q L represent the high and low boundaries of the gas storage safety operation range, respectively. S I n v q t L and S I n v q t H are two positive slack variables used to represent the deviation of the gas holder’s storage level from the safety operation range. Through optimization, the gas holder’s storage level can be controlled to minimize system operating costs while ensuring production safety.
D.
Burner Operational Constraints
Industrial boilers in steel plants are typically equipped with multiple identical burners used to introduce different types of gas. By controlling the opening and closing of burners, the gas intake can be adjusted. However, frequently opening and closing burners may lead to backfiring or incomplete combustion. Therefore, to maintain the stability of the combustion process in the boiler, the number of burner switches should be minimized. In the actual operation, a maximum of three burner state changes are allowed per time period. The total amount of gas entering the boiler during each time period is [32]
V i q t = V i q N i q t q Q x , t
where Niqt represents the number of burners that are in the “on” state during time period t. Viq denotes the consumption rate of gas type q in each burner of the energy recovery system equipment i.
Define ΔNiqt as the number of burners whose on/off status changes during time period t. The constraint equations for ΔNiqt are
Δ N i q t N i q t N i q ( t 1 ) q Q x , t
Δ N i q t N i q ( t 1 ) N i q t q Q x , t
Δ N i q t = i b n i q t 1 + i b n i q t 2 + i b n i q t 3 q Q x , t
where i b n i q t 1 , i b n i q t 2 , and i b n i q t 3 are binary variables, representing the cases where the status of 1, 2, and 3 burners changes, respectively.
E.
Objective Function
The objective function is to minimize the operational cost of the GSPS coupled system considering the TOU tariff:
Y = y coal + y ele + y pl + y p 2 + y p 3 + y p 4 + y main
y coal = t = 1 T k = 1 K C q V i q t τ t q = coal
where Cq is the price of the coal and ycoal is the purchasing coal cost,
y ele = t = 1 T C t exp × f t exp × τ t t = 1 T C t imp × f t imp × τ t
where C t exp is the electricity sale price in period t, C t imp is the electricity purchase price in period t and yele is the cost of purchasing/selling electricity from the grid,
y p 1 = t = 1 T E q e m Q q t e m
where E q e m is the penalty coefficient for gas emission of the q type of the byproduct gas and yp1 is punished by the gas dispersion,
y p 2 = t = 1 T E q L S I n v q t L + E q H S I n v q t H
where E q L is the penalty coefficient for the inventory level in gasholder q which lies in its low operational region, E q H is the penalty coefficient for the inventory level in gasholder q which lies in its high operational region and yp2 is the penalty cost of the gasholder cabinet,
y p 3 = t = 1 T i = 1 I E q S W × i b n i q t 1 + i b n i q t 2 + i b n i q t 3
where E q S W is the penalty coefficient for burner switching operations for the q type of byproduct gas and yp3 is the penalty cost of the boiler burner switch
y p 4 = t = 1 T i = 1 I E q 2 S × i b n i q t 2 + E q 3 S × i b n i q t 3
where E q 2 W is the penalty coefficient for simultaneously switching two burners related to the q type of byproduct gas in a boiler, E q 3 W is thepenalty coefficient for simultaneously switching three burners related to the q type of byproduct gas in a boiler, and yp4 is the additional penalty cost for simultaneous switches of burners in fuel boilers,
y main = t = 1 T i = 1 I C i m × S i t × τ t + t = 1 T j = 1 J C j P M × f j t × τ t + t = 1 T k = 1 K C k P M × f k t × τ t + t = 1 T m = 1 M C m P M × f m t × τ t
where C i m is the maintenance cost of boiler i, C j P M is the maintenance cost of turbine j, C k P M is the maintenance cost of CHP unit k, C k P M is the maintenance cost of waste heat and unit m, and ymain is the equipment maintenance costs.

