Active power distribution network optimization scheduling method considering price type demand response

Abstract

An active power distribution network optimal scheduling method considering price-type demand response comprises the following steps: S1, taking the daily minimum operation cost of an active power distribution network as a target function, considering the influence of the price-type demand response on active power distribution network scheduling, and constructing an active power distribution network scheduling model; s2, improving a bald eagle search algorithm by using a Tent chaotic equation and a Cauchy mutation operator; and S3, solving the active power distribution network dispatching model by adopting an improved bald eagle algorithm. The invention provides an active power distribution network optimal scheduling method considering price type demand response, which takes ADN daily minimum operation cost as a target function, considers the influence of the price type demand response on ADN scheduling, and constructs an active power distribution network scheduling model. According to the method, a Bald Eagle Search (BES) algorithm is improved by using a Tent chaos equation and a Cauchy mutation operator, so that the optimization effect of the improved Bald Eagle Search (IBES) algorithm is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid optimization dispatching, and in particular to an active distribution network optimization dispatching method considering price-type demand response. Background Art

[0002] Against the backdrop of global warming and energy crisis, vigorously developing distributed generation (DG) technology has become a basic national policy of my country. With the large-scale grid connection of photovoltaic, wind power and other DGs, the traditional distribution network has changed from passive to active, and has the structural foundation for active dispatch of power sources. Active Distribution Network (ADN) has become the future development direction. Demand response is an important strategy for ADN dispatching. By formulating relevant electricity price policies to change users' electricity consumption habits, the load curve can be improved and the safety of ADN can be improved. Therefore, considering the role of demand response in the ADN dispatching process and guiding users to participate in system dispatching are of great significance for reducing ADN operating costs and improving the absorption of DGs such as wind and solar.

[0003] At present, experts and scholars have conducted a lot of research on the application of demand response in the ADN dispatch process. Demand response models are mainly divided into price-based and incentive-based models, which have different characteristics. Zhu Chaoting et al. studied the user demand response model, and on this basis, constructed an ADN optimization dispatch model with the minimum daily operating cost as the optimization goal, and conducted simulation analysis for different proportions of user participation. The example results show that in the ADN dispatch process, the higher the user participation, the better the load curve adjustment effect, which verifies the positive role of user demand response. Ge Xiaolin et al. improved the demand response model by using the electricity price correction coefficient and incentive load opportunity constraints to address the adverse effects of demand response uncertainty on ADN dispatch, and constructed an ADN optimization dispatch model containing multiple types of demand response costs. The superiority of the proposed model was verified by combining example analysis. Lin Li et al. focused on analyzing the impact of incentive demand response on ADN dispatch, and considered the reliability power constraint, and constructed an ADN optimization dispatch model with the minimum system operation cost as the objective function. The model was solved using the particle swarm algorithm, and the compensation electricity price policy and system dispatch plan were determined. Compared with incentive-based demand response, price-based demand response mainly guides users by adjusting electricity prices, and users have a higher degree of active participation. Therefore, the ADN optimization scheduling method considering electricity price demand response needs to be further improved. Summary of the invention

[0004] The purpose of the present invention is to improve the economic efficiency of the active distribution network (ADN), provide an active distribution network optimization scheduling method considering price-based demand response, take the ADN daily minimum operating cost as the objective function, and consider the impact of price-based demand response on ADN scheduling, and construct an active distribution network scheduling model. The Bald Eagle Search (BES) algorithm is improved by using the Tent chaos equation and the Cauchy mutation operator, and the Improved Bald Eagle Search (IBES) algorithm is used to solve the ADN scheduling model.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: an active distribution network optimization scheduling method considering price-based demand response, comprising the following steps: S1. Taking the daily minimum operating cost of the active distribution network as the objective function and considering the impact of price-based demand response on the active distribution network scheduling, an active distribution network scheduling model is constructed; S2, using Tent chaos equation and Cauchy mutation operator to improve the vulture search algorithm; S3. The improved vulture algorithm is used to solve the active distribution network dispatching model.

