Research on Low-Carbon Capability Evaluation Model of City Regional Integrated Energy System under Energy Market Environment

Abstract

In the context of the “carbon peaking and carbon neutrality” goal and energy marketization, the City Regional Integrated Energy System (CRIES), as an important participant in the energy market, pursues low-carbon development as its most important goal. Without a reasonable market participation structure and a comprehensive low-carbon evaluation system, it will be difficult to meet the needs of CRIES for low-carbon development in the energy market. Therefore, this paper first proposes a framework suitable for CRIES to participate in the energy market, and under the influence of the operating characteristics of the energy market, proposes an evaluation index system suitable for CRIES’ low-carbon capabilities in the energy market. The analytic network process–criteria importance through intercriteria correlation (ANP-CRITIC) method is used to determine the subjective and objective weights of each indicator, and the comprehensive weight of each indicator is calculated by the principle of moment estimation to achieve a quantitative evaluation of the low-carbon capability of CRIES in the energy market. Finally, taking a CRIES as an example, the analysis verifies that the proposed evaluation model and method can scientifically and comprehensively evaluate the low-carbon capability of CRIES in the energy market. The results show that the CRIES low-carbon capability evaluation results of different market schemes can be improved by up to 24.9%, and a fairer market transaction mechanism can promote the low-carbon development of CRIES.

1. Introduction

In recent years, how to improve energy utilization and reduce carbon emissions has become the focus of energy development in various countries. The single traditional energy supply system has the defects of low energy efficiency and high emission, which cannot meet the current needs of low-carbon energy development [1,2]. The regional integrated energy system (RIES) can couple different energy types and promote the consumption of renewable energy, which has become a key technology for low-carbon energy development in recent years [3]. From small industrial parks to large cities, they all belong to the category of RIES. The City Regional Integrated Energy System (CRIES) is an important form of RIES [4]. It has numerous distributed energy systems and multienergy complementary systems, which are the bridge connecting the upper energy main network and the energy load side.

With the advancement of energy marketization [5], CRIES has become an important participant in the energy market due to its advantages of high economic benefits, strong low-carbon capabilities, high system reliability, and high energy utilization rates [6,7]. Among them, the low-carbon capability is an important factor that CRIES must consider when participating in the energy market. Evaluating the low-carbon capability of CRIES after participating in the energy market is an important theoretical support for promoting the consumption of renewable energy in CRIES, improving the comprehensive energy utilization rate of CRIES, scientifically planning the operation plan of CRIES participating in the energy market, and improving the low-carbon development of CRIES. Therefore, it is necessary to conduct scientific and comprehensive research on the low-carbon capability evaluation model of CRIES in the energy market.

Some scholars have carried out related research on this and obtained rich research results. Reference [8] introduced the carbon trading mechanism into the energy market clearing of integrated energy and studied the impact of the carbon trading mechanism on the RIES auction clearing strategy. References [9,10] study the low-carbon clearing strategy of IES participating in the market with uncertain demand response and new energy output. References [11,12] based their studies on the carbon emission flow (CEF) theory for low-carbon and the economical optimal scheduling of IES. References [13,14] studied the low-carbon optimization of RIES through carbon capture and carbon trading. However, most of the existing research focuses on considering RIES as a distributed energy system for the market clearing, optimal scheduling, and design planning of the system. However, in the face of RIES, such as CRIES, which covers a wide area, has a wide variety of energy sources, and has many distributed energy sources, existing research methods will not be able to satisfy CRIES’ reasonable participation in the energy market.

In terms of the RIES evaluation, Reference [15] proposed a comprehensive evaluation index with universal applicability to RIES from the links of energy, installation, the distribution network and users, and thereby proposed a scientific method for evaluating the development level of RIES. Reference [16] established a comprehensive evaluation of integrated energy systems through six characteristics of multidimensional, multivector, systematic, future, systematic, and applicability. Reference [17] proposed a decision-making method for integrated energy participation in energy market transactions, and evaluated the system considering four aspects of economy, fairness, environmental protection, and safety. Reference [18] evaluated the integrated energy system from different aspects of the integrated energy system, such as reliability under the consideration of user thermal comfort, power transaction performance, and system energy efficiency analysis. Reference [19] proposed an alternative model-assisted IES quantitative evaluation method to evaluate the operation of the IES. However, the existing evaluation system only takes low-carbon capability as a part of the evaluation system, and lacks a comprehensive evaluation model for CRIES’ low-carbon capability. If there is no comprehensive evaluation system, it will not be able to meet the development process of CRIES, which will bring great challenges to the low-carbon development of CRIES after participating in the energy market.

So, for the above two aspects, this paper proposes a CRIES low-carbon capability evaluation model under the energy market. First, by fully considering the difficulties faced by CRIES’ participation in the energy market, and then establishing a reasonable structure for CRIES to participate in the energy market; secondly, based on the operating characteristics of the energy market, an evaluation index system for CRIES’ low-carbon capability in the energy market is proposed. The ANP-CRITIC method is used to assign the objective and subjective weights of the indicators, and the moment estimation principle is used to obtain the comprehensive weight so as to realize the quantitative evaluation of the low-carbon capability of CRIES in the energy market, and provide a reference for promoting the low-carbon development of CRIES in the energy market in the future.

