Low-carbon demand response method based on adjustable carbon emission factor

Abstract

The invention belongs to the low-carbon field of power distribution networks and power systems, and discloses a low-carbon demand response method based on an adjustable carbon emission factor, which comprises the following steps of: firstly, proposing an adjustable carbon emission factor mechanism, and establishing a power distribution network low-carbon operation mathematical model based on the adjustable carbon emission factor; and solving the problem that the carbon emission and the electric quantity are in a fixed proportion in the power distribution network by adopting a universal dynamic carbon emission factor which can be adjusted along with the carbon reduction benefit, so that the constraint of the time-of-use electricity price and the shear difference of the carbon emission factor is effectively broken through. According to the method, the adjustable carbon emission factor serves as a core measurement tool, the actual emission reduction potential serves as the basis, the load can be effectively guided to achieve low-carbon electricity utilization, the flexibility of the power distribution network participating in the carbon market is improved, and the load side can benefit from low-carbon demand response.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of low-carbon distribution network and power system, and particularly relates to a low-carbon demand response method based on adjustable carbon emission factors. BACKGROUND

[0002] To achieve the "double carbon" goal and promote the low-carbon operation of the power system, it is necessary to effectively guide users to participate in low-carbon electricity consumption. The current carbon accounting method based on the node carbon potential factor (NCEF) has problems such as lack of fairness and complex calculation, which is difficult to adapt to the environment of high proportion of distributed energy access to the distribution network. At the same time, under the current time-of-use electricity price guidance, there is an uncoordinated contradiction between the carbon reduction benefit and the electricity cost of users. For example, at the time when the carbon emission factor is high at night, but it is at the valley of electricity price, from the perspective of electricity economy, a large amount of load will be transferred to the evening, so relying on the node carbon potential to guide the reduction of load to achieve low-carbon benefit is much smaller than the electricity cost at this time.

[0003] To improve the fairness and response efficiency of carbon responsibility allocation, some studies have proposed improved methods such as average carbon emission factor and electricity-carbon ratio. However, in the distribution network with energy storage, the average carbon emission factor method simplifies the accounting but blurs the carbon responsibility difference between users - users may charge during low-carbon periods, but need to bear the average cost during high-carbon periods, which weakens the accuracy and fairness of the carbon signal guidance, and it cannot provide precise marginal incentives. The method based on the electricity-carbon ratio can clearly track the two-way carbon flow generated by the charging and discharging of energy storage, and reflect the carbon emission level of unit electricity, but its carbon factor is fixed after calculation, still lacks temporal and spatial dynamics, the incentive effect is lagging, and it is difficult to achieve dynamic incentives for the demand side. Therefore, some studies introduce the "virtual carbon storage" mechanism, which improves the adjustment flexibility of users by simulating the "storage" and "borrowing" of carbon quotas, but may bring new problems such as blurred carbon responsibility accounting boundary, uncertain long-term incentive effect, and limited system operation flexibility. SUMMARY

[0004] To solve the above technical problems, the application provides a low-carbon demand response method based on adjustable dynamic carbon emission factors (ADCEF), which generates dynamic carbon emission factors that can be adjusted with carbon reduction benefits, to break the shackles between time-of-use electricity price and carbon emission factor, effectively guide the load side to achieve low-carbon electricity consumption, and improve the overall carbon reduction effect of the power system.

[0005] The low-carbon demand response method based on adjustable carbon emission factors provided by the application comprises the following steps: Step 1, analyze the characteristics of carbon emission flow of distribution network, based on the principle that the carbon emission of power generation side is entirely borne by load side, establish a mathematical model of adjustable carbon emission factor based on energy storage and load; Step 2, propose a demand response mechanism based on adjustable dynamic carbon emission factor, guide users to participate in low-carbon demand response under the framework of time-of-use price; Step 3, establish a low-carbon demand response load side model, quantify the electricity dissatisfaction cost of users participating in load adjustment, and adjust the peak-valley difference of load; Step 4, construct a target function of optimal electric-carbon comprehensive benefit of distribution network and user response benefit, and establish a distribution network dispatching model based on adjustable carbon emission factor; Step 5, solve the target function of optimal electric-carbon comprehensive benefit of distribution network and user response benefit, realize the optimization of low-carbon demand response dispatching strategy, and guide the low-carbon electricity consumption of load.

[0006] Further, in step 1, considering the characteristics of carbon emission flow of distribution network, the dynamic carbon emission factor method is adopted to calculate the carbon emission borne by load side at any time: (1), In the formula, represents the average carbon emission factor value of all load nodes at t time; represents the load power value of node i at t time; N represents the number of nodes of distribution network; represents the total carbon emission borne by load side at t time; represents the time interval, and the load power value of node i is affected by the power value of other node j.

