Supply and demand bilateral response multi-target scheduling method and system

Abstract

The invention relates to the technical field of power system optimization scheduling, in particular to a supply and demand bilateral response multi-target scheduling method and system, and the method comprises the steps: building a supply side model and a demand side model of a new energy integrated power system; inputting a conventional power system bilateral constraint condition; constructing a main objective function based on a small interference stability standard; constructing a supply side objective function based on the power generation cost; constructing a demand side objective function based on the economic indexes; obtaining a bilateral scheduling target scheme based on the main target function and the supply side target function; pricing the electricity price based on the target scheme; the demand side responds according to the electricity price and the economic target; obtaining a response result based on demand side response constraints, and calculating demand side response saturation; and performing excitation based on the difference between the demand side response and the target scheme, and guiding the user to actively participate in fine adjustment. A target scheme is output by optimizing dispatching parameters, loads are encouraged to participate in fine adjustment through excitation, and economic dispatching and stable control of a power system are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of optimal dispatching of power systems, and more specifically, to a multi-objective dispatching method and system for bilateral supply and demand response. Background Art

[0002] During the operation of a power system, it is constantly subjected to some minor disturbances. However, in the past maintenance and operation of power systems, people often only considered major disturbances or faults and ignored the impact of minor disturbances on power systems, especially during dispatching. Traditionally, dispatchers regarded the dispatching cost as the dispatching goal and ignored the problem that different power generation arrangements would have different stability impacts on the power system.

[0003] Demand-side response is a strategy for regulating the steady state of the power grid on the user side. Traditionally, demand-side response has been applied to peak shaving and valley filling, moving a certain amount of peak load to the valley period to better match power supply and demand.

[0004] The prior art discloses a multi-price demand response pricing method for peak shifting in large industrial cities, including the following steps: calculating the load response and electricity cost of users based on electricity prices, and constructing a first objective function; calculating the optimal generator power generation combination and generation cost according to the total network load, unit information, and grid information, and constructing a second objective function; integrating the first objective function and the second objective function to construct a total objective function; using the alternating direction multiplier method to solve the total objective function to obtain the optimal electricity price pricing. This solution can achieve peak shaving and valley filling, but ignores minor disturbances and the different stability impacts of different power generation arrangements on the power system. Summary of the Invention

[0005] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a multi-objective dispatching method and system for bilateral supply and demand response, effectively realizing the stable operation of the power system throughout the dispatching cycle.

[0006] To solve the above technical problems, the technical solution adopted by the present invention is:

[0007] Provide a multi-objective dispatching method for bilateral supply and demand response, including the following steps:

[0008] S1: Establish a power system supply-side model and a demand-side model integrating new energy;

[0009] S2: Input the bilateral constraint conditions of a conventional power system;

[0010] S3: Construct a main objective function based on the small-signal stability criterion;

[0011] S4: Construct a supply-side objective function based on the generation cost;

[0012] S5: Construct the demand-side objective function based on economic indicators;

[0013] S6: Obtain the bilateral scheduling objective scheme based on the main objective function and the supply-side objective function;

[0014] S7: Conduct electricity price pricing based on the objective scheme;

[0015] S8: The demand side responds according to the electricity price and economic objectives;

[0016] S9: Obtain the response result based on the demand-side response constraint, and calculate the demand-side response saturation degree;

[0017] S10: Provide incentives based on the difference between the demand-side response and the objective scheme to guide users to actively participate in fine-tuning.

[0018] For the multi-objective scheduling method of bilateral response of supply and demand of the present invention, the scheduling parameters are optimized according to the small-signal stability index of the power system, and the bilateral scheduling objective scheme that enables the system to meet the stability requirements is obtained. The objective scheme is preferentially output to avoid the inaccuracy of the scheme caused by response saturation or response error. The demand side responds, taking into account the generation cost on the supply side and the economic indicators on the demand side, and real-time incentives are provided according to the response degree to encourage the load to actively participate in the regulation of the power system stability, and solve the uncertainty caused by response saturation or response error. When the present invention schedules the power system, it can effectively achieve the economic scheduling and stable control of the power system.