[0006] Preferably, in step S1, the active distribution network dispatching model is specifically as follows: (1) Price-based demand response model Price-based demand response means that users adjust their electricity load according to the time-of-use electricity price. Therefore, the relationship between price-based response load and electricity price can be described by the load electricity price elasticity coefficient, which is expressed as: ; (1) Where: It represents the elasticity coefficient of electricity quantity and price; , They represent the initial load value and load change in the i-th period respectively; , They represent the initial electricity price and the change in electricity price in the jth period respectively; According to formula (1), the available electricity price elasticity matrix E is: ; (2) Where: n represents the scheduling period; Then we can get: ; (3) Where: is the electricity price of the nth dispatch period after adjustment; (2) Objective function After the implementation of time-of-use electricity prices, users' electricity usage habits will change, resulting in changes in the daily operating costs of ADN. This paper takes an ADN system containing photovoltaics, wind power, batteries, and small gas turbines as the research object. Since photovoltaics and wind power are clean energy, their power generation costs and maintenance costs are ignored. The minimum daily operating cost of ADN within a scheduling cycle is used as the objective function to construct an ADN optimization scheduling model. The objective function expression is: ; (4) Where C is the total daily operating cost of ADN; The cost of purchasing electricity; Cost of small gas turbines; is the system network loss cost; It is the battery maintenance cost; among which: 1) Electricity purchasing cost The power purchase cost refers to the cost of transmitting power from the upper-level power grid to the ADN, and its expression is: ; (5) Where: is the unit price of electricity purchased during period t; is the power purchased in time period t; T is the dispatching period, which is 24h in the present invention; 2) Cost of small gas turbines During operation, the cost of a small gas turbine is mainly composed of fuel cost and maintenance cost, which can be expressed as: ; (6) Where: is the number of units; is the fuel cost coefficient; is the maintenance cost coefficient; is the power generation power of gas turbine m during period t; is the conversion efficiency of the small gas turbine m; 3) System network loss cost The system network loss cost is mainly caused by line impedance, and its expression is: ; (7) Where: is the number of system nodes; , are the voltages of node i and node j at time period t respectively; is the node set whose terminal node is numbered i; is the conductance of the line between node i and node j; is the voltage phase angle difference between node i and node j during period t; 4) Battery maintenance cost During the charging and discharging process, the battery will incur certain maintenance costs, which can be expressed as: ; (8) Where: is the number of batteries; is the battery maintenance cost coefficient; is the charge and discharge power of battery z during period t. When charging, When discharging, ; (3) Constraints 1) Power balance constraints ; (9) Where; , are the active and reactive power injected by DG into node i during period t, respectively; , are the active and reactive power injected into node i by the upper level power grid during period t respectively; , is the active value and reactive value of the system load during period t; is the susceptance of the line between node i and node j; 2) Voltage Constraint ; (10) In the formula, is the voltage lower limit of node i; is the voltage upper limit of node i; 3) Current Constraint ; (11) Where: is the current in branch p; is the upper limit of the current in branch p.

[0007] 4) Output constraints ; (12) Where: , are the upper and lower limits of the output m of a small gas turbine respectively; , are the upper and lower limits of the charge and discharge power of battery z respectively; 5) Climbing rate constraint ; (13) Where: and are the lower and upper limits of the ramp rate of the small gas turbine m, respectively; 6) State Of Charge (SOC) Constraints ; (14) In the formula, is the state of charge of battery z during period t; is the lower limit of SOC of battery z; is the upper limit of SOC of battery z; is the SOC value of battery z at the beginning of the scheduling cycle; is the SOC value of battery z at the end of the scheduling period; 7) Demand response constraints ; (15) ; (16) Where: It is the peak electricity price adjustment amount; is the normal electricity price; The adjustment amount of off-peak electricity price; is the peak-to-low electricity price ratio; is the lower limit of the peak-to-low electricity price ratio, It is the upper limit of the peak-to-low electricity price ratio.