[0007] Further, when the energy storage is in charging state, the carbon emission of power generation side is borne by load and energy storage; the average carbon emission factor of load side in this state is denoted as , then (2), In the formula, is the average node carbon emission factor corresponding to all energy storage devices, , represents the carbon value when all energy storage devices in distribution network are in charging state, is the total charging efficiency of system, is the total charging power of system at t time; represents the total load power value of all energy storage devices at t time; the total carbon emission value at t time ; represents the electric power purchased from main network, is the average carbon emission factor of main network, represents the output power of generator of distribution network itself, represents the carbon emission factor of the power distribution network generator; Assuming the average carbon emission factor of the energy storage is The average carbon emission factor of the load side is: (3), When , the energy storage is in the charging state, and the currently calculated carbon storage of the energy storage is greater than the actual carbon storage. Denote the carbon storage at this time as: (4), In the formula, represents the assumed carbon emission factor of the energy storage device at time t; Based on the principle of constant total carbon, the carbon emission amount borne by the load will be smaller than the actual one. The current average carbon emission factor of the load side is calculated by formula (2): (5), The total carbon amount of the energy storage at the end of this time is: (6), In the formula, represents the carbon storage amount of the energy storage device at time ; When , the energy storage is in the discharging state, and the carbon emission amount is borne by the load side, and the calculated carbon storage of the energy storage is smaller than the actual carbon storage. The carbon emission factor corresponding to the discharging of the energy storage at this time is: (7), In the formula, represents the carbon amount changed by all energy storage devices in the power distribution network at time t, , represents the remaining power of the energy storage device at times t and t-1; Denote the carbon storage at this time as: (8), The entire dispatching period satisfies: (9), In the formula, represents the carbon emission amount when the energy storage is in the charging state; represents the carbon emission amount when the energy storage is in the discharging state; T represents the dispatching period; When the energy storage device is in the idle state, the charging carbon amount and the discharging carbon amount of the energy storage are both zero.

[0008] Further, in step 2, the demand response mechanism based on the adjustable dynamic carbon emission factor includes: In the period of high time-of-use price and low system carbon potential, the price is lowered according to the current carbon emission factor to encourage the transferable load to gather in this period; When the carbon emission factor breaks through the threshold, the price is raised to suppress the non-rigid demand and relieve the pressure of marginal coal-fired units.

[0009] Further, step 3 is specifically: Step 3-1, the total cost of the load side purchasing power from the distribution network is: (10), Wherein, is the price of unit electricity, represents the reduction of user low-carbon load at t time, represents the increase of load at t time; Step 3-2, establish the dissatisfaction cost of user side: (11), In the formula, represents the upper limit of user power consumption at t time, is a parameter, represents the load amount of user after participating in demand response, represents the expected power consumption of user at t time; When , it means that the user is dissatisfied with the current power consumption; When , it means that the user is satisfied with the current power consumption process; When , no dissatisfaction cost is generated.

[0010] Further, step 4 is specifically: (1) Construct the distribution network objective function: (12), In the formula, represents the total of distribution network electricity income and carbon income, represents the distribution network electricity sales revenue, represents the carbon reduction income of distribution network, represents the purchase cost of distribution network from main network, represents the cost of gas turbine providing power to distribution network; The Distflow optimal power flow is used to model the constraint condition, and the mathematical model is: (13), In the formula, represents all generator units located at node i, including the power generation of micro gas turbine, wind turbine and photovoltaic in the distribution network and the power purchased from the main network. This represents the energy storage charging and discharging power located at node i; This refers to the flexible load power participating in demand response, including transferable loads and interruptible loads; P represents the output power of all generator units located at node i; i This represents the active power of node i; This represents all generator units located at node i, including the reactive power generated by power purchased from the main grid, micro gas turbines in the distribution network, wind turbines, and photovoltaics. This represents the reactive power of energy storage charging and discharging located at node i; This represents the output reactive power of all generator units located at node i; This represents the reactive power of flexible loads participating in demand response; The second-order cone programming method is used to relax the quadratic nonlinearity conditions in the Distflow model. The relaxed mathematical model is as follows: (14), (15), (16), In the formula, r ij x ij These represent the resistance and reactance values ​​of the branch between node i and node j in the distribution network, respectively; h ij This represents the square of the current flowing through node i→j; v i P represents the square of the voltage magnitude at node i; j Q represents the active power at node j; j P represents the reactive power at node j; jk Q represents the active power between node j and node k; jk P represents the reactive power between node j and node k; ij Q represents the active power between node i and node j; ij The reactive power between node i and node j is represented by formula (14), which is the relaxation of node power balance constraint, formula (15) is the phase angle relaxation of the power of the first segment of the branch, and formula (16) is the convex relaxation of the power flow of the branch. (2) Safety constraints on the operation of various equipment in the power distribution network: (17), In the formula, P i gen,min P i gen,max This represents the minimum and maximum power output of all generator units located at node i, including those purchasing power from the main grid, micro-turbines in the distribution network, wind turbines, and photovoltaics; Q igen,min i gen,max Pmin,i and Pmax,i represent the minimum and maximum values of the active power of all generator units located at node i, including the minimum and maximum values of the active power of the micro gas turbine, wind turbine, and photovoltaic in the distribution network and the active power of the electricity purchased from the main grid; Qmin,i and Qmax,i represent the minimum and maximum values of the reactive power of all generator units located at node i, including the minimum and maximum values of the reactive power of the micro gas turbine, wind turbine, and photovoltaic in the distribution network and the reactive power of the electricity purchased from the main grid; i s,min i s,max Qmin,i and Qmax,i represent the minimum and maximum values of the active power of all generator units located at node i, including the minimum and maximum values of the active power of the micro gas turbine, wind turbine, and photovoltaic in the distribution network and the active power of the electricity purchased from the main grid; Qmin,i and Qmax,i represent the minimum and maximum values of the reactive power of all generator units located at node i, including the minimum and maximum values of the reactive power of the micro gas turbine, wind turbine, and photovoltaic in the distribution network and the reactive power of the electricity purchased from the main grid; i s,min i s,max Qmin,i and Qmax,i represent the minimum and maximum values of the active power of all generator units located at node i, including the minimum and maximum values of the active power of the micro gas turbine, wind turbine, and photovoltaic in the distribution network and the active power of the electricity purchased from the main grid; Qmin,i and Qmax,i represent the minimum and maximum values of the reactive power of all generator units located at node i, including the minimum and maximum values of the reactive power of the micro gas turbine, wind turbine, and photovoltaic in the distribution network and the reactive power of the electricity purchased from the main grid; i Ei represents the carbon emission factor of node i; Emin,i and Emax,i represent the minimum and maximum values of the carbon emission factor of node i; i min Emin,i and Emax,i represent the minimum and maximum values of the carbon emission factor of node i; i max Emin,i and Emax,i represent the minimum and maximum values of the carbon emission factor of node i; Pmin,i and Pmax,i represent the minimum and maximum values of the active power of all generator units located at node i, including the minimum and maximum values of the active power of the micro gas turbine, wind turbine, and photovoltaic in the distribution network and the active power of the electricity purchased from the main grid; Qmin,i and Qmax,i represent the minimum and maximum values of the reactive power of all generator units located at node i, including the minimum and maximum values of the reactive power of the micro gas turbine, wind turbine, and photovoltaic in the distribution network and the reactive power of the electricity purchased from the main grid; i soft,min Pmin,i and Pmax,i represent the minimum and maximum values of the active power of all generator units located at node i, including the minimum and maximum values of the active power of the micro gas turbine, wind turbine, and photovoltaic in the distribution network and the active power of the electricity purchased from the main grid; Qmin,i and Qmax,i represent the minimum and maximum values of the reactive power of all generator units located at node i, including the minimum and maximum values of the reactive power of the micro gas turbine, wind turbine, and photovoltaic in the distribution network and the reactive power of the electricity purchased from the main grid; i soft,max Pmin,i and Pmax,i represent the minimum and maximum values of the active power of all generator units located at node i, including the minimum and maximum values of the active power of the micro gas turbine, wind turbine, and photovoltaic in the distribution network and the active power of the electricity purchased from the main grid; Qmin,i and Qmax,i represent the minimum and maximum values of the reactive power of all generator units located at node i, including the minimum and maximum values of the reactive power of the micro gas turbine, wind turbine, and photovoltaic in the distribution network and the reactive power of the electricity purchased from the main grid; i min Pmin,i and Pmax,i represent the minimum and maximum values of the active power of all generator units located at node i, including the minimum and maximum values of the active power of the micro gas turbine, wind turbine, and photovoltaic in the distribution network and the active power of the electricity purchased from the main grid; Qmin,i and Qmax,i represent the minimum and maximum values of the reactive power of all generator units located at node i, including the minimum and maximum values of the reactive power of the micro gas turbine, wind turbine, and photovoltaic in the distribution network and the reactive power of the electricity purchased from the main grid; i max Pmin,i and Pmax,i represent the minimum and maximum values of the active power of all generator units located at node i, including the minimum and maximum values of the active power of the micro gas turbine, wind turbine, and photovoltaic in the distribution network and the active power of the electricity purchased from the main grid; Qmin,i and Qmax,i represent the minimum and maximum values of the reactive power of all generator units located at node i, including the minimum and maximum values of the reactive power of the micro gas turbine, wind turbine, and photovoltaic in the distribution network and the reactive power of the electricity purchased from the main grid; In the carbon emission stage, (18), wherein, is determined by formula (2), i.e., the value corresponding to the carbon amount value of 0 borne by the energy storage; similarly, the value of can be determined; (3) Load-side objective function: (19), (4) Within one dispatch, the transferable load should satisfy the principle of constant total electricity consumption, i.e., (20), wherein, , are the load transfer-in and transfer-out amounts of period t, respectively; (21), wherein, is the load shedding amount of period t; is the change amount of the flexible load power participating in the demand response.

