1. Introduction
Since the Industrial Revolution, high-carbon industrial operations, characterized by their massive consumption of natural resources and vast greenhouse gas emissions, has accelerated environmental issues [
1,
2]. Environmental pollution and energy security have become global challenges and pose serious threats to human survival and development [
3,
4]. A global consensus is emerging in support of the promotion of green energy economics and the expedited transition to a low-carbon future [
5,
6]. The Integrated Energy System (IES) stands as a key enabler in this paradigm, seamlessly interconnecting a diverse array of energy sources, encompassing renewables, thermal energy, and natural gas [
7]. These diverse sources are harmonized through various coupling mechanisms embedded within the system [
8]. Meanwhile, this coupling fosters interchangeability and synergy among the manifold energy resources, thereby bolstering overall energy efficiency and facilitating the seamless integration of renewable energy sources, such as wind and solar power [
9,
10]. This integration, in turn, lays the foundation for the realization of a holistic spectrum of benefits, encompassing economic efficiency, energy sustainability, and environmental preservation [
11].
Regarding the holistic energy optimization of industrial parks, ref. [
12] focused on iron and steel enterprises and established an optimization scheduling model for the by-products of gas, steam, and electricity to effectively improve the utilization efficiency of energy. A joint scheduling model for the industrial park that combines day-ahead optimization with real-time operation was proposed in [
13], but this model did not consider the constraints of production, making it difficult to apply to real industrial production. Ref. [
14] proposed a two-stage optimal scheduling model for the IES of a residential area to reduce the operational cost. But this model is not suitable for a production factory with strict processes and orderliness. An optimization model for a multi-regional integrated energy system (IES) was developed in [
15], taking into account carbon trading and the constraints imposed by the district heating network. However, the individual analysis of each region remains relatively simplistic in the existing research. Ref. [
16] proposes an integrated demand response model that includes multiple types of flexible resources and establishes a two-layer optimal dispatch strategy to achieve the optimal operation of factories and multi-energy operators and reduce the overall peak power value of the industrial park. Although the aforementioned studies coordinate the energy systems of industrial parks with various loads, they all fail to have an in-depth understanding of the production processes of different stages and workshops of the factory and incorporate them into the energy system modeling. Especially without considering the collaborative supply chain production of multiple factories in an industrial park, optimizing the energy utilization of one factory may have an impact on the energy efficiency of the entire clustered industrial park. This requires modeling and analysis of the supply chain to understand how energy consumption is distributed across the cluster to find the overall optimal solution. As a result, the dynamic processes of intra-industrial production and the supply chain coupling characteristics between factories are ignored. Thus, this leads to suboptimal outcomes in energy management, making it difficult to apply to actual industries.
The industrial sector represents a significant source of carbon emissions and occupies a pivotal role in attaining carbon peaking and carbon neutrality objectives [
17]. The integration of carbon trading mechanisms into the scheduling procedures of IES offers an effective means of reducing carbon emissions, all while ensuring the economic viability of these scheduling strategies [
18,
19,
20]. Many studies have evaluated emission reduction strategies in industrial parks, exploring avenues such as improved industrial development and emission reduction technologies. Ref. [
21] suggested a comprehensive evaluation method to assess the influence of emissions on energy consumption. Ref. [
22] examined the viability and practicality of establishing an electric–thermal carbon-neutral industrial park, while conducting economic, energy, and environmental analyses under various scenarios.