2. CRIES Structure under the Energy Market

CRIES is different from other RIES in that it has a vast area, a wide variety of energy sources, and the locations of distributed energy sources are scattered, which cannot be traded with the energy market according to the traditional system architecture [20]. Therefore, this paper proposes a three-tiered structure and a multisubject CRIES to participate in the energy market structure. The three-layer structure is divided into the market layer, the CRIES layer, and the load layer. As shown in Figure 1, it involves energy transactions such as electricity, natural gas, and heat.

The market layer includes the electricity market and the natural gas market. The electricity market consists of four main entities: the Power Trading Center (PTC), the Power Generator (PG), the City Regional Integrated Energy Trading Center (CRIETC), and the Electricity Retailer (ER). The function of each participant is that PG sells electricity, CRIETC can sell electricity or buy electricity, and ER buys electricity. PTC is the backbone of the power market, and determines the clearing and settlement results of the power market by accepting bidding information from PG, CRIETC bidding and power purchase information, and its ER power purchase information. The natural gas market consists of four main entities: Natural Gas Trading Centers (NGTC), Natural Gas Producers (NGP), CRIETC, and Natural Gas Retailers (NGR). As the price of natural gas is relatively stable, the natural gas trading center conducts clearing and settlement according to the average bidding price of NGPs.

The CRIES layer includes CRIETC and various comprehensive energy producers (CEPs). CRIETC is the hub and settlement center for CRIES to participate in the energy market, and is the link between the upper-level energy market and CEPs. It determines the purchase of energy at the market layer according to the load information and affects the clearing of the market layer and the bidding and clearing results of the decision-making CEPs. The CEPs contains the gas boiler (GB), combined heat and power (CHP), wind turbine (WT), energy storage systems (ESS), photovoltaic (PV), vapor-driven absorption refrigerating machine (VAR), etc. It is a collection of distributed nergy conversion equipment which can make bidding decisions to CRIETC according to their respective unit information.

The load layer is a collection of energy-consuming entities such as electric energy, natural gas, and thermal energy in the region. Each energy-consuming entity has the functions of energy monitoring and communication, and provides load information in the region to CRIETC.

3. CRIES Low Carbon Capacity Evaluation Index System under Energy Market

3.1. Construction of the Low-Carbon Capability Index System

The low-carbon capability evaluation of the CRIES under the energy market involves two aspects. On the one hand, considering the interior of the CRIES, which includes a variety of renewable energy equipment which can output lower-carbon clean energy to the load. The low-carbon situation indicator is the embodiment of the low-carbon state and form in the development of the RIES, including system energy consumption, efficiency, carbon dioxide emissions, and participation in the carbon market, etc. The low-carbon situation can be used as the basic element of CRIES’ low-carbon capability evaluation to characterize the low-carbon development capability of CRIES. On the other hand, CRIES participates in the exchange of external energy through the medium of energy market. As the main body of energy supply on the load side, CRIES is an indispensable part of the energy market. Therefore, the participation of CRIES in the operation of the energy market is defined as the market structure. This indicator can reflect the low-carbon capability of CRIES under the market behavior. Since the natural gas market has not formed a competitive market environment, this paper only selects the electricity market as the evaluation object.

This paper takes the low-carbon situation and market structure of the CRIES under the energy market environment as the objects of evaluation. A low-carbon capability evaluation index system consisting of two first-level indicators, six second-level indicators, and fourteen basic indicators is constructed under the principles of systematicness, science, and independence. One first-level indicator includes the summative indicators with no specific meaning, as shown in Figure 2. The basic indicator system includes three types of indicators: cost type, benefit type, and intermediate type. For the cost-type evaluations, the smaller the value is, the better the evaluation is, and for the benefit-type evaluation, the larger the value is, the better. For the intermediate type, the closer the evaluation value is to a certain value, the better the rating.

3.2. Quantification and Overview of Evaluation Indicators

3.2.1. Low-Carbon Transition

1.

(S1-1): The energy exergy efficiency of system energy is based on the second law of thermodynamics [21,22], which focuses on the conversion efficiency corresponding to the quality of energy. Compared with the comprehensive utilization rate of energy, which focuses on the quantity of energy, its exergy efficiency can better reflect the loss of energy and the level of stepped utilization, which is defined as the output of the system. The ratio of the revenue exergy to the input cost exergy.

$${\eta }_{ex}=\frac{E_{os}^e+E_{os}^h+E_{os}^c+{\displaystyle \sum _{n=1}^N\xi E_s^n}}{E_{is}^e+E_{is}^{gas}+E_{is}^w+E_{is}^s+{\displaystyle \sum _{n=1}^N(1-\xi )E_s^n}}$$

(1)

where $E_{os}^E,E_{os}^h,E_{os}^c$ is the electrical, hot, and cold exergy of the total output of the system, respectively; $E_s^n$ is the exergy of energy type; n is for the energy storage; $E_{is}^e,E_{is}^{gas},E_{is}^{ws}$ is the exergy of electricity, natural gas, and new energy input to the system; $\xi$ is the 0–1 state of the energy storage device.