4. Case Study

Taking the industrial production of a steel plant in Northeast China as an example, this study optimizes the operation strategy of the GSPS in response to the TOU tariff, while ensuring the steel production requirements. The GSPS consists of four boilers, two CHP units, and two CDQ units, with the system schematic depicted in Figure 1. The steel plant’s production schedule for the entire day is shown in Figure 2. Figure 2a illustrates the by-product gas and waste heat production for each time period in the main steel production processes. Since CDQ2 requires supplementary S2 steam, the amount of waste heat required is higher than for CDQ1. Due to the intermittent nature of the converter process, LDG is fully utilized for the blast furnace and hot rolling processes and is not included in the optimization scheduling. Figure 2b displays the demand for BFG, COG, and various grades of steam in the steel production process. Parameters for boilers, turbines, CHP units, etc. are given in the Appendix A.
The TOU electricity pricing for a typical steel enterprise is presented in Table 1 [40], where 08:00–12:00 and 19:00–23:00 are the peak prices, with the price of 0.7188 ¥/kWh, 12:00–19:00 is the moderate price by 0.4917 ¥/kWh, and 23:00–08:00 is the valley price by 0.2796 ¥/kWh. Traditional steel enterprises purchase electricity at contract prices, overlooking the impact of TOU tariffs on production costs following electricity market reforms, which hinders their ability to adapt to market mechanisms.

5. Results and Discussions

Figure 3 depicts the optimized S1 steam generation in each period. S1 steam is generated in boilers B1-B4 from the by-product gases BFG and COG. Since the S1 steam needs to meet the main steel production process demand for various types of steam, it is always in full production. The by-product gas introduced in B3 and B4 is all BFG, and its capacity is greater than that of B1 and B2. Notably, the S1 steam required for the production process is primarily supplied by B1 and B2, with the remaining demand met by the supplementary contributions from B3 and B4. The surplus S1 steam produced by B3 and B4 serves as thermal energy for the generation of electricity by TB1, TB2, and the generation of S2 and S3 steam.
Figure 4 illustrates the output of S2 steam in various devices in each time period, where S2 steam is generated by TB1, TB2, CHP1, CHP2, and CDQ2. Considering the impact of the TOU tariff, the electricity demand in the steel production process is mainly met by the grid during the valley hours (23:00–08:00). On the contrary, the coal usage for CHP units needs to be increased during the moderate and peak hours. Since the operating efficiency of CHP1 is lower than that of CHP2, priority should be given to adjusting the coal purchase of CHP1 according to the TOU tariff. Meanwhile, the S2 steam produced by CHP1 adjusts according to the TOU tariff. To ensure the steel production demand, the S2 steam produced by TB1 and TB2 changes with the CHP1 adjustment strategy, while the CHP2 and CDQ units remain at full-load operation. Considering the efficiency differences between the two CDQ units in practical operation, the more efficient CDQ2 unit is prioritized when steam generation is required. Furthermore, since the CDQ2 unit has not reached its maximum adjustable capacity, the less efficient CDQ1 unit is not adjusted for use.
Figure 5 depicts the S3 steam output in various devices in each time period. S3 steam is produced by TB1, TB2, and CHP2. Since the S2 steam increases with the rise in power generation in CHP1 during the moderate and peak hours, the S2 steam produced by TB1 and TB2 decreases, resulting in an increase in the S3 steam. To meet the requirement of S3 steam in the production process, S3 steam produced by CHP2 decreases accordingly. In the valley hours, the S3 steam presents an opposite trend to moderate and peak hours.
Figure 6 depicts the electricity generation and purchase during 24 h considering the TOU tariff. Since there is no power generation cost from by-product gases, the TB, CHP, and CDQ in the GSPS are at full capacity under NTOU states. At this time, the remaining electricity required in the steel production process is provided by the grid. Self-generated electricity accounts for 64.4% of the total power load. Under the implementation of the TOU tariff mechanism, the electricity generation in the GSPS during the valley price period (23:00 to 08:00) decreases significantly due to the lower electricity purchase cost. Meanwhile, the coal consumption in the CHP units during this time period decreases by 406.3 tons, as shown in Figure 7. On the contrary, the power generation of the GSPS at full capacity during the peak and moderate price period. If the steel enterprise is considered as a virtual power plant, its power regulation capacity when participating in the TOU tariff mechanism accounts for approximately 26.94% of the NTOU tariff. The results show that by participating in the TOU tariff mechanism, the GSPS can fully utilize the load regulation potential of steel enterprises, thereby enhancing the adaptability and flexibility of their electricity consumption behavior.
Figure 8 illustrates the electricity and coal purchase costs for the steel enterprise at different times of day. After the implementation of the TOU tariff mechanism, the coal consumption of CHP units during the valley price hours is reduced, resulting in a decrease in the cost of purchased coal. The peak of the power purchase is actually from 0:00 to 7:00 p.m., during which the amount of power purchased increases, but the cost of the power purchase decreases due to the low price of electricity at this time. Overall, the cost of energy (including electricity and coal prices) is reduced by 2.24% after the GSPS optimized dispatch under the TOU tariff mechanism.
Based on the above study, this paper further analyzes the impact of coal prices on electricity generation and purchase in the GSPS. For the coal prices of 0.3 ¥/kg, 0.7 ¥/kg, and 1 ¥/kg, the optimization and scheduling results are shown in Figure 9. In Figure 9a, when the coal price is 0.3 ¥/kg, due to the low cost of power generation, the electricity production and purchase of the GSPS have always been at the upper and lower limits, respectively. In Figure 9b, due to the steam demands of production processes, the GSPS must operate a certain number of units for power generation and cannot fully rely on purchased electricity. When the coal price increases, the electricity demands for the steel production primarily rely on the steam power generation and grid, which leads to a decrease in coal purchases and an increase in electricity purchases, especially in the valley price period. When the coal price is 1¥/kg, purchasing coal for power generation is best avoided. Therefore, the changing trends in electricity generation and purchase are similar to that of the TOU tariff.
In general, the model successfully adjusts generation levels according to the TOU tariff, maintaining the general trend of “more self-generation during high electricity price periods and more electricity purchasing during low price periods”. This demonstrates that the proposed optimization scheduling model effectively identifies the most reasonable power production strategy under varying coal price conditions, highlighting its robustness and effectiveness.

