Low-Carbon Economic Dispatch of an Integrated Electricity–Gas–Heat Energy System with Carbon Capture System and Organic Rankine Cycle

Abstract

A low-carbon economic optimization dispatch model of integrated energy system is proposed to improve the low-carbon and economic efficiency of the integrated energy systems. Firstly, the waste heat generator with the organic Rankine cycle is introduced into the combined heat and power to decouple the combined heat and power operation, and a coupled model with an organic Rankine cycle, power to gas, combined heat and power and carbon capture system is established. Then, the ladder-type carbon trading mechanism is introduced to improve the low-carbon model. Finally, the function is established to minimize the sum of energy purchase costs, operation and maintenance costs, and environmental costs. The proposed integrated energy systems’ low-carbon economic dispatch model reduces the total operating cost by 18.9% and the carbon emissions by 83.7% by setting up different models for comparative analysis.

1. Introduction

The severe consequences of the energy crisis and global climate change, which have arisen due to the overuse of fossil energy supplies since the commencement of the Industrial Revolution, have considerably affected both the economic circumstances and livelihoods of individuals [1]. During the 75th session of the United Nations General Assembly, China made a public declaration to enhance its national autonomous contribution towards mitigating CO₂ emissions and fostering the development of a low-carbon society. China set forth its commitment to reach the “carbon peaking” milestone by 2030, followed by the ultimate goal of achieving “carbon neutrality” by 2060 [2,3].

Coal-fired power plants provide about 50% of China’s power system’s total installed capacity, contributing significantly to carbon emissions [4]. Coal-fired power facilities fitted with carbon capture and storage (CCS) technologies have demonstrated their efficiency in mitigating CO₂ emissions [5]. CCS can capture CO₂ from cogeneration emissions [6]. Ref. [7] provided an overview of the present level of development and fundamental concepts of CCS, with a specific focus on post-combustion technology. The primary method of sequestering CO₂ collected by CCS involved injecting deep, porous rock formations. However, this approach was subject to many economic and safety constraints [8]. Utilizing power to gas (P2G) technology presented a viable approach to address this issue. The P2G process involves the electrolysis of water to generate hydrogen gas. This hydrogen gas is then utilized in the Sabatier reaction, which reacts with CO₂ to yield methane (CH₄) [9]. Ref. [10] presented a low-carbon economic dispatch model that incorporates the utilization of P2G and post-combustion CCS technologies in the context of electricity and natural gas systems. Ref. [11] proposed a two-tier optimum scheduling model for an integrated energy system incorporating CCS and P2G technologies. A linked CCS-P2G model was developed to incorporate carbon trading and the green certification market [12]. The study’s findings revealed a significant reduction of 50% in CO₂ emissions and a noteworthy decrease of 22.24% in operational expenses. Ref. [13] examined integrating CCS and P2G technologies to investigate the correlation between reducing carbon emissions and implementing renewable energy utilization techniques.

Combined heat and power (CHP) is one of the major components of IES, which is distinguished by energy conservation and high efficiency [14]. The inflexibility inherent in CHP systems resulted in significant wind abandonment. A proposed model for scheduling electric thermal systems incorporating CHP, thermal energy storage, and demand response aimed to mitigate wind abandonment and reduce pollutant emissions [15]. The P2G process can harness unused wind energy and convert it into CH₄. Ref. [16] presented a microgrid system that integrated combined cooling, heating, and power (CCHP) with P2G technology to enhance the efficiency of electrical connections. Ref. [17] presented an academic model known as the integrated natural gas, heat, and power dispatch (INGHPD) model. This model aimed to effectively manage the demand for natural gas, heat, and electricity by incorporating wind and P2G units. Ref. [18] presented a multi-objective optimal operation strategy for an integrated electricity–natural gas–thermal energy system. This strategy considered heat demand, P2G technology, and gas storage. The proposed method aimed to minimize both carbon emissions and operating costs. Most research efforts had concentrated on investigating the integrated operation of P2G and CHP systems. At the same time, there was a limited body of literature exploring the combined processes of P2G, CHP, and CCS technologies. Ref. [19] presented the concept of CCS to capture CO₂ emissions from a CHP plant. The captured CO₂ was then utilized as a carbon feedstock for P2G applications. Ref. [20] utilized the Organic Rankine Cycle (ORC) within a CHP system. The assessment of technical and economic costs was conducted through a life cycle approach. Ref. [21] explored the addition of solar thermal (ST) and ORC technologies in traditional combined cooling, heating, and power (CCHP) systems. This study focused on the thermodynamic evaluation of the CCHP-ST-ORC configuration. Ref. [22] conducted an experiment on IES that integrates ORC and P2G technologies. The research focused on analyzing the optimal thermoelectric ratios for various configurations of IES. Ref. [23] analyzed a low-carbon economic dispatch model for a multi-energy virtual power plant. Additionally, it introduced an ORC waste heat power generation unit to address the issue of insufficient carbon capture during peak periods in the low-load process.

Ref. [24] used the life cycle assessment approach to examine the greenhouse gas emissions associated with various energy chains within IES. Additionally, the authors evaluated the costs of carbon trading by utilizing carbon emission coefficients and incorporating stepped carbon trading incentive and penalty mechanisms. Ref. [25] employed a phased carbon pricing mechanism to enhance the limitations on carbon emissions and the low-carbon advantages of IES. Ref. [26] acknowledged the significance of integrating electricity, gas, and heat systems through waste heat recovery in P2G processes. Additionally, it proposed implementing a carbon trading mechanism to facilitate low-carbon operations in IES. Ref. [27] examined the effects of the simultaneous implementation of heat grids, P2G technology, and carbon trading mechanisms on the integrated regional energy system. This integrated approach enhanced the economic and environmental advantages of the overall energy system. Ref. [28] presented a multi-objective optimal scheduling model for IES. This model considered a multi-timescale stepped carbon trading mechanism to effectively handle the uncertainty related to renewable energy supply and load demand.

Ref. [29] proposed a low-carbon economic dispatch with source-load coordination, which utilizes the energy time-shift advantages of CCS and P2G and combines the participation of electric vehicles in demand-side response to improve the system’s low-carbon and economic performance. Ref. [30] proposed a two-layer optimization model of an integrated electricity–gas system considering carbon capture, utilization, and storage. It introduced a stepped carbon trading mechanism, effectively reducing the system’s operating cost and carbon emissions. Ref. [31] proposed an integrated electricity–heat–gas energy system incorporating coupled operation of oxygen-rich combustion capture and power to gas (OCC-P2G) and hydrogen-doped gas equipment, and the proposed model has low economic costs and carbon emissions. Ref. [32] constructed a carbon emission unit cost model with a dynamic reward and punishment pricing mechanism, which is an optimal scheduling model considering carbon emission, energy procurement, and equipment operation costs. It constructed a model with good economic efficiency. Ref. [33] proposed a synergistic optimization model for the integrated energy system of cooling–heat–electricity–gas, considering the ladder carbon trading mechanism and multi-energy demand response, which enhances the economics of the integrated energy system.

In summary, few current studies have considered the coupled operation of integrated energy systems containing an ORC CHP, CCS, and P2G. CCS can capture CHP and carbon dioxide emitted from coal-fired power plants to supply P2G to synthesize natural gas, which can be supplied to CHP for power generation and heat production. CCS and P2G can increase the consumption of new energy sources and reduce the curtailment of wind and solar energy. CCS and P2G can increase new energy consumption and reduce the curtailment of wind and light. An ORC can enable the operation of CHP thermoelectric decoupling and reduce the mutual constraints between power generation and heat production. The main contributions of this paper are summarized as follows.

1. Establishment of an optimal scheduling model containing an ORC, CHP, CCS, and P2G, which further improves the coupling of electricity, gas, and heat.