2.

(S1-2): The value-added rate of energy conversion is the profitability of the CRIES in the process of coupling different energy sources through its own energy coupling equipment and then selling it.

$$V=\left(\frac{C_{rl}}{{\eta }_{ex}}\right)/C_s$$

(2)

where V is the value-added rate of energy conversion, $C_s$ is the selling price for the system, and $C_{rl}$ is the total profit converted for the system.

3.

(S1-3): The energy conversion boundary is restricted by the CRIES hardware conditions, resource conditions, and external conditions. For example, the capacity of system equipment, the ability to absorb new energy such as wind and solar, and equipment operation constraints. In addition, it also includes factors such as the selection of system operation electric–heat ratio, demand restrictions, safety, and environmental protection restrictions. Therefore, this indicator uses relevant experts to score the qualitative evaluation.

3.2.2. Low-Carbon Technology

1.

(S2-1): In the CRIES, the energy storage device is the link between different energy sources, mainly used in the energy storage inside power systems and thermal systems. It has good spatiotemporal coupling and balancing capabilities of different energy sources. In addition, it can reduce the energy waste of the system and enhance the system regulation. The energy storage configuration ratio ${\eta }_{se}$ is the proportion of the energy storage capacity connected to the system to the installed capacity of the system.

$${\eta }_{se}=\frac{{\displaystyle \sum _{n=1}^N{W}_{ac.n}}}{{\displaystyle \sum _{s=1}^S{W}_{ec.s}}}$$

(3)

where $W_{ac.n}$ is the energy storage equipment capacity corresponding to the energy storage energy type $n$. There are a total of $S$ devices in the system, and $W_{ec.s}$ is S-th device capacity.

2.

(S2-2): The proportion of new energy installed capacity is the proportion of the installed capacity of new energy units to the installed capacity of the entire system.

$${\eta }_{ni}=\frac{{\displaystyle \sum _{i=1}^I{W}_{rn.i}}}{{\displaystyle \sum _{s=1}^S{W}_{ec.s}}}$$

(4)

3.2.3. Low-Carbon Benefits

• (S3-1): The return-on-investment in carbon emission reduction can better judge the emission reduction intensity of the CRIES, and can intuitively reflect the income generated by the investment in carbon emission reduction of the system. It is expressed as the ratio of value to the sum of investment in system projects.

$${\eta }_{eb}=\frac{{\displaystyle \sum _{k=1}^K\Delta C{E}_j\cdot C_j}}{V}$$

(5)

where $\Delta C{E}_j$ is the emission of the k-th pollutant in the system, $C_j$ is the price charged for the emission of type k pollutants, and $V$ is the total investment of the system.

2.

(S3-2): Participating in the carbon trading market is to put the excess carbon emission credits of the system into the carbon trading market, and then obtain carbon rights benefits. It is assumed that in the carbon trading market, the carbon emission allowances are obtained for each capacity unit through the baseline method, and the carbon trading market income is calculated through the stepped carbon price.

$$C{E}_{c{o}_2}^{qu}={\displaystyle \sum _s^S{P}_s}{\zeta }_s$$

(6)

where $C{E}_{c{o}_2}^{qu}$ is the total amount of carbon emission quota of the system; $P_s,{\zeta }_s$ is the carbon emission quota for the output power and unit active power output of the equipment.

$$C{E}_{c{o}_2}={\displaystyle \sum _{s=1}^S{\lambda }_s{P}_s}$$

(7)

where ${\lambda }_s$ is the carbon emission factor of the s-th emitting device and $P_s$ is the output power of the s-th device.

$$f_{c{o}_2}=\{\begin{array}{l}-30-3a,-(1+0.1a)C{E}_{c{o}_2}^{qu}\le C{E}_{c{o}_2}<0\\ 30+3a,0\le C{E}_{c{o}_2}<(1+0.1a)C{E}_{c{o}_2}^{qu}\end{array}(a=0,1,2\dots )$$

(8)

In the carbon trading market, the carbon trading price is set through the stepped carbon price, and the stepped carbon price is shown in Equation (8).

$$C_{c{o}_2}=f_{c{o}_2}(C{E}_{c{o}_2}-C{E}_{c{o}_2}^{qu})$$

(9)

where $C_{c{o}_2}$ is the benefit of the carbon trading market; when the total amount of carbon dioxide emitted by the system is greater than the total amount of carbon emission allowances, the system needs to purchase carbon emission allowances from the carbon market. On the contrary, get the benefit.

