Source-Load Coordinated Low-Carbon Economic Dispatch of Microgrid including Electric Vehicles

Abstract

As the global warming crisis becomes increasingly serious, sustainable dispatch strategies that can reduce CO₂ emissions are gradually developed. Aiming at the problems of poor synergy between carbon capture systems (CCS) and P2G as well as the potential of the source-load interaction of microgrids with electric vehicles for carbon reduction that needs to be explored, this paper proposes a source-load coordinated low-carbon economic dispatch strategy for microgrids, including electric vehicles. Firstly, considering the low-carbon operation characteristics of CCS and P2G, a comprehensively flexible and cooperative operation mode for CCS and P2G is constructed. Secondly, based on the carbon reduction potential of demand response on the energy consumption side, a demand response optimal scheduling model considering the participation of electric vehicles in the microgrid is established. Finally, based on the complementary characteristics of low-carbon resources on both sides of the microgrid, a source-load coordinated low-carbon economic dispatch strategy for the microgrid is proposed. The results show that the strategy proposed in this paper can fully use the energy time-shift advantage of CCS and P2G and can combine EVs and other load-side resources to flexibly participate in demand-side response, which effectively realizes source-load synergy and improves the low carbon and economy of the microgrid.

1. Introduction

With the gradual depletion of fossil energy sources and the improvement in sustainable development attention, efficient use of energy and reductions in carbon emissions have become urgent issues. At present, the power industry is the main source of carbon emissions in China. At the same time, the number of installed renewable energy sources, such as photovoltaics and wind power, continues to rise, and the problem of consumption has become increasingly prominent. To achieve the goals of “carbon peaking” by 2030 and “carbon neutrality” by 2060 [1], it is urgent to carry out a low-carbon transformation of traditional generation on the energy supply side and to explore the carbon emission reduction potential of various demand response resources on the energy consumption side. At the same time, it is necessary to fully use the scheduling advantages of low-carbon complementary characteristics of resources on both sides of the source and load to promote the consumption of renewable energy, improve the low-carbon performance of the system, and achieve sustainable development [2,3,4].

Carbon capture power plants and power-to-gas systems can improve the low-carbon economic operational performance of microgrids at the source side. By adding carbon capture equipment, traditional generation units can be transformed into a carbon capture power plant to realize large-scale and high-efficiency carbon emission reduction [5,6,7,8,9]. Currently, most of the carbon capture power plants are operated in diffluence type and stock solution type. In the former type, the CO₂ absorption and resolution processes are coupled with each other, which tends to conflict between the demand of the load and the carbon capture system (CCS). The latter type cannot actively discharge CO₂ according to demand, leading to poor scheduling flexibility. In contrast, by installing solution storage to form a comprehensively flexible operation mode for a carbon capture power plant, the processes of absorbing and capturing CO₂ can be decoupled from each other. The CO₂ absorbed at load peaks can be transferred to be processed at a low load period, which realizes the time-shift of carbon capture power and expands the output range of generator sets [10,11]. In [10], a generic quantitative model based on the benchmark capture plant was established to formulate the process of CO₂ capture, revealing the different characteristics between CO₂ capture power plants and conventional non-capture plants. Ref. [11] proposed a stochastic optimization model of the IEHS considering the uncertainty of wind power (WP) output and carbon capture power plants. In [12], a day-ahead scheduling model with comprehensive cost and overall carbon emission optimization was constructed based on the detailed modeling of the integrated flexible operation mode of the carbon capture power plant. However, the above references did not consider the combination of carbon capture power plants with the power-to-gas system, where the captured CO₂ can be supplied to the power-to-gas system to synthesize methane to further improve the CO₂ utilization efficiency and reduce the system’s operating cost.

The power-to-gas (P2G) system operates with electricity mainly supplied by new energy sources, which can not only solve the problem of renewable energy consumption but also realize the recycling of carbon resources [13,14,15,16,17]. Ref. [16] proposed a new economic low-carbon clean dispatching of power systems containing the P2G, considering the comprehensive influence of the multi-price factor and improving the wind power absorption capacity of the grid. In [17], a low-carbon economic scheduling model of an integrated energy system containing a carbon capture system, utilization and storage device, P2G system, and heat and power cogeneration was constructed, realizing the recycling of carbon and reducing the energy cost of the system. However, the fixed operation mode of the P2G system cuts down its ability to consume renewable energy, which is not conducive to the utilization of carbon resources and the economic benefits of microgrids. Simultaneously, existing studies have not fully discussed the principle of synergistic operation of the CCS-P2G system and failed to address the problem of the uncoordinated operational effects of the carbon capture system and P2G system. Therefore, by constructing a comprehensively flexible and cooperative operation mode for the carbon capture system and the P2G system, the utilization rate of CO₂ separation can be enhanced, thus increasing the operating benefit of the microgrid and further improving the low-carbon performance of the system.

In addition to vigorously developing carbon capture technology and P2G technology on the energy supply side, carbon emission reduction also needs to explore the potential of demand response on the energy consumption side [18,19,20,21]. Electric vehicles (EVs) can not only reduce carbon emissions but also act as an effective scheduling resource for source-load interaction. Meanwhile, EVs can participate in large-scale renewable energy consumption and effectively reduce carbon emissions from conventional units [22,23,24]. In [23], a novel low-carbon EV charging coordination approach in coupled transportation and power networks was proposed. The method effectively mitigated the global carbon emissions of the coupled networks. Ref. [24] proposed an economic–environmental dispatch model considering EV carbon quotas, which improved the carbon reduction performance of EVs while reducing user costs. The above studies illustrated the enormous decarbonization potential of effectively coordinating the charging and discharging scheduling of electric vehicles. However, the current studies have failed to fully explore the carbon emission reduction potential of EVs and other load-side demand-response resources to dispatch with the source-side carbon capture power plant and P2G system. Additionally, the existing studies did not deeply analyze the low-carbon effects of different scenarios and scales of EVs participating in microgrid electricity–carbon co-dispatch.

In order to realize the collaboration between the carbon capture system and the P2G system and to fully explore the sustainable development potential of microgrid source-load coordination, this paper proposes a source-load synergistic low-carbon economic dispatch strategy for microgrids that considers the participation of EVs. The contributions of this paper are summarized as follows:

• This paper constructs a comprehensively flexible and cooperative operation mode for carbon capture systems and P2G systems;
• This paper establishes a demand-side response model with the participation of electric vehicles and analyzes the impact of the size of electric vehicles on the economy and low carbon emissions of microgrids;
• The source-load coordinated low-carbon economic dispatch of microgrids, including electric vehicles, effectively realizes source-load synergy and mutual assistance and improves the low-carbon and economic performance of microgrids.