[0011] Further, step 5 is specifically: ​​​Step 5-1, initialize the to-be-solved power variable , given the basic information of the power system, including load power, fan, photovoltaic power, electricity price, compensation coefficient; superscript 0 represents the number of iterations; is the electricity power purchased from the main network in the dispatching period, is the power output of the distribution network itself in the dispatching period; is the carbon potential of the node in the dispatching period; is the power generation of all generator units in the dispatching period; Step 5-2, the distribution network calculates the power distribution value under the condition of maximizing the power benefit based on the time-of-use electricity price, that is, , the objective function is formula (12), and the constraints are formulas (14)-(17), that is, , under the restriction of the objective function and the constraint condition, the optimal power flow calculated by the distribution network only considers the cost; k represents the kth iteration; Step 5-3, substitute the calculated power value into formula (12) to calculate the maximum benefit of the distribution network, and calculate the dynamic carbon emission factor under the maximum carbon benefit of the distribution network, and satisfy the constraint conditions formulas (18), (20), to obtain all node carbon potentials ; Step 5-4, the node carbon potential is sent to the load side to update the load amount after the demand response, that is, the load adjustment amount of the objective function formula (19) is calculated, and the constraint conditions are (20), (21) ; Step 5-5, judge whether it is less than the accuracy requirement, if it is satisfied, the operation is ended; otherwise , the actual load value of the load side is updated, and the value is uploaded to the distribution network dispatching center, and the calculation is repeated to step 5-2.