2. Comparing the operating costs and carbon emissions of different subsystem couplings, the proposed coupling model considering stepped carbon trading and ORC, CHP, CCS, and P2G reduces the operating costs, decreases carbon emissions, and increases new energy consumption.

3. The effect of an ORC on the optimal scheduling model is investigated to analyze the optimal occupancy ratio.

Following this is the remainder of the paper. Consisting of ORC, CHP, CCS, and P2G coupling, the integrated energy system is described in Section 2. The principle of ladder-type carbon trading is examined in Section 3. The constraints and objective functions of the integrated energy system are delineated in Section 4. Section 5 verifies the validity of the proposed model by analyzing the different operation modes. Section 6 concludes the paper.

2. The Structure and Model of IES

IES comprise a diverse range of energy forms and facilitate the efficient conversion, storage, and distribution of energy using various technological devices. The configuration of IES’s frameworks is visually depicted in Figure 1. The system consists of wind turbines, PV units, coal-fired power plants, electric energy storage, power to gas, carbon capture and storage, combined heat and power, gas boilers, and thermal energy storage.

2.1. CHP System with ORC

The CHP system consists of a GT, WHB, and ORC waste heat power generation unit. Using natural gas by GT enables the production of electricity and heat. Waste heat created by GT can be captured by WHB and utilized to meet the heat demand of the integrated energy system, therefore achieving cogeneration. However, the existing reliance on “heat for electricity” imposes constraints on the flexibility of GT’s output. The ORC waste heat generator can capture waste heat from GT and convert it into electricity. This allows for flexible electric and thermal power generation, as shown in Figure 2.

$$\left[\begin{array}{c}{P}_{\mathrm{e},t}^{\mathrm{GT}}\\ P_{\mathrm{h},t}^{\mathrm{GT}}\end{array}\right]=H_{\mathrm{g}}{V}_t^{\mathrm{GT}}\left[\begin{array}{c}{\eta }_{\mathrm{e}}^{\mathrm{GT}}\\ {\eta }_{\mathrm{h}}^{\mathrm{GT}}\end{array}\right]$$

(1)

$$\left\{\begin{array}{l}{P}_{\mathrm{e},t}^{\mathrm{CHP}}=P_{\mathrm{e},t}^{\mathrm{GT}}+P_{\mathrm{e},t}^{\mathrm{ORC}}\hfill \\ P_{\mathrm{e},t}^{\mathrm{ORC}}={\lambda }^{\mathrm{ORC}}{\eta }_{\mathrm{ORC}}{P}_{\mathrm{h},t}^{\mathrm{GT}}\hfill \\ P_{\mathrm{h},t}^{\mathrm{CHP}}={\lambda }^{\mathrm{WHB}}{\eta }_{\mathrm{WHB}}{P}_{\mathrm{h},t}^{\mathrm{GT}}\hfill \end{array}\right.$$

(2)

where $P_{\mathrm{e},t}^{\mathrm{CHP}},P_{\mathrm{h},t}^{\mathrm{CHP}},P_{\mathrm{e},t}^{\mathrm{GT}},P_{\mathrm{h},t}^{\mathrm{GT}}$ are the electric and thermal power output from CHP and the electric and thermal power output from GT (MW); $P_{\mathrm{e},t}^{\mathrm{ORC}}$ is ORC’s electrical power output (MW); ${\eta }_{\mathrm{e}}^{\mathrm{GT}},{\eta }_{\mathrm{h}}^{\mathrm{GT}}$ are the gas-to-electricity and gas-to-thermal efficiencies of GT; ${\eta }_{\mathrm{WHB}}$ is the conversion efficiency for WHB; ${\eta }_{\mathrm{ORC}}$ is the heat transmission efficiency of ORC; $V_t^{\mathrm{GT}}$ is the natural gas consumption of GT (m³); $H_{\mathrm{g}}$ is the natural gas calorific value; ${\lambda }^{\mathrm{ORC}}$ is the proportionality factor of GT’s waste heat allocated to the ORC waste heat generator; ${\lambda }^{\mathrm{WHB}}$ is the proportionality factor of GT’s waste heat allocated to WHB.

2.2. P2G Model

P2G can use surplus energy to perform electrolysis of water, producing hydrogen and oxygen. Subsequently, the hydrogen may be combined with CO₂ via the Sabatier reaction to synthesize methane. This methane can then be sent to Combined Heat and Power (CHP) systems and the gas grid (GB) via a natural gas pipeline. The energy consumption and conversion efficiency of the P2G unit primarily determine the quantities of methane produced. Figure 3 shows the schematic diagram of P2G.

The response principle can be defined as

$$P_t^{\mathrm{P}2\mathrm{G}}=P_{\mathrm{wind},t}^{\mathrm{P}2\mathrm{G}}+P_{\mathrm{pv},t}^{\mathrm{P}2\mathrm{G}}$$

(3)

$$Q_t^{\mathrm{P}2\mathrm{G},\mathrm{sum}}={\alpha }^{{\mathrm{CO}}_2}{P}_t^{\mathrm{P}2\mathrm{G}}$$

(4)

$$Q_t^{\mathrm{P}2\mathrm{G},\mathrm{sum}}=Q_t^{\mathrm{P}2\mathrm{G}}+Q_t^{\mathrm{buy}}$$

(5)

$$V_t^{\mathrm{P}2\mathrm{G}}=\frac{3.6{\eta }^{\mathrm{P}2\mathrm{G}}{P}_t^{\mathrm{P}2\mathrm{G}}}{H_{\mathrm{g}}}$$

(6)

where $P_t^{\mathrm{P}2\mathrm{G}}$ is P2G energy consumption (MW); $P_{\mathrm{wind},t}^{\mathrm{P}2\mathrm{G}},P_{\mathrm{pv},t}^{\mathrm{P}2\mathrm{G}}$ are the wind and light energy sources abandoned (MW); $Q_t^{\mathrm{P}2\mathrm{G},\mathrm{sum}}$ is the total amount of CO₂ consumed by P2G (t); $Q_t^{\mathrm{P}2\mathrm{G}}$ is the quantity of CO₂ supplied by CCS to P2G (t); $Q_t^{\mathrm{buy}}$ is the quantity of CO₂ bought by P2G (t); $V_t^{\mathrm{P}2\mathrm{G}}$ is the quantity of natural gas produced by P2G (m³); ${\eta }^{\mathrm{P}2\mathrm{G}}$ is the conversion efficiency of artificially produced natural gas in P2G; ${\alpha }^{{\mathrm{CO}}_2}$ is the quantity of CO₂ required to generate a single natural gas unit (t/MW).

2.3. New Energy Power

The new energy power described in this paper consists of wind and photovoltaic power generation. The excess production from the new energy power can be supplied to the P2G and CCS, increasing the consumption of new energy and decreasing the amount of electric power required to operate the P2G and CCS.

$$P_{\mathrm{P}2\mathrm{G},t}^{\mathrm{wind}}+P_{\mathrm{CCS},t}^{\mathrm{wind}}+P_{\mathrm{e},t}^{\mathrm{wind}}+P_{\mathrm{cut},t}^{\mathrm{wind}}=P_t^{\mathrm{wind}}$$

(7)

$$P_{\mathrm{P}2\mathrm{G},t}^{\mathrm{pv}}+P_{\mathrm{CCS},t}^{\mathrm{pv}}+P_{\mathrm{e},t}^{\mathrm{pv}}+P_{\mathrm{cut},t}^{\mathrm{pv}}=P_t^{\mathrm{pv}}$$

(8)

where $P_t^{\mathrm{wind}},P_{\mathrm{CCS},t}^{\mathrm{wind}},P_{\mathrm{e},t}^{\mathrm{wind}},P_{\mathrm{cut},t}^{\mathrm{wind}}$ are the wind power forecast power, wind power supply CCS power, wind power feed-in power, and wind power curtailment (MW); $P_t^{\mathrm{pv}},P_{\mathrm{CCS},t}^{\mathrm{pv}},P_{\mathrm{e},t}^{\mathrm{pv}},P_{\mathrm{cut},t}^{\mathrm{pv}}$ are the anticipated PV power, CCS power supplied by the PV, PV feed-in power, and curtailment (MW).

