Optimal scheduling method for integrated energy system based on source-load interaction to stimulate low-carbon transformation

Abstract

The application discloses a kind of based on source load interaction excitation low-carbon transformation comprehensive energy system optimization scheduling method, belong to electric power system technical field.The present application is based on CEF theory and constructs IES source-network-load whole chain carbon tracking method, accurately measures IES whole link carbon emissions, and at the same time, the responsibility of source side carbon emission is apportioned to load side, to obtain spatial dynamic carbon emission factor as the standard for measuring CCER market user side carbon reduction amount.Second, relying on the carbon reduction value of CCER market, to build CET-CCER joint market operation mode, use carbon emission factor to drive user side energy consumption behavior low carbonization, to orderly guide the smooth implementation of LCDR.Finally, form source load interaction excitation low-carbon transformation mode, realize the interaction coupling between source and load, and improve the carbon efficiency level of system from source and load.The present application is a kind of based on source load interaction excitation low-carbon transformation comprehensive energy system optimization scheduling method, effectively reduces the carbon cost and overall carbon emissions under different wind power penetration rate scenarios.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power systems, and particularly relates to a comprehensive energy system optimal scheduling method based on source-load interaction and low-carbon transformation. BACKGROUND

[0002] Accurate measurement and analysis of carbon emissions are the basis and premise for the power sector to achieve early decarbonization. China will also develop a "scale" for carbon emission data to improve the carbon measurement traceability system. In this regard, accurate carbon measurement of all links of the energy system has become one of the key scientific problems of the power system under the background of low-carbon development.

[0003] Accurate carbon measurement is conducive to the healthy and orderly development of the Chinese certified emission reduction (CCER) market mechanism. On January 22, 2024, the national voluntary greenhouse gas emission trading market was officially restarted in Beijing. The CCER market, as an important carbon reduction market type in addition to the carbon emission trading (CET) market in China, can trade certified emission reductions to offset carbon quotas. Carbon emission factors are one of the key indicators for certifying carbon reductions, and extending carbon emission factors to multiple energy fields is conducive to building a unified carbon management system for integrated energy systems (IES).

[0004] Carbon emission factors calculated based on carbon emission flow (CEF) theory can effectively guide users to achieve active low-carbon demand response (LCDR) by adjusting energy use timing, further improving the low-carbon level of society as a whole. Carbon emission factors, as a direct signal to measure carbon emissions, can effectively represent the carbon level of the system, and in combination with the CCER market, can provide a reasonable and orderly user-side carbon reduction incentive mechanism. In the LCDR process, it can break away from the price signal and rely directly on carbon emission factors to encourage low-carbon use behavior, thereby promoting green and low-carbon transformation of the energy system.

[0005] Therefore, it is currently difficult to rely solely on price signals to encourage low-carbon use behavior on the user side, and there is an urgent need for a new technical solution to solve this problem in the prior art. SUMMARY

[0006] The technical problem to be solved by the present application is to provide a comprehensive energy system optimal scheduling method based on source-load interaction and low-carbon transformation, which solves the problem of difficulty in relying on price signals to encourage low-carbon use behavior on the user side in the prior art.

[0007] The technical scheme adopted by the present application is to provide a comprehensive energy system optimal scheduling method based on source-load interaction incentive low-carbon transformation, which comprises the following steps:

[0008] Step 1: Construct an IES source-grid-load full-chain carbon tracking method based on the CEF theory, accurately measure the IES full-link carbon emissions, and at the same time, apportion the source-side carbon emission responsibility to the load side to obtain a spatial dynamic carbon emission factor as a standard for measuring the CCER market user-side carbon reduction amount;

[0009] Step 2: Relying on the carbon reduction value of the CCER market, construct a CET-CCER joint market operation mode, and use the carbon emission factor to drive the user-side energy use behavior to be low-carbonized, so as to orderly guide the smooth implementation of the LCDR;

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

[0011] Step 4: Construct a source-load interaction incentive double-layer optimal scheduling model to obtain the optimal solution, so that the system reaches the optimal low-carbon state.