In [
23], a model for optimizing demand response within an electric–thermal IES was introduced, predicated on a fixed carbon price, thus enabling the realization of both economic and low-carbon operation of the IES. Ref. [
24] pioneered the incorporation of a stepped carbon trading mechanism into the scheduling of a pumped storage power station equipped with battery energy storage, resulting in a notable reduction in carbon emissions. Furthermore, ref. [
25] presented an optimization scheduling model tailored to an electric–gas interconnected IES, which took into account carbon trading mechanisms. This model systematically analyzed the impact of carbon trading on both economic cost and carbon emissions. Similarly, ref. [
26] introduced a stepped carbon trading mechanism and devised an integrated energy system model encompassing electric, gas, and heat components, effectively curbing overall system carbon emissions. However, the existing body of academic literature primarily focuses on single factories or comprehensive energy management systems within communities. There remains a dearth of research on integrating carbon trading mechanisms into the comprehensive energy management systems of clustered industrial users. This limitation hinders the seamless coordination of production processes among various factories. In summary, the introduction of carbon trading mechanisms into the energy scheduling model of clustered industrial parks is an imperative step in propelling the industry’s transition from high-carbon emissions to a low-carbon paradigm.
Based on the aforementioned reviews, this paper puts forth an energy optimization strategy encompassing the entire production process within each factory situated in a clustered industrial park. It takes into account the stepped carbon trading mechanism and integrates supply chain coupling. The remaining sections of this paper are structured as follows:
Section 2 introduces the production workshop model for every factory, delineates the diverse energy unit models within the park, and elaborates on the stepped carbon trading mechanism. In
Section 3, we proceed to establish and analyze the optimized scheduling model for energy management. The efficacy of the proposed approach is scrutinized in
Section 4.
Section 5 encapsulates our findings with the future work being prospected.
4. Experimental Verification
4.1. Case Analysis
A pulp and paper industry park located in the Zhejiang Province, China, is selected as the subject of our case study. The following provides an overview of the factory production within this industrial park. The paper production process in a factory can typically be categorized into four primary stages, each corresponding to a specific workshop, as illustrated in
Figure 4 for Factory 1 and Factory 2. Furthermore, there are auxiliary factories, such as Factory 3, which supply semi-finished products for further processing. Moreover, the wastewater treatment workshop (WTW) at Factory 4 employs a combination of physical, chemical, and biological processes to eliminate or reduce pollutants present in the wastewater generated by various paper factories. This approach ensures that the wastewater adheres to the environmental regulations and discharge standards, allowing for subsequent recycling and reuse, thus safeguarding the environment and public health.
In addition to the paper production lines and associated equipment, paper factories need an Auxiliary Production System (APS) to support production and management, thereby ensuring product quality and employee safety. This system comprises an enterprise operation management system, a production guidance management system, as well as various departments and units within the factory area, including workshop facilities, water boiling stations, canteens, and more.
(1) The pulping workshop (PUW) is a specialized industrialized facility dedicated to the production of pulp. Raw materials utilized can encompass wood, waste paper, bamboo, and various other sources. By subjecting these raw materials to a series of chemical or mechanical processes, they are disintegrated into fibers. Subsequent processes, such as cleaning, bleaching, and filtering, are employed to extract pure pulp. The workshop is equipped with key machinery including pulping machines, digesters, bleaching machines, filters, and more.
(2) The paper-making workshop (PAW) is responsible for the conversion of pulp, manufactured in the pulping workshop, into paper products. Typically, it comprises four principal processes: forming, pressing, drying, and winding, as depicted in
Figure 5. The specific production process unfolds as follows: pulp is conveyed into the forming wire via the Headbox. Subsequently, a sequence of dewatering mechanisms, encompassing vacuum pressure within the forming section, mechanical pressing in the pressing section, and steam-assisted drying in the drying section, is employed to eliminate moisture and produce large, dried paper sheets. Ultimately, these paper sheets are wound onto substantial rolls by a winder for further processing. It is crucial to note that this workshop demands a substantial amount of steam for paper drying, with electricity and heat consumption accounting for 40% to 50% of the overall energy consumption in paper production. Hence, it stands as the most energy-intensive workshop within the facility.
(3) The coating and printing workshop (COW) primarily assumes responsibility for applying surface coatings and performing printing treatments on the manufactured paper to enhance its quality, visual appeal, and functionality. Key equipment within the COW comprises coating machines, printing machines, and calendars.