2.4. GB Model

The GB unit is the primary heating component within an integrated energy system, capable of generating heat loads via natural gas. This unit’s magnitude of heat loads is primarily contingent upon its efficiency.

$$P_t^{\mathrm{GB}}=H_{\mathrm{g}}{V}_t^{\mathrm{GB}}{\eta }^{\mathrm{GB}}$$

(9)

where $P_t^{\mathrm{GB}}$ is the thermal power output from GT (MW); ${\eta }^{\mathrm{GB}}$ is the unit gas transfer efficiency for GB; $V_t^{\mathrm{GT}},V_t^{\mathrm{GB}}$ are the natural gas consumption of GT and GB (m³).

2.5. Coupled Modeling of CCS, P2G, and CHP with ORC

In this paper, the coupled model is shown in Figure 4. Carbon capture and storage (CCS) technology is used to collect emissions from coal-fired units, gas turbine power to gas applications and sequestration. Additionally, the electricity required by the CCS system may be provided by coal-fired units, organic Rankine cycle waste heat generators, and surplus new energy. The P2G equipment employs the CO₂ collected from the CCS system to produce hydrogen and oxygen using water electrolysis using surplus new energy. Subsequently, it synthesizes CH₄ by reacting the hydrogen with the CO₂ obtained by the CCS system using Sabatier’s reaction. The Sabatier reaction is used to make methane by combining hydrogen and carbon dioxide that have been collected by carbon capture and storage methods. The resulting methane is transported via a natural gas pipeline to gas turbine and gas boiler units. These units utilize the methane to generate electricity and heat, which are then used to meet the demands of electric and thermal loads. This research selects the post-combustion carbon capture technique since it is considered the most mature and frequently used technology among the three carbon capture methods for CCS: pre-combustion, post-combustion, and oxygen-enriched combustion. Hence, this study employs the post-combustion carbon capture technique.

Carbon capture and storage (CCS) technology is used to capture carbon dioxide (CO₂) emissions originating from coal-fired power plants, gas boilers, and combined heat and power (CHP) systems. This approach serves as an effective means of mitigating carbon emissions within the integrated energy system. Coal-fired power stations, Organic Rankine Cycle (ORC) waste heat producers, and wind photovoltaic (PV) units can provide the needed electrical energy for Carbon Capture and Storage (CCS) systems.

$$P_{\mathrm{CCS},t}=P_{\mathrm{CCS},t}^{\mathrm{CFPP}}+P_{\mathrm{CCS},t}^{\mathrm{CHP}}+P_{\mathrm{CCS},t}^{\mathrm{GB}}$$

(10)

$$P_{\mathrm{CCS},t}^1+P_{\mathrm{CCS},t}^{\mathrm{ORC}}+P_{\mathrm{CCS},t}^{\mathrm{wind}}+P_{\mathrm{CCS},t}^{\mathrm{pv}}=P_{\mathrm{CCS},t}$$

(11)

$$P_t=P_{\mathrm{A}}+P_{\mathrm{CCS},t}$$

(12)

$$P_{\mathrm{CCS},t}^{\mathrm{CFPP}}=\theta Q_{\mathrm{CCS},t}^{\mathrm{CFPP}}$$

(13)

$$\left[\begin{array}{c}{P}_{\mathrm{CCS},t}^{\mathrm{CHP}}\\ P_{\mathrm{CCS},t}^{\mathrm{GB}}\end{array}\right]=\theta \left[\begin{array}{c}{Q}_{\mathrm{CCS},t}^{\mathrm{CHP}}\\ Q_{\mathrm{CCS},t}^{\mathrm{GB}}\end{array}\right]$$

(14)

where $P_{\mathrm{CCS},t}$ is the total energy consumption of CCS (MW); $P_{\mathrm{CCS},t}^{\mathrm{CFPP}},P_{\mathrm{CCS},t}^{\mathrm{CHP}},P_{\mathrm{CCS},t}^{\mathrm{GB}}$ are the power required to absorb the CO₂ emitted by CFPP, CHP, and GB (MW); $P_{\mathrm{CCS},t}^1,P_{\mathrm{CCS},t}^{\mathrm{ORC}},P_{\mathrm{CCS},t}^{\mathrm{wind}},P_{\mathrm{CCS},t}^{\mathrm{pv}}$ for CFPP, ORC waste heat generator, wind power, photovoltaic to provide electricity for CCS to capture CO₂ (MW); $P_t$ is the total quantity of energy used by CCS (MW); $P_{\mathrm{A}}$ is the fixed energy consumption of CCS (MW); $\theta$ is the energy consumed per unit of CO₂ captured (MW/t); $Q_{\mathrm{CCS},t}^{\mathrm{CFPP}},Q_{\mathrm{CCS},t}^{\mathrm{CHP}},Q_{\mathrm{CCS},t}^{\mathrm{GB}}$ are the amount of CFPP, CHP, and GB emitted CO₂ captured by CCS (t).

The amount of CO₂ captured by CCS is mainly related to the efficiency of CCS capture and the intensity of CO₂ emissions from CFPP, CHP, and GB.

$$Q_t^{\mathrm{CFPP}}={\gamma }_t^{\mathrm{CFPP}}{P}_t^{\mathrm{CFPP}}$$

(15)

$$\left[\begin{array}{c}{Q}_t^{\mathrm{CHP}}\\ Q_t^{\mathrm{GB}}\end{array}\right]={\gamma }_t^{\mathrm{g}}\left[\begin{array}{c}{P}_{\mathrm{h},t}^{\mathrm{CHP}}+{\lambda }^{\mathrm{e}\text{-}\mathrm{h}}{P}_{\mathrm{e},t}^{\mathrm{CHP}}\\ P_t^{\mathrm{GB}}\end{array}\right]$$

(16)

$$\left[\begin{array}{c}\begin{array}{l}{Q}_{\mathrm{CCS},t}^{\mathrm{CFPP}}\\ Q_{\mathrm{CCS},t}^{\mathrm{CHP}}\end{array}\\ Q_{\mathrm{CCS},t}^{\mathrm{GB}}\end{array}\right]=\beta \left[\begin{array}{c}\begin{array}{l}{Q}_t^{\mathrm{CFPP}}\\ Q_t^{\mathrm{CHP}}\end{array}\\ Q_t^{\mathrm{GB}}\end{array}\right]$$

(17)

$$Q_t=Q_t^{\mathrm{CP}}+Q_t^{\mathrm{CHP}}+Q_t^{\mathrm{GT}}$$

(18)

$$Q_t^{\mathrm{CCS}}=Q_{\mathrm{CCS},t}^{\mathrm{CP}}+Q_{\mathrm{CCS},t}^{\mathrm{CHP}}+Q_{\mathrm{CCS},t}^{\mathrm{GB}}$$

(19)

where $P_t^{\mathrm{CFPP}}$ is the total output of CFPP (MW); $Q_t^{\mathrm{CFPP}},Q_t^{\mathrm{CHP}},Q_t^{\mathrm{GB}}$ are the amount of CO₂ emitted by CFPP, CHP, and GB (t); $Q_t$ is the total carbon emissions of IES (t); $Q_t^{\mathrm{CCS}}$ is the entire CO₂ captured by CCS (t); $\beta$ is CCS capture efficiency; ${\gamma }_t^{\mathrm{CFPP}}$ is the CO₂ emission intensity of coal-fired power plants (t/MW); ${\gamma }_t^{\mathrm{g}}$ is the CO₂ emission intensity of gas-fired units (t/MW); ${\lambda }^{\mathrm{e}\text{-}\mathrm{h}}$ is the electricity generation to heat supply conversion factor for CHP.