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

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

[0014]

[0015] In the formula: is the carbon potential of the 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 of branch w injected into node n at time t; is the carbon emission intensity of unit h connected to node n; n G is the set of generator units connected to node n; n L is the set of all power flow injected nodes n;

[0016] The carbon potential calculation principle of the gas network node is consistent with that of the power grid, and the specific calculation formula is represented as:

[0017]

[0018] In the formula: is the carbon potential of the natural gas network node n at time t; Q w,n,t is the gas quantity injected into node n by branch w at time t; Q h,n,t is the power injected into node n by gas source h at time t; Carbon flow density of branch w of injection node n at time t; Carbon emission intensity of gas source h connected with node n; E is the carbon emission factor of natural gas; B is the heat value of natural gas; Branch set of all natural gas flow injection nodes n; All gas source sets connected with node n;

[0019] Carbon transfer relationship in EH:

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

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

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

[0023]

[0024] In the formula: is the net node carbon potential of the CCPP output end at time t; P CCPP,t is the net online power of the CCPP at time t; E SG,t is the carbon capture amount of the liquid storage tank at time t; E G,t is the carbon emission amount of the CCPP at time t; β is the carbon capture efficiency; δ is the flue gas diversion ratio;

[0025] The carbon transfer equation of EH is as follows:

[0026]

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

[0028] The heat network topology is composed of an inlet pipeline and a return pipeline, and the calculation principles of the carbon flow models of the two are the same, and the specific calculation formula is expressed as:

[0029]

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

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

[0032]

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

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

[0035]

[0036] In the formula: is the CCER obtained by the wind power; P w,t is the on-grid power of the wind power at time t;

[0037] wherein the grid baseline carbon emission factor e CM The calculation method is represented as follows:

[0038] e CM = 75%·e OM + 25%·e BM

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

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

[0041]

[0042] In the formula: is the CCER obtained by the LCDR; is the response amount of the i-th type of load at time t; is the response amount of the i-th type of load at time t; k is the number of load types.

[0043] The upper IES service provider and the lower IES energy consumer in step 3 can participate in the CET-CCER joint operation market; the upper IES service provider formulates an optimal unit output dispatching plan according to the comprehensive operation cost, and provides the lower IES energy consumer with a spatial dynamic carbon emission factor calculated based on the CEF theory; the lower IES energy consumer receives the carbon emission factor, and through the CET-CCER joint market, encourages the user side to transfer low-carbon energy consumption behavior, and updates the load demand and returns it to the upper layer, and the output is optimized and adjusted again, the carbon emission factor is updated, and the iteration optimization is repeated, and finally the system reaches an optimal low-carbon state.

[0044] The upper layer of the source-load interaction incentive double-layer optimization dispatching model in step 4 is the objective function of the comprehensive energy service provider dispatching model, and the upper IES service provider optimizes the unit output with the minimum total system comprehensive operation cost as the target, which is specifically expressed as follows:

[0045]

[0046] In the formula: F is the total comprehensive operation cost of the system; F e,t is the energy consumption cost of the system in the t period; F yw,t is the operation and maintenance cost of each unit in the t period; F qt,t is the start-stop cost of each unit in the t period; F c,t is the carbon cost of the system in the t period; F curt,t is the wind curtailment cost of the system in the t period; is the CCER income obtained by wind power on-grid;

[0047] 1) Energy cost:

[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, and the total cost of the two is specifically expressed as follows:

[0049]

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

[0051] 2) Operation and maintenance cost:

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

[0053]

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

[0055] 3) Start-stop cost:

[0056]

[0057] where U i,t is the start-stop state of thermal power unit i in the t period, U i,t = 1 indicates that thermal power unit i is started in the t period, U i,t = 0 indicates that thermal power unit i is stopped in the t period; U i,t = 0 is the start-stop cost of thermal power unit i;

[0058] 4) Carbon cost:

