Comprehensive energy system optimization scheduling method based on source-load interactive excitation low-carbon transformation

Abstract

The invention discloses an integrated energy system optimization scheduling method based on source-load interactive excitation low-carbon transformation, and belongs to the technical field of power systems. According to the method, an IES source-network-load full-chain carbon tracking method is constructed based on the CEF theory, the carbon emission of the whole link of the IES is accurately metered, and meanwhile, the carbon emission responsibility of the source side is allocated to the load side, so that a spatial dynamic carbon emission factor is obtained to serve as a standard for measuring the carbon reduction of the user side of the CCER market. And secondly, a CET-CCER combined market operation mode is constructed based on the carbon reduction value of the CCER market, and a carbon emission factor is utilized to drive the energy consumption behavior of the user side to be low-carbon, so that the smooth development and implementation of the LCDR are orderly guided. And finally, a source-load interactive excitation low-carbon transformation mode is formed, interactive coupling between the source load and the load is realized, and the carbon efficiency level of the system is improved from two ends of the source load. According to the comprehensive energy system optimization scheduling method based on the source-load interactive excitation low-carbon transformation, the carbon cost and the overall carbon emission in different wind power permeability scenes are effectively reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and in particular relates to an optimization scheduling method for an integrated energy system based on source-load interaction to stimulate low-carbon transformation. Background Art

[0002] In order to achieve the "dual carbon" goal, decarbonization of electricity is the key to breaking the deadlock. The accurate measurement and analysis of carbon emissions is the basis and prerequisite for the power sector to achieve early decarbonization. The United Nations Intergovernmental Panel on Climate Change proposed the "three cans" principle of "measurable, reportable, and verifiable" for carbon measurement methods. my country will also formulate a "ruler" for carbon emission data to improve the carbon measurement transmission and traceability system. From this point of view, accurate carbon measurement of carbon emissions in all links of the energy system has become one of the key scientific issues of the power system under the background of low-carbon development.

[0003] Accurate carbon measurement and accounting is conducive to the healthy and orderly development of the national certified emission reduction (CCER) market mechanism. On January 22, 2024, the national greenhouse gas voluntary emission reduction trading market was officially restarted in Beijing. As an important carbon emission reduction market type in my country besides the carbon emission trading (CET) market, the CCER market allows certified emission reductions to be traded to offset carbon quotas. The carbon emission factor is one of the key indicators of certified carbon emission reductions. Extending the carbon emission factor to multiple energy fields is conducive to building a unified carbon management system for the integrated energy system (IES).

[0004] The carbon emission factor calculated based on the carbon emission flow (CEF) theory can effectively guide users to achieve active low carbon response (low carbon demand response, LCDR) by adjusting the energy usage sequence, and further improve the low carbon level of the whole society. As a direct signal to measure carbon emissions, the carbon emission factor can effectively characterize the carbon level of the system. It can provide a reasonable and orderly user-side carbon emission reduction incentive mechanism in cooperation with the CCER market. In the LCDR process, it can get rid of price signals and directly rely on carbon emission factors to encourage low carbonization of its own energy consumption behavior, thereby promoting the green and low-carbon transformation of the energy system.

[0005] Therefore, it is currently difficult to incentivize low-carbon energy consumption behavior on the user side by relying solely on price signals. A new technical solution is urgently needed in existing technologies to solve this problem. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide an integrated energy system optimization scheduling method based on source-load interaction to stimulate low-carbon transformation, which is used to solve the problem that it is difficult to rely on price signals to incentivize low-carbon energy consumption behavior on the user side in the existing technology.

[0007] The technical solution adopted by the present invention is to provide an integrated energy system optimization scheduling method based on source-load interaction to stimulate low-carbon transformation, including the following steps:

[0008] Step 1: Based on the CEF theory, the IES source-grid-load full-chain carbon tracking method is constructed to accurately measure the carbon emissions of all links of IES. At the same time, the carbon emission responsibility on the source side is allocated to the load side, and the spatial dynamic carbon emission factor is obtained as a standard for measuring the carbon reduction on the user side of the CCER market;

[0009] Step 2: Relying on the carbon reduction value of the CCER market, build a CET-CCER joint market operation model, use carbon emission factors to drive the low-carbonization of user-side energy consumption, and thus guide the smooth implementation of LCDR in an orderly manner;

[0010] Step 3: Form a source-load interaction incentive low-carbon transformation model, realize interactive coupling between source and load, and improve the carbon efficiency of the system from both ends of the source and load;

[0011] Step 4: Construct a two-layer optimization scheduling model of source-load interaction incentives to obtain the optimal solution and enable the system to reach the optimal low-carbon state.