[0019] Preferably, step S1 includes:

[0020] S101: Linearize the differential-algebraic of the dynamic characteristics of the power system at the steady-state operating point:

[0021]

[0022] In the formula, respectively represent the submatrices of the power system state matrix, which are related to the order arrangement of the power system state variables, the form of the network equation, and each dynamic component; Δx represents the state variable of the dynamic characteristics of the power system; Δy represents the operating parameter of the power system; t represents time;

[0023] S102: Eliminate the operating parameter Δy to obtain the mathematical model for small-signal stability analysis:

[0024]

[0025] In the formula, A represents the power system state matrix, which determines the stability of the power system;

[0026] S103: Solve the eigenvalues of the power system state matrix and calculate the damping ratio ξ;

[0027]

[0028] Wherein, σ represents the real part of the critical oscillation mode eigenvalue, reflecting the damping of the power system; ω represents the imaginary part of the critical oscillation mode eigenvalue, reflecting the oscillation frequency.

[0029] Preferably, in step S2, the conventional bilateral constraint conditions of the power system include conventional supply-side constraints and conventional demand-side constraints, where:

[0030] The conventional supply-side constraints include:

[0031] Generator output constraint:

[0032]

[0033] Wherein, represents the minimum active power of the i-th unit; represents the maximum active power of the i-th unit; P i represents the output power of the i-th unit;

[0034] Output ramp constraint:

[0035] P i(t+1) -P i(t) ≤UR i ;

[0036] P i(t) -P i(t-1) ≤DR i ;

[0037] Wherein, P i(t) represents the output power of the i-th generator at time t; UR i represents the output ramp limit of the i-th unit; DR i represents the output decline limit of the i-th unit;

[0038] Load balance constraint:

[0039]

[0040] Wherein, N g represents the total number of dispatched units; N l represents the number of load nodes; P l represents the input power of the l-th load;

[0041] The conventional demand-side constraints include:

[0042] Response saturation constraint:

[0043]

[0044] Wherein, ΔP n→m The actual load change amount transferred from time n to time m before and after demand - side response; Represents the maximum load change amount that can be transferred from time n to time m before and after demand - side response.

[0045] Preferably, in step S3, a damping ratio greater than 0.03 is used as the threshold for judging that the system reaches strong stability:

[0046]

[0047] Wherein, threshold(ξ) represents the threshold function.

[0048] Preferably, in step S4, a supply - side objective function is constructed based on the power generation cost:

[0049]

[0050] Wherein, COST supply Represents the supply - side objective function of the conventional scheduling model; N t Represents the number of unit scheduling time periods; N g Represents the total number of units participating in scheduling; P i Represents the output power of the i - th unit; a i , b i , c i Respectively represent the fuel cost coefficients.

[0051] Preferably, in step S5, a supply - side objective function is constructed based on the power generation cost:

[0052] min(COST demand ) = C buy +C OP ;

[0053] Wherein, COST demand Represents the demand - side objective function of the conventional scheduling model; C buy Represents the power purchase cost; C OP Represents the operation and maintenance cost.

[0054] Preferably, in step S6, the process of obtaining the bilateral scheduling target scheme is as follows:

[0055] S601: Initialize the scheduling parameters, including the output power of all generators and all load transfer amounts:

[0056]

[0057] Wherein, X(M) represents the population matrix to be optimized; M represents the total number of population particles; P N Represents the power to be optimized for the N - th generator; Pi→j represents the output power transferred from the i-th generator to the j-th generator;

[0058] S602: Use a solver or artificial intelligence algorithm for parameter optimization and iterative solution;

[0059] S603: Output the globally optimal particle to form a bilateral scheduling target scheme.

[0060] Preferably, in step S7, the electricity price pricing includes:

[0061] S701: Form a price-demand elasticity matrix between nodes:

[0062]

[0063] In the formula, E bus (s,r) represents the price-demand elasticity matrix between nodes; z represents the total number of nodes in the power system, and e sr represents the elasticity coefficient of the load at node s with respect to the electricity price at node r: represents the change in load at node s after demand-side response; represents the initial load at node s; Δρ r represents the change in electricity price at node r after demand-side response; ρ r represents the initial electricity price at node r;

[0064] S702: Construct a relationship between the electricity price and the change in load at node s:

[0065]

[0066] In the formula, ΔP s→r represents the actual change in load transferred from node s to node r before and after demand-side response; represents the change in unit load transferred from node s to node r before and after demand-side response; E(s,r) represents the element in the s-th row and r-th column of matrix E bus (s,r); represents the change in unit electricity price at node r after demand-side response;

[0067] S703: Form a price-demand elasticity matrix between time periods:

[0068]

[0069] In the formula, E time (n,m) represents the price-demand elasticity matrix between time periods; Z represents the number of time periods; e nm represents the elasticity coefficient of the load at time n with respect to the electricity price at time m; ΔP n represents the change in load at time n after demand-side response; P nRepresents the initial load at time n; Δρ m Represents the change in electricity price at time m after demand - side response; ρ m Represents the initial electricity price at time m;

[0070] S704: Construct a relational expression between electricity price and the change in load at time n:

[0071]

[0072] In the formula, ΔP n→m Represents the actual change in load transferred from time n to time m before and after demand - side response; Represents the change in unit load transferred from time n to time m before and after demand - side response; E(n, m) represents the element in the n - th row and m - th column of matrix E time in; Represents the change in unit electricity price at time m after demand - side response;

[0073] S705: Taking the bilateral scheduling target scheme as the load transfer target, conduct electricity price pricing according to the two relational expressions between electricity price and load change.

[0074] Preferably, step S9 includes:

[0075] S901: Construct a load transfer rate model based on the Logistic function:

[0076]

[0077] In the formula, Δp represents the electricity price difference; λ represents the load transfer rate; a, c, μ respectively represent the known quantities of the Logistic function; b represents the variable parameter;

[0078] S902: Construct a user actual response model

[0079]

[0080] In the formula, Represents the actual load transfer rate; Represents the load transfer rate predicted by pessimistic response; Represents the load transfer rate predicted by optimistic response; a pv 、b pv respectively represent the dividing - point of the division region of the electricity price difference; Q represents the optimistic response membership degree, used to represent the probability of estimating the optimistic response of user load;

[0081] S903: Calculate the response difference:

[0082]

[0083] Wherein, ΔP represents the load change actually responded by the user; P represents the unit load change actually responded by the user.

[0084] The present invention also provides a multi-objective scheduling system for bilateral supply and demand response, which is used to execute the above-mentioned multi-objective scheduling method for bilateral supply and demand response. The system includes:

[0085] A day-ahead optimal scheduling module; before the target scheduling day, collect the prediction data including new energy and load, optimize the scheduling parameters according to the small-signal stability index of the power system, and obtain a bilateral scheduling target scheme that enables the system to meet the stability requirements. The scheduling target scheme includes the output power of all generators on the supply side at all times, as well as the transfer power of all loads between nodes and between times. Determine the electricity price according to the scheduling target scheme;

[0086] An intra-day optimal scheduling module; on the target scheduling day, the demand side responds according to the proposed electricity price, and the dispatcher gives real-time incentives according to the response degree to encourage the load to actively participate in the regulation of the power system stability.

[0087] Compared with the prior art, the present invention has the following beneficial effects:

[0088] Optimize the scheduling parameters according to the small-signal stability index of the power system, obtain a bilateral scheduling target scheme that enables the system to meet the stability requirements, and give priority to outputting the target scheme to avoid the inaccuracy of the scheme caused by response saturation or response error. The demand side responds, taking into account the generation cost on the supply side and the economic indicators on the demand side, and gives real-time incentives according to the response degree to encourage the load to actively participate in the regulation of the power system stability, solve the uncertainty caused by response saturation or response error, and effectively realize the stable operation of the power system throughout the scheduling cycle. Description of the Drawings

[0089] Figure 1 It is a flowchart of the multi-objective scheduling method for bilateral supply and demand response in an embodiment of the present invention. Detailed Embodiments

[0090] The present invention will be further described below in conjunction with the detailed embodiments.

[0091] Embodiment 1

[0092] This embodiment is the first embodiment of the multi-objective scheduling method for bilateral supply and demand response. As Figure 1 shown, it includes the following steps:

[0093] S1: Establish a supply-side model and a demand-side model of a power system integrated with new energy;

[0094] S2: Input the conventional bilateral constraints of the power system;

[0095] S3: Construct the main objective function based on the small-signal stability criterion;

[0096] S4: Construct the supply-side objective function based on the power generation cost;

[0097] S5: Construct the demand-side objective function based on the economic indicators;

[0098] S6: Obtain the bilateral dispatch target scheme based on the main objective function and the supply-side objective function;

[0099] S7: Conduct electricity price pricing based on the target scheme;

[0100] S8: The demand side responds according to the electricity price and economic objectives;

[0101] S9: Obtain the response result based on the demand-side response constraint, and calculate the demand-side response saturation degree;