3.2.4. Market Subject

• (S4-1): The market concentration index can reflect the energy concentration of the CRIES in the energy market environment and the overall competition level of the multienergy market. This paper adopts the Herfindahl–Hirschman Index (HHI), which is commonly used in industrial economics, as an indicator of market concentration.

$$M_{HHI}={\displaystyle \sum _{d=1}^D{R}_d^2}\cdot \mathrm{10,000}$$

(10)

where $R_d$ is the CRIES d’s share in the multienergy market; for HHI, the value is between [0–10,000]. The larger the value, the more concentrated the market. According to the differentiation rule, when the HHI value is [500, 1800], the market is a competitive market.

2.

(S4-2): The market fairness index is to evaluate the fairness of the energy trading results of the CRIES under different market mechanisms, and to characterize whether the CRIES has the same trading status as other market entities in the participating market.

$$S=\frac{1}{L}\frac{{\left({\displaystyle \sum _{g=1}^G{C}_g}\right)}^2}{{\displaystyle \sum _{g=1}^G{\left(C_g\right)}^2}}$$

(11)

where $S$ is the market fairness index. The larger the value is between [0, 1], the fairer the market; $C_g$ is the operational benefit of the CRIES; $G$ is the total number of CRIES;$L$ is the number of evaluation systems.

3.2.5. Market Operation

• (S5-1): The new energy clearing ratio is the proportion of the system’s renewable energy clearing energy to the total system clearing energy. By default, the electricity purchased from the upper-level power grid is generated by thermal power units.

$${\eta }_{ne}=\frac{{\displaystyle \sum _{i=1}^I{E}_{rn}^i}}{E_{load}^e+{\alpha }_h{Q}_{load}^h+{\alpha }_c{Q}_{load}^c}$$

(12)

where $E_{load}^e,Q_{load}^h,Q_{load}^c$ is the demand side electricity, heating, and cooling loads corresponding to the system; α_h, α_c is the energy conversion factor for heat and cold; $E_{rn}^i$ is the power generation of new energy equipment i.

2.

(S5-2): Clearing price and new energy indicators can analyze the relationship between market energy prices and renewable energy clearing capacity. This paper uses the Spearman correlation coefficient in statistics for characterization.

$${\vartheta }_{n,p}=1-\frac{6{\displaystyle \sum X^2}}{n(n^2-1)}$$

(13)

where ${\vartheta }_{n,p}$ is the Spearman correlation coefficient; $X$ is the difference between the data of clearing price and renewable energy output arranged from small to large; $n$ is the total number of both data; for ${\vartheta }_{n,p}$, its value exists between [−1, 1]. The larger the absolute value of the value, the stronger the correlation, and the closer it is to 0, the weaker the correlation.

3.

(S5-3): The equivalent utilization rate of the system is the ratio of the operating power of each device in the system to the total operating power that can be dispatched by each device in the system when the system participates in the energy market. It can effectively reflect the utilization degree of the CRIES resources under the participation in the energy market.

$${\eta }_{ur}=\frac{{\displaystyle \sum _{s=1}^S{P}_s^{ao}}}{{\displaystyle \sum _{s=1}^S{P}_s^{to}}}$$

(14)

where $P_s^{ao},P_s^{to}$ is the actual operating power and the maximum operating power of the device S.

3.2.6. Market Benefit

• (S6-1): The market price volatility is the fluctuation of the market clearing price in the energy market. When the value is large, the market price may fluctuate violently, causing the energy market risk to increase. When it is too small, it is not conducive to a reasonable response to the market supply and demand relationship.

$${\eta }_{pf}=\frac{P_{std,T}}{P_{av,T}}$$

(15)

where $P_{std,T},P_{av,T}$ is the standard deviation of the market price and the average price of the market during the evaluation period.

2.

(S6-2): The social net income index refers to the economic benefits brought by CEPs satisfying the load-side demand under market rules and deciding on energy purchase and clearing decisions according to their own and market clearing rules. It can be reflected in the social welfare level under the energy market.

4. Evaluation and Calculation of CRIES Low-Carbon Capability under Energy Market Environment Based on ANP-CRITIC

4.1. Subjective Weight Calculation

The Analytic Network Process (ANP) [23,24] is a subjective empowerment method. It consists of two layers: a control layer and a network layer. It can analyze and calculate the network structure that is mutually influenced and dependent on the principle of the super matrix, so that it can obtain more scientific index weights.

Since the first-level indicators are summative indicators, no weight assignment is performed. The secondary and tertiary indicators are the main body of the evaluation indicators, and the ANP is used for weighting. Therefore, the second layer is subordinate to the control layer, and the 14 interdependent indicators constitute the network layer. The set of control layers for the evaluation of the low-carbon capability of the CRIES under the energy market environment is $S=\left\{S_1,S_2,\cdots S_6\right\}$. The network layer factor group should be $S_i=\left\{S_{i1},S_{i2},\cdots S_{ij}\right\}(i=1,2,\dots 6)$ . The subjective weight calculation steps are as follows:

Firstly, the control layer $S_i$ is used as the criterion, the element $S_{il}(l=1,2\cdots n_i)$ in $S_i$ is the secondary criterion, and the elements in the control layer $S_j$ are used to compare the dominance of $S_{il}$ by the 1–9 scale method according to their influence. Moreover, through the consistency test, the influence judgment matrix of the network layer element corresponding to the control layer $S_i$ on the network layer element corresponding to $S_j$ is obtained. If the two indicators are not affected by each other, then $w_{ij}=0$ will be used, and the initial supermatrix $W$ will be finally constructed, as shown in Formula (16) shown.

$$W=\left[\begin{array}{cccc}{w}_{11}& w_{12}& \cdots & w_{1n}\\ w_{21}& w_{22}& \cdots & w_{2n}\\ ⋮& ⋮& \cdots & ⋮\\ w_{n1}& w_{n2}& \cdots & w_{nn}\end{array}\right]$$

(16)

Since $W$ is not a column normalization matrix, it needs to be weighted and normalized, and the weighted matrix $A$ is obtained by comparing each column pairwise, as shown in Formula (17).

$$A=\left[\begin{array}{cccc}{a}_{11}& a_{12}& \cdots & a_{1n}\\ a_{21}& a_{22}& \cdots & a_{2n}\\ ⋮& ⋮& \cdots & ⋮\\ a_{n1}& a_{n2}& \cdots & a_{nn}\end{array}\right]$$

(17)

The weighted supermatrix $\overline{W}$ is obtained by processing $A·W$. In order to calculate the relationship between the factors, it is necessary to stabilize $\overline{W}$ by Formula (18), calculate the limit relative sorting vector, and if it converges and is unique, the result obtained is the limit matrix, and finally calculate the normalized eigenvectors to obtain the indicators of each network layer weight $w^{\prime }$.

$$\underset{k\to \infty }{\lim }(\frac{1}{n}){\displaystyle {\sum }_{k=1}^n{w}^k}$$

(18)

4.2. Objective Weight Calculation

The CRITIC method determines the objective weight of each indicator by quantifying the dispersion of each indicator value [25]. Compared with the traditional entropy method, it not only considers the contrast strength between the indicators, but also considers the contradiction between the indicators to deal with the mutual influence between the indicators. Make the weight results more scientific and reasonable. The steps are as follows:

First, it is necessary to perform dimensionless processing on the original data matrix $S={[s_{ij}]}_{m\times n}$, assuming that the number of evaluation samples is $m$ and the number of evaluation indicators is $n$. The benefit-type index and the cost-type index are, respectively, processed by Formulas (19) and (20) to obtain a dimensionless evaluation matrix $S^{\prime }$.

$${s^{\prime }}_{ij}=\frac{s_{ij}-\min (s_j)}{\max (s_j)-\min (s_j)}$$

(19)

$${s^{\prime }}_{ij}=\frac{\max (s_j)-s_{ij}}{\max (s_j)-\min (s_j)}$$

(20)

CRITIC determines the amount of information by calculating the variability and conflict so as to determine the objective weight of each indicator.

The index variability is expressed by the standard deviation, as shown in Formula (21), where ${\zeta }_j$ is the standard deviation of the j-th index.

$${\zeta }_j=\sqrt{[{\displaystyle \sum _{i=1}^n{s}_{ij}-{\overline{s}}_j{}^{\prime }}]/(n-1)}$$

(21)

The index conflict is expressed by the conflict coefficient $C_j$ as in Equation (22), where $c_{ij}$ is the correlation coefficient between the i-th index and the j-th index, expressed by Equation (23).

$$C_j={\displaystyle \sum _{i=1}^n(1-c_{ij})}$$

(22)

$$c_{ij}=\frac{{\displaystyle \sum _{u=1}^m({s^{\prime }}_{ui}-{{\overline{s}}^{\prime }}_{ui})({s^{\prime }}_{ui}-{{\overline{s}}^{\prime }}_{ui})}}{\sqrt{{\displaystyle \sum _{u=1}^m{({s^{\prime }}_{ui}-{\overline{s}}_i{}^{\prime })}^2{\displaystyle \sum _{i=1}^n{(s_{uj}-{\overline{s}}_j{}^{\prime })}^2}}}}$$

(23)

The amount of information of the $j$-th index is $I_j$, and its calculation is shown in Formula (24). $I_j$ is the fusion of index variability and index conflict. The greater the amount of information, the greater the weight it occupies.

$$I_j={\zeta }_j{C}_j$$

(24)

Finally, the objective weight $w^{″}$ of the j-th index is obtained by Formula (25).

$$w^{″}=I_j{\left[{\displaystyle \sum _{j=1}^n{I}_j}\right]}^{-1}$$

(25)

4.3. Comprehensive Weight and Scoring Mechanism

After the subjective and objective weights are obtained by ANP-CRITIC, the moment estimation principle is used to calculate the comprehensive weight. According to the principle, the coupling coefficient corresponding to the subjective weight and the objective weight is first calculated by Formula (26).

$$\{\begin{array}{l}{\varsigma }_j={w^{\prime }}_j/\left({w^{\prime }}_j+{w^{″}}_j\right)\\ {\ell }_j={w^{″}}_j/\left({w^{\prime }}_j+{w^{″}}_j\right)\end{array}$$