[0012] The beneficial effect of the application is that: the method generates a general dynamic carbon emission factor ADCEF which can be adjusted according to the carbon reduction benefit, breaks the limitation of the fixed proportion of carbon emission and power in the distribution network, and promotes the distribution network and the load side to achieve the goal of carbon emission reduction, avoids the high-dimensional calculation burden of node carbon potential, and increases the double flexibility of the carbon emission factor in the distribution network side and the load side, provides a theoretical basis and practical path for encouraging users in the distribution network to participate in low-carbon demand response. The application not only increases the flexibility of the distribution network participating in the carbon market, but also enables the load side to benefit from low-carbon demand response. The application discusses the influence of electricity price subsidies on reducing the scissors difference between the current time-of-use electricity price and the carbon emission factor and on demand response, provides a basis for subsequent research on hierarchical incentive mechanism and for the distribution network participating in carbon market transactions. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 is a flowchart of the method of the application; Figure 2 is a carbon conservation principle diagram; Figure 3 is a total carbon amount diagram during the energy storage charging process; Figure 4 is a demand response diagram based on electric-carbon coupling; Figure 5 is a low-carbon demand response strategy calculation flowchart; Figure 6 is a dynamic carbon emission factor comparison curve; Figure 7 is a demand response load curve comparison; Figure 8 is the income, cost and carbon reduction amount under different electricity price adjustment coefficients; Figure 9 is an improved 33-node distribution network system diagram. DETAILED DESCRIPTION

[0014] In order to make the content of the application more easily understood, the application will be further described in detail below according to specific embodiments and in conjunction with the drawings.

[0015] As shown in Figure 1 , the low-carbon demand response method based on an adjustable carbon emission factor according to the application comprises the following steps: Step 1, analyze the carbon emission flow characteristics of the distribution network, based on the principle that all carbon emissions on the power generation side are borne by the load side, establish a mathematical model of an adjustable carbon emission factor based on energy storage and load; Step 2: Propose a demand response mechanism based on adjustable dynamic carbon emission factors to guide users to participate in low-carbon demand response under the time-of-use pricing framework; Step 3: Establish a low-carbon demand response load-side model, quantify the cost of insufficient electricity consumption under user-participated load adjustment, and regulate the load peak-valley difference; Step 4: Construct an objective function that optimizes the overall electricity-carbon benefits of the distribution network and the user response benefits, and establish a distribution network dispatch model based on an adjustable carbon emission factor; Step 5: Solve the objective function that optimizes the overall benefits of electricity and carbon in the distribution network and the benefits of user response, thereby optimizing the low-carbon demand response scheduling strategy and guiding loads to use electricity in a low-carbon manner.

[0016] In step 1, considering the "fairness principle" of load participation in low-carbon demand response node locations, a dynamic carbon emission factor method is adopted to accurately calculate the carbon emissions borne by the load side at any given time, i.e.: (1), In the formula, This represents the average carbon emission factor value of all load nodes at time t; This represents the load power value at node i at time t, and N represents the number of nodes in the distribution network; This represents the total carbon emissions borne by the load side at time t.

[0017] When the distribution network purchases electricity solely from the main grid to meet the load power demand, then The carbon emission factor is equal to the average carbon emission factor of the main grid. However, the current distribution network widely integrates renewable energy generation units and distributed power sources such as energy storage. To accurately calculate the carbon emissions of the load, it is necessary to determine the carbon emission factor of the load node. The value of is closely coupled with the power flow. Given a fixed power flow, ... It is a fixed value, which limits the guiding role of dynamic carbon emission factors on the demand side under the current time-of-use pricing mechanism. For example, during the off-peak electricity price period at night, which is actually a high-carbon emission time, a large amount of load will be adjusted to this time, which will aggravate the carbon emissions of the distribution network. If the distribution network will... If the carbon emission factor is lowered, the carbon emissions will decrease compared to a fixed average carbon emission factor. In this case, the corresponding electricity price should be increased to inhibit the shift of load to high-carbon periods. Therefore, establishing a universal dynamic carbon emission factor suitable for the distribution network can enhance the dual carbon reduction effect of both the distribution network and the load.

[0018] like Figure 2As shown, when energy storage is added to the distribution network for carbon emission responsibility division, energy storage is equivalent to load when charging, absorbing CO2 carried in current, and is equivalent to power source when discharging, releasing stored CO2, so that in the charging process, the energy storage system is a state of accumulating carbon flow, and the accumulation amount is related to the carbon potential of the node at the charging time and the charging time length. Therefore, when the energy storage is in the charging state, the carbon emission of the power generation side will be borne by the load and the energy storage together, as shown in state ① in FIG. 6. Figure 2 The power sources in the distribution network mainly include power purchase from the main grid, power generation equipment of the distribution network itself, and renewable energy power. At this time, the energy storage is regarded as a general load, and it is assumed that the carbon storage value of the energy storage at the charging time is The average carbon emission factor of the load side in this state is denoted as Therefore, there is: (2), In the formula, is the total carbon storage value of all energy storage devices in the distribution network, and the total charging power of the system at this time is The real average node carbon potential factor corresponding to all energy storage devices is Considering the "zero carbon" attribute of the renewable power generation unit, the total carbon emission value is Assuming that the average carbon emission factor of the energy storage is The average carbon emission factor of the load side can be calculated by formula (2) as follows: (3), In actual operation may be greater than or less than the real value, but no matter is greater than or less than the real value, in any case, the carbon emission at the current time is borne by the energy storage and the load together.

[0019] Assuming , as shown in (a) of FIG. 7, the current calculated carbon storage of the energy storage is greater than the actual carbon storage, and the carbon storage at this time is denoted as Figure 3 (4), Based on the principle of constant total carbon, the carbon emission amount borne by the load will be smaller than the actual value, and the average carbon emission factor of the load side at the current time can be calculated by formula (1) as follows: (5), And the total carbon amount of the energy storage at the end of this time will become (6), In the formula, indicates the carbon storage value of the energy storage device at the end of the charging time ​Carbon storage at any given moment.