Carbon sequestration technology allows the carbon dioxide captured by CCS to be sequestered and then supplied to P2G for synthesizing methane. CCS does not capture all the carbon dioxide emitted by CFPP, CHP, and GB, so a portion is still emitted into the atmosphere.

$$Q_t^{\mathrm{CS}}=Q_t^{\mathrm{CCS}}-Q_t^{\mathrm{P}2\mathrm{G}}$$

(20)

$$Q_t^{\mathrm{N}}=Q_t-Q_t^{\mathrm{CCS}}$$

(21)

where $Q_t^{\mathrm{CS}}$ is carbon sequestration (t); $Q_t^{\mathrm{N}}$ is the net carbon emissions of IES (t).

2.6. Energy Storage Equipment

Energy storage devices enhance load-side flexibility by recharging stored energy during periods of low demand to sustain system stability and unit output during periods of high demand.

$$E_t^X=E_{t-1}^X\left(1-{\delta }^X\right)+{\eta }^{X,\mathrm{c}}{P}_t^{X,\mathrm{c}}-\frac{P_t^{X,\mathrm{d}}}{{\eta }^{X,\mathrm{d}}}$$

(22)

where $E_t^X,E_{t-1}^X$ are the energy stored in the device at time $t-1$, $t$; $X$ is the type of energy, denoted by es and hs for electrical and thermal (MW); ${\delta }^X$ is the energy storage device’s rate of self-loss; ${\eta }^{X,\mathrm{c}},{\eta }^{X,\mathrm{d}}$ are the charging/discharging performance of the energy storage device; $P_t^{X,\mathrm{c}},P_t^{X,\mathrm{d}}$ are the energy storage device charging and discharging power (MW).

3. Ladder-Type Carbon Trading Mechanism

China has established a carbon trading mechanism that treats carbon emission quotas as commodities, implements a free-trading mechanism, and accounts for the cost of carbon trading in enterprise production to reduce CO₂ emissions.

3.1. Unpaid Carbon Emission Allowance Modeling

China’s allocation of carbon emission allowances is predominately based on gratuitous appropriation. Gratuitous distribution is based on the energy structure of the region, power generation, heat supply, and unit energy consumption mode related to the allocation of free carbon emission credits to the system in advance.

$$E^{\mathrm{IES},0}=E^{\mathrm{CFPP},0}+E^{\mathrm{GB},0}+E^{\mathrm{CHP},0}$$

(23)

$$E^{\mathrm{CFPP},0}={\gamma }^{\mathrm{CFPP}}{\displaystyle \sum }_{t=1}^T{P}_t^{\mathrm{CFPP}}$$

(24)

$$E^{\mathrm{GB},0}={\gamma }^{\mathrm{g}}{\displaystyle \sum }_{t=1}^T{P}_t^{\mathrm{GB}}$$

(25)

$$E^{\mathrm{CHP},0}={\gamma }^{\mathrm{g}}{\displaystyle \sum }_{t=1}^T({\lambda }^{\mathrm{e}\text{-}\mathrm{h}}{P}_{\mathrm{e},t}^{\mathrm{CHP}}+P_{\mathrm{h},t}^{\mathrm{CHP}})$$

(26)

where $E^{\mathrm{IES},0},E^{\mathrm{CFPP},0},E^{\mathrm{GB},0},E^{\mathrm{CHP},0}$ are carbon emission allowances for IES, CFPP, GB, and CHP (t); ${\gamma }^{\mathrm{CFPP}}$ is the CFPP baseline carbon emission allowance per unit of electricity (t/MW); ${\gamma }^{\mathrm{g}}$ is a baseline carbon credit for grid heat supply from gas-fired units (t/MW).

3.2. Real Carbon Emissions Modeling

In IES, CCS captures a portion of carbon emissions, P2G equipment consumes a bit, and the actual carbon emissions are produced by CFPP, GB, and CHP, excluding the carbon emissions from power purchases from the upper grid.

$$E_{\mathrm{all}}^{\mathrm{IES}}=E^{\mathrm{CFPP}}+E^{\mathrm{GB}}+E^{\mathrm{CHP}}-E^{\mathrm{CCS}}$$

(27)

$$E^{\mathrm{CFPP}}={\displaystyle \sum }_{t=1}^T{Q}_t^{\mathrm{CFPP}}$$

(28)

$$E^{\mathrm{GB}}={\displaystyle \sum }_{t=1}^T{Q}_t^{\mathrm{GB}}$$

(29)

$$E^{\mathrm{CHP}}={\displaystyle \sum }_{t=1}^T{Q}_t^{\mathrm{CHP}}$$

(30)

$$E^{\mathrm{CHP}}={\displaystyle \sum }_{t=1}^T{Q}_t^{\mathrm{CCS}}$$

(31)

where $E_{\mathrm{all}}^{\mathrm{IES}},E^{\mathrm{CP}},E^{\mathrm{GB}},E^{\mathrm{CHP}}$ are the actual carbon emissions of IES, CFPP, GB, and CHP (t); $E^{\mathrm{CCS}}$ is the total quantity of CO₂ captured during the cycle of dispatch (t).

3.3. Ladder-Type Carbon Trading Costs

IES can derive their carbon trading amount from carbon emission allowances and actual carbon emissions. To further restrict CO₂ emissions, this paper adopts the ladder-type carbon trading mechanism, which divides the traditional unified carbon trading mechanism into multiple sub-intervals and introduces compensation and penalty factors. With the increase in purchased carbon emission allowances comes the corresponding need to bear a more significant economic burden. The cost of stepped carbon trading is as follows:

$$E=E_{\mathrm{all}}^{\mathrm{IES}}-E^{\mathrm{IES},0}$$

(32)

$$C_{\mathrm{ca}}=\left\{\begin{array}{l}\lambda E,\mathrm{E}\le l\hfill \\ \lambda (1+\delta )(E-l)+\lambda l,l\le \mathrm{E}\le 2l\hfill \\ \lambda \left(1+2\delta \right)\left(E-2l\right)+\lambda \left(2+\delta \right)l,2l\le \mathrm{E}\le 3l\hfill \\ \lambda \left(1+3\delta \right)\left(E-3l\right)+\lambda \left(3+3\delta \right)l,3l\le \mathrm{E}\le 4l\hfill \\ \lambda \left(1+4\delta \right)\left(E-4l\right)+\lambda \left(4+6\delta \right)l,\mathrm{E}\ge 4l\hfill \end{array}\right.$$

(33)

where $E$ is the amount of carbon credits traded by IES (t); $C_{\mathrm{ca}}$ is the ladder-type carbon trading cost (yuan); $\lambda$ is the carbon trading base price (yuan); $\delta$ is the price growth rate; $l$ is the carbon emission interval length.