The rest of the paper is organized as follows: Section 2 constructs a model of the carbon capture system and P2G system in microgrids. Section 3 develops a model of demand-side flexible response in microgrids. Section 4 constructs a low-carbon economic dispatch model of microgrids with source-load coordination. Section 5 analyzes the reasonableness and effectiveness of the proposed strategy through simulation comparison. Section 6 concludes the paper.

2. Model of Carbon Capture System and P2G System in Microgrid

2.1. Model of Microgrid System

In this paper, a microgrid containing CCS, P2G systems, and demand-response resources is constructed, as shown in Figure 1.

The source side of the microgrid includes coal-fired units, natural-gas-fired combined heat and power units, and wind farms. Among them, carbon capture equipment is installed in coal-fired and gas-fired units to capture and separate the CO₂ emitted by the units, and carbon storage equipment is connected to the CCS and P2G systems to generate methane from the stored CO₂ for recycling with the units.

On the load side, the economic operation of the source side is assisted by the comprehensive coordination of various types of demand response resources, including electric vehicles and flexible electric and thermal loads, to improve the level of renewable energy consumption and reduce the carbon emissions of the system.

2.2. Model of Carbon Capture System Based on Comprehensively Flexible Operation Mode

The comprehensive, flexible operation of the carbon capture system [9,10] is to introduce both a flue gas bypass system and a solvent storage tank in the carbon capture system, which can transfer the energy consumption of carbon capture from the peak load period to the low load period to reduce the carbon emission of the unit while mitigating the conflict between the demand of the load and the carbon capture system. At the same time, it can actively release CO₂ into the air according to the demand of the microgrid, which can improve the economy and dispatchability of the unit.

The total unit output of the CCS includes two parts: the net unit output and the capture energy consumption [12]. Since the separating energy and compression energy in the capture energy consumption are much higher than the absorption energy consumption, only the analyzing energy and compression energy of carbon capture are considered in this paper.

The CO₂ emission of the generating unit $i$ during time $t$ is as follows:

$$E_{i,t}^G=e_i^{C{O}_2}{P}_{i,t}^G$$

(1)

where $P_{i,t}^G$ is the total output power of the unit $i$ at time $t$; $e_i^{C{O}_2}$ is the carbon emission intensity of unit $i$.

The total amount of CO₂ absorbed with the carbon capture system of unit $i$ at time $t$ is as follows:

$$\begin{array}{l}{E}_{i,t}^{SG}=E_{i,t-1}^{SG}+\beta {\delta }_i{E}_{i,t}^G-E_{i,t}^{C{O}_{2}out}\\ E_{i,t}^{C{O}_{2}out}\le \eta \beta e_i^{C{O}_2}{P}_{i,\max }^G\end{array}$$

(2)

where $E_{i,t}^{SG}$ is the amount of CO₂ to be separated fixed in the solvent storage tank of unit $i$; $\beta$ is the carbon sequestration efficiency of carbon capture system; ${\delta }_i$ is the flue gas split ratio of unit $i$; $E_{i,t}^{C{O}_{2}out}$ is the amount of CO₂ separated by unit $i$ at time $t$; $\eta$ is the maximum working state coefficient of regeneration tower and compressor; and $P_{i,\max }^G$ is the maximum unit output when the unit is starting.

The carbon capture energy $P_{i,t}^{C{O}_2}$ of unit $i$ at time $t$ is as follows:

$$P_{i,t}^{C{O}_2}=\lambda E_{i,t}^{C{O}_{2}out}$$

(3)

where $\lambda$ is the energy consumption of separating unit CO₂.

The total output power of unit $i$ at time $t$ is shown as follows:

$$P_{i,t}^G=P_{i,t}^D+P_{i,t}^T+P_{i,t}^{C{O}_2}$$

(4)

where $P_{i,t}^D$ is the electric power output of unit $i$ for the load supply at time $t$; $P_{i,t}^T$ is the fixed energy consumption for the unit carbon capture system.

From Equations (1)–(4), the energy consumption range of a carbon capture system based on a comprehensively flexible operation capture mode is as follows:

$$P_{i,t}^T\le P_{i,t}^{C{O}_2}+P_{i,t}^T\le \lambda \eta \beta {\delta }_{i,\max }{e}_i^{C{O}_2}{P}_{i,\max }^G+P_{i,t}^T.$$

(5)

The derivation showing the net output range is as follows:

$$P_{i,\min }^G-\lambda \eta \beta {\delta }_{i,\max }{e}_i^{C{O}_2}{P}_{i,\max }^G-P_{i,t}^T\le P_{i,t}^D\le P_{i,\max }^G-P_{i,t}^T.$$

(6)

From the above analysis, it is demonstrated that the carbon capture power plant operating in a comprehensively flexible operation mode has a larger net output range and stronger adjustment ability than the traditional mode.

The solvent storage tank is an important part of the comprehensively flexible operation mode of a carbon capture power plant. The CO₂ in the solvent storage tank is stored in the alcohol amine solution in the form of a compound, and the mass of CO₂ extracted from the solvent storage tank can be replaced with the liquid volume [11]:

$$V_{i,t}^{C{O}_2}=\frac{E_{i,t}^{C{O}_2}{M}_{\mathrm{MEA}}}{M_{{\mathit{CO}}_2}\theta C_{\mathrm{R}}{\rho }_{\mathrm{R}}}$$

(7)

where $V_{i,t}^{C{O}_2}$ is the liquid volume of the solvent storage tank releasing CO₂ at time $t$; $M_{\mathrm{MEA}}$ and $M_{{\mathit{CO}}_2}$ are the molar masses of alcohol amine and CO₂, respectively; $\theta$ is the separated quantity of regeneration tower; $C_{\mathrm{R}}$ is the concentration of amine solution; and ${\rho }_{\mathrm{R}}$ is the density of the amine solution.

The model of the solvent storage tank is as follows:

$$\left\{\begin{array}{l}{V}_{i,t}^{\mathrm{FY}}=V_{i,t-1}^{\mathrm{FY}}-V_{i,t}^{C{O}_2}\hfill \\ V_{i,t}^{\mathrm{PY}}=V_{i,t-1}^{\mathrm{PY}}+V_{i,t}^{C{O}_2}\hfill \\ V_{i,0}^{\mathrm{FY}}=V_{i,24}^{\mathrm{FY}}\hfill \\ V_{i,0}^{\mathrm{PY}}=V_{i,24}^{\mathrm{PY}}\hfill \end{array}\right.$$

(8)

where $V_{i,t}^{\mathrm{FY}}$ and $V_{i,t}^{\mathrm{PY}}$ are the volumes of the rich liquid storage and the lean liquid storage at time $t$, respectively; $V_{i,0}^{\mathrm{FY}}$ and $V_{i,0}^{\mathrm{PY}}$ are the initial volumes of the rich liquid and the poor liquid storage, respectively; $V_{i,24}^{\mathrm{FY}}$ and $V_{i,24}^{\mathrm{PY}}$ are the volumes of the rich liquid and the poor liquid storage at the end of the scheduling period, respectively.