[0012] The calculation method of the spatial dynamic carbon emission factor in step 1 is as follows:

[0013] The calculation formula of the carbon potential of the power grid node is expressed as:

[0014]

[0015] Where: is the carbon potential of power network node n at time t; P h,n,t is the power injected into node n by unit h at time t; P w,n,t is the power injected into node n by branch w at time t; is the carbon flow density injected into branch w of node n at time t; is the carbon emission intensity of unit h connected to node n; G is the set of generators connected to node n; L The set of branches for all flows injected into node n;

[0016] The calculation principle of carbon potential of gas grid nodes is consistent with that of the power grid. The specific calculation formula is as follows:

[0017]

[0018] Where: is the carbon potential of natural gas network node n at time t; Q w,n,t is the amount of gas injected from branch w into node n at time t; Q h,n,t is the power injected into node n by gas source h at time t; is the carbon flow density injected into branch w of node n at time t; is the carbon emission intensity of the gas source h connected to node n; E is the carbon emission factor of natural gas; B is the calorific value of natural gas; The set of branches for all gas flow injection nodes n; is the set of all gas sources connected to node n;

[0019] Carbon transfer relationship within EH:

[0020] P in e in =P out e out

[0021] Where: P in is the input power; e in is the input node carbon potential; P out is the output power; e out is the output node carbon potential;

[0022] According to the principle of conservation of carbon emissions, a net node carbon potential calculation model for liquid storage CCPP is constructed, which is specifically expressed as follows:

[0023]

[0024] Where: is the net nodal carbon potential at the CCPP output end at time t; P CCPP,t E is the net power of CCPP at time t; SG,t is the amount of carbon to be captured in the liquid storage tank at time t; E G,t is the carbon emission of CCPP at time t; β is the carbon capture efficiency; δ is the flue gas split ratio;

[0025] The carbon transfer equation of EH is shown below:

[0026]

[0027] Where: is the carbon potential of the natural gas node input to the combined heat and power (CHP) and gas boiler (GB) at time t; is the carbon potential of each energy conversion device output node at time t; η P2G , η GB The conversion efficiency of each energy conversion device;

[0028] The topological structure of the heat network consists of an inlet pipe and a return pipe. The calculation principle of the carbon flow model of the two is the same. The specific calculation formula is expressed as:

[0029]

[0030] Where: is the carbon potential of thermal network node n at time t; L h is the set of pipes connected to node n; m l,t is the flow velocity in pipe l at time t; is the outlet temperature of the pipe l connected to the node n at time t; is the loss temperature of the pipeline l connected to node n at time t; is the carbon flow density injected into pipeline l of node n at time t;

[0031] The calculation formula of spatial dynamic carbon emission factor is expressed as:

[0032]

[0033] Where: Z is the node set of the defined spatial area; e i,t is the spatial dynamic carbon emission factor of the i-th load at time t; is the power of the i-th load node j at time t; is the carbon potential of the i-th load node j at time t.

[0034] The CCER obtained by wind power in step 2 is expressed as follows:

[0035]

[0036] Where: CCER obtained for wind power; P w,t is the wind power grid-connected power at time t;

[0037] Among them, the grid baseline carbon emission factor e CM The calculation method is expressed as follows:

[0038] e CM =75% e OM +25% e BM

[0039] Where: e OM is the marginal carbon emission factor of electricity; e BM is the capacity marginal carbon emission factor;

[0040] The CCER obtained by the LCDR is expressed as follows:

[0041]

[0042] Where: CCERs obtained for LCDR; is the load response transfer quantity of the i-th type at time t; is the load response transfer amount of the i-th type at time t; k is the number of load types.

[0043] In step 3, both the upper-level IES service providers and the lower-level IES energy users can participate in the CET-CCER joint operation market; the upper-level IES service providers formulate the optimal unit output dispatch plan based on the comprehensive operation cost, and calculate the spatial dynamic carbon emission factor based on the CEF theory and provide it to the lower-level IES energy users; the lower-level IES energy users receive the carbon emission factor, and encourage the user side to shift to low-carbon energy consumption behavior through the CET-CCER joint market. The updated load demand is transmitted back to the upper layer, and the output is optimized and adjusted again, and the carbon emission factor is updated. The optimization is iterated in a reciprocating cycle, and finally the system reaches the optimal low-carbon state.

[0044] The upper layer of the source-load interactive incentive two-layer optimization scheduling model in step 4 is the objective function of the integrated energy service provider scheduling model. The upper-layer IES service provider optimizes the unit output with the goal of minimizing the total cost of system comprehensive operation, which is specifically expressed as follows:

[0045]

[0046] Where: F is the total operating cost of the system; F e,t is the energy consumption cost of the system in period t; F yw,t is the operation and maintenance cost of each unit in period t; F qt,t is the start-up and shutdown cost of each unit in period t; F c,t is the system carbon cost in period t; F curt,t is the system’s wind curtailment cost during period t; CCER income obtained from wind power grid access;

[0047] 1) Energy costs:

[0048] The primary energy consumed by the system mainly includes the cost of purchased natural gas and the cost of coal consumption of thermal power units. The total cost of the two is specifically expressed as follows:

[0049]

[0050] Where: P Gi,t is the total output of thermal power unit i in period t; a i , b i 、c i is the coal consumption characteristic coefficient of thermal power unit i; π coal is the unit standard coal price; π1 is the unit natural gas price; Q g,t is the gas consumption of purchased natural gas in the system during period t;

[0051] 2) Operation and maintenance costs:

[0052] It is mainly calculated based on the operation and maintenance coefficient of each device in IES, which is specifically expressed as follows:

[0053]