4. Model of IES Low-Carbon Economy Optimization with ORC and P2G-CHP-CCS Coupling

4.1. Objective Function

This paper uses a low-carbon economic optimization model to minimize the daily operating cost while simultaneously satisfying the safety, economy, and low-carbon requirements. The operation cost consists primarily of three components: energy acquisition cost $C_{buy}$, operation and maintenance cost $C_{op}$, and environmental cost $C_{hj}$.

$$\min F=C_{\mathrm{buy}}+C_{\mathrm{op}}+C_{\mathrm{hj}}$$

(34)

(1)

Energy acquisition cost

The primary components of the cost of purchased energy are the mass of coal utilized by the CFPP, the amount of natural gas used by the CHP and GB, the quantity of carbon dioxide used by the P2G, the expenses associated with carbon sequestration, and the cost of grid-sourced power incurred by the system.

$$C_{\mathrm{buy}}=C^{\mathrm{CFPP}}+C_{}^{\mathrm{CHP}-\mathrm{GB}}+C_{}^{\mathrm{P}2\mathrm{G}}+C_{}^{\mathrm{CS}}+C_{}^{\mathrm{ES}}$$

(35)

The expense of CFPP is mainly determined by the cost factor and the power generated.

$$C_{}^{\mathrm{CFPP}}={\displaystyle \sum _{t=1}^T(a+b{P}_t^{\mathrm{CFPP}}+c{(P_t^{\mathrm{CFPP}})}^2)}$$

(36)

where $C_t^{\mathrm{CFPP}}$ is the cost of fuel purchased by CFPP; $a,b,c$ are CFPP cost factors.

Natural gas is purchased external to the system because the amount of methane produced by P2G is insufficient for CHP and GB, resulting in its high cost.

$$C_{}^{\mathrm{CHP}-\mathrm{GB}}={\displaystyle \sum _{t=1}^T{A}^{{\mathrm{CH}}_4}{V}_t^{\mathrm{buy}}}$$

(37)

$$V_t^{\mathrm{buy}}=V_t^{\mathrm{GB}}+V_t^{\mathrm{GT}}-V_t^{\mathrm{P}2\mathrm{G}}$$

(38)

where $C_t^{\mathrm{CHP}-\mathrm{GB}}$ is natural gas purchased by CHP and GB; $A^{{\mathrm{CH}}_4}$ is the price per unit of natural gas (yuan/m³); $V_t^{\mathrm{buy}}$ is the amount of natural gas purchased (m³);

The quantity of carbon dioxide purchased is primarily influenced by the demand for P2G and the amount captured by the CCS. In cases where the carbon dioxide supplied by the CCS to the P2G fails to satisfy the request, external carbon dioxide purchases become necessary.

$$C_{}^{\mathrm{P}2\mathrm{G}}={\displaystyle \sum _{t=1}^T{A}^{{\mathrm{CO}}_2}{Q}_t^{\mathrm{buy}}}$$

(39)

$$Q_t^{\mathrm{buy}}=Q_t^{\mathrm{P}2\mathrm{G},\mathrm{sum}}-Q_t^{\mathrm{P}2\mathrm{G}}$$

(40)

where $C_t^{\mathrm{P}2\mathrm{G}}$ is the cost of purchasing CO₂; $A^{{\mathrm{CO}}_2}$ is the price per unit of CO₂ (yuan/t).

Sequestering carbon dioxide captured by CCS also consumes a portion of the cost. $Q_t^{\mathrm{CS}}$ In Equation (20)

$$C_{}^{\mathrm{CS}}={\displaystyle \sum _{t=1}^T{A}^{\mathrm{CS}}{Q}_t^{\mathrm{CS}}}$$

(41)

where $C_t^{\mathrm{CS}}$ is the cost of carbon sequestration; $A^{\mathrm{CS}}$ is the price per sequestered unit of CO₂ (yuan/t);

Even if the unit is operating at total capacity during the peak period of electricity consumption, it will be unable to satisfy the load demand of the system. In such cases, electricity must be purchased from a higher power grid.

$$C_{}^{\mathrm{ES}}={\displaystyle \sum _{t=1}^T{A}_t^{\mathrm{ES}}{P}_t^{\mathrm{ES}}}$$

(42)

where $C_t^{\mathrm{ES}}$ is the cost of purchasing electricity; $A_t^{\mathrm{ES}}$ is the grid electricity price (yuan/MW); $P_t^{\mathrm{ES}}$ is the quantity of electricity purchased from the grid (MW).

(2)

Operation and maintenance cost

The operation and maintenance cost is an essential unit expense proportional to the unit’s type and the burden it produces.

$$C_{\mathrm{op}}^1={\displaystyle \sum }_{t=1}^T{\displaystyle \sum }_{L=1}^{10}{\omega }^L{P}_t^L$$

(43)

$$C_{\mathrm{op}}^2={\displaystyle \sum }_{t=1}^T{\omega }^{\mathrm{CCS}}{Q}_t^{\mathrm{CCS}}+{\omega }^{\mathrm{CS}}{Q}_t^{\mathrm{CS}}$$

(44)

$$C_{\mathrm{op}}=C_{\mathrm{op}}^1+C_{\mathrm{op}}^2$$

(45)

where $L$ is taken as 1, 2, …, 10 for PW, PV, CP, P2G, GT, GB, ORC, WHB, TES, and EES, respectively; ${\omega }^L$ is the number of O&M lines of equipment $L$ (yuan/MW); $P_t^L$ is the output of equipment $L$ (MW); ${\omega }^{\mathrm{CCS}},{\omega }^{\mathrm{CS}}$ are the number of O&M systems for CCS and carbon sequestration equipment (yuan/t).

(3)

Environmental cost

Due to the counter-regulatory and stochastic nature of new energy sources (plenty of wind power in the early hours of the morning but little load demand, the exact opposite of load demand), a penalty factor is implemented to encourage the use of these sources. This factor imposes a penalty when the wind and PV unit outputs fail to meet the predicted outputs.

$$C_{\mathrm{hj}}=C_{\mathrm{ca}}+C_{\mathrm{cut}}^{\mathrm{pv}}+C_{\mathrm{cut}}^{\mathrm{wind}}$$

(46)

$$C_{\mathrm{cut}}^{\mathrm{pv}}={\displaystyle \sum }_{t=1}^T{\phi }^{\mathrm{pv}}{P}_{\mathrm{cut},t}^{\mathrm{pv}}$$

(47)

$$C_{\mathrm{cut}}^{\mathrm{wind}}={\displaystyle \sum }_{t=1}^T{\phi }^{\mathrm{wind}}{P}_{\mathrm{cut},t}^{\mathrm{wind}}$$

(48)

where $C_{\mathrm{cut}}^{\mathrm{pv}},C_{\mathrm{cut}}^{\mathrm{wind}}$ are the cost of curtailment PV and wind power (yuan); ${\phi }^{\mathrm{pv}},{\phi }^{\mathrm{wind}}$ are the unit costs of PV and wind power curtailment (yuan/MW).

4.2. Constraint Conditions

(1)

Electric power balance

The balance of power comprises two primary components. The power supply consists primarily of wind power, CFPP, CHP, PV, battery discharge, and grid-purchased power; the demand for electrical energy consists mainly of electrical loads, battery charging, CFPP, and ORC waste heat generating units to supply electricity to CCS.

$$P_t^{\mathrm{CP}}+P_{\mathrm{e},t}^{\mathrm{CHP}}+P_{\mathrm{e},t}^{\mathrm{pv}}+P_{\mathrm{e},t}^{\mathrm{wind}}+P_t^{\mathrm{es},\mathrm{d}}+P_t^{\mathrm{ES}}=P_t^{\mathrm{EL}}+P_t^{\mathrm{es},\mathrm{c}}+P_{\mathrm{CCS},t}^1+P_{\mathrm{CCS},t}^{\mathrm{ORC}}$$

(49)

where $P_t^{\mathrm{EL}},P_t^{\mathrm{es},\mathrm{d}},P_t^{\mathrm{es},\mathrm{c}}$ are the quantity of electrical load, storage device discharge, and storage capacity (MW).

(2)

Thermal power balance

$$P_{\mathrm{h},t}^{\mathrm{CHP}}+P_t^{\mathrm{GB}}+P_t^{\mathrm{hs},\mathrm{d}}=P_t^{\mathrm{HS}}+P_t^{\mathrm{hs},\mathrm{c}}$$

(50)

where $P_t^{\mathrm{HS}},P_t^{\mathrm{hs},\mathrm{d}},P_t^{\mathrm{hs},\mathrm{c}}$ are the user heat load, heat release from storage equipment, and heat storage (MW).