In summary, the energy time-shift characteristics of the comprehensively flexible operation mode make the carbon capture power plant have a wider adjustment range, which is conducive to the consumption of surplus wind power, the realization of peak shaving and valley filling, and the reduction in carbon emission in the microgrid.

2.3. Model of Carbon Storage Equipment and P2G System

The carbon capture power plants based on a comprehensively flexible operation mode generate CO₂ during operation, and the efficient operation of the P2G system during the peak of renewable energy output can better improve the economic benefits of the microgrid. In order to solve the problem that the two pieces of equipment do not cooperate efficiently in operation, this paper adds carbon storage equipment between P2G and CCS to ensure sufficient carbon sources to synthesize methane for gas turbine recycling when P2G is started.

The model of carbon storage equipment is as follows:

$$M_{s,t}^{{\mathit{CO}}_2}=M_{s,t-1}^{{\mathit{CO}}_2}+\left(1-{\lambda }_s\right)M_{s,t}^{{\mathit{CO}}_2}-M_{s,t}^{{\mathit{CO}}_{2}out}$$

(9)

where $M_{s,t-1}^{{\mathit{CO}}_2}$ and $M_{s,t}^{{\mathit{CO}}_2}$ are the storage capacities of the carbon storage device $s$ at time $t-1$ and $t$; $M_{s,t}^{{\mathit{CO}}_{2}out}$ is the CO₂ output of the carbon storage device $s$ at time $t$; and ${\lambda }_s$ is the carbon storage loss coefficient.

The power of P2G consuming electricity to produce natural gas is as follows [17]:

$$V_{i,t}^{\mathrm{P}2\mathrm{G}}=\alpha P_{i,t}^{\mathrm{P}2\mathrm{G}}$$

(10)

where $P_{i,t}^{\mathrm{P}2\mathrm{G}}$ is the operating power of the P2G system; $\alpha$ is the electrical conversion efficiency of P2G gas production.

Correspondingly, the amount of CO₂ $W_{i,t}^{{\mathit{CO}}_2}$ required for P2G is as follows:

$$W_{i,t}^{{\mathit{CO}}_2}=\gamma P_{i,t}^{\mathrm{P}2\mathrm{G}}$$

(11)

where $\gamma$ is the coefficient for calculating the amount of CO₂ required for P2G conversion.

By compressing and storing the captured carbon dioxide, not only can the system’s operating cost be reduced through carbon trading market revenue but the gas turbine can also use it through the P2G cycle to improve the economy of the microgrid.

3. Model of Demand-Side Flexible Response in Microgrid

In this paper, the source side adopts a comprehensively flexible operation mode of CCS-P2G connected to a carbon storage device. However, only the low-carbon scheduling on the source side has certain limitations. By shifting and reducing the electric and thermal loads on the load side and by using the electric vehicles as flexible demand response resources to participate in the low-carbon economic dispatch of the microgrid, it can further exploit the sustainable development potential of the microgrid.

3.1. Demand Response Model of Electric Vehicles

The battery characteristics of EVs and the travel characteristics of users jointly determine the charging demand. Due to the spatio-temporal randomness of EV charging and discharging, it is difficult to model the travel characteristics of individual EV users. However, the fitting results of EV cluster travel data conform to the probability distribution, which can be mathematically modeled [25,26,27]. Most of the existing studies analyzing travel characteristics are based on the vehicle travel statistics from the 2009 National Highway Traffic Safety Administration NHTS2009 (National Household Travel Survey 2009), and the statistical results are fitted for analyzing the behavioral characteristics of EVs.

In this paper, the probability distribution model obtained from the travel characteristics is used to describe the commuting behavior of electric vehicles in urban areas, taking into full consideration for the driving patterns of users and the stochastic nature of their charging behavior. The commuting behavior characteristics of vehicles and their initial state of charge (SOC) values can be obtained with Monte Carlo sampling.

The arrival time of private cars obeys the normal distribution t~N (8.5, 1.0²) between [0, 24], and its probability density function can be expressed as follows [26]:

$$\left\{\begin{array}{l}f\left(t_{\mathrm{arr}}\right)=\frac{1}{{\sigma }_{\mathrm{arr}}\sqrt{2\pi }}\exp \left(-\frac{{\left(t_{\mathrm{arr}}-{\mu }_{\mathrm{arr}}\right)}^2}{2{\sigma }_{\mathrm{arr}}^2}\right)\hfill \\ 0\le t_{\mathrm{arr}}\le 24\hfill \end{array}\right.$$

(12)

where ${\sigma }_{\mathrm{arr}}=8.5$, ${\mu }_{\mathrm{arr}}=1.0$, and $t_{\mathrm{arr}}$ represent the arrival times of private cars.

The departure time between [0, 24] obeys the normal distribution t~N (17.5, 1.0²), and its probability density function can be expressed as follows:

$$\left\{\begin{array}{l}f\left(t_{\mathrm{dept}}\right)=\frac{1}{{\sigma }_{\mathrm{dept}}\sqrt{2\pi }}\exp \left(-\frac{{\left(t_{\mathrm{dept}}-{\mu }_{\mathrm{dept}}\right)}^2}{2{\sigma }_{\mathrm{dept}}^2}\right)\hfill \\ 0\le t_{\mathrm{dept}}\le 24\hfill \end{array}\right.$$

(13)

where ${\sigma }_{\mathrm{dept}}=17.5$, ${\mu }_{\mathrm{dept}}=1.0$, and $t_{\mathrm{dept}}$ represent the departure times of private cars.

The initial SOC value of the electric vehicle obeys the normal distribution SOC~N (0.5, 0.4²) between [0.7, 0.95] [28].

$$\left\{\begin{array}{l}f\left(S_{v,t_0}^{\mathrm{EV}}\right)=\frac{1}{{\sigma }_{\mathrm{s}}\sqrt{2\pi }}\exp \left(-\frac{{\left(S_{v,t_0}^{\mathrm{EV}}-{\mu }_{\mathrm{s}}\right)}^2}{2{\sigma }_{\mathrm{s}}^2}\right)\hfill \\ 0.8\le S_{v,t_0}^{\mathrm{EV}}\le 0.95\hfill \end{array}\right.$$

(14)

where ${\sigma }_{\mathrm{s}}=0.5$, ${\mu }_{\mathrm{s}}=0.4$, and $S_{v,t_0}^{\mathrm{EV}}$ represent the SOC values of the v-th electric vehicle.