[0059] The carbon cost includes CET cost and carbon sequestration cost. The carbon quota is allocated free of charge by the baseline method. In the joint operation mode of CET-CCER market, part of the CCER amount obtained by the system can offset the carbon quota, reduce the compliance pressure, and the carbon cost is specifically represented as follows:

[0060]

[0061] where π j is the carbon trading price; π s is the unit mass carbon dioxide sequestration price; F CET,t is the carbon trading cost of the system in the t period; F cs,t is the carbon sequestration cost of the system in the t period; E Ji,t is the net carbon emission amount of the system in the t period; α1, α2 are carbon quota coefficients; Q CHP,t , Q GB,t are the gas consumption amounts of CHP and GB in the t period; E duty is the amount of free allocation of carbon quota; is the total carbon dioxide capture amount of the system in the t period; is the carbon dioxide utilization amount of the system in the t period; is the CCER amount used to offset the carbon quota;

[0062] 5) Wind curtailment cost:

[0063]

[0064] where P curt,t is the wind curtailment power in the t period; P WY,t is the wind power prediction power in the t period; k curtPenalty coefficient for unit wind curtailment;

[0065] 6) Wind power CCER revenue:

[0066] The CCER revenue obtained by the wind power on the source side is specifically represented as follows:

[0067]

[0068] In the formula: π ccer is the unit price of CCER.

[0069] The constraint condition of the upper layer comprehensive energy service provider scheduling model of the source-load interaction incentive double-layer optimal scheduling model in step 4 is:

[0070] 1) Power balance constraint

[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] In the formula: P load,t is the electric load power in the t period; P CHP,t is the electric power generated by the CHP in the t period; is the electric load transfer-in and transfer-out amount in the t period; H GB,t , H CHP,t are the heat load power supplied by the GB and the CHP in the t period respectively; is the heat load transfer-in and transfer-out amount in the t period; Q buy,t is the amount of natural gas purchased by the system in the t period; Q P2G,t is the amount of power-to-gas (P2G) gas in the t period; Q load,t is the gas load amount; is the gas load transfer-in and transfer-out amount in the t period; Qmin , Q max upper and lower limits of the gas source output;

[0076] 2) CCPP operation constraints:

[0077]

[0078] wherein: P Y,t is the operation energy consumption of the CCPP in the t period; P D is the fixed energy consumption of the CCPP; λ is the electricity consumption for capturing unit CO2; e gi,t is the carbon emission intensity of the thermal power unit i; P Gi,max is the maximum power generation of the CCPP; η is the maximum working coefficient of the regenerative tower and compressor;

[0079] 3) Thermal power unit start-stop constraints:

[0080]

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

[0082] 4) Rotational reserve constraints:

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

[0084]

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

[0086] 5) CCER offset quota amount constraints:

[0087]

[0088] wherein: ψ is the maximum proportion of the system allowed to clear the carbon quota, and the current CCER offset carbon quota should not exceed 5% of the total amount.

[0089] The lower layer of the source-load interaction excitation double-layer optimization scheduling model in step 4 is a comprehensive energy consumer response model objective function, and the lower layer IES consumer takes the minimum comprehensive energy consumption cost as the target, and the consumer can participate in the LCDR, and the response emission reduction amount can be converted into an equivalent CCER for sale, and is specifically expressed as follows:

[0090]

[0091] In the formula, f is the total comprehensive energy consumption cost, L i,t is the load amount of the t period after response, and i is the energy price of the i type load, and min is the unit time scale (1h).

[0092] The constraint condition of the lower layer comprehensive energy consumer response model of the source-load interaction excitation double-layer optimization scheduling model in step 4 includes a user transfer amount constraint, a user response amount constraint and a user satisfaction constraint.

[0093]

[0094] In the formula, is the load value before response of the i type load at t time; u is a single-point load transfer limit value; M is user satisfaction; and min is the minimum user satisfaction.