[0020] At the moment of energy storage discharge, the load side will bear the carbon released jointly by the power generation unit and the energy storage device. Due to the adjustable nature of the energy storage carbon emission factor, the calculation of the carbon emission factor corresponding to the energy storage discharge at this moment is related to the amount of carbon stored in the previous moment and the remaining electricity, that is: (7), In the formula, This represents the amount of carbon released from the stored energy at time t. , This indicates the remaining electrical charge of the energy storage device at times t and t-1. The calculated carbon storage capacity is less than the actual carbon storage capacity. Let this be the calculated carbon storage capacity. (8), If the amount of carbon stored during discharge is equal to the amount of carbon stored during charging, then the carbon content in the energy storage device will return to its initial state at the end of the discharge.

[0021] when At times, such as Figure 3 As shown in (b), the carbon emissions borne by the energy storage side are small while those borne by the load side are large. Similarly, based on the above analysis, when the calculated carbon stock of energy storage is less than the actual value, the corresponding carbon emissions during energy storage discharge will also decrease.

[0022] Extending the above analysis to the entire scheduling cycle, that is, satisfying the following throughout the entire system operation: (9); This relationship ensures both the flexibility of the carbon emission factor for energy storage and the principle of load-side carbon responsibility. Furthermore, equations (3) and (7) allow for the rapid acquisition of the average dynamic carbon emission factor corresponding to the load side, without being limited by the distribution network structure and parameters.

[0023] When the energy storage device is idle, both the carbon loading and carbon release of the energy storage are zero, and the carbon content of the distribution network still meets the principle that it is borne by the load side.

[0024] Figure 3 middle, The power of the load, The power of energy storage operation is represented by P, which indicates the power on the horizontal axis. This represents the amount of carbon emissions borne by the load at time t. t represents the amount of carbon emissions borne by energy storage at time t, and e represents the carbon emission factor on the vertical axis.

[0025] In step 2, in order to effectively motivate the load side to actively participate in low-carbon demand response, it is necessary to effectively guide users to participate in low-carbon demand response under the existing time-of-use electricity price framework to reduce the impact of the scissors difference, and to enable the user side to actively reduce electricity consumption during high-carbon periods or shift to low-carbon periods according to dynamic carbon emission factor information, and the measurable and verifiable carbon emission reduction contribution of their behavior should be substantially rewarded or compensated. The mechanism proposes an electricity-carbon coupling regulation model, as shown in Figure 4 When the time-of-use electricity price is high and the system carbon potential is low, the price is adjusted downward according to the current carbon emission factor to motivate the transferable load to gather in this period. When the carbon emission factor exceeds the threshold (such as the evening peak dominated by thermal power), the price is adjusted upward to suppress non-rigid demand and relieve the pressure on marginal coal-fired units. Through direct adjustment of the price lever, the load side is encouraged to shift electricity consumption to low-carbon periods, reducing overall carbon emissions from the demand side, achieving a positive cycle of electricity consumption behavior optimization and carbon emission reduction, and reducing the impact of the scissors difference by combining electricity price with carbon emission factor. At the same time, in order to ensure the interests of the distribution network, the reduction of electricity sales revenue caused by the reduction of electricity price needs to be compensated, so a carbon reduction revenue feedback mechanism is established: the carbon emission reduction achieved by the system through load migration is included in the carbon market transaction, and the carbon reduction revenue obtained is used to compensate for the loss caused by the reduction of electricity price, and the subsidy intensity of the user is increased, forming a benefit community of distribution network and load side for collaborative carbon reduction.

[0026] Further, step 3 includes: (1) The total cost of the load side purchasing electricity from the distribution network is: (10), wherein, is the electricity price per unit of electricity, represents the reduction of low-carbon load of the user at time t, represents the increase of load at time t; (2) When the user participates in demand response, the electricity consumption satisfaction is closely related to the expected electricity consumption and the maximum electricity consumption, for example, the expected electricity consumption of the user at time t is When , it indicates that the user is not satisfied with the current electricity consumption; when , it indicates that the user is satisfied with the current electricity consumption process; when , no dissatisfaction cost is generated; accordingly, the dissatisfaction cost of the user side is established as: (11), wherein, represents the upper limit of electricity consumption of the user at time t, is a parameter, represents the load of the user after participating in demand response, The user's desired power consumption is represented. Through modeling by formula (11), it can be clearly quantified that the user can effectively control the power dissatisfaction cost and alleviate the problem of excessive peak-valley difference of load under the participation of load adjustment.

[0027] Further, step 4 comprises: (1) The objective function and constraint condition of the power distribution network: (12), In order to ensure the safe and stable operation of the power distribution network, the application adopts Distflow optimal power flow to model the constraint condition: (13), In the formula, Pgen,i represents all generator units located at node i, including the power generation of micro gas turbines, wind turbines and photovoltaic power from the main network and the power distribution network, Pstorage,i represents the charging and discharging power of the energy storage located at node i, Pflex,i represents the flexible load power participating in demand response, including transferable load and interruptible load.

[0028] The second-order cone programming method is used to relax the quadratic nonlinear condition in the Distflow model, and the mathematical model after relaxation is: (14), (15), (16), In the formula, r ij , x ij respectively represent the resistance and reactance value of the branch between node i and node j in the power distribution network, h ij represents the square value of the current flowing through node i→j, and v i represents the square value of the voltage amplitude of node i.

[0029] (2) The safe operation constraint of each device in the operation of the power distribution network: (17), In the formula, , respectively represent the upper and lower limits of the variable to be solved.

[0030] Since the load side always bears the responsibility of all carbon emissions throughout the operation, the operation range of the adjustable carbon emission factor is subject to the actual carbon value, so in the carbon emission stage: (18), In the formula, is determined by formula (2), that is, the value corresponding to the carbon value borne by the energy storage at 0; similarly, the value of .