[0008] Preferably, the step S2 is specifically as follows: (1) Tent Chaos Mapping For the optimization algorithm, the uneven distribution of the initial solution has a great impact on its performance. The initial solution of the BES algorithm is randomly generated, which easily leads to insufficient diversity of the vulture population and premature convergence of the algorithm. The Tent chaos equation can improve the distribution of the initial solution, and its expression is: ; (17) Where: is the initial position sequence of the vulture; is the sequence of condor positions after mapping; is the chaos coefficient, usually taken as 0.499.

[0009] After using the Tent chaos equation to obtain the initial position of the vulture, its position distribution became more uniform, and the population diversity increased significantly, which solved the premature problem of the BES algorithm. (2) Cauchy mutation operator Spiral flight is a unique search method for vultures. This method is conducive to the vulture population to fully carry out global search, but it is not conducive to the rapid convergence of the algorithm in the later stage. For this reason, the present invention uses the Cauchy mutation operator to perturb the position of the best vulture individual, and accelerates the convergence of the algorithm through mutation. The specific perturbation formula is: ; (18) Where: new locations for individual vultures; The current optimal location for the condor population; is a random number that follows the Cauchy distribution.

[0010] Preferably, step S3 comprises the following steps: 1) Set ADN system parameters, including DG output information, load changes, electricity price information, and various constraints; 2) Set the IBES algorithm parameters, including the upper limit of the number of iterations, position parameters, trajectory parameters, number of vultures, intensity coefficient, etc.; 3) Generate the initial population of vultures through the Tent chaos equation, calculate its initial fitness using equation (4), and save the current optimal one; 4) The vulture begins to look for hunting space, and the position of the vulture is updated using the first-stage position update formula; 5) The vulture starts searching for prey, and the position of the vulture is updated using the second-stage position update formula; 6) The vulture preys by diving, and the position of the vulture is updated using the third stage position update formula; 7) Use the Cauchy mutation operator to perturb the position of the best vulture individual and select the vulture with better fitness to continue iterating; 8) Determine whether the iteration is terminated. When the upper limit of the number of iterations has been reached, the iteration is terminated and the minimum daily operating cost of ADN is output. If the upper limit of the number of iterations has not been reached, set t=t+1 and the program returns to step 4).

[0011] The present invention provides an active distribution network optimization scheduling method considering price-type demand response, taking the daily minimum operating cost of ADN as the objective function, and considering the influence of price-type demand response on ADN scheduling, an active distribution network scheduling model is constructed. The Bald Eagle Search (BES) algorithm is improved by using the Tent chaos equation and the Cauchy mutation operator, so that the optimization effect of the Improved Bald Eagle Search (IBES) algorithm is improved. The ADN scheduling model is solved by using the IBES algorithm, and different scenarios are built using the improved IEEE node system for simulation analysis. The results show that when considering demand response, the peak-to-valley difference of the load curve is smaller, and the demand response transfers part of the peak load to the valley period, realizing the load peak shaving and valley filling. Under the action of demand response, the daily minimum operating cost of ADN is smaller and the economy is better. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The present invention will be further described below in conjunction with the accompanying drawings and embodiments: Figure 1 The flow chart of solving the ADN scheduling model by the IBES algorithm of the present invention; Figure 2 To improve the IEEE33 node system diagram; Figure 3 It is the load demand and wind and solar output curve; Figure 4 This is the scheduling result diagram for scenario 1; Figure 5 This is the scheduling result diagram for scenario 2; Figure 6 Load curve diagrams for two scheduling scenarios; Figure 7 Convergence curves of each algorithm. DETAILED DESCRIPTION

[0013] An active distribution network optimization scheduling method considering price-based demand response includes the following steps: S1. Taking the daily minimum operating cost of the active distribution network as the objective function and considering the impact of price-based demand response on the active distribution network scheduling, an active distribution network scheduling model is constructed; S2, using Tent chaos equation and Cauchy mutation operator to improve the vulture search algorithm; S3. The improved vulture algorithm is used to solve the active distribution network dispatching model.