[0054] Where: P i,t is the total output of the system for device i in period t; s i is the operation and maintenance coefficient of equipment i;

[0055] 3) Start-stop costs:

[0056]

[0057] Where: U i,t is the start / stop status of thermal power unit i during period t, U i,t =1 means that thermal power unit i is turned on during period t, U i,t =0 means that thermal power unit i is shut down during period t; U i,t =0 is the start-up and shutdown cost of thermal power unit i;

[0058] 4) Carbon cost:

[0059] Carbon cost includes CET cost and carbon storage cost. Carbon quota is allocated free of charge using the baseline method. Under the joint operation mode of CET-CCER market, part of CCER obtained by the system can offset carbon quota and reduce the pressure of compliance. The specific carbon cost is as follows:

[0060]

[0061] Where: π j is the carbon trading price; s is the storage price of carbon dioxide per unit mass; F CET,t is the carbon trading cost of the system in period t; F cs,t is the carbon storage cost of the system in period t; E Ji,t is the net carbon emission of the system in period t; α1 and α2 are carbon quota coefficients; Q CHP,t , Q GB,t is the gas consumption of CHP and GB in period t; E duty The amount of carbon allowances allocated for free; is the total carbon dioxide captured by the system during period t; is the carbon dioxide utilization of the system during period t; The CCER amount used to offset carbon quota;

[0062] 5) Wind curtailment costs:

[0063]

[0064] Where: P curt,t P is the wind power abandoned during period t; WY,t is the predicted wind power during period t; k curt is the penalty coefficient for unit abandoned wind volume;

[0065] 6) Wind power CCER income:

[0066] The CCER benefits obtained from the grid connection of wind power on the source side are specifically expressed as follows:

[0067]

[0068] Where: π ccer It is the unit price of CCER.

[0069] The constraints of the upper-level integrated energy service provider dispatching model of the source-load interactive incentive dual-level optimization dispatching model in step 4 are:

[0070] 1) Power balance constraints

[0071]

[0072] H GB,t +H CHP,t =H load,t +ΔH t in -ΔH t out

[0073] Q buy,t +Q P2G,t -Q CHP,t -Q GB,t =Q load,t +ΔQ t in -ΔQ t out

[0074] Q min ≤Q buy,t ≤Q max

[0075] Where: P load,t P is the electric load power in period t; CHP,t is the electric power generated by CHP during period t; H is the amount of load transfer in and out during period t; GB,t , H CHP,t are the heat load powers supplied by GB and CHP in period t respectively; Q is the amount of heat load transferred in and out during period t; buy,tQ is the amount of natural gas purchased by the system during period t; P2G,t Q is the gas production from power to gas (P2G) during period t; load,t is the gas load; Q is the amount of gas load transferred in and out during period t; min , Q max The upper and lower limits of the gas source output;

[0076] 2) CCPP operation constraints:

[0077]

[0078] Where: P Y,t is the operating energy consumption of CCPP during period t; P D is the fixed energy consumption of CCPP; λ is the electricity consumed per unit CO2 captured; e gi,t is the carbon emission intensity of thermal power unit i; P Gi,max is the maximum power generation of CCPP; η is the maximum working coefficient of the regeneration tower and compressor;

[0079] 3) Constraints on start and stop of thermal power units:

[0080]

[0081] Where: T S is the minimum shutdown time of the thermal power unit; T O The minimum startup time of the thermal power unit;

[0082] 4) Spinning reserve constraints:

[0083] The spin reserve credibility opportunity constraint expression is as follows:

[0084]

[0085] Where: Cr{} is the confidence expression; is the maximum value of the net output of thermal power unit i in period t; is the maximum value of CHP power output during period t; is the fuzzy representation of load and wind power; R g , are the ramp rates of CHP and thermal power plant units respectively; α is the confidence level of spinning reserve;

[0086] 5) CCER offset quota constraints:

[0087]

[0088] In the formula: ψ is the maximum proportion of carbon quotas allowed to be cleared by the system. The current CCER can offset carbon quotas that should not exceed 5% of the total.

[0089] The lower layer of the source-load interactive incentive two-layer optimization scheduling model in step 4 is the comprehensive energy consumer response model objective function. The lower layer IES consumer takes the minimum comprehensive energy cost as the goal. The consumer can participate in LCDR and convert the response emission reduction into an equivalent CCER for sale. The specific expression is as follows:

[0090]

[0091] Where: f is the total comprehensive energy cost; L i,t is the load in the period t after the response; π i is the energy price of the i-th type of load; Δt is the unit time scale (1h).

[0092] The constraints of the lower-level comprehensive energy consumer response model of the source-load interactive incentive dual-level optimization scheduling model in step 4 include user transfer quantity constraints, user response quantity constraints, and user satisfaction constraints:

[0093]

[0094] Where: is the pre-response load value of the i-th type load at time t; u is the single-point load transfer limit; M is the user satisfaction; M min Minimum user satisfaction.

[0095] Through the above design scheme, the present invention can bring the following beneficial effects:

[0096] 1. After the system was introduced into the CCER market, the carbon costs of three different wind power penetration scenarios all decreased by 1.88%, 5.01%, and 4.83%, respectively. This proves the good low-carbon economic benefits brought by the CET-CCER market joint operation mode constructed by the present invention.