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

[0096] 1. After the system introduces the CCER market, the carbon costs of three different wind power penetration rate scenarios are all reduced, and are reduced by 1.88%, 5.01% and 4.83% respectively. It is proved that the CET-CCER market joint operation mode constructed by the application brings good low-carbon economic benefits.

[0097] 2. The CCER market can effectively guide the energy side to carry out the LCDR, encourage users to low-carbonize their own energy consumption behavior, so that the overall carbon emissions of the system under three different wind power penetration rates are reduced by 1.78%, 4.48% and 2.9% respectively. The total cost is reduced by 1.34%, 2.24% and 0.92% respectively. It is proved that the low-carbon economic benefits under the low-carbon transformation mode of the source-load interaction excitation are good.

[0098] 3. The application analyzes the influencing factors of the peak-valley difference level of the carbon emission factor of the electric-thermal-gas three systems, and research and comparison find that the peak-valley difference level of the carbon emission factor of the power system is more obvious, and the user obtains a larger carbon reduction space through the electric load demand response. DETAILED DESCRIPTION

[0099] Figure 1This is a schematic diagram of the IES source-grid-load full-chain carbon tracking method of the present invention, which is based on an integrated energy system optimization scheduling method that stimulates low-carbon transformation through source-load interaction;

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

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

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

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

[0104] Figure 6 This is a schematic diagram of the LCDR of electric load under high wind power penetration rate according to the present invention's integrated energy system optimization scheduling method based on source-load interaction to stimulate low-carbon transformation;

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

[0106] The present invention will be 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 As shown. 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 energy carbon emissions "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 load-side energy consumption. All three meet the "three-can" 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] To measure the indirect carbon emissions of power users, this patent calculates the carbon potential of user-side nodes based on the CEF theory of the proportional sharing principle, as the carbon potential of a node is only affected by the injected power flow. IES is not an independent individual, 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 energies. The carbon transfer relationship between EHs is shown in FIG. 1. Figure 2 The carbon transfer relationship inside the EH needs to comply with the carbon emission conservation principle, that is, the input carbon emission needs to be equal to the output carbon emission, but for single-input-multiple-output devices, there is a carbon allocation problem between multiple energies at the output port, which can be allocated according to the efficiency allocation principle, that is, the carbon emissions of heterogeneous energies are inversely proportional to their energy conversion efficiency. Thus, the carbon transfer relationship inside the EH can be described.

[0110] CEF will cause the carbon potential of the load node close to the clean unit to be at a relatively stable low level; otherwise, the carbon potential of the node far from the clean unit is at a relatively stable high level. However, users do not have the freedom to choose the location of the access node, and it is not fair to analyze the carbon emissions of each node according to the carbon potential of the node. Therefore, the spatial granularity of the node carbon potential can be relaxed to a certain area for averaging processing 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: Joint operation mode of CET-CCER market

[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 joint market operation mode of CET-CCER can not only give subsidies to source-side clean energy emission reduction projects, but also drive the load side users to carry out LCDR.

[0113] The full-chain carbon tracking method effectively allocates the carbon responsibility of the system, and gives the carbon emission factor of the spatial dynamic carbon emission factor, which is a carbon measurement index for encouraging the load side carbon emission reduction of the CCER market. The user side can measure the carbon emissions caused by its own energy use behavior according to the spatial dynamic carbon emission factor, and consider the low-carbonization transfer of its own energy use behavior to actively seek the maximum economic value in the transfer process, and improve the situation that the LCDR is difficult to carry out according to the price incentive signal in the past.

[0114] The certified emission reduction of clean energy emission reduction projects is the grid baseline emission factor. The latest published “2019 Annual Emission Reduction Project China Regional Grid Baseline Emission Factor” announced the marginal carbon emission factor in different regions of China.

[0115] The user's participation in the process of LCDR also produces a certain amount of carbon emission reduction, and the carbon value produced in this part is often ignored. The present application measures the carbon emission reduction produced by LCDR according to the proposed spatial dynamic carbon emission factor. The certified carbon emission reduction can be exchanged for an equivalent amount of CCER in the CCER market, so as to realize the economic conversion of the carbon value in the user's response process.