The commuting arrival time, the departure time, and the initial SOC value are independent of each other, so the charging demand of v-th electric vehicles in the urban office area on weekdays can be obtained via Monte Carlo simulation:

$$\left\{\begin{array}{l}{S}_{v,t}^{\mathrm{EV}}=S_{v,t-1}^{\mathrm{EV}}+{\eta }_v^{\mathrm{vc}}{g}_{v,t}^{\mathrm{vc}}-\frac{g_{v,t}^{\mathrm{vd}}}{{\eta }_v^{\mathrm{vd}}}-E_v{d}_{v,t}^{\mathrm{tr}}\hfill \\ S_{v,0}^{\mathrm{EV}}=S_{v,24}^{\mathrm{EV}}\hfill \end{array}\right.$$

(15)

where $S_{v,t}^{\mathrm{EV}}$ is the power storage capacity of the v-th EV at time $t$; ${\eta }_v^{\mathrm{vc}}$ and ${\eta }_v^{\mathrm{vd}}$ are the charging and discharging efficiencies of the v-th EV; $g_{v,t}^{\mathrm{vc}}$ and $g_{v,t}^{\mathrm{vd}}$ are the charging and discharging powers of the v-th EV at time $t$; $E_v$ is the power consumption per unit mile of EV; $d_{v,t}^{\mathrm{tr}}$ is the driving distance of the v-th EV at time $t$; $S_{v,0}^{\mathrm{EV}}$ is the initial SOC value of the electric vehicle; and $S_{v,24}^{\mathrm{EV}}$ is the SOC value at the end of the electric vehicle scheduling cycle.

The charging and discharging power balance constraints of electric vehicles are shown in (16)–(18) as follows:

$$0\le g_{v,t}^{\mathrm{vc}}\le g_v^{\mathrm{vc},\max }{\mu }_{v,t}^{\mathrm{vc}}$$

(16)

$$0\le g_{v,t}^{\mathrm{vc}}\le g_v^{\mathrm{vc},\max }{\mu }_{v,t}^{\mathrm{vc}}$$

(17)

$${\mu }_{v,t}^{\mathrm{vc}}+{\mu }_{v,t}^{\mathrm{vd}}={\mu }_{v,t}^{\mathrm{I}}$$

(18)

where $g_v^{\mathrm{vc},\max }$ and $g_v^{\mathrm{vd},\max }$ are the upper limits of charging and discharging power for the v-th EV; ${\mu }_{v,t}^{\mathrm{I}}$ represents whether the v-th EV is connected to the power grid during the period. If yes, it is set to 1, otherwise it is set to 0.

By flexibly adjusting the charging and discharging time and power of electric vehicles, it can assist the source side of the microgrid to carry out low-carbon economic dispatch and further explore the carbon emission reduction potential of the system based on improving the income of both sides.

3.2. Electricity and Heat Load Demand Response Model

The electric and heat loads that can be shifted and reduced on the load side are also a flexible demand response resource for low-carbon economic dispatch of microgrid.

The electrical load $P_t^{\mathrm{load}}$ in the microgrid at time $t$ is composed of three parts: fixed electrical load $P_t^{\mathrm{f}}$, shiftable electrical load $P_t^{\mathrm{tran}}$, and reduced electrical load $P_t^{\mathrm{cut}}$, which can be expressed as follows [29]:

$$P_t^{\mathrm{load}}=P_t^{\mathrm{f}}+P_t^{\mathrm{tran}}+P_t^{\mathrm{cut}}.$$

(19)

The shiftable electrical load needs to satisfy the following constraints:

$${\displaystyle \sum _{t=1}^T}{P}_t^{\mathrm{tran}}\mathsf{\Delta }t=0.$$

(20)

The electric load that can be reduced needs to satisfy the following constraints:

$$0\le P_t^{\mathrm{cut}}\le P_{t,\max }^{\mathrm{cut}}$$

(21)

where $P_{t,\max }^{\mathrm{cut}}$ indicates the upper limit value of the participating demand response in each period of load reduction that can be reduced to ensure the users’ energy quality demand.

The actual heat load $H_t^{\mathrm{load}}$ in the microgrid is also divided into three parts: fixed heat load $H_t^{\mathrm{f}}$, translatable heat load, and reducible heat load $H_t^{\mathrm{cut}}$:

$$H_t^{\mathrm{load}}=H_t^{\mathrm{f}}+H_t^{\mathrm{trans}}+H_{\mathrm{t}}^{\mathrm{cut}}.$$

(22)

The shifted heat load needs to satisfy the following constraint:

$${\displaystyle \sum _{t=1}^T}{H}_t^{\mathrm{tran}}\mathsf{\Delta }t=0.$$

(23)

The heat load can be reduced needs to satisfy the following constraint:

$$0\le H_t^{\mathrm{cut}}\le H_{i,\max }^{\mathrm{cut}}.$$

(24)

4. A Low-Carbon Economic Dispatch Model of Microgrids with Source-Load Coordination

4.1. Objective Function

Based on the source and load models of the microgrid established in the second and third sections, the microgrid, considering source-load coordinated operation, takes the optimal system cost as the objective function, and a low-carbon economic dispatch model is constructed:

$$f_{MG}=\min (C_{\mathrm{H}}+C_G+C_q+C_T+C_{\mathrm{R}}+C_{\mathrm{DR}}+C_{EV}+C_{\mathrm{z}}).$$

(25)

In the formula, $f_{MG}$ is the overall operation scheduling cost of the microgrid, and each cost is specifically expressed as follows:

(1)

Operation cost of a thermal power unit:

The operating cost $C_{\mathrm{H}}$ of a thermal power unit includes coal consumption cost and start-stop cost is as follows:

$$C_{\mathrm{H}}={\displaystyle \sum _{t=1}^T}{\displaystyle \sum _{i=1}^N}{U}_{i,t}\left(a_p{P}_{i,t}^{G^2}+b_p{P}_{i,t}^G+c_p\right)$$

(26)

where $N$ is the number of thermal power units; $U_{i,t}$ is the start-up state of the unit at time $t$; and $a_p$, $b_p$, $c_p$ represent the coal consumption cost coefficient of the unit.

(2)

Operation cost of natural gas source cogeneration unit combined heat and power:

The operation cost of CHP units is as follows:

$$C_G={\displaystyle \sum _{t=1}^T}{\displaystyle \sum _{i=1}^N}{\lambda }_t^{C{H}_4}(V_{i,t}^{buy}-V_{i,t}^{P2G})$$

(27)

where $V_{i,t}^{buy}$ is the purchase amount of natural gas; ${\lambda }_t^{C{H}_4}$ is the purchase price for natural gas.