[0014] Preferably, in step S1, the active distribution network dispatching model is specifically as follows: (1) Price-based demand response model Price-based demand response means that users adjust their electricity load according to the time-of-use electricity price. Therefore, the relationship between price-based response load and electricity price can be described by the load electricity price elasticity coefficient, which is expressed as: ; (1) Where: It represents the elasticity coefficient of electricity quantity and price; , They represent the initial load value and load change in the i-th period respectively; , They represent the initial electricity price and the change in electricity price in the jth period respectively; According to formula (1), the available electricity price elasticity matrix E is: ; (2) Where: n represents the scheduling period; Then we can get: ; (3) Where: is the electricity price of the nth dispatch period after adjustment; (2) Objective function After the implementation of time-of-use electricity prices, users' electricity usage habits will change, resulting in changes in the daily operating costs of ADN. This paper takes an ADN system containing photovoltaics, wind power, batteries, and small gas turbines as the research object. Since photovoltaics and wind power are clean energy, their power generation costs and maintenance costs are ignored. The minimum daily operating cost of ADN within a scheduling cycle is used as the objective function to construct an ADN optimization scheduling model. The objective function expression is: ; (4) Where C is the total daily operating cost of ADN; The cost of purchasing electricity; Cost of small gas turbines; is the system network loss cost; It is the battery maintenance cost; among which: 1) Electricity purchasing cost The power purchase cost refers to the cost of transmitting power from the upper-level power grid to the ADN, and its expression is: ; (5) Where: is the unit price of electricity purchased during period t; is the power purchased in time period t; T is the dispatching period, which is 24h in the present invention; 2) Cost of small gas turbines During operation, the cost of a small gas turbine is mainly composed of fuel cost and maintenance cost, which can be expressed as: ; (6) Where: is the number of units; is the fuel cost coefficient; is the maintenance cost coefficient; is the power generation power of gas turbine m during period t; is the conversion efficiency of the small gas turbine m; 3) System network loss cost The system network loss cost is mainly caused by line impedance, and its expression is: ; (7) Where: is the number of system nodes; , are the voltages of node i and node j at time period t respectively; is the node set whose terminal node is numbered i; is the conductance of the line between node i and node j; is the voltage phase angle difference between node i and node j during period t; 4) Battery maintenance cost During the charging and discharging process, the battery will incur certain maintenance costs, which can be expressed as: ; (8) Where: is the number of batteries; is the battery maintenance cost coefficient; is the charge and discharge power of battery z during period t. When charging, When discharging, ; (3) Constraints 1) Power balance constraints ; (9) Where: , are the active and reactive power injected by DG into node i during period t, respectively; , are the active and reactive power injected into node i by the upper level power grid during period t respectively; , is the active value and reactive value of the system load during period t; is the susceptance of the line between node i and node j; 2) Voltage Constraint ; (10) In the formula, is the voltage lower limit of node i; is the voltage upper limit of node i; 3) Current Constraint ; (11) Where: is the current in branch p; is the upper limit of the current in branch p.

[0015] 4) Output constraints ; (12) Where: , are the upper and lower limits of the output m of a small gas turbine respectively; , are the upper and lower limits of the charge and discharge power of battery z respectively; 5) Climbing rate constraint ; (13) Where: and are the lower and upper limits of the ramp rate of the small gas turbine m, respectively; 6) State Of Charge (SOC) Constraints ; (14) In the formula, is the state of charge of battery z during period t; is the lower limit of SOC of battery z; is the upper limit of SOC of battery z; is the SOC value of battery z at the beginning of the scheduling cycle; is the SOC value of battery z at the end of the scheduling period; 7) Demand response constraints ; (15) ; (16) Where: It is the peak electricity price adjustment amount; is the normal electricity price; The adjustment amount of off-peak electricity price; is the peak-to-low electricity price ratio; is the lower limit of the peak-to-low electricity price ratio, It is the upper limit of the peak-to-low electricity price ratio.