[0116] Step 3: Source-load interaction incentive low-carbon transformation mode

[0117] The whole-chain carbon tracking method proposed in the present application allocates the source-side carbon emission responsibility to the load side, realizes the interactive coupling between the source and the load, and provides an important index for stimulating the user-side low-carbon energy use transfer in the CET-CCER joint market operation mode. In summary, the source-load interaction incentive low-carbon transformation mode can be constructed, and the low-carbon and green transformation of the energy system is further promoted. The operation framework of the source-load interaction incentive low-carbon transformation mode is as shown in Figure 3

[0118] In this mode, the upper-layer IES service provider and the lower-layer IES energy consumer can both participate in the CET-CCER joint operation market. The upper-layer IES service provider formulates an optimal unit output dispatching plan according to the comprehensive operation cost, and provides the lower-layer IES energy consumer with a spatial dynamic carbon emission factor calculated based on the CEF theory. The lower-layer IES energy consumer receives the carbon emission factor, stimulates the user-side low-carbon energy use behavior transfer through the CET-CCER joint market, updates the load demand, and transmits it back to the upper layer, optimizes and adjusts the output again, updates the carbon emission factor, and iteratively optimizes in a loop, so that the system finally reaches an optimal low-carbon state.

[0119] Step 4: Source-load interaction incentive double-layer optimization dispatching model

[0120] The double-layer optimization dispatching model includes the objective function of the upper-layer comprehensive energy service provider dispatching model and the objective function of the lower-layer comprehensive energy consumer response model.

[0121] In order to verify the effectiveness of the low-carbon dispatching method proposed in the present application, 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: only consider the CET market.

[0123] Strategy 2: on the basis of strategy 1, introduce CCER, and consider the CET-CCER market joint operation mode.

[0124] Strategy 3: on the basis of strategy 2, introduce LCDR, and consider the source-load interaction incentive low-carbon transformation mode.

[0125] The cost and carbon emission amount of the three strategies under different wind power penetration rates are shown in Table 1. ​

[0126] The operating cost and carbon emissions of the system in each scenario in Table 1

[0127]

[0128] As can be seen from Table 1, compared with strategy 1, the CCER income obtained by the system gradually increases with the increase of wind power penetration after the introduction of CCER in strategy 2, and the carbon cost under different wind power penetration rates decreases by 0.53, 1.22, and 1.1 thousand yuan, i.e. decreases by 1.88%, 5.01%, and 4.83%. It can be seen that in the market joint operation mode, the participation of the CCER market can alleviate the pressure of carbon quota fulfillment of the system, and the clean benefits of renewable energy are also economically subsidized to some extent, which reflects the carbon reduction value of renewable energy and is conducive to the further development of renewable energy in the future.

[0129] The CCER market not only gives economic subsidies to service providers, but also stimulates energy consumers to shift to low-carbon energy consumption behavior. Strategy 3 uses a spatial dynamic carbon emission factor to assist the CCER market to guide energy consumers to perform LCDR. As can be seen from Table 1, compared with strategy 2, the total cost of the system after responding under different wind power penetration rates decreases by 3.48, 5.93, and 2.63 thousand yuan, i.e. decreases by 1.34%, 2.24%, and 0.92%. In the source-load interaction incentive low-carbon transformation mode, the economic benefit of the system is effectively improved.

[0130] After the introduction of LCDR in strategy 3, compared with strategy 2, the carbon emissions under different wind power penetration rates also decrease by 61.4, 129.8, and 77.1 tons, i.e. decreases by 1.78%, 4.48%, and 2.9%. It can be seen that the source-load interaction incentive low-carbon transformation mode not only allocates the carbon emission responsibility of the source side to the load side, but also encourages energy consumers to achieve LCDR, thereby improving the low-carbon benefit of the system. Due to the different response characteristics under different wind power penetration rates, the emission reduction amount of the system after LCDR is also different.