(26)

where ${\varsigma }_j$ is the subjective coupling coefficient; ${\ell }_j$ is the objective coupling coefficient; the comprehensive weight ${\chi }_j$ can be obtained from Formula (27).

$${\chi }_j=({\varsigma }_j{w^{\prime }}_j+{\ell }_j{w}^{″})/{\displaystyle \sum _j^n({\varsigma }_j{w^{\prime }}_j+{\ell }_j{w}^{″})}$$

(27)

In order to display the evaluation results more intuitively in the actual project, this paper multiplies and sums the actual data of each indicator and the comprehensive weight of each indicator, and finally obtains the final score of the low-carbon capability evaluation of the CRIES under the energy market environment.

5. Case Study

This paper takes a CRIES in a certain area as an example. The basic structure is shown in Figure 3, which includes a nine-node power network and a seven-node thermal network. G1, WP1, GB1, ESS1, and G2, as well as WP2, GB2, and ESS2 are the CHP units, wind and solar units, gas boilers, and energy storage equipment belonging to CEP1 and CEP2, respectively. CHP operates in the way of constant heat and electricity, and the system heat load is supplied by the CHP unit and the GB unit. The cooling load of the system is supplied by an absorption chiller, so the heat load node can be replaced with a cooling load node. Select the typical daily operation data in this area, and use MATLAB to fit the electricity, heating, and cooling loads. The fitting curve is shown in Figure 4.

The region is currently in the transitional stage of the CRIES participating in the market and has all the hardware conditions and policy support for participating in the market. Combined with the actual situation in the region, the operation plan of reference [26] and equipment constraints [7] are used to calculate the index data of each system.

Table 1. CRIES specific scheme.

Scheme	CEPs	Participate in the Market Scheme
1	CEP1	A
2	CEP2	A
3	CEP1	B
4	CEP2	B
5	CEP1	C
6	CEP2	C

and

Table 2. The parameters of each device in CEPs.

CEPs	WT	ESS	CHP	GB
CEP1	70 MW	40 MW	240 MW	200 MW
CEP2	180 MW	80 MW	240 MW	200 MW

show the specific schemes.

For the design of the market participation scheme: the clearing price of Scheme A is calculated using the peak–valley electricity price, and the reverse power sales to the power grid is not considered; the clearing price of Scheme B is calculated using the real-time electricity price, and the reverse power selling to the power grid is not considered; Scheme C adopts the market real-time electricity price and sells excess electricity to the grid. From Scheme A to Scheme C, the market opening degree has gradually deepened, gradually transitioning from not participating in the market to fully participating in the market. The unit price of natural gas is 2.70 CYN/m³.

5.1. Calculation Results of Each Indicator under Different Market Participation Schemes

Due to the limited space, only a brief analysis of the power system output is made here. Figure 5, Figure 6 and Figure 7 is the power system output diagram under different market participation schemes. In the vertical comparison, with the deepening of the market openness, the power purchased from outside the system gradually decreases. The output of wind turbines continues to increase, and the system gradually changes from a single load mode to a power mode to sell electricity to the upper-level power grid.

According to the calculation method of each indicator in the low-carbon capability evaluation system (Formulas (1)–(15)), the indicators of the low-carbon situation and market structure are calculated, respectively, and the calculation results are shown in

Table 3. Calculation results of low-carbon situation indicators under each scheme.

Scheme	1	2	3	4	5	6
S1-1/%	56.113	57.95	58.454	64.184	62.928	66.803
S1-2/%	36.231	42.265	45.848	48.367	48.213	54.268
S1-3	74.16	84.23	74.12	84.22	74.11	84.24
S2-1/%	7.1	11.5	7.1	11.5	7.1	11.5
S2-2/%	12.7	25.7	12.7	25.7	12.7	25.7
S3-1/%	7.2	13.42	7.41	13.65	7.64	13.71
S3-2/KCNY	-72	12.1	-34	62	17.5	71

and

Table 4. Calculation results of market structure indicators under each scheme.

Scheme	1	2	3	4	5	6
S4-1	4800	4800	3500	3500	1600	1600
S4-2	0.667	0.671	0.756	0.761	0.891	0.868
S5-1/%	18.94	24.07	22.82	30.46	23.62	34.98
S5-2/%	21.46	25.1	28.17	32.64	46.18	48.82
S5-3/%	75.21	78.16	78.64	80.12	83.41	85.13
S6-1/%	23.6	24.1	42.5	41.9	56.4	56.7
S6-1/KCYN	243.0	267.8	275.4	305.9	321.8	349.6

.