(4) The cutting and packaging workshop (CUW) typically encompasses equipment such as paper cutters, reel cutters, cross cutters, packaging machines, box sealers, and bundling machines. This workshop is tasked with the precise task of cutting the large paper rolls into smaller-sized sheets as per specific requirements. The cut paper sheets are subsequently sorted and packaged according to specified criteria and quality standards, facilitating their transportation and eventual sale.
4.2. Parameter Settings
The IES within the pulp and paper park includes various components such as wind turbines, photovoltaic panels, gas turbines, gas boilers, coal-fired units, heat storage tanks, and batteries. The length of dispatching time period T is 24 h with a time interval t of 1 h. The pertinent parameters of the BA and HST can be found in
Table A1 and
Table A2 of
Appendix A, respectively. Additionally, the relevant parameters for the CFU, GT, and GB are detailed in
Table A3,
Table A4 and
Table A5 in
Appendix A. The coefficients of the carbon emission model are provided in
Table A6 of the same appendix. The base price for carbon trading
=
$20.63 per ton; the rate of price growth
= 0.25; and the length of the carbon emission
= 13 ton. Note that both the APS and the WTW operate continuously and are not subject to optimization scheduling. In order to facilitate the analysis, this paper considers the APS as an additional workshop within Factory 1, and the WTW as an additional workshop within Factory 3. The capacity of each workshop in the paper mill of each factory is shown in
Table A7,
Table A8 and
Table A9 of
Appendix A. The time-of-use price for industrial and commercial users during summertime in the Zhejiang Province is adopted as the retail price. During the valley period from 1:00 to 7:00, the electricity price is
$0.034 per kWh. The flat periods from 8:00 to 10:00, 16:00 to 18:00, and 22:00 to 24:00 have an electricity price of
$0.073 per kWh. The electricity price during the peak period from 11:00 to 15:00 and 19:00 to 21:00 is
$0.11 per kWh. The price of natural gas is fixed at
$0.34 per m
3, and the calorific value is set at 9.7 kWh/m
3. The price of raw coal is fixed at
$0.12 per kg, and the calorific value is set at 5.81 kWh/m
3. Based on the LSTM prediction [
30], the predicted power output for the wind turbines and photovoltaic panels is illustrated in
Figure 6. Finally, the number of production tasks for finished paper for Factory 1 and Factory 2 are designated as
R1 = 15 and
R2 = 16, respectively.
4.3. Optimized Result
To assess the low-carbon and economic implications of the optimized scheduling model proposed in this paper, we established the following scenarios for comparative analysis:
Scenario 1: The production workshops of each factory operate as per their original plans.
Scenario 2: Optimization scheduling of the IES is conducted, accounting for supply chain coupling while excluding carbon emissions.
Scenario 3: Optimization scheduling of the IES is performed, taking carbon emissions into account while excluding supply chain coupling.
Scenario 4: The model proposed in this paper considers both supply chain coupling and carbon emissions.
Table 1 presents the total operational and carbon trading cost, as well as the actual carbon trading volumes for each scenario. Scenario 1 incurs high operating cost, with a total operational expense of
$15,984. This outcome reflects that when the factory production workshops adhere strictly to their original plans and the IES is not involved in scheduling, it results in an impractical energy usage strategy and low energy utilization efficiency. In Scenario 2, as opposed to Scenario 1, inventory levels across various factories and storage workshops are adjusted, with most workshops scheduled to operate during off-peak hours. The IES purchases a significant amount of electricity during this period, which results in reduced operating cost. Scenario 3, introduced after implementing the carbon trading mechanism, prioritizes the dispatch of the GB and GT. However, the cost of purchasing natural gas is higher than coal, leading to an increase in operating cost. Lastly, comparing Scenario 4 with Scenario 1, which considers both supply chain coupling and carbon trading, adjusting the inventory levels in each factory workshop incurs some additional cost. This approach allows for more flexible adjustments to the energy network, resulting in a reduction of 28.658 tons in carbon emissions. Moreover, the overall cost of the industrial park are still reduced by 13.46% compared to Scenario 1, indicating the effectiveness of the scheduling model in lowering park operating expenses while curbing carbon emissions. Therefore, the model presented in this paper effectively strikes a balance between operational cost-efficiency and carbon emissions reduction.