6. Conclusions

This study investigates the optimal energy scheduling strategy for the GSPS in steel enterprises, taking into account the TOU tariff mechanism. The model integrates various forms of energy such as electricity, heat, and gas, as well as various items of energy conversion and transmission equipment, such as boilers, steam turbines, CHP units, and CDQ units. Based on the MILP method, an innovative GSPS optimization model is developed to solve the distribution strategy of various energies during the dispatching period, while ensuring energy supply stability and production safety. The results show that after participating in the TOU tariff mechanism, more efficient CHP units play a central role in demand response. The GSPS adjusts the relationship between coal, steam power generation, and purchased electricity according to the TOU tariff in the dispatching period to reduce the energy costs. Specifically, by increasing the amount of purchased electricity and reducing coal consumption during the valley price period, a 2.24% reduction in GSPS energy costs was achieved.
In contrast to previous studies, this study added the burner switching problem with TOU tariffs. Firstly, the burner switching problem involving fuel boilers in actual production is considered. In actual production, the change in the fuel flow rate needs to be controlled by the burner switch, and the switch is affected by the safety of production and cannot be changed arbitrarily. Most importantly, we added TOU tariffs to the energy system model, which provides a more flexible way of using electricity in steel mills.
Under the influence of the TOU tariff mechanism, the load regulation capability of the steel enterprise increased by 26.94%. Meanwhile, the load regulation strategy is also affected by coal prices. By optimizing the secondary energy allocation within the steel enterprises, this study not only reduces the overall energy cost, but also enhances the flexibility of the demand response, providing theoretical support for the integration of renewable energy in steel enterprises. This research provides an important theoretical basis for optimizing energy scheduling, reducing costs, and enhancing flexibility in steel enterprises, with positive implications for promoting their participation in sustainable energy market development.

Author Contributions

Methodology, Y.J. and H.M.; Software, Y.Z.; Investigation, Y.J.; Resources, Q.H.; Data curation, Y.Z. and Q.H.; Writing—original draft, J.Y.; Writing—review & editing, N.M.; Visualization, M.Z.; Supervision, N.M. All authors have read and agreed to the published version of the manuscript.

Funding

This work is supported by the State Grid Corporation of China Headquarters Management of Science and Technology Project (5400-202328576A-3-2-ZN).

Data Availability Statement

Dataset available on request from the authors. The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

Authors Jun Yan, Qianpeng Hao and Minhao Zhang were employed by the company Inner Mongolia Power (Group) Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Nomenclature