[0097] 2. The CCER market can effectively guide the energy user to conduct LCDR, and encourage users to use low-carbon energy, so that the overall carbon emissions of the system under three different wind power penetration rates decreased by 1.78%, 4.48%, and 2.9%, respectively. The total cost decreased by 1.34%, 2.24%, and 0.92%, respectively. This proves the low-carbon economic benefits of the source-load interaction incentive low-carbon transformation model proposed in this invention.

[0098] 3. The present invention analyzes the influencing factors of the peak-to-valley difference level of the carbon emission factor under the three systems of electricity, heat and gas. The research and comparison found that the peak-to-valley difference level of the carbon emission factor of the power system is more obvious, and users have more room for carbon reduction through electric load demand response. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] Figure 1It is a schematic diagram of an IES source-grid-load full-chain carbon tracking method of the present invention based on an integrated energy system optimization scheduling method for source-load interaction incentive low-carbon transformation;

[0100] Figure 2 A schematic diagram of carbon transfer in an energy hub of an integrated energy system optimization scheduling method based on source-load interaction to stimulate low-carbon transformation according to the present invention;

[0101] Figure 3 A schematic diagram of a source-load interaction incentive low-carbon transformation mode of an integrated energy system optimization scheduling method based on source-load interaction incentive low-carbon transformation of the present invention;

[0102] Figure 4 It is a schematic diagram of LCDR of electric load under low wind power penetration rate of an integrated energy system optimization dispatching method based on source-load interaction to stimulate low-carbon transformation of the present invention;

[0103] Figure 5 It is a schematic diagram of electric power balance of strategy 2 under low wind power penetration rate of an integrated energy system optimization scheduling method based on source-load interaction incentive low-carbon transformation of the present invention;

[0104] Figure 6 It is a schematic diagram of LCDR of electric load under high wind power penetration rate of an integrated energy system optimization dispatching method based on source-load interaction incentive low-carbon transformation of the present invention;

[0105] Figure 7 This is a schematic diagram of electric power balance of strategy 2 under high wind power penetration rate of the present invention, based on the integrated energy system optimization scheduling method for source-load interaction incentive low-carbon transformation. DETAILED DESCRIPTION

[0106] The present invention is further described below with reference to the accompanying drawings and specific embodiments:

[0107] Step 1: IES Source-Grid-Load Full Chain Carbon Tracking Method

[0108] IES source-grid-load full chain carbon tracking method Figure 1 At present, CEF theory is one of the most practical carbon measurement methods. It relies on the carbon meter system to achieve carbon measurement in all aspects of the power system, making the carbon emissions of energy consumption "traceable". Figure 1 In the full-chain carbon tracking method shown, carbon emission intensity can measure the carbon emissions of source-side power generation units; network carbon emission flow can allocate the carbon emission responsibility on the source side to the load side, and at the same time measure the carbon transfer relationship between various energy systems; carbon emission factor can measure the carbon emissions generated by energy consumption on the load side. All three meet the "three-capable" measurement principle and will become important indicators for measuring the carbon reduction amount of carbon emission reduction projects and mobilizing the user side to shift to low-carbon energy consumption behavior in the future.

[0109] In order to measure the indirect carbon emissions generated by electricity users, this patent calculates the carbon potential of user-side nodes based on the CEF theory of proportional sharing principle, because the node carbon potential is only affected by the injection flow. IES is not an independent operating entity, and heterogeneous energy systems carry the carbon emission transfer relationship after energy interaction. The concept of energy hub (EH) can effectively express the various conversion relationships between heterogeneous energy sources. The carbon transfer relationship between EH is as follows: Figure 2 As shown. The carbon transfer relationship within the EH must comply with the principle of conservation of carbon emissions, that is, the input carbon emissions must be equal to the output carbon emissions. However, for single-input-multiple-output devices, there is a problem of carbon allocation between multiple energy sources at the output port, which can be allocated according to the efficiency allocation principle, that is, the carbon emissions generated by heterogeneous energy are inversely proportional to their energy conversion efficiency. This can depict the carbon transfer relationship within the EH.

[0110] CEF will cause the carbon potential of the nodes close to the clean units to be at a relatively stable low level; on the contrary, the carbon potential of the nodes far away from the clean units will be at a relatively stable high level. However, users are not free to choose the access node location. If the carbon emissions are analyzed according to the carbon potential of each node, the position fairness will be lost. Therefore, the spatial granularity of the node carbon potential can be relaxed from the node to a certain area for averaging to obtain a unified spatial dynamic carbon emission factor. The specific spatial division needs to be carried out according to the actual operation of the energy system.

[0111] Step 2: CET-CCER market joint operation model

[0112] The CCER market is a beneficial supplement to the CET market. The establishment of the IES full-chain carbon tracking method can guide the orderly development of the joint operation of the CET-CCER market. The establishment of the CET-CCER joint market operation model can not only provide subsidies for clean energy emission reduction projects on the source side, but also drive users on the load side to carry out LCDR.