(3)

Coal-fired power plant constraints

$$P_{t,\min }^{\mathrm{CFPP}}\le P_t^{\mathrm{CFPP}}\le P_{t,\max }^{\mathrm{CFPP}}$$

(51)

$$\left|P_{t+1}^{\mathrm{CFPP}}-P_t^{\mathrm{CFPP}}\right|\le \Delta P_t^{\mathrm{CFPP}}$$

(52)

where $P_{t,\min }^{\mathrm{CP}},P_{t,\max }^{\mathrm{CP}}$ are the min/max power of CCFP output (MW); $\Delta P_t^{\mathrm{CP}}$ is CFPP output climb rate constraint (MW).

(4)

Combined heat and power constraints

$$P_{\min }^{\mathrm{GT}}\le P_t^{\mathrm{GT}}\le P_{\max }^{\mathrm{GT}}$$

(53)

$$P_{\min }^{\mathrm{ORC}}\le P_t^{\mathrm{ORC}}\le P_{\max }^{\mathrm{ORC}}$$

(54)

$$\left|P_{t+1}^{\mathrm{GT}}-P_t^{\mathrm{GT}}\right|\le \Delta P^{\mathrm{GT}}$$

(55)

$$\left|P_{t+1}^{\mathrm{ORC}}-P_t^{\mathrm{ORC}}\right|\le \Delta P^{\mathrm{ORC}}$$

(56)

$${\lambda }^{\mathrm{ORC}}+{\lambda }^{\mathrm{WHB}}=1$$

(57)

$$0\le {\lambda }^{\mathrm{ORC}}\le 1$$

(58)

$$0\le {\lambda }^{\mathrm{WHB}}\le 1$$

(59)

where $P_{\min }^{\mathrm{GT}},P_{\max }^{\mathrm{GT}}$ are the min/max power of GT (MW); $P_{\min }^{\mathrm{ORC}},P_{\max }^{\mathrm{ORC}}$ are the min/max power of the ORC (MW); $\Delta P^{\mathrm{GT}},\Delta P^{\mathrm{ORC}}$ are the GT and ORC output climb rate constraints (MW).

(5)

Gas boiler constraints

$$P_{t,\min }^{\mathrm{GB}}\le P_t^{\mathrm{GB}}\le P_{t,\max }^{\mathrm{GB}}$$

(60)

$$\left|P_{t+1}^{\mathrm{GB}}-P_t^{\mathrm{GB}}\right|\le \Delta P^{\mathrm{GB}}$$

(61)

where $P_{t,\min }^{\mathrm{GB}},P_{t,\max }^{\mathrm{GB}}$ are the min/max power of GB (MW); $\Delta P^{\mathrm{GB}}$ is GB output climb rate constraint (MW).

(6)

Power to gas constraints

$$P_{t,\min }^{\mathrm{P}2\mathrm{G}}\le P_t^{\mathrm{P}2\mathrm{G}}\le P_{t,\max }^{\mathrm{P}2\mathrm{G}}$$

(62)

$$\left|P_{t+1}^{\mathrm{P}2\mathrm{G}}-P_t^{\mathrm{P}2\mathrm{G}}\right|\le \Delta P^{\mathrm{P}2\mathrm{G}}$$

(63)

where $P_{t,\min }^{\mathrm{P}2\mathrm{G}},P_{t,\max }^{\mathrm{P}2\mathrm{G}}$ are the min/max power of P2G (MW); $\Delta P^{\mathrm{P}2\mathrm{G}}$ is P2G output climb rate constraint (MW).

(7)

Energy storage constraints

$$0\le P_t^{X,\mathrm{c}}\le P_{t,\max }^{X,\mathrm{c}}{\mu }_t^{X,\mathrm{c}}$$

(64)

$$0\le P_t^{X,\mathrm{d}}\le P_{t,\max }^{X,\mathrm{d}}{\mu }_t^{X,\mathrm{d}}$$

(65)

$$0\le {\mu }_t^{X,\mathrm{c}}+{\mu }_t^{X,\mathrm{d}}\le 1$$

(66)

$$E_{t,\min }^X\le E_t^X\le E_{t,\max }^X$$

(67)

$$E_0^X=E_{24}^X$$

(68)

where $P_{t,\max }^{X,\mathrm{c}},P_{t,\max }^{X,\mathrm{d}}$ are the energy storage device’s maximum charging and discharging capacities (MW); ${\mu }_t^{X,\mathrm{c}},{\mu }_t^{X,\mathrm{d}}$ are Boolean variables that indicate whether the energy storage device is charging or discharging, respectively. Yes is a 1 and No is a 0; $E_{t,\min }^X,E_{t,\max }^X$ are the upper and lower limits of the energy storage device (MW); $E_0^X,E_{24}^X$ are the states at the beginning and end of the energy storage device (MW); X is the type of energy, namely electrical and thermal energy.

The optimized scheduling framework of the integrated energy system constructed in this paper is shown in Figure 5.

4.3. Linearization of the Model

Due to the quadratic Equation (36), the proposed model is a mixed-integer nonlinear model, which is transformed into a mixed-integer linear model via the segmental linearization method. The commercial solution software CPLEX is invoked via MATLAB + YALMIP to solve the model.

The process of segmentation is as follows:

• Step 1: Take Q + 1 segmentation points $[{\tau }_1,{\tau }_2,\cdots ,{\tau }_{Q+1}]$ divide the original function into Q intervals;
• Step 2: Add Q + 1 continuous auxiliary variables $[{\upsilon }_1,{\upsilon }_2,\cdots ,{\upsilon }_{Q+1}]$ and Q binary auxiliary variables $[{\rho }_1,{\rho }_2,\cdots {\rho }_Q]$, and satisfy Equation (65).

  $$\left\{\begin{array}{l}{\upsilon }_1+{\upsilon }_2+\cdots +{\upsilon }_{Q+1}=1\\ {\rho }_1+{\rho }_2+\cdots +{\rho }_Q=1\\ {\upsilon }_1\ge 0,{\upsilon }_2\ge 0,\cdots ,{\upsilon }_{Q+1}\ge 0,\\ {\upsilon }_1\le {\rho }_1,{\upsilon }_2\le {\rho }_1+{\rho }_2,\cdots ,{\upsilon }_{Q+1}\le {\rho }_Q\end{array}\right.$$

  (69)
• Step 3: Replace the nonlinear function with a linear expression, as shown below

  $$P_t^{\mathrm{CFPP}}={\displaystyle \sum _{q=1}^{Q+1}{\tau }_q{\upsilon }_q}$$

  (70)

  $$C_{}^{\mathrm{CFPP}}={\displaystyle \sum _{q=1}^{Q+1}{\tau }_q{C}_{}^{\mathrm{CFPP}}({\upsilon }_q)}$$

  (71)

5. Case Study

5.1. Basic System Parameters

This paper’s low-carbon economy optimization dispatch model selects a typical day in a northern region with an operating period T of 24 h and a 1 h dispatch interval. The time-of-day tariffs are shown in Figure 6; the forecast values of electric and thermal loads, as well as wind power and photovoltaic, are displayed in Figure 7;

Table 1. Parameters of each IES units.

Equipment Name	Symbol	Output Limit/MW	Lower Output Limit/MW	Climbing Restrictions/MW
Coal-fired power plant	CFPP	100	50	20
Gas turbine	GT	70	20	15
Power to gas	P2G	50	0	10
Organic rankine cycle	ORC	30	0	10
Gas boiler	GB	150	0	30
Carbon capture and storage	CCS	30	5	10
Waste heat boiler	WHB	50	0	10

,

Table 2. Energy storage equipment parameters.