[0031] (3) Load side objective function: (19).

[0032] (4) Within one dispatch, the transferable load should meet the principle of constant total electricity consumption, that is: (20), wherein, , are the load transfer-in and transfer-out values of the time period t respectively; (21).

[0033] In step 5, the electric-carbon coupling demand response dispatching strategy under the adjustable carbon emission factor proposed by the application is realized in the manner of power distribution network-user, wherein the power distribution network solves the problem of maximizing electric income and carbon income, and sends the calculated optimal power distribution and the corrected electricity price to the user, and the user adjusts the load according to the electricity price to realize the benefit optimization of participating in low-carbon demand response. When the calculated load value does not need to be adjusted, it indicates that the program running is completed; and when the calculated load value of the load side still does not meet the convergence condition after adjustment, the adjusted load value is uploaded to the power distribution network until the convergence adjustment is met, and the steps are as follows, and the specific process is shown in Figure 5 .

[0034] The calculation steps of the demand response according to the adjustable carbon emission factor are as follows: Step 5-1, initialization of to-be-solved power variable , and given basic information of the power system (load power, wind turbine, photovoltaic power, electricity price, compensation coefficient, etc.); Step 5-2, kth iteration, the power distribution network performs power income maximization operation calculation based on time-of-use electricity price, and calculates the power distribution value under the condition of , that is, formula (12) as the objective function and formula (14)-(17) as the constraint, that is, Under the limitation of the objective function and the constraint condition, the optimal power flow calculated by the power distribution network only considers the cost; Step 5-3, substitute the calculated power value into formula (12) to calculate the maximum power distribution network income, and calculate the dynamic carbon emission factor under the maximum carbon income of the power distribution network, and meet the constraint conditions formula (18), (20), and obtain all node carbon potentials ; Step 5-4, substitute the node carbon potential The load side is issued to update the load amount after demand response, that is, the load adjustment amount of the constraint conditions (20) and (21) is calculated in the target function formula (19) ; Step 5-5, judging whether it is less than the accuracy requirement, if yes, the running is ended, otherwise the actual load value of the load side is updated, and the value is uploaded to the power distribution network dispatching center, and the program jumps to step 2 to repeat the calculation.

[0035] Based on the adjustable carbon emission factor, the improved IEEE 33-node power distribution network is used to analyze and verify the proposed strategy in three scenarios: Scenario 1, low-carbon demand response scheduling model under the classical carbon flow theory; Scenario 2, low-carbon demand response scheduling strategy under the adjustable carbon emission factor; Scenario 3, low-carbon demand response scheduling strategy under the adjustable carbon emission factor, considering demand response under different subsidy prices.

[0036] To verify the effectiveness of the proposed low-carbon demand response strategy based on the adjustable carbon emission factor, simulation and verification are carried out on the improved IEEE 33-node power distribution network, and the system topology is as shown in Figure 9 The power distribution network is connected to the main grid through node 1, the photovoltaic is located at node 6 with a capacity of 3MW, and the wind turbine is located at node 3 with a capacity of 2MW. The energy storage devices are connected to nodes 7, 14 and 24 with maximum charge and discharge power of 0.5MW, 0.4MW and 0.8MW respectively, and the charging efficiency is 0.95 and the discharging efficiency is 0.92. The gas turbines are located at nodes 12 and 25 with rated power of 1MW and 1.2MW respectively, and the corresponding cost coefficients are a1=0.015, b1=20, c1=100; a2=0.02, b2=35, c2=150. The running period in the simulation is set to 24 hours a day, and the time-of-use electricity price is as shown in Table 1.

[0037] Table 1 Time-of-use electricity price

[0038] The time-of-use electricity price scheme divides 24 hours a day into 5 time periods, forming a significant peak-valley price difference structure. The carbon price of the power distribution network selling carbon reduction amount to obtain income is the intermediate price index 73.65 yuan / ton given by the Fudan carbon price index in August 2025. The carbon emission intensity of the gas turbine is calculated as 0.503 tCO2 / MWh.

[0039] The flexibility of the adjustable carbon emission factor is shown by comparing the results of scenario 1 and scenario 2, and the running results of the same setting are shown in Figure 6 , which shows the change trend of key carbon indicators of the two scenarios within 24 hours. Figure 6 It can be seen from Figure 6 that the electric-carbon coupling mechanism can more flexibly adjust the node carbon potential and couple the electricity price signal to effectively guide load transfer. Scenario 2 reduces the carbon potential during the low-carbon period and cooperates with the electricity price subsidy to promote the user load to gather in the low-carbon period, and moderately increases the electricity price during the high-carbon period to suppress the demand and weaken the influence of the scissors difference between the time-of-use electricity price and the carbon emission factor. Through the adjustment, the carbon reduction benefit of the distribution network is promoted and the electricity cost of the user is reduced, and the results of the demand response are shown in Figure 7 . Scenario 2 not only reduces the total carbon emission of the distribution network by 7% and increases the benefit by 2.6%, but also reduces the electricity cost of the user by 3.87%. The method realizes accurate accounting of the carbon emission of the load side, improves the adaptability of the system to energy fluctuations and load changes, and forms a win-win pattern of collaborative emission reduction of the distribution network and the user. The present application compares the low-carbon demand response of scenarios 1 and 2, and the specific results are shown in Table 2. The dispatching strategy effectively optimizes the low-carbon target and economic benefit, and more fully excavates the emission reduction potential of the distribution network.

