User demand response modeling and load side resource cooperative scheduling method

Abstract

The invention relates to the technical field of electric energy management, in particular to a user demand response modeling and load side resource collaborative scheduling method. A power distribution network, distributed photovoltaic, distributed energy storage and flexible load cooperative scheduling model is provided, load side distributed resources are coordinated through the power distribution network, and a user demand response modeling and load side resource cooperative scheduling method is provided under the condition of considering power flow constraints. Therefore, the total operation cost of the power distribution network, the integrated distributed photovoltaic, the distributed energy storage and the flexible load is minimized. According to the design of the invention, efficient integration and scheduling of the resources are realized, distributed resource management is optimized, and the operation efficiency and stability of the power distribution network are improved; the response speed and the fault-tolerant capability of the power grid to emergencies are effectively improved, and the toughness, the safety, the flexibility and the reliability of the whole power system are enhanced; and through flexible load adjustment, expensive peak power purchase is avoided, the electric charge expenditure is directly reduced, and the cost is greatly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy management, and in particular to a method for user demand response modeling and load-side resource coordinated scheduling. Background Art

[0002] With the surge in load-side resources such as distributed power generation (DG), distributed energy storage, and flexible loads, the coordinated scheduling of these resources has become a thorny issue. With the acceleration of the global energy transition and the continued growth of electricity demand, the importance of load-side resources in the power system has become increasingly prominent. In recent years, these resources have experienced explosive growth. Distributed photovoltaics, a prime example of distributed power generation, are widely used in residential, commercial, and industrial sectors due to their clean and renewable nature. More and more users are choosing to install distributed photovoltaic systems on rooftops or in open spaces for power generation. Distributed energy storage has also made significant progress. Various battery energy storage technologies continue to mature, their costs gradually decrease, and they are widely used in power systems to regulate power supply and demand and improve power quality. Furthermore, with the development of smart grid technology and increasing user demand for flexible electricity consumption, the scale of flexible loads is also expanding, allowing users to adjust their electricity consumption based on electricity price signals and incentives.

[0003] However, the surge in these load-side resources has also brought about a series of serious coordinated scheduling issues. From a resource perspective, distributed power sources are significantly affected by natural conditions (such as sunlight and wind), resulting in intermittent and fluctuating power generation, making their power generation difficult to accurately predict and stably control. For example, distributed photovoltaic power generation can significantly drop or even cease on cloudy days or at night, posing a significant challenge to the power balance of the power system. While distributed energy storage can alleviate the volatility of distributed power sources to a certain extent, it faces numerous challenges in developing charging and discharging strategies, configuring capacity, and coordinating with other resources. If the timing of charging and discharging is not properly selected, not only will the regulatory role of energy storage be lost, but operating costs may also increase. The electricity consumption behavior of flexible loads is highly uncertain, and users' electricity usage habits and needs vary. This makes load forecasting more difficult and effective scheduling and management challenging.

[0004] Traditional power system dispatch mechanisms, designed primarily for centralized power generation and rigid loads, are unable to cope with new changes in load-side resources. Under traditional dispatch models, distributed power sources, energy storage, and flexible loads often operate in isolated islands, lacking effective coordination and integration. The electricity generated by distributed photovoltaics cannot be promptly and efficiently absorbed by local loads, resulting in a large amount of curtailment and energy waste. Interaction between distributed energy storage and the distribution network is also not smooth, preventing the full utilization of energy storage in peak shaving, frequency regulation, and backup. Furthermore, because the adjustable nature of flexible loads is not taken into account, traditional dispatch cannot utilize them for load balancing, making it difficult to improve the operational efficiency and economic efficiency of the power system.

[0005] The continuous reform and development of the power market has placed higher demands on the operational efficiency, stability, and economic efficiency of power systems. In this context, the coordinated dispatch of load-side distributed power generation, distributed energy storage, and flexible loads has become a key issue that needs to be addressed in the power sector. This issue not only affects the reliable supply of power systems but also plays a vital role in promoting energy transition, reducing carbon emissions, and improving social and economic benefits. To address this, we propose a method for modeling user demand response and coordinating the dispatch of load-side resources. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for user demand response modeling and load-side resource coordinated scheduling to solve the problems raised in the above background technology.

[0007] To solve the above technical problems, the purpose of the present invention is to provide a user demand response modeling and load-side resource coordinated scheduling method, propose a distribution network, distributed photovoltaic, distributed energy storage and flexible load coordinated scheduling model, coordinate the load-side distributed resources through the distribution network, and propose a user demand response modeling and load-side resource coordinated scheduling method under the consideration of power flow constraints to minimize the total operating cost of the distribution network (network), integrated distributed photovoltaic (source), distributed energy storage (storage) and flexible load (load); specifically, the following steps are included:

[0008] S1. Analyze the objective function of the distribution network: The distribution network integrates load-side resources, including distributed photovoltaics, distributed energy storage, and demand-response flexible power loads. Its objective function is to minimize the total operating cost.

[0009] S2. Analyze the operating constraints and establish a user demand response modeling and load-side resource coordinated scheduling method; among them, the operating constraints include distribution network operating constraints, distributed photovoltaic operating constraints, distributed energy storage operating constraints and flexible load operating constraints; combined with the objective function and operating constraints of the distribution network, construct the algorithm equation of the user demand response modeling and load-side resource coordinated scheduling method.

[0010] As a further improvement of this technical solution, in S1, the objective function of the distribution network is:

[0011] minC=C DN +C PV +C ES +C LA (1)

[0012] Where minC is the minimum total operating cost of the distribution network, C DN 、C PV 、C ES and C LA They are the operating costs of distribution network, distributed photovoltaic, distributed energy storage and flexible load respectively.