[0102] S10: Provide incentives based on the difference between the demand-side response and the target scheme, and guide users to actively participate in fine-tuning

[0103] The above multi-objective dispatch method for bilateral supply-demand response optimizes dispatch parameters according to the small-signal stability index of the power system, obtains a bilateral dispatch target scheme that enables the system to meet the stability requirements, preferentially outputs the target scheme, avoids inaccuracies in the scheme caused by response saturation or response errors. The demand side responds, taking into account the power generation cost on the supply side and the economic indicators on the demand side, and conducts real-time incentives according to the response degree, encouraging the load to actively participate in the regulation of the power system stability, and solving the uncertainty caused by response saturation or response errors. When dispatching the power system in this embodiment, it can effectively achieve the economic dispatch and stable control of the power system.

[0104] Embodiment 2

[0105] This embodiment is the second embodiment of the multi-objective dispatch method for bilateral supply-demand response. This embodiment is similar to Embodiment 1, and the difference lies in:

[0106] Step S1 includes:

[0107] S101: Linearize the differential-algebraic of the dynamic characteristics of the power system at the steady-state operating point:

[0108]

[0109] In the formula, respectively represent the sub-matrices of the power system state matrix, which are related to the order arrangement of the power system state variables, the form of the network equation, and each dynamic component; Δx represents the state variable of the dynamic characteristics of the power system; Δy represents the operating parameter of the power system; t represents time;

[0110] S102: Eliminate the operating parameter Δy to obtain the mathematical model for small-signal stability analysis:

[0111]

[0112] Wherein, A represents the power system state matrix, which determines the stability of the power system;

[0113] S103: Solve the eigenvalues of the power system state matrix and calculate the damping ratio ξ:

[0114]

[0115] Wherein, σ represents the real part of the eigenvalue of the critical oscillation mode, which reflects the damping of the power system; ω represents the imaginary part of the eigenvalue of the critical oscillation mode, which reflects the oscillation frequency.

[0116] In step S2, the conventional bilateral constraints of the power system include conventional supply-side constraints and conventional demand-side constraints, where:

[0117] The conventional supply-side constraints include:

[0118] Generator output constraint:

[0119]

[0120] Wherein, represents the minimum active power of the i-th unit; represents the maximum active power of the i-th unit; P i represents the output power of the i-th unit;

[0121] Output ramp constraint:

[0122] P i(t+1) -P i(t) ≤UR i ;

[0123] P i(t) -P i(t-1) ≤DR i ;

[0124] Wherein, P i(t) represents the output power of the i-th generator at time t; UR i represents the output ramp limit of the i-th unit; DR i represents the output decline limit of the i-th unit;

[0125] Load balance constraint:

[0126]

[0127] Wherein, N gRepresents the total number of scheduling units; N l Represents the number of load nodes P l Represents the input power of the l-th load;

[0128] Conventional demand-side constraints include:

[0129] Response saturation constraint:

[0130]

[0131] In the formula, ΔP n→m Actual load change amount transferred from time n to time m before and after demand-side response; Represents the maximum load change amount that can be transferred from time n to time m before and after demand-side response.

[0132] In step S3, a damping ratio greater than 0.03 is used as the threshold for judging that the system reaches strong stability:

[0133]

[0134] In the formula, threshold(ξ) represents the threshold function.

[0135] In step S4, a supply-side objective function is constructed based on the generation cost:

[0136]

[0137] In the formula, COST supply Represents the supply-side objective function of the conventional scheduling model; N t Represents the number of unit scheduling periods; N g Represents the total number of units participating in scheduling; P i Represents the output power of the i-th unit; a i , b i , c i Respectively represent the fuel cost coefficients.

[0138] In step S5, a supply-side objective function is constructed based on the generation cost:

[0139] min(COST demand ) = C buy +C OP ;

[0140] In the formula, COST demand Represents the demand-side objective function of the conventional scheduling model; C buy Represents the power purchase cost; C OP Represents the operation and maintenance cost.

[0141] In step S6, the process of obtaining the bilateral scheduling target scheme is:

[0142] S601: Initialize scheduling parameters, including the output power of all generators and all load transfer amounts:

[0143]

[0144] In the formula, X(M) represents the population matrix to be optimized; M represents the total number of population particles; P N represents the power to be optimized for the Nth generator; P i→j represents the output power transferred from the ith generator to the jth generator;

[0145] S602: Use a solver or artificial intelligence algorithm for parameter optimization and iterative solution;

[0146] S603: Output the global optimal particle to form a bilateral scheduling target scheme.