[0040] Table 2 Comparison of low-carbon demand response in different scenarios

[0041] Scenario 3 corrects the existing time-of-use electricity price with a dynamic carbon emission factor, increases the electricity price during the high-carbon emission factor to guide the user to reduce electricity consumption, and reduces the electricity price during the low-carbon emission factor to guide the user to increase electricity consumption, and sets different subsidy prices to evaluate the influence on demand response. With the increase of the subsidy price, the net benefit of the distribution network first increases and then decreases, the incentive is limited at low subsidy, the user participation is low, and the net benefit relies on electricity sales; high subsidy improves user participation and increases the optimal dispatching of transferable load, and the carbon benefit increases. But too high will make the net benefit decrease, as shown in Figure 8 . The electricity cost of the user decreases with the increase of the subsidy, and the electricity cost and the dissatisfaction cost decrease significantly, which reflects the incentive of the subsidy to low-carbon behavior.

[0042] The application is to explore the method of reducing carbon emissions under the current time-of-use pricing mechanism of power distribution network and load side, and proposes a low-carbon demand response scheduling strategy based on adjustable carbon emission factor, breaks the fixed proportion limit of carbon emission and power in the power distribution network, discusses the influence of price subsidy on reducing the scissors difference between the current time-of-use pricing and carbon emission factor and on demand response, and verifies the method through specific cases. The simulation results show that the method can increase the flexibility of the power distribution network participating in the carbon market and make the load side benefit from the low-carbon demand response, and also can increase the carbon income of the power distribution network and promote the active carbon reduction of the load side, and the method can break through the restriction of the scissors difference between the time-of-use pricing and the carbon emission factor.

[0043] The above only describes the preferred scheme of the application, and is not intended to further limit the application, and various equivalent changes made by using the content of the specification and drawings are within the protection scope of the application.

Claims

1. A low carbon demand response method based on adjustable carbon emission factors, characterized in that, The method comprises the following steps: Step 1, analyze the carbon emission flow characteristics of the power distribution network, and based on the principle that the carbon emission of the power generation side is entirely borne by the load side, a mathematical model of adjustable carbon emission factor based on energy storage and load is established; Step 2, a demand response mechanism based on the adjustable dynamic carbon emission factor is proposed, which guides users to participate in low-carbon demand response under the framework of time-of-use electricity price; Step 3, a low-carbon demand response load side model is established, and the electricity dissatisfaction cost of users participating in load adjustment is quantified, and the load peak-valley difference is adjusted; Step 4, a target function of the power distribution network electric-carbon comprehensive benefit and the user response benefit optimization is constructed, and a power distribution network scheduling model based on the adjustable carbon emission factor is established; Step 5, the target function of the power distribution network electric-carbon comprehensive benefit and the user response benefit optimization is solved, the low-carbon demand response scheduling strategy optimization is realized, and the low-carbon electricity consumption of load is guided.

2. The low carbon demand response method based on adjustable carbon emission factor according to claim 1, characterized in that, In step 1, considering the carbon emission flow characteristics of the power distribution network, the dynamic carbon emission factor method is used to calculate the carbon emission borne by the load side at any time: (1)， wherein, represents the average carbon emission factor value of all load nodes at time t; represents the load power value of node i at time t; N represents the number of nodes of the power distribution network; represents the total carbon emission amount borne by the load side at time t; represents the time interval during which the load power value of node i is affected by the power value of other node j.

3. The low carbon demand response method based on adjustable carbon emission factor according to claim 3, characterized in that, When the energy storage is in the charging state, the carbon emission of the power generation side is borne by the load and the energy storage together; the average carbon emission factor of the load side in this state is denoted as Then, we have: (2)， wherein, is the average node carbon emission factor corresponding to all real energy storage devices, , represents the carbon value when all energy storage devices in the distribution network are in the charging state, is the total charging efficiency of the system, is the total charging power of the system at time t; represents the total load power value of all energy storage devices at time t; total carbon emission value at time t ; represents the electrical power purchased from the main grid, is the average carbon emission factor of the main grid, represents the output power of the own generator of the distribution grid, represents the carbon emission factor of the generator of the distribution grid; Assuming the average carbon emission factor of energy storage is , the average carbon emission factor of load side is: (3)， When the energy storage is in a charged state, the currently calculated carbon stock of the energy storage is greater than the actual carbon stock, the carbon stock at this time is recorded as: (4)， wherein represents the hypothetical carbon emission factor of the energy storage device t at time t. Based on the principle that the total carbon amount is constant, the carbon emission borne by the load will be smaller than the actual one, and the current average carbon emission factor of the load side is calculated by formula (2): (5)， The total carbon amount of the energy storage at the end of this time is: (6)， In the formula, Indicates that the energy storage device is in Carbon storage at any given moment; When the energy storage is in a discharge state, the carbon emission is borne by the load side, and the calculated carbon storage of the energy storage is less than the actual carbon storage; The carbon emission factor corresponding to the discharge of the energy storage at this time is: (7)， In the formula, represents the carbon amount value of all energy storage devices in the power distribution network at time t, , represents the residual power of the energy storage device at time t, t-1. Let the carbon storage at this time be: (8)， The entire scheduling period satisfies: (9)， In the formula, represents the carbon emission amount in the energy storage charging state; represents the carbon emission amount in the energy storage discharging state; T represents a dispatching period; When the energy storage device is in an idle state, the charge carbon amount and the discharge carbon amount of the energy storage are both zero.

4. The low carbon demand response method based on adjustable carbon emission factor according to claim 1, characterized in that, In step 2, the demand response mechanism based on the adjustable dynamic carbon emission factor includes: In the period when the time-of-use electricity price is high and the system carbon potential is low, the electricity price is lowered according to the current carbon emission factor to encourage the transferable load to gather in this period; When the carbon emission factor exceeds the threshold, the electricity price is raised to suppress the non-rigid demand and relieve the pressure on marginal coal-fired power units.