[0113] The full-chain carbon tracking method allows the system's carbon responsibility to be effectively shared, and it provides the CCER market with a carbon measurement indicator to incentivize carbon emission reduction on the load side - the spatial dynamic carbon emission factor. The user side can measure the carbon emissions caused by its own energy use behavior based on the spatial dynamic carbon emission factor, and consider the low-carbon transfer of its own energy use behavior, so as to actively seek to maximize the economic value in the transfer process, and improve the situation in which LCDR was difficult to carry out based on price incentive signals in the past.

[0114] The indicator for verifying carbon reduction in clean energy emission reduction projects is the grid baseline emission factor. my country's most recent publication, "2019 China Regional Grid Baseline Emission Factors for Emission Reduction Projects," announced marginal carbon emission factors in different regions of my country.

[0115] The process of users participating in LCDR will also generate a certain amount of carbon emission reduction, and the carbon value generated by this part is often ignored. The present invention measures the carbon emission reduction generated by LCDR according to the proposed spatial dynamic carbon emission factor. The certified carbon reduction can be exchanged for an equal amount of CCER in the CCER market, thereby realizing the economic conversion of carbon value in the user response process.

[0116] Step 3: Source-load interaction encourages low-carbon transformation model

[0117] The full-chain carbon tracking method proposed in this invention allocates the carbon emission responsibility on the source side to the load side, realizing the interactive coupling between the source and the load. It also provides important indicators to encourage the low-carbon energy transfer on the user side under the CET-CCER joint market operation mode. In summary, a low-carbon transformation model of source-load interactive incentive can be constructed to further promote the low-carbon green transformation of the energy system. The operation framework of the low-carbon transformation model of source-load interactive incentive is as follows: Figure 3 shown.

[0118] In this mode, both upper-level IES service providers and lower-level IES energy users can participate in the CET-CCER joint operation market. The upper-level IES service providers formulate the optimal unit output dispatch plan based on the comprehensive operating cost, and calculate the spatial dynamic carbon emission factor based on the CEF theory and provide it to the lower-level IES energy users. The lower-level IES energy users receive the carbon emission factor, and through the CET-CCER joint market, they incentivize the user side to transfer low-carbon energy consumption behavior. The updated load demand is transmitted back to the upper layer, and the output is optimized and adjusted again, and the carbon emission factor is updated. The optimization is repeated and iterated, and the system finally reaches the optimal low-carbon state.

[0119] Step 4: Source-load interaction incentive dual-layer optimization scheduling model

[0120] It includes the objective function of the upper-level integrated energy service provider dispatch model and the objective function of the lower-level integrated energy user response model.

[0121] In order to verify the effectiveness of the low-carbon dispatch method proposed in this invention, three low, medium and high wind power penetration rate operation scenarios of 45%, 70% and 90% are constructed, and each scenario is compared and analyzed according to the following three strategies:

[0122] Strategy 1: Consider only the CET market.

[0123] Strategy 2: Based on Strategy 1, CCER is introduced and the CET-CCER market joint operation model is considered.

[0124] Strategy 3: Based on Strategy 2, LCDR is introduced to consider the source-load interaction incentive low-carbon transformation model.

[0125] The simulation examples are carried out for the three strategies under different wind power penetration rates, and the comparison between cost and carbon emissions is shown in Table 1.

[0126] Table 1 System operation cost and carbon emissions under various scenarios

[0127]

[0128] As can be seen from Table 1, after the introduction of CCER, compared with Strategy 1, the CCER benefits obtained by the system gradually increase with the increase of wind power penetration rate, and the carbon costs under different wind power penetration rates have all decreased, respectively by 5,300 yuan, 12,200 yuan, and 11,000 yuan, that is, a decrease of 1.88%, 5.01%, and 4.83%. It can be seen that under the market joint operation mode, the participation of the CCER market can alleviate the pressure of carbon quota compliance of the system, and the clean benefits of renewable energy have also received certain economic subsidies, reflecting the carbon emission reduction value of renewable energy, which is conducive to the further development of renewable energy in the future.

[0129] The CCER market not only provides economic subsidies to service providers for clean units, but also stimulates energy users to shift to low-carbon energy use behaviors. Strategy 3 uses spatial dynamic carbon emission factors to assist the CCER market in guiding energy users to conduct LCDR. As can be seen from Table 1, compared with Strategy 2, the total cost has decreased after the system responded under different wind power penetration rates, which decreased by 34,800 yuan, 59,300 yuan, and 26,300 yuan, respectively, that is, a decrease of 1.34%, 2.24%, and 0.92%. Under the source-load interaction incentive low-carbon transformation model, the economic benefits of the system have been effectively improved.

[0130] After the introduction of LCDR in Strategy 3, compared with Strategy 2, carbon emissions at different wind power penetration rates also decreased, by 61.4 tons, 129.8 tons, and 77.1 tons, respectively, or by 1.78%, 4.48%, and 2.9%. It can be seen that the proposed source-load interaction incentive low-carbon transformation model not only allocates the carbon emission responsibility on the source side to the load side, but also encourages energy users to achieve LCDR, thereby improving the low-carbon benefits of the system. Due to the different response characteristics under different wind power penetration rates, the system emission reduction after LCDR is also different.