[0016] Preferably, the step S2 is specifically as follows: The BES algorithm has a simple principle, good robustness and is easy to implement. However, like other heuristic algorithms, it has a high probability of falling into local optimality in the later stage. In order to solve this problem, the present invention improves the BES algorithm by using the Tent chaos equation and the Cauchy mutation operator, so that the optimization effect of the IBES algorithm is improved.

[0017] (1) Tent Chaos Mapping For the optimization algorithm, the uneven distribution of the initial solution has a great impact on its performance. The initial solution of the BES algorithm is randomly generated, which easily leads to insufficient diversity of the vulture population and premature convergence of the algorithm. The Tent chaos equation can improve the distribution of the initial solution, and its expression is: ; (17) Where: is the initial position sequence of the vulture; is the sequence of condor positions after mapping; is the chaos coefficient, usually taken as 0.499.

[0018] After using the Tent chaos equation to obtain the initial position of the vulture, its position distribution became more uniform, and the population diversity increased significantly, which solved the premature problem of the BES algorithm. (2) Cauchy mutation operator Spiral flight is a unique search method for vultures. This method is conducive to the vulture population to fully carry out global search, but it is not conducive to the rapid convergence of the algorithm in the later stage. For this reason, the present invention uses the Cauchy mutation operator to perturb the position of the best vulture individual, and accelerates the convergence of the algorithm through mutation. The specific perturbation formula is: ; (18) Where: new locations for individual vultures; The current optimal location for the condor population; is a random number that follows the Cauchy distribution.

[0019] The Tent chaos equation and Cauchy mutation operator significantly improve the optimization performance of the IBES algorithm. Therefore, this paper uses the IBES algorithm to solve the ADN scheduling model. The solution steps are as follows. The main process is shown in Figure 1 .

[0020] 1) Set ADN system parameters, including DG output information, load changes, electricity price information, and various constraints; 2) Set the IBES algorithm parameters, including the upper limit of the number of iterations, position parameters, trajectory parameters, number of vultures, intensity coefficient, etc.; 3) Generate the initial population of vultures through the Tent chaos equation, calculate its initial fitness using equation (4), and save the current optimal one; 4) The vulture begins to look for hunting space, and the position of the vulture is updated using the first-stage position update formula; 5) The vulture starts searching for prey, and the position of the vulture is updated using the second-stage position update formula; 6) The vulture preys by diving, and the position of the vulture is updated using the third stage position update formula; 7) Use the Cauchy mutation operator to perturb the position of the best vulture individual and select the vulture with better fitness to continue iterating; 8) Determine whether the iteration is terminated. When the upper limit of the number of iterations has been reached, the iteration is terminated and the minimum daily operating cost of ADN is output. If the upper limit of the number of iterations has not been reached, set t=t+1 and the program returns to step 4).

[0021] Hashrate Analysis 1. System and parameters Use Figure 2 The ADN composed of the improved IEEE33 node system is analyzed in the example. The system standard voltage is 12.66kV, the standard power is 1MVA, and the Figure 2In the figure, PV, WT, MT, and ESS represent photovoltaic power stations, wind turbines, small gas turbines, and batteries, respectively. The PV access location is node 15, and its rated power is 500kW; the WT access location is node 33, and its rated power is 650kW; the MT access location is node 21, and its rated power is 400kW. The fuel cost coefficient and maintenance cost coefficient are 0.16 yuan / (kW·h) and 0.21 yuan / (kW·h), respectively, and the conversion efficiency is 35%. There are two groups of ESS, with access locations at node 15 and node 33. Their rated capacities are both 320kW, and the charging and discharging efficiencies are both 95%. The allowable SOC range is 20%~90%, the initial value is 50%, and the maintenance cost coefficient is 0.1 yuan / (kW·h).