5.2. Analysis of Indicator Results

Through the comparative analysis of the data in Table 3 in Section 5.1, under the same market participation scheme, the energy exergy efficiency of CEP2 can be improved by up to 9.8% compared with CEP1 because CEP2 has larger capacity new energy units, and new energy units rely on renewable energy such as wind energy instead of consuming fossil energy. Therefore, the input exergy of the new energy unit is considered to be zero [27], so the CEP2 with a large installed capacity of the new energy units has a higher exergy efficiency. Under different market participation schemes, from Scheme A to Scheme C, the system can sell more electric energy generated by clean energy in the region to the energy market, reducing the occurrence of energy waste and improving the exergy efficiency of the system. CEP2, with larger energy storage capacity, can sell electricity when the load is high and generate electricity when the load is low. It has a stronger energy translation ability and reduces the cost of electricity, so it can obtain higher income and improve the value-added rate of energy conversion. In the face of different CEPs, the qualitative evaluation results of relevant experts on the energy conversion boundary are consistent with the actual situation, and CEPs with more new energy and energy storage equipment capacity obtain higher qualitative evaluation results. For CEP2, from Scheme A to Scheme C, the profit obtained in the carbon market increased from 12.1 KCYN to 71 KCYN. This is because, with the deepening of the market opening, it can promote the consumption of new energy, thus increasing the emission rights sold in the carbon market. However, for CEP1 under Scheme A, due to the low capacity of new energy and energy storage equipment, and the inability to sell electricity to the upper power grid, the wind and solar energy are seriously abandoned, not only unable to make profits in the carbon market, but also needing to purchase carbon emission rights in the carbon market. With the deepening of market openness, it can improve the consumption of new energy, so that it has more carbon emission rights, and carbon benefits can be obtained under the final Scheme C.

From the data in Table 4 in Section 5.1, the Herfindahl–Hirschman Index for Scheme A is higher. This is because, at this time, the energy market only takes the role of CEPs as energy receivers, and they do not have the ability to compete in the energy market. With the deepening of the market openness, the HHI index value gradually decreases, and the HHI index value is within the range of the competitive market in the case of Scheme C. With the deepening of the market openness, its market fairness also becomes relatively fair. The proportion of new energy clearing has a positive correlation with the capacity of new energy equipment, and a more open market is more conducive to the increase in the proportion of new energy clearing. For CEP2, the proportion of new energy clearing in Scheme C has increased by 45.3% compared to Scheme A. The increase in the proportion of energy storage equipment can promote the space–time coupling and balancing ability of different energy sources, thereby improving the equivalent utilization rate of the system. The degree of market openness affects the relationship between supply and demand in the market, and the relationship between supply and demand guides the fluctuation of market prices. Therefore, the price volatility of a market with a high degree of openness maintains a higher level than other market solutions. Under Scheme C, CEPs can participate in the market competition as the main body of the energy market and obtain more social benefits in the energy market, while under Scheme A, CEPs can only passively act as energy receivers to obtain lower social benefits.

5.3. Calculation Results of Index Weights Based on ANP-CRITIC

Through the ANP-CRITIC indicator weight calculation method proposed in Section 4, and the actual indicator data of each scheme, the subjective and objective weights and comprehensive weights of the secondary and tertiary indicators are obtained, as shown in

Table 5. Calculation results of three-level indicator weights.

Indicator Number	Type	Subjective Weight	Objective Weight	Comprehensive Weight
S1-1	benefit-type	0.0892	0.0397	0.0737
S1-2	benefit-type	0.0230	0.0369	0.0769
S1-3	benefit-type	0.0067	0.0386	0.0289
S2-1	benefit-type	0.0173	0.0781	0.0872
S2-2	benefit-type	0.0552	0.0826	0.0704
S3-1	benefit-type	0.0222	0.1054	0.0775
S3-2	benefit-type	0.1081	0.1181	0.1104
S4-1	cost-type	0.0196	0.0830	0.0605
S4-2	benefit-type	0.0589	0.0786	0.0399
S5-1	benefit-type	0.1172	0.0384	0.0835
S5-2	intermediate-type	0.0245	0.0599	0.0423
S5-3	benefit-type	0.0710	0.0439	0.0518
S6-1	benefit-type	0.0556	0.0794	0.0592
S6-2	benefit-type	0.3339	0.1185	0.1571

and Figure 8.

From the weight distribution of secondary indicators, it can be seen that market benefit accounts for the highest proportion. This is because the CRIES first pursues the maximization of social welfare in the process of participating in the market, so that the main body of integrated energy can be motivated to improve the energy service level, optimize the system operation plan and upgrade, and invest in lower-carbon and efficient equipment good positive cycle. The high proportion of low-carbon transition and low-carbon technical indicators reflects higher energy coupling efficiency, stronger energy space–time translation capability, and lower-carbon and efficient equipment, which can minimize primary energy consumption and build a green energy consumption model to improve the system low-carbon capacity. The market operation indicator is the embodiment of the system’s low-carbon capability in the market transaction mechanism, which can reflect the relationship between the system’s new energy output, supply and demand, and energy prices in the energy market. A reasonable market transaction mechanism can promote the system’s low-carbon capability improve. Each weight is consistent with the actual low-carbon performance of the system, which also verifies the scientificity and rationality of the indicators proposed in this paper.