tperiod
Rsteam
Jsteam turbine
KCHP unit
MCDQ unit
Qgdifferent types of gases
Sirtflow rate of steam, ton/h
S i t w feedwater flow rate of boiler, ton/h
Viqtfeed flow rate of fuel q, km3/h
ηthermal efficiency of boiler
hvqheating value of fuels
henthalpy
Sjtsteam flow of the steam turbine, ton/h
Sjrtflow of grade r steam of the steam turbine, ton/h
fj,tpower generation of steam turbine, MW/h
fktpower output of CHP, MW/h
Skrtflow rate of steam, ton/h
fmtpower generation of the CDQ, MW/h
E in m t total waste heat recovered by the CDQ, GJ/h
f t dem demand for power, MW
f t imp electricity purchase, MW
f t exp electricity sales, MW
S r t dem steam demand, km3
ycosts
V q t gen generation rate of gas, km3/h
Vqtuconsumption rate of gas, km3/h
Qemissionflaring amount of gas, km3
DRdemand response
GSPSgas–steam–power system
TOUtime-of-use
MILPmixed-integer linear programming
CDQcoke dry quenching
NTOUnot time-of-use
CHPcombined heat and power
COGgas recovered from the coking process
BFGgas recovered from blast furnace
LDGgas recovered from the converter process

Appendix A

Table A1. Data for boilers.
Table A1. Data for boilers.
BoilerEfficiency (ηi) V i b f g max
(m3/h)
V i c o g max
(m3/h)
S i , H P min
(t/h)
S I , H P max ,
(t/h)
h v i min
(GJ/m3)
B10.894060 350.0035
B20.924060350.0035
B30.84120701300.0035
B40.86120701300.0035
Table A2. Initial burner switching data in boilers and CHP units.
Table A2. Initial burner switching data in boilers and CHP units.
BoilersInitial NumberInlet Flow Rate per Burner (m3/h)
COGBFGCOGBFG
B1240.55
B2240.55
B30230.55
B40230.55
CHP11416210
CHP21416210
Table A3. Gasholders capacity.
Table A3. Gasholders capacity.
VolumeCOGBFGLDG
Minimum capacity (m3)405015
Low operational inventory level (m3)8014025
Normal operational inventory level (m3)10018040
High operational inventory level (m3)12022055
Maximum capacity (m3)13526070
Table A4. Calorific value of gases, coal, and natural gas, and enthalpy of boiler feedwater and steam.
Table A4. Calorific value of gases, coal, and natural gas, and enthalpy of boiler feedwater and steam.
COG (GJ/m3)BFG (GJ/m3)LDG (GJ/m3)Coal
Calorific value0.0180.00350.00750.0218
ItemBoiler feed water (GJ/kg)S1 (GJ/kg)S2 (GJ/kg)S3 (GJ/kg)
Enthalpy0.000105380.00330.00310.00293
Table A5. Parameters such as the maximum inlet and outlet power of the plant, penalty coefficient, etc.
Table A5. Parameters such as the maximum inlet and outlet power of the plant, penalty coefficient, etc.
ParametersValue
Maximum imported power (MW)130
Maximum exported power (MW)100
Penalty coefficient for gas emission (¥/m3)100
Penalty coefficient for violation of high operational level (¥/m3)10
Penalty coefficient for violation of low operational level (¥/m3)5
Penalty coefficient for burner switches (¥/switch)800
Penalty coefficient for simultaneous switches of two burners (¥/instance)100
Penalty coefficient for simultaneous switches of three burners (¥/instance)200
Maintenance cost of boilers (¥/t)6
Maintenance cost of turbines (¥/kWh)0.06
Maintenance cost of CHP units (¥/kWh)0.08
Maintenance cost of CDQ units(¥/kWh)0.06
Coal purchase cost (¥/t)500
Electricity sale price (¥/kWh)0.2
Table A6. Data for steam turbine units.
Table A6. Data for steam turbine units.
TBEfficiency
(ηj)
S j , H P in , min
(t/h)
S j , H P in , max
(t/h)
S j , M P out , min
(t/h)
S j , M P out , max
(t/h)
S j , L P out , min
(t/h)
S j , L P out , max
(t/h)
f j min
(MW)
f j max
(MW)
DRj
(MW/h)
URj
(MW/h)
TB10.8260130060010002555
TB20.8560130060010002555
Table A7. Data for CHP units.
Table A7. Data for CHP units.
CDQEfficiency
m)
S m , H P min
(t/h)
S m , H P max
(t/h)
S m , L P min
(t/h)
S m , L P max
(t/h)
f m min
(MW)
f m max
(MW)
DRm
(MW/h)
URm
(MW/h)
CDQ10.7304006003055
CDQ20.7504006003055
Table A8. Data for CDQ units.
Table A8. Data for CDQ units.
CHPEfficiency
k)
V k BFG , max
(m3/h)
V k COG , max
(m3/h)
h v k min
(GJ/m3)
S k , M P min
(t/h)
S k , M P max
(t/h)
S k , L P min
(t/h)
S k , l P max
(t/h)
f k min
(MW)
f k max
(MW)
DRk
(MW/h)
URk
(MW/h)
CHP10.3801300.004608001102203002020
CHP20.3901300.004608001102203002020
Table A9. Gas generation rate, steam and electricity demand, and CDQ recovery.
Table A9. Gas generation rate, steam and electricity demand, and CDQ recovery.
PeriodGases (m3/h)CDQ (GJ)Power (MW)Steam (t/h)
BFGCOGLDGCDQ1CDQ2 S1S2S3
01514179.5047138.965247.40069868233255
11523175.5046138.965244.36569066234253
21525177.7045138.965252.94068569247253
31505177.8045138.965259.55069168244257
41522175.7049138.965266.54070768234250
51508176.8038138.965272.54070965228255
61523175.5047138.965244.36769066234253
71505179.5046138.965259.54969168233257
81514177.8045138.965247.40369868244255
91505177.8045138.965259.55569168244257
101522175.7049138.965266.53770768234250
111508176.8038138.965272.54270965228255
121514179.5047138.965247.40369868233255
131523175.5046138.965244.36369066234253
141525177.7045138.965252.93768569247253
151525177.7045138.965252.94268569247253
161514179.5049138.965247.39669868233255
171522175.7038138.965266.54270768234250
181508176.8047138.965272.53670965228255
191523175.5046138.965244.37169066234253
201525177.7045138.965252.94268569247253
211505177.8038138.965272.53870965228255
221522175.7038138.965266.54370768234250
231522175.7049138.965247.39969868233255
241508176.8045138.965247.40469868233255