[0131] The peak-to-valley difference levels of carbon emission factors under different systems are shown in Table 2.

[0132] Table 2 Peak-to-valley difference levels of dynamic carbon emission factors under different systems

[0133]

[0134] As can be seen from Table 2, the peak-to-valley difference of carbon emission factors under different systems of IES presents different characteristics. The low-carbon area during the high wind period at night is wider than that during the low wind period at noon, and the carbon emission factor of electricity at this time is also lower than that during the noon period. It can be seen that the peak-to-valley difference level of the power system is greatly affected by wind power, and the "anti-peaking" characteristics of wind power output lead to a large peak-to-valley difference level of carbon emission factors in a day.

[0135] The peak-to-valley difference of the carbon emission factor of the gas grid is related to the output of P2G and gas source, and most of the gas load is supplied by the gas source. P2G only converts part of the abandoned wind power into natural gas, resulting in the peak-to-valley difference of the carbon emission factor of the gas grid not being drastic. The heat sources of the thermal system are all supplied by CHP and GB, resulting in the peak-to-valley difference of the thermal system being affected by both the power system and the natural gas system, resulting in an increase in the peak-to-valley difference of the carbon emission factor of the thermal grid compared to the natural gas system. From this point of view, the carbon emission factor based on carbon flow has a more obvious carbon reduction space for the response of electric load.

[0136] The LCDR diagram of electric load under low wind power penetration is as follows Figure 4 As shown. Figure 4 It can be seen that under the low wind power penetration scenario, the carbon emission factor change of the system generally shows an increase-decrease characteristic, and a carbon emission factor inflection point appears at this time.

[0137] The power balance diagram of strategy 2 under low wind power penetration rate is as follows Figure 5 As shown. Figure 5 It can be seen that the inflection point of the carbon emission factor appears under low wind power penetration rate because the output of low-carbon energy (wind power) accounts for a low proportion, and the carbon emission factor of the power system is mainly dominated by high-carbon energy (thermal power). In the period of 00:00-9:00, as the load gradually increases, the thermal power units need to meet the load level, the output increases accordingly, and the carbon emission factor shows an upward trend. In the low wind period of 10:00-14:00, the thermal power units need to increase their output, and the CCPP needs to reduce its own capture energy consumption to increase the net output to meet the peak load level. At this time, the carbon emissions of the system increase, and the carbon emission factor also reaches the peak. In this case, the peak-to-valley difference of the system load is reduced by 32.81MW, which has the effect of peak shaving and valley filling.

[0138] The LCDR diagram of electric load under high wind power penetration is as follows Figure 6 As shown. Figure 6 It can be seen that under the condition of high wind power penetration rate, the system carbon emission factor shows a decrease-increase-decrease-increase situation, and three carbon emission factor inflection points appear at this time.

[0139] The LCDR diagram of electric load under high wind power penetration is as follows Figure 7 As shown. Figure 7It can be seen that the three carbon emission factor inflection points under high wind power penetration are due to the large proportion of low-carbon energy (wind power) output, and the carbon emission factor of the power system is mainly dominated by low-carbon energy (wind power). However, wind power has the characteristics of "anti-peak regulation". In the period of 00:00-8:00, the load gradually increases, which improves the consumption level of wind power, and the carbon emissions of the system decreases accordingly, and the carbon emission factor also decreases accordingly. In the peak load period of 9:00-14:00, wind power is in a low-generation state, and high-carbon energy (thermal power) needs to increase its output, causing the carbon emission factor to reach its peak. In the period of 15:00-19:00, the wind power output increases, the system shows a cleaner trend, and the carbon emission factor decreases accordingly. In the period of 20:00-24:00, the load power gradually decreases, while the wind power gradually increases, and the amount of wind power consumed also gradually decreases, resulting in a slow increase in the carbon emission factor. In this case, the peak-to-valley difference of the system load increased by 29.11MW, which may increase the complexity of scheduling.

[0140] The implementation methods of the present invention are not limited to the above-mentioned embodiments, and any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principle of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. An integrated energy system optimization scheduling method based on source-load interaction to stimulate low-carbon transformation, characterized by: It includes the following steps: Step 1: Based on the CEF theory, the IES source-grid-load full-chain carbon tracking method is constructed to accurately measure the carbon emissions of all links of IES. At the same time, the carbon emission responsibility on the source side is allocated to the load side, and the spatial dynamic carbon emission factor is obtained as a standard for measuring the carbon reduction on the user side of the CCER market; Step 2: Relying on the carbon reduction value of the CCER market, build a CET-CCER joint market operation model, use carbon emission factors to drive the low-carbonization of user-side energy consumption, and thus guide the smooth implementation of LCDR in an orderly manner; Step 3: Form a source-load interaction incentive low-carbon transformation model, realize interactive coupling between source and load, and improve the carbon efficiency of the system from both ends of the source and load; Step 4: Construct a two-layer optimization scheduling model of source-load interaction incentives to obtain the optimal solution and enable the system to reach the optimal low-carbon state.