5.4. Analysis of Evaluation Results

Based on the index calculation results calculated above, the comprehensive evaluation results of the six schemes and the second-level index evaluation results are shown in

Table 6. Valuation results and ranking.

Scheme	1	2	3	4	5	6
Fraction	2.186	2.4305	2.3268	2.601	2.516	3.037
sort	6	4	5	2	3	1

and Figure 9.

It can be seen from Table 6 and Figure 9 that the comprehensive evaluation results under different market schemes are, from high to low, Scheme 6, Scheme 4, Scheme 5, Scheme 2, Scheme 3, Scheme 1. Among the six schemes, Scheme 1 cannot conduct energy interaction with the energy market, and due to the small energy storage capacity and insufficient energy translation capability, the phenomenon of energy abandonment is relatively serious. Therefore, the low-carbon capacity evaluation result is the lowest. Scheme 5 has a higher degree of market openness, so the market subject indicator it has a higher score, but due to the small capacity of new energy and energy storage equipment and serious energy abandonment, the evaluation results of low-carbon technology and market operation are low. So, the final evaluation result is in the third place. In contrast, Scheme 4 has a high proportion of new energy and energy storage, which can promote the coupling efficiency and space–time translation capability of different energy sources in the system, so that low-carbon transition, low-carbon technology, and market operations have high scores. So, it comes in second. For Scheme 6, it can participate in the competition in the energy market and sell the new energy that cannot be absorbed in the region in the energy market to reduce the occurrence of energy waste in the system. Therefore, the evaluation result is the best. For CEP1 and CEP2 from Scheme A to Scheme C, the low-carbon capacity assessment results increased by 15.08% and 24.9%, respectively.

5.5. Comparative Analysis of Evaluation Methods

In order to verify the effectiveness and superiority of the ANP-CRITIC method, the original data were compared with three traditional methods of the fuzzy analytic hierarchy process (Fuzzy-AHP) [28], entropy weight method (EWM) [29], and AHP–antientropy weight method (AHP-AEWM) [30]. The final comprehensive evaluation results are shown in

Table 7. Results and rankings under different methods.

Evaluation Methodology	Fraction/Sort
	1		2		3		4		5		6
Fuzzy-AHP	5.424	6	6.361	4	6.184	5	6.764	2	6.657	3	7.154	1
EWM	0.473	6	0.697	4	0.618	5	0.748	3	0.794	2	0.869	1
AHP-AEWM	0.846	6	1.268	4	1.195	5	1.593	2	1.386	3	1.895	1
ANP-CRITIC	2.1862	6	2.4305	4	2.3268	5	2.601	2	2.516	3	3.037	1

.

It can be seen from Table 7 that the results obtained by the other methods, except EWM, are the same, which also verifies the effectiveness of the evaluation model proposed in this paper. The EWM relies too much on objective indicator data. Although it can reflect the correlation between indicators, it ignores the guiding role of decision-makers in the low-carbon development of CRIES, which leads to deviations in the evaluation results. Although the Fuzzy-AHP and AHP-AEWM are the same as the comprehensive evaluation results of the method proposed in this paper, the Fuzzy-AHP is too much affected by the subjective factors of decision-makers and cannot reflect the objective impact of system data on the evaluation of CRIES low carbon capacity in the energy market, which will adversely affect the final evaluation result. Although the AHP-AEWM considers both subjective and objective factors, it lacks the consideration of the correlation between the indicators. The method proposed in this paper makes up for the shortcomings of traditional methods, and can obtain more detailed and comprehensive scientific evaluation results for the CRIES low-carbon capability evaluation model in the energy market.

6. Conclusions

This paper fully considers the impact of the energy market on the low-carbon capability of CRIES and takes into account the characteristics of low-carbon development and energy market operation to construct a low-carbon capability evaluation system for CRIES under the energy market. The ANP-CRITIC comprehensive empowerment method is used to evaluate the low-carbon capacity of CRIES under six different schemes. The following conclusions are drawn from the analysis:

(1)

The low-carbon capacity evaluation system and comprehensive empowerment method constructed in this paper can quantitatively analyze and compare the low-carbon capacity of the CRIES, and then provide the system construction and policies for the CRIES to participate in the energy market under the low-carbon target. It provides a useful reference for the formulation and improvement of market rules.

(2)

Improving the installed capacity and consumption level of new energy, promoting the coupling efficiency and translation ability of different energy sources in the system, and a more open energy market are the key factors for improving the low-carbon capability of the CRIES.

(3)

Establishing a fairer market transaction mechanism and taking CRIES as a participant in the energy market to participate in the energy market competition rather than just the role of energy receiver. It can enable CRIES to obtain more market benefits in the energy market. Promoting the low-carbon upgrade of CRIES equipment has a positive effect on the low-carbon development of CRIES.

This paper establishes the CRIES low-carbon capability index under the current energy market. However, with the continuous development of the CRIES and the advancement of energy marketization, it is necessary to continuously improve and refine the index system for the evaluation of the CRIES low-carbon capability in the energy market environment in the future.