Equipment Name	Symbol	Energy Storage Limit/MW	Lower Energy Storage Limit/MW	${\mathit{\delta }}^{\mathit{X}}/\%$	${\mathit{\eta }}^{\mathit{X},\mathbf{c}}$	${\mathit{\eta }}^{\mathit{X},\mathbf{d}}$
Electric energy storage	EES	80	10	0.1	0.95	0.95
Thermal energy storage	TES	40	10	1	0.88	0.88

and

Table 3. System parameters.

Parameters	Retrieve Value	Parameters	Retrieve Value	Parameters	Retrieve Value
${\eta }_{\mathrm{e}}^{\mathrm{GT}}$	0.35	${\eta }_{\mathrm{h}}^{\mathrm{GT}}$	0.65	${\eta }^{\mathrm{GB}}$	0.75
${\eta }_{\mathrm{WHB}}$	0.8	${\eta }_{\mathrm{ORC}}$	0.8	${\eta }^{\mathrm{P}2\mathrm{G}}$	0.55
${\alpha }^{{\mathrm{CO}}_2}/(\mathrm{t}/\mathrm{MW})$	1.02	$A^{{\mathrm{CH}}_4}{/(\mathrm{yuan}/\mathrm{m}}^3)$	3	$A^{{\mathrm{CO}}_2}/(\mathrm{yuan}/\mathrm{t})$	120
$A^{\mathrm{CS}}/(\mathrm{yuan}/\mathrm{t})$	30	${\phi }^{\mathrm{pv}}/(\mathrm{yuan}/\mathrm{MW})$	260	${\phi }^{\mathrm{wind}}/(\mathrm{yuan}/\mathrm{MW})$	260
a	26	b	125	c	0.0016
$\lambda /\yen$	120	$\delta$	0.2	l/t	1000
$\beta$	0.85	$\theta /(\mathrm{MW}/\mathrm{t})$	0.23	${\gamma }_t^{\mathrm{CCPP}}/(\mathrm{t}/\mathrm{MW})$	1.1
${\gamma }_t^{\mathrm{g}}/(\mathrm{t}/\mathrm{MW})$	0.234	${\gamma }^{\mathrm{CCPP}}/(\mathrm{t}/\mathrm{MW})$	0.728	${\gamma }^{\mathrm{g}}/(\mathrm{t}/\mathrm{MW})$	0.367
${\lambda }^{\mathrm{e}\text{-}\mathrm{h}}$	1.67	$P_{\mathrm{A}}/\mathrm{MW}$	5	$H_g{/(\mathrm{MJ}/\mathrm{m}}^3)$	39

indicate the capacity of each device within IES and the associated parameters.

5.2. Simulation Setup

To verify the efficiency of the optimal scheduling model for the low-carbon economy proposed in this paper, a total of six cases were created:

• Case 1: Integrated energy systems without carbon capture and storage (CCS), organic Rankine cycle (ORC), and power to gas (P2G) technologies, without carbon trading;
• Case 2: Systems in Case 1 plus an ORC facility, without carbon trading;
• Case 3: Systems in Case 2 plus a P2G facility, without carbon trading;
• Case 4: Systems in Case 2 plus a P2G facility, with carbon trading;
• Case 5: Systems in Case 3 plus a CCS facility, without carbon trading;
• Case 6: Systems in Case 3 plus a CCS facility, with carbon trading.

The six cases presented have the same conditions except for the variables mentioned above.

The simulation results of the six cases are displayed in

Table 4. Scheduling results for six cases.

Cases	1	2	3	4	5	6
Power purchase costs/Yuan	0	0	5340	5430	353,725	31,5871
Gas purchase cost/Yuan	1,332,601	1,451,908	857,201	860,869	933,034	967,346
Coal-fired power plant cost/Yuan	204,249	194,104	247,230	245,085	208,527	205,971
Cost of wind and light curtailment/Yuan	102,511	151,263	40,386	38,902	12,216	11,488
Carbon trading cost/Yuan	0	0	0	9988	0	−305,869 *
Cost/Yuan	1,864,029	2,025,606	1,564,702	1,575,100	1,815,032	1,512,367
CO2 emissions/t	2704	2720	3295	3261	439	442

* “−”for sale. 1 yuan = 0.14 dollars.

.

5.2.1. Model Comparisons

The results for cases 1, 2, 3, and 5 are shown in

Table 5. Cases 1, 2, 3, and 5 results.

	Case 1	Case 2	Case 3	Case 5
Power purchase costs/Yuan	0	0	5340	353,725
Gas purchase cost/Yuan	1,332,601	1,451,908	857,201	933,034
Coal-fired power plant cost/Yuan	204,249	194,104	247,230	208,527
Cost of wind and light curtailment/Yuan	102,511	151,263	40,386	12,216
Cost/Yuan	1,864,029	2,025,606	1,564,702	1,815,032
CO2 emissions/t	2704	2720	3295	439

.

This paper devised six distinct cases, with a minimum of 15,123,367 yuan for Case 6 and a maximum of 2,025,606 yuan for Case 2.

By comparing Cases 1 and 2, it becomes evident that a combined heat and power (CHP) system with organic Rankine cycle (ORC) waste heat power generation enables the decoupled operation of the CHP system and facilitates the power of surplus heat via the ORC waste heat unit. As the electrical output of combined heat and power (CHP) systems grows, there is a corresponding drop in the PW output. Incorporating Organic Rankine Cycle (ORC) technology into Integrated Energy Systems (IES) enhances the flexibility of IES but at the expense of elevated system costs and carbon emissions. Case 2 is less economical and results in lower carbon.

A comparison between Cases 2 and 3 reveals that in Case 2, the addition of P2G is observed within the IES. P2G employs surplus renewable energy to facilitate the production of methane, which is then utilized by combined heat and power (CHP) systems and the gas grid (GB). This process effectively mitigates the expenses associated with procuring natural gas. In Case 3, the introduction of P2G technology results in a rise in energy consumption. However, to satisfy the electric load requirements, there is a corresponding increase in the production of coal-fired power plants, leading to a subsequent increase in carbon dioxide emissions.

A comparison of Cases 3 and 5 demonstrates that IES plus CCS increase wind and light consumption. At the same time, CCS captures CO₂ emissions from the system, substantially reducing carbon emissions. Carbon capture by CCS is proportional to the power consumption of the system. Therefore, the power consumption is increased to meet the system’s operation. So, IES procure electricity from the higher grid, which results in an escalation of the system’s operating expenses.

5.2.2. Analysis of Carbon Trading Mechanisms

The results for cases 1, 2, 3, 5, and 6 are shown in

Table 6. Cases 1, 3, 4, 5, and 6 results.

	Case 1	Case 3	Case 4	Case 5	Case 6
Gas purchase cost/Yuan	1,332,601	857,201	860,869	933,034	967,346
Coal-fired power plant cost/Yuan	204,249	247,230	245,085	208,527	205,971
Cost of wind and light curtailment/Yuan	102,511	40,386	38,902	12,216	11,488
Carbon trading cost/Yuan	0	0	9988	0	−305,869 *
Cost/Yuan	1,864,029	1,564,702	1,575,100	1,815,032	1,512,367
CO2 emissions/t	2704	3295	3261	439	442

* “−”for sale. 1 yuan = 0.14 dollars.

.

Comparing Cases 3 and 4, the carbon trading mechanism under consideration in Case 4 results in a marginal reduction in the total cost and carbon emissions. The system lacks carbon capture and storage (CCS) technology, and coal-fired units have higher carbon trading expenses than gas-fired units. As a result, the system augments the output of gas-fired units and new energy while constraining the output of coal-fired power plants.