5. The low carbon demand response method based on adjustable carbon emission factor according to claim 3, characterized in that, Step 3 is specifically: Step 3-1, the total cost of the load side for purchasing electric energy from the power distribution network is: (10)， wherein, a unit of electric power, represents a low-carbon load reduction amount of the user at time t, represents an increase amount of the load at time t; Step 3-2, the dissatisfaction cost of the user side is established: (11)， In the formula, represents the upper limit of the user's electricity consumption at time t, is a parameter, represents the load amount of the user after participating in demand response, represents the expected electricity consumption of the user at time t; When the user is dissatisfied with the current power consumption; When the user is satisfied with the current power consumption process; When then no unsatisfied cost is generated.

6. The low carbon demand response method based on adjustable carbon emission factor according to claim 3, characterized in that, Step 4 is specifically: (1) The target function of the power distribution network is constructed: (12)， wherein, represents the sum of the electricity revenue and the carbon revenue of the distribution grid, represents the electricity revenue of the distribution grid to the load, represents the carbon reduction revenue of the distribution grid, represents the electricity purchase cost of the distribution grid from the main grid, represents the cost of the gas turbine providing electricity to the distribution grid; The Distflow optimal power flow is used to model the constraint condition, and the mathematical model is obtained as: (13)， wherein, Pgi represents the generation power of all generator units located at node i; Pgi represents the storage charge-discharge power located at node i; Pgi represents the flexible load power participating in demand response, including transferable load and interruptible load; Pgi represents the output power of all generator units located at node i; P i Pgi represents the active power of node i; Pgi represents the generation reactive power of all generator units located at node i; Pgi represents the storage charge-discharge reactive power located at node i; Pgi represents the output reactive power of all generator units located at node i; Pgi represents the flexible load reactive power participating in demand response; The second-order cone programming method is used to relax the quadratic nonlinear condition in the Distflow model, and the mathematical model after relaxation is: (14)， (15)， (16)， where r ij , x ij are the resistance and reactance of the branch between node i and node j in the distribution network, respectively; h ij is the square of the current flowing through node i→j; v i is the square of the voltage amplitude of node i; P j is the active power of node j; Q j is the reactive power of node j; P jk is the active power between node j and node k; Q jk is the reactive power between node j and node k; P ij is the active power between node i and node j; Q ij is the reactive power between node i and node j; formula (14) is the relaxation of the node power balance constraint, formula (15) is the phase angle relaxation of the power of the first section of the branch, and formula (16) is the convex relaxation of the branch power flow; (2) The operation safety constraint of each device in the power distribution network operation: (17)， where P i gen,min , P i gen,max denotes the minimum and maximum of the generated power of all generator units located at node i; Q i gen,min , Q i gen,max denotes the minimum and maximum of the generated reactive power of all generator units located at node i; P i s,min , P i s,max denotes the minimum and maximum of the energy storage charging and discharging power located at node i; Q i s,min , Q i s,max denotes the minimum and maximum of the energy storage charging and discharging reactive power located at node i; E i denotes the carbon emission factor of node i; E i min , E i max denotes the minimum and maximum of the carbon emission factor of node i; P i soft,min , P i soft,max denotes the minimum and maximum value of the flexible load power participating in demand response, including shiftable load and interruptible load; V i min , V i max denotes the minimum and maximum value of the square value of the voltage amplitude of node i; In the carbon emission stage, (18)， In the formula, is the value corresponding to the case where the carbon amount assumed to be taken by the actual energy storage is 0; is the value corresponding to the case where the carbon amount assumed to be taken by the hypothetical energy storage is 0; (3) The target function of the load side: (19)， (4) In a scheduling, the transferable load should satisfy the principle that the total electricity consumption is constant, that is: (20)， In the formula, , are the load transfer-in and transfer-out amounts of the period t, respectively. (21)， In the formula, is the load shedding amount for the time period t; is the change amount of the flexible load power participating in demand response.

7. The low carbon demand response method based on adjustable carbon emission factor according to claim 6, characterized in that, Step 5 is specifically: Step 5-1, initialize the to-be-solved power variable , given the basic information of the power system, including load power, fan, photovoltaic power, electricity price, compensation coefficient; superscript 0 represents the number of iterations; is the electricity power purchased from the main network within the dispatching period, is the power output of the distribution network itself within the dispatching period; is the carbon potential of the node within the dispatching period; is the power generation of all generator units within the dispatching period; Step 5-2, the power distribution network calculates power benefit maximization operation based on time-of-use electricity price, and the power distribution network calculates power distribution value under the condition that formula (12) is the objective function and formulas (14)-(17) are constraints, that is Under the limitation of the objective function and the constraint condition, the optimal power flow calculated by the power distribution network only considers cost; k represents the kth iteration;​ Step 5-3, calculating the power value The power value is substituted into equation (12) to maximize the distribution network benefit, and the dynamic carbon emission factor under the condition of maximizing the distribution network carbon benefit is calculated, and the constraint conditions equation (18), (20) are satisfied to obtain the carbon potential of all nodes ; Step 5-4, the node carbon potential The load side is issued to update the load amount after demand response, that is, the load adjustment amount calculated by the objective function formula (19) and the constraint conditions (20) and (21) ; Step 5-5, judging whether it is less than the accuracy requirement, if yes, the operation ends; otherwise the actual load value of the load side is updated, and the value is uploaded to the power distribution network dispatching center, and the calculation is repeated in step 5-2.

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