[0131] The peak-valley difference level of the carbon emission factor under different systems, and the results are shown in Table 2.

[0132] Table 2 Peak-valley difference level of dynamic carbon emission factor under different systems

[0133]

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

[0135] The peak-valley difference level of the gas network carbon emission factor is related to P2G and gas source output, 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 a not sharp peak-valley difference level of the gas network carbon emission factor. The heat source of the heat supply system is supplied by CHP and GB, resulting in that the peak-valley difference level of the heat supply system is affected by both the electric power system and the natural gas system, and the peak-valley difference level of the heat network carbon emission factor is increased compared with that of the natural gas system. In this way, the carbon emission factor based on carbon flow is more obvious for the carbon reduction space of the electric load response.

[0136] The electric load LCDR diagram under low wind power penetration is shown in Figure 4 As can be seen from Figure 4 , under the scenario of low wind power penetration, the carbon emission factor of the system generally presents the characteristics of increase-decrease, and an inflection point of the carbon emission factor appears.

[0137] The electric power balance diagram under strategy 2 under low wind power penetration is shown in Figure 5 As can be known from Figure 5 , the inflection point of the carbon emission factor under low wind power penetration is due to the low proportion of low-carbon energy (wind power) output, and the carbon emission factor of the electric power system is mainly dominated by high-carbon energy (thermal power). During the period of 00:00-9:00, as the load gradually increases, the thermal power unit needs to meet the load level, and the output increases, and the carbon emission factor presents an upward trend. During the period of 10:00-14:00, the thermal power unit needs to increase the output, and the CCPP needs to reduce the energy consumption to increase the net output to meet the load peak level. At this time, the carbon emission of the system increases, and the carbon emission factor also reaches the peak value. Under this condition, the peak-valley difference of the system load is reduced by 32.81 MW, which has the effect of peak shaving and valley filling.

[0138] The electric load LCDR diagram under high wind power penetration is shown in Figure 6 As can be known from Figure 6 , under the condition of high wind power penetration, the carbon emission factor of the system presents the characteristics of decrease-increase-decrease-increase, and three inflection points of the carbon emission factor appear.

[0139] The electric load LCDR diagram under high wind power penetration is shown in Figure 7 As can be known from 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-peaking", during the period of 00:00-8:00, the load gradually increases, which improves the wind power consumption level, and the carbon emission of the system decreases, and the carbon emission factor also decreases. During the period of 9:00-14:00, the load peak period, wind power is in a low state, and high-carbon energy (thermal power) needs to increase output, which leads to the peak value of the carbon emission factor. During the period of 15:00-19:00, the wind power output rises, the system shows a more clean trend, and the carbon emission factor decreases. During the period of 20:00-24:00, the load power gradually decreases, while the wind power gradually increases, and the wind power consumption gradually decreases, which leads to the slow increase of the carbon emission factor. Under this condition, the peak-valley difference of the system load increases by 29.11 MW, which may increase the complexity of scheduling.

[0140] The embodiments of the present application are not limited by the above examples, and any changes, modifications, substitutions, combinations and simplifications made without departing from the spirit and principles of the present application shall be equivalent replacement methods and shall be included in the protection scope of the present application.

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 carbon emission flow CEF theory, a carbon tracking method for the IES source-grid-load full chain of the integrated energy system is constructed to accurately measure the carbon emissions of all links in the integrated energy system IES. At the same time, the carbon emission responsibility on the source side is allocated to the load side, and a spatial dynamic carbon emission factor is obtained as a standard for measuring the carbon reduction on the user side of the national certified emission reduction (CCER) market; Step 2: Relying on the carbon reduction value of the National Certified Emission Reduction (CCER) market, build a carbon trading-National Certified Emission Reduction (CET)-CCER joint market operation model, use carbon emission factors to drive low-carbon energy consumption behavior on the user side, and thus guide the smooth implementation of low-carbon response LCDR in an orderly manner; Step 3: Form a source-load interaction incentive low-carbon transformation model to achieve interactive coupling between sources and loads, and improve the carbon efficiency of the system from both the source and load sides; Step 4: Construct a two-layer optimization scheduling model with source-load interaction incentives to find the optimal solution and enable the system to achieve the optimal low-carbon state; 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; n G is the set of generators connected to node n; 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 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 from branch w into node n 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; is the set of branches of all natural gas flows injected into node 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 nodal 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 is the net on-grid power of CCPP at time t; E 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) unit and the 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 topology of the heat network consists of an inlet pipe and a return pipe. The calculation principle of the carbon flow model for 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 pipe l connected to the node n at time t; is the carbon flux density injected into the 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.