[0022] Select 24 hours as a cycle for ADN scheduling. The load demand curve within the scheduling cycle, wind power and photovoltaic output curves are as follows: Figure 3 shown.

[0023] ADN uses time-of-use electricity prices to purchase electricity from the first-level power grid. The electricity price information is shown in Table 1. The reference electricity price is set to the normal electricity price. The price-based demand response model is used to correct the current electricity purchase price. The corrected valley, normal and peak electricity prices are 0.25 yuan / (kW·h), 0.65 yuan / (kW·h) and 1.07 yuan / (kW·h), respectively. From this, the electricity price elasticity matrix shown in Table 2 can be obtained.

[0024] Table 1 Electricity price information

[0025] Table 2 Electricity price elasticity matrix

[0026] The IBES algorithm parameters are set to [14-15]: the upper limit of the number of iterations kmax=300, the position parameter =2, trajectory parameter a=8, R=1.5, number of vultures N=20, intensity coefficient =2, =2.

[0027] 2. Analysis of the results of the example In order to verify the role of price-based demand response, two typical dispatch scenarios are used for comparative analysis. Scenario 1 does not consider price-based demand response for ADN dispatch, and scenario 2 considers price-based demand response for ADN dispatch. The IBES algorithm is used to optimize the two different dispatch scenarios. The dispatch results of the IBES algorithm for scenario 1 and scenario 2 are respectively Figure 4 and Figure 5 shown.

[0028] from Figure 4From the dispatch results of scenario 1, it can be seen that the battery charging period is mainly concentrated in 1h~4h, 14h-16h and 23h-24h. Among them, 1h~4h and 23h-24h are valley hours, and the electricity price is the lowest during valley hours. The battery has the priority to store more electricity. In 14h-16h, the combined output of wind and light is large, and battery charging can make better use of clean energy. In the periods of 9h~13h and 17h-21h, the load demand is large and the electricity purchase price is also high. In order to reduce the amount of electricity purchased, the battery discharges to reduce the cost of electricity purchase. In addition, during the peak period of electricity consumption, the small gas turbine as a controllable power source increases its output to meet the power demand of the system.

[0029] from Figure 5 The dispatch results of scenario 2 show that the working conditions of the battery and small gas turbine are basically the same as those of scenario 1, but under the effect of price-based demand response, the load curve has changed slightly, mainly manifested in a slight decrease in load during peak hours and a slight increase in load during valley hours. The change in the load curve also caused a change in the purchased power, which was generally manifested in a decrease in the purchased power during the day and an increase in the purchased power in the early morning. Such changes are conducive to reducing the cost of purchased power.

[0030] In order to further verify the role of demand response in peak load shifting, Figure 6 The load curves in the scheduling period of scenario 1 and scenario 2 are given, and the statistics are Figure 6 The load peak-to-valley value and peak-to-valley difference in the load are shown in Table 3. Figure 6 It can be seen from Table 3 that the peak load and valley load of scenario 1 are 2976.78kW and 1585.90kW respectively, with a peak-to-valley difference of 1390.88kW; while the peak load and valley load of scenario 2 are 2935.38kW and 1627.09kW respectively, with a peak-to-valley difference of 1308.29kW. Compared with scenario 1, the peak-to-valley difference of scenario 2 is smaller. It can be seen that under the action of demand response, part of the peak load has been transferred to the valley period, and the peak shaving and valley filling effect is significant.

[0031] Table 3 Comparison of loads in different scenarios

[0032] The various costs of scenario 1 and scenario 2 are shown in Table 4. By comparing the data in Table 4, it can be seen that under the effect of demand response, the various costs in scenario 2 have been reduced, among which the cost of purchasing electricity has been reduced the most significantly, by 953.59 yuan. It can be seen that demand response has shifted the peak and valley loads and significantly reduced the cost of purchasing electricity. The daily minimum operating costs of scenario 1 and scenario 2 are 27,524.03 yuan and 26,504.69 yuan respectively. Compared with scenario 1, the daily minimum operating cost of ADN has been reduced by 3.7%. It can be seen that demand response can improve the economic efficiency of ADN operation.