(3)

Costs of carbon transaction:

At present, the domestic quota method mainly adopts free distribution. The initial quota model is as follows:

$$\left\{\begin{array}{l}{E}_s=E_{\mathrm{G}}+E_{\mathrm{GT}}\hfill \\ E_{\mathrm{G}}={\chi }_{\mathrm{e}}{\displaystyle \sum _{t=1}^T}{P}_{\mathrm{G}}(t)\hfill \\ E_{\mathrm{GT}}={\chi }_{\mathrm{h}}{\displaystyle \sum _{t=1}^T}\left({\chi }_{\mathrm{e},\mathrm{h}}{P}_{\mathrm{GT}}(t)+H_{\mathrm{GT}}(t)\right)\hfill \end{array}\right.$$

(28)

where $E_s$, $E_{\mathrm{G}}$, and $E_{\mathrm{GT}}$ are the carbon emission quotas of microgrids, coal-fired units and gas units, respectively; ${\chi }_{\mathrm{e}}$ and ${\chi }_{\mathrm{h}}$ are the carbon emission quotas for generating unit electric power and unit thermal power, respectively; ${\chi }_{\mathrm{e},\mathrm{h}}$ is the electric and thermal power conversion coefficient; $P_{\mathrm{G}}(t)$ represents the electric power output of the coal-fired unit at time $t$; $P_{\mathrm{GT}}(t)$ and $H_{\mathrm{GT}}(t)$ are the electric and thermal power outputs by the gas unit at time $t$; and $T$ is the scheduling cycle.

The carbon trading volume that the microgrid can participate in the carbon trading market is as follows:

$$E_{s,\mathrm{t}}=\left|E_{s,\mathrm{a}}-E_s\right|$$

(29)

where $E_{s,\mathrm{a}}$ is the actual carbon emissions of the microgrid. When the actual carbon emissions exceed the initial quota of carbon emissions, it is necessary to purchase carbon quotas from the carbon trading market; when the actual carbon emissions are less than the initial carbon quota, the carbon quota can be sold to the superior carbon trading market.

It can be obtained that the carbon transaction cost $C_T$ of microgrid participation is as follows:

$$C_T={\lambda }_{{co}_2}{E}_{s,t}$$

(30)

where $C_T$ is the carbon transaction costs; ${\lambda }_{{co}_2}$ is the carbon trading price.

(4)

Wind curtailment penalty cost of the microgrid:

The wind curtailment penalty of the microgrid is as follows:

$$C_q={\displaystyle \sum _{t=1}^T}{K}_{\mathrm{q}}\left(P_{\mathrm{w},t}^{pre}-P_{\mathrm{w},t}\right)$$

(31)

where $K_{\mathrm{q}}$ is the penalty cost for unit abandoned air volume; $P_{\mathrm{w},t}^{pre}$ is the predicted wind power; and $P_{\mathrm{w},t}$ is the on-grid wind power at time $t$.

(5)

Cost of solvent loss:

The cost of solvent loss $C_{\mathrm{R}}$ in the carbon capture process is as follows:

$$C_{\mathrm{R}}={\displaystyle \sum _{t=1}^{24}}{\displaystyle \sum _{i=1}^N}{K}_{\mathrm{R}}\phi E_{i,t}^{C{O}_{2}out}$$

(32)

where $K_{\mathrm{R}}$ is the cost coefficient of ethanolamine solvent; $\phi$ is the solvent loss coefficient.

(6)

Demand response cost of electricity and heat load:

The cost of demand response $C^{DR}$ is as follows:

$$C^{DR}={\displaystyle \sum _{t=1}^T}\left({\lambda }_e^{\mathrm{cut}}{P}_{i,t}^{\mathrm{cut}}+{\lambda }_e^{\mathrm{tran}}{P}_{i,t}^{\mathrm{tran}}+{\lambda }_h^{\mathrm{cut}}{H}_{i,t}^{\mathrm{cut}}+{\lambda }_h^{\mathrm{tran}}{H}_{i,t}^{\mathrm{tran}}\right)$$

(33)

where ${\lambda }_e^{\mathrm{cut}}$, ${\lambda }_e^{\mathrm{tran}}$ and ${\lambda }_h^{\mathrm{cut}}$ are the compensation unit prices of shiftable loads and reducible loads, respectively.

(7)

The cost of electric vehicles:

The cost is the cost of EV battery loss. When the discharge reaches a certain number of times, the EV battery needs to be replaced, which can be expressed as follows:

$$C^{EV}={\displaystyle \sum _{t=1}^T}{\displaystyle \sum _{v=1}^{n_v}}\frac{C_v^{\mathrm{b}}}{L_v^{\mathrm{c}}{S}_v^{\mathrm{EV}}{d}_v^{\mathrm{DOD}}}\left(\frac{g_{v,t}^{\mathrm{vd}}}{{\eta }_v^{\mathrm{vd}}}+E_v{d}_{v,t}^{\mathrm{tr}}\right)$$

(34)

where $n_{EV}$ is the number of EVs; $C_v^{\mathrm{b}}$ is the cost of purchasing the battery for the v-th EV; $L_v^{\mathrm{c}}$ is the number of charge and discharge cycles in the life cycle of the v-th EV battery; $S_v^{\mathrm{EV}}$ is the battery capacity of the v-th EV; $d_v^{\mathrm{DOD}}$ is the available battery discharge depth of the v-th EV; $g_{v,t}^{\mathrm{vd}}$ is the discharge power of the v-th EV at time $t$, which is the decision variable; ${\eta }_v^{\mathrm{vd}}$ is the discharge efficiency of the v-th EV; $d_{v,t}^{\mathrm{tr}}$ is the driving distance of the EV in the period; and $E_v$ it is the energy demand of the v-th EV, which represents the power consumed per unit driving distance of the EV.

(8)

Cost of carbon storage equipment:

The cost of carbon storage equipment $C_{\mathrm{z}}$ includes the cost of carbon storage equipment and depreciation cost, which can be expressed as follows:

$$C_{\mathrm{z}}=\frac{C_{\mathrm{FL}}{(1+r)}^{N_{\mathrm{ZJ}}}r}{365\left({(1+r)}^{N_{\mathrm{ZJ}}}-1\right)}$$

(35)

where $C_{\mathrm{FL}}$ and $N_{ZJ}$ are the cost of carbon storage equipment and depreciation year, respectively; $r$ is the discount rate for carbon storage equipment.