[0147] In step S7, the electricity price pricing includes:

[0148] S701: Form a price-demand elasticity matrix between nodes:

[0149]

[0150] In the formula, E bus (s, r) represents the price-demand elasticity matrix between nodes; z represents the total number of nodes in the power system, and e sr represents the elasticity coefficient of the load at node s with respect to the electricity price at node r: represents the change in load at node s after demand-side response; represents the initial load at node s; Δρ r represents the change in electricity price at node r after demand-side response; ρ r represents the initial electricity price at node r;

[0151] S702: Construct a relationship between the electricity price and the change in load at node s:

[0152]

[0153] In the formula, ΔP s→r represents the actual change in load transferred from node s to node r before and after demand-side response; represents the change in unit load transferred from node s to node r before and after demand-side response; E(s, r) represents the element in the sth row and rth column of matrix E bus (s, r); represents the change in unit electricity price at node r after demand-side response;

[0154] S703: Form a price-demand elasticity matrix between time instants:

[0155]

[0156]

[0157] Wherein, E time (n, m) represents the price-demand elasticity matrix between time intervals; Z represents the number of time intervals; e nm represents the elasticity coefficient of the load at a certain time with respect to the electricity price at that time; ΔP n represents the change in load at time n after demand-side response; P n represents the initial load at time n; Δρ m represents the change in electricity price at time m after demand-side response; ρ m represents the initial electricity price at time m;

[0158] S704: Construct a relational expression between the electricity price and the change in load at time n:

[0159]

[0160] Wherein, ΔP n→m represents the actual change in load transferred from time n to time m before and after demand-side response; represents the unit change in load transferred from time n to time m before and after demand-side response; E(n, m) represents the element in the nth row and the mth column of matrix E time ; represents the unit change in electricity price at time m after demand-side response;

[0161] S705: Taking the bilateral dispatching target scheme as the load transfer target, conduct electricity price pricing according to the two relational expressions between the electricity price and the load change.

[0162] Step S9 includes:

[0163] S901: Construct a load transfer rate model based on the Logistic function:

[0164]

[0165] Wherein, Δp represents the electricity price difference; λ represents the load transfer rate; a, c, and μ respectively represent the known quantities of the Logistic function; b represents the variable parameter;

[0166] S902: Construct a user actual response model

[0167]

[0168] Wherein, represents the actual load transfer rate; represents the load transfer rate predicted by pessimistic response; represents the load transfer rate predicted by optimistic response; apv , b pv respectively represent the division region demarcation points of the electricity price difference; Q represents the optimistic response membership degree, which is used to represent the probability of the user load optimistic response estimation;

[0169] S903: Calculate the response difference:

[0170]

[0171] In the formula, ΔP represents the actual load change amount of the user's response; P represents the actual unit load change amount of the user's response.

[0172] Example 3

[0173] A multi-objective scheduling system for bilateral supply and demand response, which is used to execute the multi-objective scheduling method for bilateral supply and demand response in Example 1 or Example 2, includes:

[0174] The day-ahead optimal scheduling module; before the target scheduling day, collect the prediction data including new energy and load, optimize the scheduling parameters according to the small-signal stability index of the power system, obtain the bilateral scheduling target scheme that enables the system to meet the stability requirements, and the scheduling target scheme includes the output power of all generators on the supply side at all times, and the transfer power of all loads between nodes and between times, and conduct electricity price pricing according to the scheduling target scheme;

[0175] The within-day optimal scheduling module; on the demand side, respond according to the proposed electricity price within the target scheduling day, and the dispatcher conducts real-time incentives according to the response degree to encourage the load to actively participate in the regulation of the power system stability.

[0176] Specifically, in the day-ahead optimal scheduling module, collect all the prediction data including new energy and load, take the output power of each generator and the transfer power of each load as the parameters to be optimized, take making the damping ratio reach the threshold requirement as the main objective, take minimizing the generation cost as the supply-side objective, output the bilateral scheduling target scheme for the supply side and the demand side, and the bilateral scheduling target scheme includes the output power of all generators on the supply side at all times, and the transfer power of all loads between nodes and between times, and conduct electricity price pricing according to the relationship formula between the electricity price and the load transfer; in the within-day optimal scheduling module, take minimizing the response cost as the demand-side objective, where the response cost includes the electricity purchase cost and the operation and maintenance cost, the demand side responds, transfers the load in the direction beneficial to the power system stability, calculates the mismatch degree between the actual transfer power and the target transfer power caused by factors such as response saturation and response error, and conducts real-time incentives to encourage the load to actively participate in the regulation of the power system stability.