[0033] Table 4 Comparison of costs in different scheduling scenarios

[0034] In order to verify the superiority of the IBES algorithm, the BES algorithm, particle swarm optimization (PSO) algorithm, and cuckoo search (CS) algorithm are used to optimize the ADN scheduling in scenario 2. The convergence curves of each algorithm are shown in Figure 2. Figure 7 The optimization results are shown in Table 5.

[0035] Table 5 Comparison of algorithm optimization results

[0036] comprehensive Figure 7 As can be seen from Table 5, in terms of the number of iterations, the number of iterations for the IBES algorithm, BES algorithm, PSO algorithm and CS algorithm to converge to the optimal solution is 45, 82, 128 and 144 respectively, which shows that the IBES algorithm has a higher evolution efficiency; in terms of the optimal solution, the optimal solutions of the IBES algorithm, BES algorithm, PSO algorithm and CS algorithm are 26504.69 yuan, 26991.24 yuan, 27115.72 yuan and 27374.84 yuan respectively, which shows that the IBES algorithm has a higher calculation accuracy; in terms of calculation time, the calculation time of the IBES algorithm, BES algorithm, PSO algorithm and CS algorithm is 5.2s, 9.6s, 11.24s and 12.37s respectively, which shows that the IBES algorithm converges faster. In summary, the optimization effect of the IBES algorithm obtained by improving the Tent chaos equation and the Cauchy mutation operator is comprehensively improved, and its superiority is obvious.

[0037] The above embodiments are only preferred technical solutions of the present invention and should not be regarded as limiting the present invention. The protection scope of the present invention shall be the technical solutions recorded in the claims, including equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. An active distribution network optimization scheduling method considering price-based demand response, characterized in that: The following steps are involved: S1. Taking the daily minimum operating cost of the active distribution network as the objective function and considering the impact of price-based demand response on the active distribution network scheduling, an active distribution network scheduling model is constructed; S2, using Tent chaos equation and Cauchy mutation operator to improve the vulture search algorithm; S3. The improved vulture algorithm is used to solve the active distribution network dispatching model.