4.2. Constraint Condition

(1)

Electric and thermal power balance constraints are as follows:

$$P_{\mathrm{w},t}+{\displaystyle {\displaystyle \sum _{i=1}^N}{P}_{i,t}^G}+{\displaystyle {\displaystyle \sum _{i=1}^N}{P}_{i,t}^{G\mathrm{T}}}+{\displaystyle {\displaystyle \sum _{v=1}^V}{g}_{v,t}^{vd}}+{\displaystyle {\displaystyle \sum _{m=1}^M}{P}_{m,t}^{essd}}=P_t^{load}+{\displaystyle {\displaystyle \sum _{v=1}^N}{g}_{v,t}^{\mathrm{vc}}}+{\displaystyle {\displaystyle \sum _{m=1}^M}{P}_{m,t}^{essd}}+{\displaystyle {\displaystyle \sum _{n=1}^N}{P}_{n,t}^{EB}}$$

(36)

$${\displaystyle {\displaystyle \sum _{i=1}^N}{H}_{i,t}^{EB}}+{\displaystyle {\displaystyle \sum _{i=1}^N}{H}_{i,t}^{GT}}+{\displaystyle {\displaystyle \sum _m^M}{H}_{m,t}^{essd}}=H_t^{load}+{\displaystyle {\displaystyle \sum _m^M}{H}_{m,t}^{essc}}$$

(37)

where $P_{m,t}^{ess}$ and $H_{m,t}^{ess}$ are the energy storage and heat storage power for the microgrid; $P_{n,t}^{EB}$ is the power consumption for the electric boiler; and $H_{i,t}^{EB}$ is the power of the electric boiler to transfer electricity to heat.

(2)

Gas balance constraint is as follows:

$${\displaystyle {\displaystyle \sum _{i=1}^N}{V}_{i,t}^{buy}}+{\displaystyle {\displaystyle \sum _{i=1}^N}{V}_{i,t}^{\mathrm{P}2\mathrm{G}}}={\displaystyle {\displaystyle \sum _{i=1}^N}{V}_{i,t}^{GT}}.$$

(38)

(3)

Wind power output constraint is as follows:

$$0\le P_{\mathrm{w},t}\le P_{\mathrm{w},t}^{pre}.$$

(39)

(4)

Thermal power unit constraints:

The output constraint of the thermal power unit is as follows:

$$\left\{\begin{array}{l}{P}_{i,\min }^G\le P_{i,t}^G\le P_{i,\max }^G,U_{i,t}=1\hfill \\ P_{i,\max }^G=0,U_{i,t}=0\hfill \end{array}\right..$$

(40)

The climbing constraint of the thermal power unit is as follows:

$$\left\{\begin{array}{l}{P}_{i,t}^G-P_{i,t-1}^G\le U_{i,t}{R}_i^{\mathrm{u}}\hfill \\ P_{i,t-1}^G-P_{i,t}^G\le U_{i,t-1}{R}_i^{\mathrm{d}}\hfill \end{array}\right.$$

(41)

where $R_i^{\mathrm{u}}$ and $R_i^{\mathrm{d}}$ are the uphill and downhill climbing rates of the unit.

(5)

The electric boiler constraint is as follows:

The heat production power of the electric boiler is as follows:

$$H_t^{EB}={\eta }_{EB}{P}_t^{EB}$$

(42)

where $P_t^{EB}$ is the power consumption of the electric boiler; ${\eta }_{GB}$ is the thermal efficiency of the electric boiler.

(6)

Carbon storage equipment constraint:

Carbon storage equipment needs to satisfy the constraints of carbon storage capacity, which can be expressed as follows:

$$M_{s,\min }^{{\mathit{CO}}_2}\le M_{s,t}^{{\mathit{CO}}_2}\le M_{s,\max }^{{\mathit{CO}}_2}$$

(43)

where $M_{s,\min }^{{\mathit{CO}}_2}$ and $M_{s,\max }^{{\mathit{CO}}_2}$ are the upper and lower limits of carbon storage, respectively.

5. Case Analysis

5.1. Parameter Settings

In this paper, the scheduling cycle is 24 h. The prediction of wind power output and the electric and heat load demand of the microgrid is shown in Appendix A—Figure A1; thermal power and CHP unit output parameters are shown in Appendix A—

Table A2. Parameters of thermal power units.

Unit Number	Maximum Output/MW	Minimum Output/MW	Energy Consumption Coefficient			Carbon Emission Intensity (t/(MW·h))
			a (CNY/MW2)	b (CNY/MW)	c (CNY)
1	40	20	0.0048	162	10,000	0.91
2	45	12	0.0031	173	9700	0.95
3	20	10	0.002	166	7000	0.98

and

Table A3. Parameters of CHP units.

Unit Number	Maximum Output/MW	Minimum Output/MW	Carbon Emission Intensity (t/(MW·h))
1	15	12	0.91
2	7	5	0.95

; time-of-use electricity price reference is shown in Appendix A—Figure A2; the remaining parameters are detailed in Appendix A—

Table A1. Parameter table.

Parameter	Value
$\beta$ (Carbon capture efficiency)	0.9
$\eta$ (Maximum operating condition factor)	1.05
$\lambda$ (Energy consumption per unit of CO2 separated)	0.269
$S_v^{\mathrm{EV}}$ (Electric Vehicle SOC)/(kW·h)	60
$E_v$ (Power consumption per unit mile traveled by EV)/(kW·h)	0.228
${\lambda }_{c{o}_2}$ (Carbon trading price)/(CNY/t)	100
${\lambda }_t^{C{H}_4}$ (Natural gas purchase price)/(CNY/m3)	3
$M_{\mathrm{MEA}}$ (MEA molar mass)/(g/mol)	61.08
$M_{{\mathit{CO}}_2}$ (CO2 molar mass)/(g/mol)	44
$\theta$ (Regeneration tower resolution)/(mol/mol)	0.3
$C_{\mathrm{R}}$ (Amine solution concentration)/%	30
${\rho }_{\mathrm{R}}$ (Alcohol amine solution density)/(g/mL)	1.01
$\alpha$ (P2G electric-to-gas efficiency)	0.55
$\gamma$ (CO2 volume factor required for P2G conversion)	1.02
$C_{\mathrm{FL}}$ (Cost of carbon storage equipment)/CNY	1.65 × 105
$N_{ZJ}$ (Depreciable lives of carbon storage equipment)/Year	15
$r$ (Discount rate for carbon storage equipment)/%	8

. The nonlinear terms included in the model are converted to linear form with the big m method. After converting the optimization model into a mixed-integer linear programming problem, CPLEX is used to solve it in order to achieve efficient computation.

5.2. Benefit Analysis of Comprehensively Flexible Operation Mode of CCS and P2G System

In order to compare and verify the effectiveness of the comprehensively flexible operation strategy of carbon capture and P2G systems proposed in this paper, the low-carbon economic dispatch results of microgrids under four scenarios without demand-side response are compared and analyzed:

(1)

Based on the fixed operation mode of the CCS and P2G systems [6];

(2)

Based on the flexible operation of CCS [12] and the fixed operation mode of the P2G system;

(3)

Based on the fixed operation of CCS and the flexible operation mode of the P2G system;

(4)

Based on the comprehensively flexible operation mode of the CCS-P2G system.

The low-carbon economic dispatch results of the microgrid under Scenarios 1–4 are shown in

Table 1. Comparison of different operation methods of CCS and P2G systems.