[0177] In the specific content of the above specific embodiments, each technical feature can be combined arbitrarily without contradiction. For the sake of concise description, not all possible combinations of the above technical features are described. However, as long as the combinations of these technical features do not exist in contradiction, they should all be considered as within the scope described in this specification.

[0178] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, rather than limitations on the embodiments of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made on the basis of the above description. It is not necessary and impossible to enumerate all the embodiments here. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the claims of the present invention.

Claims

1. A multi-objective scheduling method for bilateral response of supply and demand, characterized in that It includes the following steps: S1: Establish a supply-side model and a demand-side model for a new energy integrated power system; S2: Input the bilateral constraints of a conventional power system; S3: Construct the main objective function based on the small-signal stability criterion; S4: Construct the supply-side objective function based on the generation cost; S5: Construct the demand-side objective function based on economic indicators; S6: Obtain the bilateral dispatch target scheme based on the main objective function and the supply-side objective function; S7: Conduct electricity price pricing based on the target scheme; S8: The demand side responds according to the electricity price and economic objectives; S9: Obtain the response result based on the demand-side response constraint and calculate the demand-side response saturation; S10: Provide incentives based on the difference between the demand-side response and the target scheme to guide users to actively participate in fine-tuning.

2. The multi-objective scheduling method for supply-demand bilateral response according to claim 1, characterized in that Step S1 includes: S101: Linearize the differential-algebraic equations of the dynamic characteristics of the power system at the steady-state operating point; wherein, respectively represent the sub-matrices of the power system state matrix, which are related to the order arrangement of the power system state variables, the form of the network equations, and each dynamic component; Δx represents the state variables of the dynamic characteristics of the power system; Δy represents the operating parameters of the power system; t represents time; S102: Eliminate the operating parameter Δy to obtain the mathematical model for small-signal stability analysis: In the formula, A represents the power system state matrix, which determines the stability of the power system; S103: Solve the eigenvalues of the power system state matrix and calculate the damping ratio ξ: In the formula, σ represents the real part of the eigenvalue of the critical oscillation mode, which reflects the damping of the power system; ω represents the imaginary part of the eigenvalue of the critical oscillation mode, which reflects the oscillation frequency.

3. The multi-objective scheduling method for supply-demand bilateral response according to claim 2, wherein In step S2, the bilateral constraints of a conventional power system include conventional supply-side constraints and conventional demand-side constraints, where: The conventional supply-side constraints include: Generator output constraint: In the formula, represents the minimum value of the active power of the i-th unit; represents the maximum value of the active power of the i-th unit; P i represents the output power of the i-th unit; Output ramp constraint: P i(t+1) -P i(t) ≤UR i ; P i(t) -P i(t-1) ≤DR i ; Where, P i(t) represents the output power of the i-th generator at time t; UR i represents the output power ramp-up limit of the i-th unit; DR i represents the output power ramp-down limit of the i-th unit. Load balance constraint: Where N g represents the total number of dispatching units; N l represents the number of load nodes; P l represents the input power of the l-th load; The conventional demand-side constraints include: Response saturation constraint: where, ΔP n→m is the actual load change amount transferred from time n to time m before and after the demand-side response; represents the maximum load change amount that can be transferred from time n to time m before and after the demand-side response.

4. The multi-objective scheduling method for supply-demand bilateral response according to claim 3, characterized in that In step S3, a damping ratio greater than 0.03 is used as the threshold for judging that the system reaches strong stability: In the formula, threshold(ξ) represents the threshold function.

5. The multi-objective scheduling method for bilateral supply and demand response according to claim 4, characterized in that In step S4, construct the supply-side objective function based on the generation cost: where, COST supply represents the supply-side objective function of the conventional scheduling model; N t represents the number of time periods for unit scheduling; N g represents the total number of units participating in scheduling; P i represents the output power of the i-th unit; a i , b i , c i respectively represent the fuel cost coefficients.