2. According to claim 1, a method for optimizing and dispatching active distribution networks considering price-based demand response, characterized in that: In step S1, the active distribution network dispatching model is specifically as follows: (1) Price-based demand response model Price-based demand response means that users adjust their electricity load according to the time-of-use electricity price. Therefore, the relationship between price-based response load and electricity price can be described by the load electricity price elasticity coefficient, which is expressed as: ；（1） Where: It represents the elasticity coefficient of electricity quantity and price; , They represent the initial load value and load change in the i-th period respectively; , They represent the initial electricity price and the change in electricity price in the jth period respectively; According to formula (1), the available electricity price elasticity matrix E is: ；（2） Where: n represents the scheduling period; Then we can get: ；（3） Where: is the electricity price of the nth dispatch period after adjustment; (2) Objective function After the implementation of time-of-use electricity prices, users' electricity consumption habits will change, which will lead to changes in the daily operating cost of ADN. Taking the minimum daily operating cost of ADN within a scheduling cycle as the objective function, an ADN optimization scheduling model is constructed. The objective function expression is: ；（4） Where C is the total daily operating cost of ADN; The cost of purchasing electricity; Cost of small gas turbines; is the system network loss cost; It is the battery maintenance cost; among which: 1) Electricity purchasing cost The power purchase cost refers to the cost of transmitting power from the upper-level power grid to the ADN, and its expression is: ；（5） Where: is the unit price of electricity purchased during period t; is the power purchased during period t; T is the dispatching period; 2) Cost of small gas turbines During operation, the cost of a small gas turbine is mainly composed of fuel cost and maintenance cost, which can be expressed as: ；（6） Where: is the number of units; is the fuel cost coefficient; is the maintenance cost coefficient; is the power generation power of gas turbine m during period t; is the conversion efficiency of the small gas turbine m; 3) System network loss cost The system network loss cost is mainly caused by line impedance, and its expression is: ；（7） Where: is the number of system nodes; , are the voltages of node i and node j at time period t respectively; is the node set whose terminal node is numbered i; is the conductance of the line between node i and node j; is the voltage phase angle difference between node i and node j during period t; 4) Battery maintenance cost During the charging and discharging process, the battery will incur certain maintenance costs, which can be expressed as: ；（8） Where: is the number of batteries; is the battery maintenance cost coefficient; is the charge and discharge power of battery z during period t. When charging, When discharging, ; (3) Constraints 1) Power balance constraints ；（9） Where: , are the active and reactive power injected by DG into node i during period t, respectively; , are the active and reactive power injected into node i by the upper level power grid during period t respectively; , is the active value and reactive value of the system load during period t; is the susceptance of the line between node i and node j; 2) Voltage Constraint ；（10） In the formula, is the voltage lower limit of node i; is the voltage upper limit of node i; 3) Current Constraint ；（11） Where: is the current in branch p; is the upper limit of the current in branch p; 4) Output constraints ；（12） Where: , are the upper and lower limits of the output m of a small gas turbine respectively; , are the upper and lower limits of the charge and discharge power of battery z respectively; 5) Climbing rate constraint ；（13） Where: and are the lower and upper limits of the ramp rate of the small gas turbine m, respectively; 6) State of charge constraints ；（14） In the formula, is the state of charge of battery z during period t; is the lower limit of SOC of battery z; is the upper limit of SOC of battery z; is the SOC value of battery z at the beginning of the scheduling cycle; is the SOC value of battery z at the end of the scheduling period; 7) Demand response constraints ；（15） ；（16） Where: It is the peak electricity price adjustment amount; is the normal electricity price; The adjustment amount of off-peak electricity price; is the peak-to-low electricity price ratio; is the lower limit of the peak-to-low electricity price ratio, It is the upper limit of the peak-to-low electricity price ratio.

3. According to claim 2, a method for optimizing and dispatching active distribution networks considering price-based demand response is characterized in that: The step S2 is specifically as follows: (1) Tent Chaos Mapping The Tent chaos equation can improve the distribution of the initial solution, and its expression is: ；（17） Where: is the initial position sequence of the vulture; is the sequence of condor positions after mapping; is the chaos coefficient; After using the Tent chaos equation to obtain the initial position of the vulture, its position distribution became more uniform, and the population diversity increased significantly, which solved the premature problem of the BES algorithm. (2) Cauchy mutation operator The specific perturbation formula is: ；（18） Where: new locations for individual vultures; The current optimal location for the condor population; is a random number that follows the Cauchy distribution.

4. The method for optimizing and dispatching an active distribution network considering price-based demand response according to claim 3, characterized in that: The step S3 comprises the following steps: 1) Set ADN system parameters, including DG output information, load changes, electricity price information, and various constraints; 2) Set the IBES algorithm parameters, including the upper limit of iterations, position parameters, trajectory parameters, number of vultures, and intensity coefficient; 3) Generate the initial population of vultures through the Tent chaos equation, calculate its initial fitness using equation (4), and save the current optimal one; 4) The vulture begins to look for hunting space, and the position of the vulture is updated using the first-stage position update formula; 5) The vulture starts searching for prey, and the position of the vulture is updated using the second-stage position update formula; 6) The vulture preys by diving, and the position of the vulture is updated using the third stage position update formula; 7) Use the Cauchy mutation operator to perturb the position of the best vulture individual and select the vulture with better fitness to continue iterating; 8) Determine whether the iteration is terminated. When the upper limit of the number of iterations has been reached, the iteration is terminated and the minimum daily operating cost of ADN is output. If the upper limit of the number of iterations has not been reached, set t=t+1 and the program returns to step 4).

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