Figure 7 illustrates the energy distribution within the entire paper-making cluster industrial park for two distinct operating scenarios: Scenario 1 and Scenario 4. In Scenario 1, each factory adheres to its original planned schedule without accounting for time-of-use electricity pricing. Consequently, numerous workshops continue to operate during peak electricity pricing periods, resulting in a substantial procurement of electricity when prices are high and the subsequent inflation of operating cost.
In contrast, Scenario 4 meticulously incorporates time-of-use electricity pricing. Due to the limitations associated with the power capacity of energy equipment and the available thermal power capacity within the industrial park, it is not feasible to schedule all loads exclusively during low-priced electricity periods. However, Scenario 4 optimizes the scheduling of controllable devices, which includes battery storage systems, thermal storage tanks, and distributed energy sources such as photovoltaic and wind power. It dynamically allocates the operating statuses of workshops across various time intervals and determines the corresponding transfer quantities for storage workshops. This strategic approach effectively shifts the overall load from high-priced electricity periods to valley and flat periods, ensuring the economic efficiency of the energy utilization strategy.
As demonstrated in
Figure 8, after participating in the optimization scheduling of the IES, the various workshops within each factory achieve different degrees of load shifting, resulting in the redistribution of electrical energy from high-priced and high-load periods to low-priced and low-load periods, while adhering to the constraints of the production sequence. Moreover, the energy consumption types vary among different workshops within each factory, and a certain coupling relationship exists between electrical and thermal energy. As seen in
Figure 7b, if the electrical load is shifted, the corresponding thermal energy is also adjusted accordingly.
This observation underscores the interconnected nature of energy consumption within the industrial park. The optimization scheduling process not only accounts for electricity load but also considers the associated thermal energy requirements. By strategically shifting the load, a more balanced and efficient overall energy utilization within the system is achieved.
4.4. Analysis of IES Scheduling
The distribution of electrical and thermal energy within the IES in the paper-making industry park is depicted in
Figure 9 and
Figure 10 for Scenario 1 and Scenario 4, respectively. From
Figure 9, the following related issues can be identified. Firstly, the energy distribution in the park is fixed, and the operational status of the workshops has not been adjusted accordingly based on external factors such as fluctuations in energy prices. This may result in the park being unable to flexibly respond to price changes when purchasing energy, thus failing to minimize energy procurement cost to the greatest extent possible. Secondly, distributed energy has not been fully utilized in the park. Distributed energy refers to the energy generated within the park, such as solar energy, wind energy, etc., which is beneficial for reducing external energy purchases and carbon emissions. The above indicates that the park has potential efficiency losses in energy consumption.
On the contrary, the model proposed in this paper addresses these issues. As shown in
Figure 10, the WT dispatch their electrical power during the valley period. This decision is driven by the fact that the amount of carbon emissions exceeding the allocated quota of the WT is negative, implying that it can offset the surplus carbon emissions. Although the operational cost of the WT is higher than purchasing electricity from the grid during the valley period, factoring in the cost of carbon trading ultimately results in superior economic benefits. Furthermore, the maintenance cost of the WT is lower than procuring electricity from the main power grid during peak and flat periods. Consequently, the IES prioritizes the dispatch of the WT to provide energy. In summary, the WT operate at full load throughout all periods.
The BA significantly contribute to “peak shaving and valley filling” throughout the entire scheduling cycle. They charge during the valley period from 5:00 to 7:00 and discharge during peak periods, capitalizing on the price differential between peak and valley electricity rates to curtail system operating cost. The HST play a pivotal role in “peak load compensation” across the scheduling cycle. The workshops are efficiently dispatched to the valley period, and their operation, which operates at or near the rated power of the GT, GB and CFU, may not fully meet the system’s heat energy demand. In this scenario, the HST operate in a discharging state to fulfill the additional heat energy demand.