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Figure 1. Diagram of the GSPS in the iron and steel enterprise.
Figure 1. Diagram of the GSPS in the iron and steel enterprise.
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Figure 2. (a) energy generation; (b) energy demand.
Figure 2. (a) energy generation; (b) energy demand.
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Figure 3. Steam generation in various boilers.
Figure 3. Steam generation in various boilers.
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Figure 4. S2 Steam generation at TOU tariff.
Figure 4. S2 Steam generation at TOU tariff.
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Figure 5. S3 Steam generation at TOU tariff.
Figure 5. S3 Steam generation at TOU tariff.
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Figure 6. Electricity generation and purchase during various time periods.
Figure 6. Electricity generation and purchase during various time periods.
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Figure 7. Coal purchase during various time periods.
Figure 7. Coal purchase during various time periods.
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Figure 8. Electricity and coal purchase costs for the steel enterprise.
Figure 8. Electricity and coal purchase costs for the steel enterprise.
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Figure 9. Electricity generation and purchase at different coal prices.
Figure 9. Electricity generation and purchase at different coal prices.
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Table 1. TOU electricity price in a typical steel plant.
Table 1. TOU electricity price in a typical steel plant.
Peak PriceModerate PriceValley Price
Time interval08:00–12:0012:00–19:0023:00–08:00
19:00–23:00
Price (¥/kWh)0.71880.49170.2796
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Yan, J.; Zhao, Y.; Hao, Q.; Ji, Y.; Zhang, M.; Ma, H.; Meng, N. Energy Scheduling Strategy for the Gas–Steam–Power System in Steel Enterprises Under the Influence of Time-Of-Use Tariff. Energies 2025, 18, 721. https://doi.org/10.3390/en18030721

AMA Style

Yan J, Zhao Y, Hao Q, Ji Y, Zhang M, Ma H, Meng N. Energy Scheduling Strategy for the Gas–Steam–Power System in Steel Enterprises Under the Influence of Time-Of-Use Tariff. Energies. 2025; 18(3):721. https://doi.org/10.3390/en18030721

Chicago/Turabian Style

Yan, Jun, Yuqi Zhao, Qianpeng Hao, Yu Ji, Minhao Zhang, Huan Ma, and Nan Meng. 2025. "Energy Scheduling Strategy for the Gas–Steam–Power System in Steel Enterprises Under the Influence of Time-Of-Use Tariff" Energies 18, no. 3: 721. https://doi.org/10.3390/en18030721

APA Style

Yan, J., Zhao, Y., Hao, Q., Ji, Y., Zhang, M., Ma, H., & Meng, N. (2025). Energy Scheduling Strategy for the Gas–Steam–Power System in Steel Enterprises Under the Influence of Time-Of-Use Tariff. Energies, 18(3), 721. https://doi.org/10.3390/en18030721

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