Scheduling Results	Scenario 1	Scenario 2	Scenario 3	Scenario 4
Thermal power coal consumption cost/CNY	970,880.0	1,063,194.2	978,757.1	1,052,915.2
Solvent loss cost/CNY	1254.0	5252.0	1854.7	17,512.2
Carbon transaction cost/CNY	108,394.0	22,261.5	37,607.1	−78,817.5
Wind curtailment cost/CNY	1,250,824.8	0	0	0
Gas cost/CNY	132,036.0	38,770.3	107,843.8	93,053.0
Microgrid cost/CNY	2,463,388.8	1,129,869.0	1,126,498.1	1,085,152.3
Carbon emission/t	2867.2	2268.4	2077.7	1217.4
Carbon emission reduction/t	102.0	427.0	150.8	1423.8
Wind curtailment volume/(MW·h)	217.8	0	0	0

, and the detailed results are shown in Appendix B from Figure A3, Figure A4, Figure A5 and Figure A6.

It can be seen from Table 1 that when the CCS and P2G systems operate in the fixed mode described in [6], the source-side regulation capability of the microgrid is the worst, resulting in serious wind curtailment of the microgrid. At the same time, its total operating cost is the highest, and the carbon emission reduction effect is the lowest.

In Scenario 2, the fixed P2G system realizes the full conversion of the captured CO₂, so it produces the most methane. However, due to the large power consumption of the P2G system, the energy consumption of the carbon capture system is reduced accordingly, and the amount of CO₂ captured is reduced. Hence, although the flexible operation mode of the carbon capture system constructed in [12] can achieve better low-carbon effects, the problem that it is not synchronized with the operation of the P2G system still leads to a low carbon emission reduction level in the microgrid.

The fixed operation mode of the CCS in scenario 3 has a great impact on the flexibility of its coordination with P2G, so the P2G conversion efficiency is still low, which increases the cost of the microgrid to purchase natural gas outward. At the same time, a large amount of CO₂ is not stored and utilized during the peak load period, thus the low-carbon effect of the microgrid is still not ideal.

Scenario 4 considers the comprehensively flexible scheduling of the CCS and P2G systems. Compared with Scenario 1, Scenario 2, and Scenario 3, its carbon emission reduction increases by 1321.8 t, 996.8 t, and 1273 t, respectively, and the degree of carbon emission reduction reaches 53.91%. The total cost of the microgrid is reduced by 55.94%, 3.96%, and 3.67% compared with Scenarios 1, 2, and 3, respectively. The carbon emissions are reduced by 57.54%, 46.33%, and 41.40% compared to Scenarios 1, 2, and 3, respectively. In Scenario 4, the CO₂ separated from the carbon capture system is transferred using the carbon storage equipment, which can be supplied to the P2G system to generate natural gas when the wind power consumption of the microgrid is low. Its flexible operation mode will not affect the operation state of the carbon capture system, so its carbon emission reduction effect and carbon trading income are significantly higher than those in Scenario 2. Compared with Scenario 3, since the carbon source can be flexibly precipitated using the carbon capture system and the CO₂ supply is sufficient, the coordinated P2G not only improves the wind curtailment consumption but also reduces the natural gas purchase cost of the microgrid.

It can be seen from Table 1, Figure 2, Figure 3 and Figure 4 and Figure A8 in Appendix B that there is a large amount of wind curtailment in Scenario 1. This is due to the working mode of the cogeneration unit, which determines the power through heat. Under the condition of satisfying a certain heat load, the forced electric output of the gas turbine is too high, which conflicts with the anti-peak regulation characteristics of wind power, resulting in a large amount of wind curtailment. At the same time, because the thermal power and gas units reach the upper limit of output during the peak period of power and heat load, they can provide less power for CCS and P2G systems, so most of the CO₂ is not stored and utilized; in the low load period, because the system has no carbon storage device, the energy time-shift cannot be realized. The CCS and P2G systems can only realize the absorption and conversion of CO₂ emitted by the current unit, and the utilization efficiency of wind power is low, resulting in high overall carbon emissions from the microgrid.

As shown in Figure 2, the period of 11:00–13:00 and 18:00–19:00 in Scenario 2 is the peak period of load, so the energy microgrid supplying the P2G system is low. Since the P2G system is not equipped with a carbon storage device, the amount of CO₂ released using the carbon capture system to supply the conversion of the P2G system is also reduced accordingly, so the carbon emission of Scenario 2 is still high.

Since the carbon capture system needs to fully separate the CO₂ generated by the operation of each unit at each time in Scenario 3, the unit’s ability to adjust the load change is reduced. At the same time, because the CCS cannot be coordinated and flexibly scheduled with P2G, the microgrid supplies less power for the operation of the P2G system. Therefore, compared with Scenarios 2 and 4, the microgrid purchases natural gas at a higher cost.

In Scenario 4, during the period of high wind power output, the CO₂ emitted by the unit is fully analyzed and effectively utilized through the system’s carbon storage devices, giving full play to the energy time-shift advantages of CCS and P2G systems and improving the source-side low-carbon synergy, as shown in Figure 2.

It can be seen from Figure 4 that the trends of carbon emissions in Scenarios 1, 2, 3, and 4 are the same. Scenario 4 consumes CO₂ in the storage device and converts CO₂ in the carbon storage device during the periods of 00:00–4:00 and 20:00–24:00 when the wind power output is high. In the period of 13:00–16:00, when the load demand is low, the CCS and P2G systems are scheduled to participate in carbon emission reduction. In the period of 21:00–23:00, not only all the CO₂ emitted in this period is completely resolved, but also the stored CO₂ is resolved through the storage device, which realizes the effective time-shift of energy, the minimum carbon emission of microgrids and the lowest operating cost.

In summary, compared with the microgrid that flexibly dispatches the carbon capture system or the P2G system alone, the comprehensively flexible operation mode based on the CCS-P2G system is more effective in improving the wind power consumption of the microgrid, reducing the carbon emissions of the system, and reducing the operating cost of the microgrid. The performance of the CCS and P2G had significantly improved, and the sustainable development potential of the microgrid is fully explored.

5.3. Benefit Analysis of the Low-Carbon Economic Operation of Microgrid Considering Source-Load Coordinated Operation

Based on the comprehensively flexible operation mode of the CCS-P2G system, considering the controllable resources on the demand side, the low-carbon economic dispatch of microgrid with source-load coordination under three scenarios are compared and analyzed:

Scenario 4: no demand response on the load side (without electric vehicles participating in the orderly scheduling of V2G);

Scenario 5: demand response on the load side [29] (with electric vehicles participating in the orderly scheduling of V2G);

Scenario 6: demand response on the load side (with electric vehicles participating in the orderly scheduling of V2G).