2. The method for optimizing and dispatching an integrated energy system based on source-load interaction to stimulate low-carbon transformation according to claim 1 is characterized by: The calculation method of the spatial dynamic carbon emission factor in step 1 is as follows: The calculation formula of the carbon potential of the power grid node is expressed as: Where: is the carbon potential of power network node n at time t; P h,n,t is the power injected into node n by unit h at time t; P w,n,t is the power injected into node n by branch w at time t; is the carbon flow density injected into branch w of node n at time t; is the carbon emission intensity of unit h connected to node n; G is the set of generators connected to node n; L The set of branches for all flows injected into node n; The calculation principle of carbon potential of gas grid nodes is consistent with that of the power grid. The specific calculation formula is as follows: Where: is the carbon potential of natural gas network node n at time t; Q w,n,t Q is the amount of gas injected into node n from branch w at time t; h,n,t is the power injected into node n by gas source h at time t; is the carbon flow density injected into branch w of node n at time t; is the carbon emission intensity of the gas source h connected to node n; E is the carbon emission factor of natural gas; B is the calorific value of natural gas; The set of branches for all gas flow injection nodes n; is the set of all gas sources connected to node n; Carbon transfer relationship within EH: P in And in =P out And out Where: P in is the input power; e in is the input node carbon potential; P out is the output power; e out is the output node carbon potential; According to the principle of conservation of carbon emissions, a net node carbon potential calculation model for liquid storage CCPP is constructed, which is specifically expressed as follows: Where: is the net nodal carbon potential at the CCPP output end at time t; P CCPP,t E is the net power of CCPP at time t; SG,t is the amount of carbon to be captured in the liquid storage tank at time t; E G,t is the carbon emission of CCPP at time t; β is the carbon capture efficiency; δ is the flue gas split ratio; The carbon transfer equation of EH is shown below: Where: is the carbon potential of the natural gas node input to the combined heat and power (CHP) and gas boiler (GB) at time t; is the carbon potential of the output nodes of each energy conversion device at time t; η P2G , η GB The conversion efficiency of each energy conversion device; The topological structure of the heat network consists of an inlet pipe and a return pipe. The calculation principle of the carbon flow model of the two is the same. The specific calculation formula is expressed as: Where: is the carbon potential of thermal network node n at time t; L h is the set of pipes connected to node n; m l,t is the flow velocity in pipe l at time t; is the outlet temperature of the pipe l connected to the node n at time t; is the loss temperature of the pipeline l connected to node n at time t; is the carbon flow density injected into pipeline l of node n at time t; The calculation formula of spatial dynamic carbon emission factor is expressed as: Where: Z is the node set of the defined spatial area; e i,t is the spatial dynamic carbon emission factor of the i-th load at time t; is the power of the i-th load node j at time t; is the carbon potential of the i-th load node j at time t.

3. The method for optimizing and dispatching an integrated energy system based on source-load interaction to stimulate low-carbon transformation according to claim 1 is characterized by: The CCER obtained by wind power in step 2 is expressed as follows: Where: CCER obtained for wind power; P w,t is the wind power grid-connected power at time t; Among them, the grid baseline carbon emission factor e CM The calculation method is expressed as follows: And CM =75%·and OM +25%·and BM Where: e OM is the marginal carbon emission factor of electricity; e BM is the capacity marginal carbon emission factor; The CCER obtained by the LCDR is expressed as follows: Where: CCERs obtained for LCDR; is the load response transfer quantity of the i-th type at time t; is the load response transfer amount of the i-th type at time t; k is the number of load types.

4. The method for optimizing and dispatching an integrated energy system based on source-load interaction to stimulate low-carbon transformation according to claim 1 is characterized by: In step 3, both the upper-level IES service providers and the lower-level IES energy users can participate in the CET-CCER joint operation market; the upper-level IES service providers formulate the optimal unit output dispatch plan based on the comprehensive operation cost, and calculate the spatial dynamic carbon emission factor based on the CEF theory and provide it to the lower-level IES energy users; the lower-level IES energy users receive the carbon emission factor, and encourage the user side to shift to low-carbon energy consumption behavior through the CET-CCER joint market. The updated load demand is transmitted back to the upper layer, and the output is optimized and adjusted again, and the carbon emission factor is updated. The optimization is iterated in a reciprocating cycle, and finally the system reaches the optimal low-carbon state.