Comparing Cases 5 and 6, it can be seen that the systems in both cases incorporate CCS, so the carbon emissions are substantially reduced in both cases. Case 5 does not consider the carbon trading mechanism, and CCS can only play the role of carbon reduction in the system, but the economic benefits are not good. Case 6 considers a carbon trading mechanism whereby CCS captures carbon emissions from the system and sells excess carbon credits for financial gain, increasing the incentive for companies to participate.

Comparing Cases 4 and 6, it shows that both cases consider carbon trading mechanisms. Both are comparable in operating costs, but they are much different regarding carbon emissions. Although Case 6 made a profit of 305,869 yuan on carbon trading, CCS costs electricity to operate. In other words, the profit from carbon trading is balanced by the cost of the electricity needed for CCS, and carbon emissions are significantly reduced.

Comparing Cases 1 and 6, the low-carbon dispatch model of the integrated energy system proposed in this paper with the basic integrated energy system performs more outstandingly in terms of economy and low carbon.

5.2.3. Integrated Energy System Power Balance Analysis

The integrated energy system power balance is shown in Figure 8.

As depicted in Figure 8a, in Case 1, the electric load is shared by the power storage equipment, CFPP, PV, PW, and GT, and the load is minimized at hour 3. The peak-to-valley difference in PW output is also at this time, which is more impactful to the grid and affects the security of the system. The power load peaks at hours 10–15 and CFPP output also peaks and continues until hour 20; the GT generates power as a reserve, and the CFPP produces a more minor amount of energy than the GT spends without considering the environment.

As shown in Figure 8b, with the addition of ORC waste heat power generation equipment in Case 2, the minimum wind power output and peak-to-valley difference is also at 3, which is a decrease relative to Case 1. The CFPP reaches its entire output state at hour 16. The coal-fired unit is running at total capacity only in three periods because the ORC unit takes on a portion of the generating output at 13:00, which is reduced by half of the time concerning Case 1.

As shown in Figure 8c, the P2G is added to Case 3, and the generation output is relatively smooth compared to the previous two models. The production of each unit is slick, and the load is relatively low at hours 7–9, but the coal-fired power plant is operating at total capacity. The storage equipment is discharged at hour 8 to meet the demand for electric load. The GT’s output increases at hour 14 and continues until hour 21, when it must purchase power from a higher-level power grid to satisfy the IES’s power demand.

As shown in Figure 8d, CCS is added to Case 5. To process the CO₂ emitted by IES, the IES’s electricity consumption rise, necessitating a rise in power purchases from the higher grid. To reduce carbon emissions, the output of coal-fired power facilities is decreased, the production of relatively clean GT units is increased, and the ORC units operate at maximum load for half the time.

As shown in Figure 8e, the carbon trading mechanism is considered in Case 6. The relative clean GT unit output increases and the ORC operates at full power 70% of the time, increasing the consumption of new energy, and the output of each unit is relatively smooth.

In summary, including an ORC waste heat generating unit cuts the peak-to-valley difference in the system and shares a portion of the output of the coal-fired plant, reducing the full-load operating time. Plus, P2G units increase the system power demand, and coal-fired power plants are at total capacity most of the time, leading to increased carbon emissions. However, the smooth output of the units in the system reduces the impact on the grid. After the system is added to CCS, the system’s output cannot meet the electricity demand, so electricity is purchased from the higher grid. To reduce CCS output and carbon emissions, the output of coal-fired power plants is reduced and the output of GT is increased. Under the carbon trading mechanism, the system prioritizes GT power supply to minimize system costs, and the GT economic benefits are better than coal-fired power plants in terms of carbon trading costs.

5.2.4. Thermal Output Balance Analysis

The heat output of Case 6 is shown in Figure 9, and GB and WHB share the heat load in the IES. Because the production unit spends less heat energy on GB units than WHB units, the GB units carry the vast majority of the heat load.

5.2.5. Wind Abandonment Results and Analysis

The amount of wind curtailment is shown in Figure 10, where Case 2 has the highest amount at hour 3. The time when each case experiences the most wind curtailment is at hour 3. To reduce the quantity of wind curtailment, the storage apparatus begins storing electricity at hour 2 and releases it at hour 3. The wind curtailment is greater at hour 3 due to the low demand for electricity and the need for each unit to meet the minimum power output. Cases 5 and 6 add CCS to take up a portion of the wind power.

The wind curtailment in Case 2 is greater than Case 1 in all time periods due to the addition of the ORC that takes up a portion of the output. At hour 2, Cases 3 and 4 with P2G have greater wind curtailment than Cases 1 and 2, and due to the large quantity of energy stored by the energy storage device at hour 2, the graph will demonstrate that models 3 and 4 have greater wind curtailment. Since the carbon trading mechanism is considered, the CFPP will reduce its output and make up the difference with renewable energy.

The occurrence of wind curtailment is predominantly concentrated in the early morning hours. This is because, despite sufficient wind to generate a substantial amount of power, load demand is low and system units have to meet minimum output requirements. Consequently, wind abandonment is more prevalent during this time of day. The implementation of energy storage devices mitigates wind curtailment by enabling the storage of surplus wind energy for subsequent utilization during periods of high load.

5.2.6. Carbon Emissions Analysis

Carbon emissions are shown in Figure 11, with Case 3 exhibiting the most substantial ones. P2G is incorporated into Cases 3 and 4, augmenting the new energy consumption capacity and occupying a portion of the new energy output. To satisfy the electric discharge demand, CFPP escalates the work, consequently increasing carbon emissions. After considering the carbon trading mechanism, the carbon emissions of Cases 3 and 4 are not very different, and the enterprises do not participate actively. After adding CCS, which captures most of the carbon emissions in the system, the system’s carbon emissions plummet.

5.2.7. The Effect of the ORC Percentage on the System

As shown in Figure 12, the overall operating cost of the system decreases as the ORC share decreases. This is because the ORC unit incurs more extraordinary expenses in generating an equivalent quantity of electricity. Consequently, an increase in the ORC share escalates the system’s total operating cost. As the ORC share rises, there is a corresponding increase in the system power purchase cost. This is because the GT unit will operate at a reduced output stage and the coal-fired plant will overwork capacity, increasing power purchase expenses. At an ORC percentage of 0.2, the overall operating cost of the system is at its minimum.

6. Conclusions

In this paper, based on the stepped carbon trading model and considering combined heat and power (CHP) with an organic Rankine cycle (ORC) waste heat generating unit, a low carbon economic dispatch model coupled with carbon capture and storage (CCS), power to gas (P2G), and CHP is established to reduce the system’s carbon emissions and operation expense. By analyzing and comparing six cases, the following conclusions can be drawn:

• ORC and GB had a decoupling effect on the cogeneration of CHP, and the introduction of an ORC increased the daily operating costs by 8.7%. At the same time, the curtailment of wind and photovoltaic output increased. With the introduction of P2G, the capacity to consume wind scenery was significantly increased, the amount of abandoned wind scenery was decreased, and the total operating cost was reduced by 22.7%; however, daily carbon emissions were increased by 21.1%.
• The introduction of a stepped carbon trading mechanism can promote the consumption of new energy. After adding CCS, the total operating cost increased by 16%, carbon emissions decreased by 86.7%, and the impact of carbon emissions was evident. Introducing the stepped carbon trading mechanism can reduce total operating costs while marginally increasing carbon emissions. Carbon emissions increased by 0.68%, while operating expenses decreased by 16.7%.
• ORC can make CHP a decoupled operation, not a “heat to set electricity” limitation; the ORC waste heat ratio of the coefficient will affect the system’s operating cost; at a ratio of 0.2, the system operating cost is the lowest.

ORC allocation factors significantly impacted a system’s operating costs. Additional research is required to determine how to select the optimal allocation factor to minimize the operational costs and carbon emissions of IES. Multi-objective optimization by integrating operating costs and carbon emission targets should be studied.