The operation results are shown in

Table 2. Comparison of low-carbon economic operation benefits of Scenarios 4–6.

Scheduling Results	Scenario 4	Scenario 5	Scenario 6
Demand response cost/CNY	0	146.5	146.5
Thermal power coal consumption cost/CNY	1,052,915.2	1,052,915.2	1,053,954.1
Solvent loss cost/CNY	17,512.2	17,827.4	18,234.5
Carbon transaction cost/CNY	−78,817.5	−81,409.9	−84,569.5
Gas cost/CNY	93,053.0	93,748.1	94,425.0
Microgrid cost/CNY	1,085,152.3	1,083,672.4	1,082,618.1
Electric vehicle cost/CNY	2076.5	2076.5	138.9
Carbon emission/t	1217.4	1189.7	1162.3
Carbon emission reduction/t	1423.8	1449.4	1482.5

. In Scenario 4, the electric vehicles are connected to the microgrid in a disorderly charging mode, and their charging peak is located at 8:00–10:00 and 18:00–20:00, as shown in Figure A7 in Appendix B, which coincides with the peak period of the microgrid electric load. Therefore, the microgrid scheduling economy is the worst, the charging cost of electric vehicle users is the highest, and the carbon emissions are also the highest.

Scenario 5 establishes the load-side response strategy described in Ref. [29]. Compared with Scenario 4, which only considers source-side optimization, the microgrid scheduling cost and carbon emission level are reduced. However, compared with scenario 6, with electric vehicles participating in V2G orderly scheduling, its low-carbon scheduling effect can still be improved.

As is shown in Figure 5, scenario 6 adopts the scheduling method of electric vehicles participating in V2G of microgrids and electric-thermal loads participating in demand response. Therefore, during the peak period of electric load, that is, 9:00–11:00, electric vehicles can be scheduled to reverse the power supply to the grid to reduce the transfer of part of the electric load and reduce the power supply pressure of coal and gas units, thus reducing the carbon emissions that cannot be resolved due to the upper limit of the unit’s power supply output. Compared with Scenario 5, the carbon emissions are reduced by 55.1t, the operating cost of the microgrid is reduced by CNY 2534.2, and the income of EV users is increased by 93.31%. Compared with scenario 6, the carbon emissions are reduced by 27.4 t, the operating cost of microgrids is reduced by CNY 1054.3, and the income of EV users is also increased by 93.31%.

It can be seen from Figure 6a,b that the electric vehicles participating in V2G discharge in the peak period of 9:00–11:00 are equivalent to the wind power at the load valley instead of the high-carbon unit output at the peak load; that is, the zero-carbon discharge force replaces the high-carbon unit output, and at the same time, it can also improve the income of the electric vehicle user side. The charging of electric vehicles in a period of high wind power output has little influence on the operation of CCS and P2G systems and has little influence on the output of thermal power and gas units in a microgrid, which can better realize the comprehensively flexible scheduling on both sides of source and load.

Due to the different effects of different scales of electric vehicles participating in the low-carbon economic dispatch of microgrids, this section further compares and analyzes the low-carbon economic dispatch of 100, 150, 200, 250, 300, and 350 electric vehicles participating in V2G, as is shown in

Table 3. Comparison of dispatch results of different sizes of electric vehicles participating in V2G.

Quantity	EV Cost/CNY	Average EV Cost/CNY	Microgrid Cost/CNY	Carbon Emission Reduction/t	Carbon Emissions/t
100	326.7	3.2	1,082,812.9	1465.7	1174.9
150	233.9	1.6	1,082,745.6	1472.4	1170.1
200	270.0	1.4	1,082,728.8	1476.8	1166.8
250	138.9	0.56	1,082,618.1	1482.5	1162.3
300	140.6	0.47	1,082,590.3	1486.9	1159.0
350	385.0	1.1	1,082,720.9	1489.6	1157.4
400	495.3	1.23	1,082,726.0	1493.9	1154.2
100	326.7	3.2	1,082,812.9	1465.7	1174.9
150	233.9	1.6	1,082,745.6	1472.4	1170.1

.

It can be seen from Figure 7 and Table 3 that when electric vehicles participate in the low-carbon economic dispatch of microgrid in the form of V2G, as the number of electric vehicles increases from 150 to 300, its schedulable capacity also increases, so its effect of assisting microgrid in carbon emission reduction gradually becomes better, and the operation cost of microgrid and the cost of charging and discharging of users also gradually decrease. As the number of electric vehicles increases to 300–400, the charging power of the EV load increases. During the peak period of electric load, the reverse power supply of electric vehicles the microgrid needs reaches the upper limit. Therefore, the operating energy consumption of CCS and P2G systems decreases. At the same time, because thermal power and gas units need to charge the electric vehicle load, the carbon emissions of the unit also increase greatly, and the cost of the microgrid and the users also increase accordingly. The participation of 300 to 350 electric vehicles in V2G can achieve the best low-carbon economic dispatch effect of the microgrid.

In summary, the microgrid source-load coordinated low-carbon economic dispatch strategy can achieve the best results in reducing carbon emissions, microgrid costs, and user costs, and it can realize the sustainable development of the microgrid.

6. Conclusions

This paper proposes a microgrid source-load coordinated low-carbon economic dispatch method considering CCS and P2G that fully uses the energy time-shift advantages of the source-side CCS and P2G. It also combines the load-side flexible resources to participate in the demand-side response, effectively realizing the source-load coordination and mutual assistance, promoting wind power consumption, improving the sustainable development potential and economic benefits of the microgrid, and increasing the income of electric vehicle users to achieve a win-win situation. The validity of the proposed model has been verified via simulation, and the following conclusions are drawn:

(1)

The comprehensively flexible and coordinated operation mode of CCS and P2G systems can significantly reduce the total cost of microgrid operation and carbon emissions. Compared with the flexible operation mode of the CCS or P2G system alone, this mode can give full play to the advantage of energy time-shift;

(2)

The cooperation of the comprehensively flexible operation of the CCS, P2G system, and electric vehicles promotes the consumption of renewable energy and fully explores the low-carbon potential of microgrids, with a significant reduction in net carbon emissions compared to the period before the cooperative operation;

(3)

The scale of electric vehicles participating in microgrid charging and discharging scheduling is not the bigger the better, and the capacity and cost of microgrid should be considered comprehensively. When the scale exceeds the demand for microgrid scheduling, its economy and low carbon emissions may decline.

The method proposed in this paper solves the problem that the low-carbon performance of resources on both sides of source-load is not fully explored in the traditional method, and the problem of source-load complementary collaborative scheduling is realized. However, the influence of carbon price on the participation of electric vehicles in the economic dispatch of microgrids has not been considered in the model of this paper. In the follow-up study, research will be carried out on the participation of electric vehicles in microgrid carbon trading.