5. The method for optimizing and dispatching an integrated energy system based on source-load interaction to stimulate low-carbon transformation according to claim 1 is characterized by: The upper layer of the source-load interactive incentive two-layer optimization scheduling model in step 4 is the objective function of the integrated energy service provider scheduling model. The upper-layer IES service provider optimizes the unit output with the goal of minimizing the total cost of system comprehensive operation, which is specifically expressed as follows: Where: F is the total operating cost of the system; F e,t is the energy consumption cost of the system in period t; F yw,t is the operation and maintenance cost of each unit in period t; F qt,t is the start-up and shutdown cost of each unit in period t; F c,t is the system carbon cost in period t; F curt,t is the system’s wind curtailment cost during period t; CCER income obtained from wind power grid access; 1) Energy costs: The primary energy consumed by the system mainly includes the cost of purchased natural gas and the cost of coal consumption of thermal power units. The total cost of the two is specifically expressed as follows: Where: P Gi,t is the total output of thermal power unit i in period t; a i , b i 、c i is the coal consumption characteristic coefficient of thermal power unit i; π coal is the unit standard coal price; π1 is the unit natural gas price; Q g,t is the gas consumption of purchased natural gas in the system during period t; 2) Operation and maintenance costs: It is mainly calculated based on the operation and maintenance coefficient of each device in IES, which is specifically expressed as follows: Where: P i,t is the total output of the system for device i in period t; s i is the operation and maintenance coefficient of equipment i; 3) Start-stop costs: Where: U i,t is the start / stop status of thermal power unit i during period t, U i,t =1 means that thermal power unit i is turned on during period t, U i,t =0 means that thermal power unit i is shut down during period t; U i,t =0 is the start-up and shutdown cost of thermal power unit i; 4) Carbon cost: Carbon costs include CET costs and carbon storage costs. The baseline method is used to allocate carbon quotas for free. Under the CET-CCER market joint operation mode, part of the CCER volume obtained by the system can offset the carbon quota and reduce the pressure of compliance. The specific carbon costs are as follows: Where: π j is the carbon trading price; s is the price of carbon dioxide storage per unit mass; F CET,t is the carbon trading cost of the system in period t; F cs,t is the carbon storage cost of the system in period t; E Ji,t is the net carbon emission of the system in period t; α1 and α2 are carbon quota coefficients; Q CHP,t , Q GB,t is the gas consumption of CHP and GB in period t; E duty The amount of carbon allowances allocated for free; is the total carbon dioxide captured by the system during period t; is the carbon dioxide utilization of the system during period t; The CCER amount used to offset carbon quota; 5) Wind curtailment costs: Where: P curt,t P is the wind power abandoned during period t; WY,t is the predicted wind power during period t; k curt is the penalty coefficient for unit abandoned wind volume; 6) Wind power CCER income: The CCER benefits obtained from the grid connection of wind power on the source side are specifically expressed as follows: Where: π ccer It is the unit price of CCER.

6. The method for optimizing and dispatching an integrated energy system based on source-load interaction to stimulate low-carbon transformation according to claim 5 is characterized by: The constraints of the upper-level integrated energy service provider dispatching model of the source-load interactive incentive dual-level optimization dispatching model in step 4 are: 1) Power balance constraints H GB,t +H CHP,t =H load,t +ΔH t in -ΔH t out Q buy,t +Q P2G,t -Q CHP,t -Q GB,t =Q load,t +ΔQ t in -ΔQ t out Q min ≤Q buy,t ≤Q max Where: P load,t P is the electric load power in period t; CHP,t is the electric power generated by CHP during period t; H is the amount of load transfer in and out during period t; GB,t , H CHP,t are the heat load powers supplied by GB and CHP in period t respectively; Q is the amount of heat load transferred in and out during period t; buy,t Q is the amount of natural gas purchased by the system during period t; P2G,t Q is the gas production from power to gas (P2G) during period t; load,t is the gas load; Q is the amount of gas load transferred in and out during period t; min , Q max The upper and lower limits of the gas source output; 2) CCPP operation constraints: Where: P Y,t is the operating energy consumption of CCPP during period t; P D is the fixed energy consumption of CCPP; λ is the electricity consumed per unit CO2 captured; e gi,t is the carbon emission intensity of thermal power unit i; P Gi,max is the maximum power generation of CCPP; η is the maximum working coefficient of the regeneration tower and compressor; 3) Constraints on start and stop of thermal power units: Where: T S is the minimum shutdown time of the thermal power unit; T O The minimum startup time of the thermal power unit; 4) Spinning reserve constraints: The spin reserve credibility opportunity constraint expression is as follows: Where: Cr{} is the confidence expression; is the maximum value of the net output of thermal power unit i in period t; is the maximum value of CHP power output during period t; is the fuzzy representation of load and wind power; R g , are the ramp rates of CHP and thermal power plant units respectively; α is the confidence level of spinning reserve; 5) CCER offset quota constraints: In the formula: ψ is the maximum proportion of carbon quotas allowed to be cleared by the system. The current CCER can offset carbon quotas that should not exceed 5% of the total.

7. The method for optimizing and dispatching an integrated energy system based on source-load interaction to stimulate low-carbon transformation according to claim 1 is characterized by: The lower layer of the source-load interactive incentive two-layer optimization scheduling model in step 4 is the comprehensive energy consumer response model objective function. The lower layer IES consumer takes the minimum comprehensive energy cost as the goal. The consumer can participate in LCDR and convert the response emission reduction into an equivalent CCER for sale. The specific expression is as follows: Where: f is the total comprehensive energy cost; L i,t is the load in the period t after the response; π i is the energy price of the i-th type of load; Δt is the unit time scale (1h).

8. The method for optimizing and dispatching an integrated energy system based on source-load interaction to stimulate low-carbon transformation according to claim 7 is characterized by: The constraints of the lower-level comprehensive energy consumer response model of the source-load interactive incentive dual-level optimization scheduling model in step 4 include user transfer quantity constraints, user response quantity constraints, and user satisfaction constraints: Where: is the pre-response load value of the i-th type load at time t; u is the single-point load transfer limit; M is the user satisfaction; M min Minimum user satisfaction.

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