2. The method for optimizing and dispatching an integrated energy system based on source-load interaction to encourage 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 as follows: And CM =75%·e 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 type i at time t; is the load response output of type i at time t; k is the number of load types.

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: In step 3, 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, incentivize the user side to shift to low-carbon energy consumption behavior. The updated load demand is transmitted back to the upper level, and the output is optimized and adjusted again, and the carbon emission factor is updated. This iterative optimization cycle is repeated until the system finally reaches the optimal low-carbon state.

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: 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. The specific expression is as follows: Where: F is the total operating cost of the system; F e,t is the energy consumption cost of the system during 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 consumed by 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 price of unit standard coal; π1 is the price of unit natural gas purchased; 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 during period t; s i is the operation and maintenance coefficient of equipment i; 3) Start-up and shutdown costs: Where: U i,t is the start and stop status of thermal power unit i during period t, U i,t =1 means that thermal power unit i is started 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 costs: Carbon costs include CET costs and carbon sequestration costs. The baseline method is used to allocate carbon quotas free of charge. Under the CET-CCER market joint operation model, part of the CCERs obtained by the system can offset carbon quotas and reduce compliance pressure. The specific carbon costs are as follows: Where: π j is the carbon trading price; s is the price per unit mass of carbon dioxide storage; F CET,t is the carbon transaction cost of the system during period t; F cs,t is the carbon sequestration cost of the system during 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 during period t; E duty The amount of carbon allowances allocated free of charge; is the total amount of carbon dioxide captured by the system during period t; is the carbon dioxide utilization of the system during period t; The amount of CCER used to offset carbon quotas; 5) Wind curtailment costs: Where: P curt,t is the wind power abandoned during period t; P WY,t is the predicted wind power during period t; k curt is the penalty coefficient for unit wind curtailment; 6) Wind power CCER income: The CCER benefits obtained from the grid connection of source-side wind power are specifically expressed as follows: Where: π ccer The unit price of CCER.

5. The method for optimizing and dispatching an integrated energy system based on source-load interaction to stimulate low-carbon transformation according to claim 4 is characterized by: The constraints of the upper-level integrated energy service provider dispatch model of the source-load interactive incentive two-level optimization dispatch 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; is the amount of electric load transferred in and out during period t; H GB,t 、H CHP,t are the heat load powers supplied by GB and CHP during 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 is the gas production from power-to-gas (P2G) during period t; Q 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 operating coefficient of the regeneration tower and compressor; 3) Thermal power unit start-up and shutdown constraints: Where: T S is the minimum shutdown time of the thermal power unit; T O The minimum startup time of thermal power units; 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: Where: 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.

6. The method for optimizing and dispatching an integrated energy system based on source-load interaction to encourage 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 objective function of the comprehensive energy consumer response model. The lower layer IES energy consumer aims to minimize the comprehensive energy cost. The energy consumer can participate in LCDR and convert the response emission reduction into an equivalent amount of CCER for sale. The specific expression is as follows: Where: f is the total comprehensive energy cost; L i,t is the load during the t period after the response; π i is the energy price of the i-th type of load; Δt is the unit time scale (1h).

7. The method for optimizing and dispatching an integrated energy system based on source-load interaction to incentivize low-carbon transformation according to claim 6 is characterized by: The constraints of the lower-layer comprehensive energy consumer response model of the source-load interactive incentive two-layer 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.

Images