6. The multi-objective scheduling method for bilateral supply and demand response according to claim 5, characterized in that In step S5, construct the supply-side objective function based on the generation cost: min(COST demand ) = C buy + C OP ; Wherein, COST demand represents the demand-side objective function of the conventional scheduling model; C buy represents the electricity purchase cost; C OP represents the operation and maintenance cost.

7. The multi-objective scheduling method for bilateral supply and demand response according to claim 6, wherein In step S6, the process of obtaining the bilateral dispatch target scheme is as follows: S601: Initialize the dispatch parameters, including the output power of all generators and all load transfer amounts; where X(M) represents the population matrix to be optimized; M represents the total number of population particles; P N represents the power to be optimized for the Nth generator; P i→j represents the output power transferred from the ith generator to the jth generator; S602: Use a solver or artificial intelligence algorithm for parameter optimization and iterative solution; S603: Output the global optimal particle to form the bilateral dispatch target scheme.

8. The multi-objective scheduling method for supply-demand bilateral response according to claim 7, characterized in that In step S7, the electricity price pricing includes: S701: Form the price-demand elasticity matrix between nodes; In the formula, E bus (i, j) represents the price-demand elasticity matrix between nodes; z represents the total number of nodes in the power system, and e sr represents the elasticity coefficient of the load of node s with respect to the electricity price of node r: represents the change in the load of node s after demand-side response; represents the initial load of node s; Δρ r represents the change in the electricity price of node r after demand-side response; ρ r represents the initial electricity price of node r; S702: Construct the relationship between the electricity price and the load change of node s; where, ΔP s→r represents the actual load change amount transferred from node s to node r before and after the demand-side response; represents the unit load change amount transferred from node s to node r before and after the demand-side response; E(s, r) represents the element in the s-th row and r-th column of matrix E bus (s, r); represents the unit electricity price change amount of node r after the demand-side response; S703: Form the price-demand elasticity matrix between time intervals; Where, E time (n, m) represents the price-demand elasticity matrix between time intervals; Z represents the number of time intervals; e nm represents the elasticity coefficient of the load at a certain time with respect to the electricity price at that time; ΔP n represents the change in load at time n after demand-side response; P n represents the initial load at time n; Δρ m represents the change in electricity price at time m after demand-side response; ρ m represents the initial electricity price at time m; S704: Construct the relationship between the electricity price and the load change at time n; where, ΔP n→m represents the actual load change amount transferred from time n to time m before and after the demand-side response; represents the unit load change amount transferred from time n to time m before and after the demand-side response; E(n, m) represents the element in the n-th row and the m-th column of matrix E time ; represents the unit electricity price change amount at time m after the demand-side response; S705: Use the bilateral dispatch target scheme as the load transfer target and conduct electricity price pricing according to the two relationships between the electricity price and the load change.

9. The multi-objective scheduling method for bilateral supply and demand response according to claim 8, wherein Step S9 includes: S901: Construct a load transfer rate model based on the Logistic function: In the formula, Δp represents the electricity price difference; λ represents the load transfer rate; a, c, and μ respectively represent the known quantities of the Logistic function; b represents the variable parameter; S902: Construct the actual response model of users In the formula, represents the actual load transfer rate; represents the load transfer rate predicted by pessimistic response; represents the load transfer rate predicted by optimistic response; a pv , b pv respectively represent the division region demarcation points of the electricity price difference; Q represents the optimistic response membership degree, which is used to represent the probability of the user load optimistic response estimation; S903: Calculate the response difference: Wherein, ΔP represents the amount of load change actually responded by the user; P represents the unit load change actually responded by the user.

10. A multi-objective scheduling system for bilateral supply and demand response, which is used to execute the multi-objective scheduling method for bilateral supply and demand response according to any one of claims 1 to 9, characterized in that the system Including: A day-ahead optimal scheduling module; before the target scheduling day, collect the prediction data including new energy and load, optimize the scheduling parameters according to the small-signal stability index of the power system, obtain a bilateral scheduling target scheme that enables the system to meet the stability requirements. The scheduling target scheme includes the output power of all generators on the supply side at all times, as well as the transfer power of all loads between nodes and between times, and conduct electricity price pricing according to the scheduling target scheme; An intra-day optimal scheduling module; on the demand side, respond according to the proposed electricity price within the target scheduling day, and the dispatcher conducts real-time incentives according to the response degree to encourage the load to actively participate in the regulation of the power system stability.

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