In the flat periods from 8:00 to 10:00 and 16:00 to 18:00, the electricity supply is primarily sourced from the GT, WT, and PV. During this timeframe, the GT, GB and CFU provide thermal energy. Conversely, during the peak periods from 11:00 to 15:00 and 19:00 to 20:00, the cost of electricity generation from distributed energy sources, such as the PV and WT, is lower than that from the main power grid. As a result, the scheduling strategy obtained maximizes the utilization of these distributed energy sources, thereby reducing the electricity demand from the main power grid.
4.5. Inventory Analysis
Figure 11 illustrates the changes in intermediate product inventory for each hour after the scheduling of the three factories. During the valley periods from 1:00 to 2:00, the inventory levels of the storage warehouse of the PAW in Factory 11 significantly decreased. This is because, during these periods, Factory 2 transfers a portion of semi-finished products to other factories to alleviate inventory constraints between production workshops in other factories. This is beneficial for scheduling more workshops to operate during the valley periods, further reducing the energy cost of the system. This approach ensures that the factories can meet their daily production targets while effectively responding to the comprehensive energy demand.
4.6. Analysis of Carbon Emissions in Paper Production
After taking into account the carbon trading mechanism, the IES considers two factors. Firstly, for natural gas, the carbon emissions exceeding the allocated quota are relatively small. In this case, the IES will operate two types of energy equipment, namely GB and GT. Secondly, the carbon emissions exceeding the allocated quota for PV and WT are smaller than others, even with negative values for the WT. These can absorb the overall carbon emissions of the industrial park. As a result, the IES prioritizes scheduling the output of these distributed energy sources, leading to a significant reduction in carbon emissions within the industrial park.
Figure 12 provides an overview of the carbon trading volume for Scenarios 1 and 4 across different time periods. As depicted in the figure, it exhibits higher carbon emissions during the price valley period in Scenario 4. This can be attributed to the majority of the paper mill’s operations being scheduled during this period, resulting in significant energy consumption and carbon emissions. Conversely, during the price peak period, carbon emissions decrease significantly for two main reasons. Firstly, the contribution of PV and WT helps reduce carbon emissions in Scenario 4. Secondly, during this period, the operating workshops in every factory have a lower load, resulting in lower carbon emissions. For example, in Scenario 1, the factories in the park adhere to their original plans, and the differences in carbon emissions in these scenarios are primarily due to the workshop operations, which obviously bring high carbon emissions.
Figure 13 illustrates the relationship between the volume of carbon trading and the carbon trading price in the industrial park. The graph reveals that as carbon emissions increase, the cost of carbon trading exhibits an upward trend. Towards the end of the graph, a steep rise in carbon trading cost is evident due to the combination of the highest baseline carbon trading price and a significant volume of carbon emissions. By imposing incrementally higher carbon trading cost, businesses face economic incentives to reduce carbon emissions and explore cleaner, lower-carbon production methods. This mechanism serves as a driver for carbon reduction and contributes to the realization of sustainable development goals.
5. Conclusions
In this paper, through an analysis of the paper production process and the parallel production constraints within supply chain coupling, an economic scheduling model based on stepped carbon trading is established to optimize the operation of the IES within a paper-making park. To demonstrate the effectiveness of this model, we selected a paper cluster industrial park as an example simulation, which resulted in the reduction of overall cost by 13.46% and overall carbon emissions by 28.658 tons. With the consideration of supply chain coupling, factories can flexibly adjust workshop operations and maximize energy utilization. The introduction of a stepped carbon trading mechanism results in reduced carbon emissions and an overall decrease in operating cost within the paper industry park. This strategy effectively enhances both the economic and low-carbon dimensions of energy utilization within the pulp and paper industry and promotes the development of the industry in a more environmentally friendly and sustainable direction.
Since various industrial production links are different, the supply chain is complex, and the load characteristics vary greatly, this paper only studies the paper-making industry. Future research endeavors may include applying the model to other types of industries, and further refining the model and operational constraints of the IES to ensure its practicality in real-world applications.