[0013] As a further improvement of this technical solution, the distribution network operating cost is mainly composed of three parts: power loss cost, power purchase cost and power sales income, as shown in the following formula:

[0014]

[0015] Where, π loss is the power loss cost coefficient; is the power loss of distribution network branch ij; The power exchanged with the upper-level power grid, with positive values ​​representing purchasing power and negative values ​​representing selling power; and are the electricity sales and purchase prices of the upper-level power grid in time period t, T is the operation cycle, and B is the branch set of the distribution network.

[0016] As a further improvement of this technical solution, the distributed photovoltaic operation cost is composed of the power abandonment penalty cost and the maintenance cost, as shown in the following formula:

[0017]

[0018] Where, ε pv is the penalty cost coefficient, τ pv is the maintenance cost coefficient, and are the actual power and predicted power of distributed photovoltaic connected to busbar i, and N is the busbar set of the distribution network.

[0019] As a further improvement to this technical solution, in the operating cost of the distributed energy storage, the discharging and charging of the distributed energy storage will generate maintenance costs, as shown in the following formula:

[0020]

[0021] Where minC ES is the minimum operating cost of distributed energy storage, τ es is the distributed energy storage maintenance cost coefficient; and are the charging and discharging power of distributed energy storage at bus i respectively.

[0022] As a further improvement to this technical solution, the flexible load cost is the operating cost of the demand-response flexible load. The load aggregator is responsible for the aggregation and demand response of the flexible load, but the demand response and adjustment of the load will generate discomfort costs. Therefore, the operating cost of the aggregated flexible load is expressed as follows:

[0023]

[0024] Where minC LA is the minimum flexible load cost, β la is the discomfort cost coefficient, and are the actual load and predicted load of bus i respectively.

[0025] As a further improvement of this technical solution, in S2, the distribution network operation constraint is:

[0026]

[0027] Where δ(j) is the set of nodes whose parent node is j; r ij and x ij are the resistance and reactance of branch ij respectively; and are the active power and reactive power of branch ij respectively; is the square value of the bus voltage i, is the square value of the current in branch ij; and Represent the square values ​​of the maximum and minimum values ​​of the i-th bus voltage respectively; and Respectively represent the maximum and minimum reactive power of the static VAR compensator (SVC); is the reactive power of the static VAR compensator SVC at bus i.

[0028] As a further improvement of this technical solution, the operating constraints of the distributed photovoltaic are:

[0029]

[0030] Where, and are the actual power and predicted power of distributed photovoltaic connected to busbar i respectively.

[0031] As a further improvement of this technical solution, the operating constraints of the distributed energy storage are:

[0032]

[0033] Where, and are respectively the charging and discharging power of the i-bus distributed energy storage, is the energy storage state at time slot t, and are the minimum and maximum values ​​of the i-bus distributed energy storage state, and are the maximum charge / discharge power of distributed energy storage at the i-th bus, w i,t It is a binary variable with values ​​of 0 or 1.

[0034] As a further improvement to this technical solution, in the operational constraints of the flexible loads, assuming that the flexible loads can participate in price-based demand response projects and a certain proportion can be adjusted, reduced, or transferred to other time periods, the following are obtained:

[0035]

[0036] Where, γ i is the flexible load adjustment ratio of the i-th busbar;

[0037] Then, the algorithm of user demand response modeling and load-side resource collaborative scheduling method is formulated as follows:

[0038]

[0039] Among them, st(2)-(9) is the set of formulas of the above formulas (2)-(9).

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1. This paper addresses the collaborative scheduling issues caused by the surge in load-side resources, including distributed power sources, distributed energy storage, and flexible loads. It proposes a collaborative scheduling model for distribution networks, distributed photovoltaics, distributed energy storage, and flexible loads. From a social welfare perspective, this paper considers power flow constraints and develops an optimal collaborative scheduling model to minimize operating costs and maintain operational safety.

[0042] 2. This user demand response modeling and load-side resource collaborative scheduling method leverages the capabilities of distributed power sources, energy storage, and flexible loads on the load side. Through intelligent algorithms, it achieves efficient integration and scheduling of these resources, resolving the problem of isolated resource operations under traditional scheduling mechanisms, optimizing distributed resource management, and improving the operational efficiency and stability of the distribution network.

[0043] 3. This user demand response modeling and load-side resource coordinated scheduling method incorporates advanced algorithms to account for complex and changing grid conditions and uncertainties, effectively improving the grid's response speed and fault tolerance to emergencies, and enhancing the resilience, security, flexibility, and reliability of the entire power system.

[0044] 4. This user demand response modeling and load-side resource coordinated scheduling method reduces reliance on external power sources and lowers electricity purchase costs through intelligent deployment of renewable energy generation and energy storage. At the same time, flexible load regulation avoids expensive peak power purchases, directly reducing electricity bills and significantly lowering costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a flow chart of an exemplary collaborative scheduling method in the present invention;

[0046] Figure 2 This is an exemplary architecture diagram of a user demand response modeling and load-side resource collaborative scheduling method based on an improved IEEE33 bus distribution network in the present invention;

[0047] Figure 3 The following is a line graph showing the predicted power of distributed photovoltaic systems of bus 8 and bus 20, which are exemplary systems of the present invention;

[0048] Figure 4 1 is a line chart showing the predicted power of the flexible loads of busbar 13 and busbar 23 as an example in the present invention;

[0049] Figure 5 This is an exemplary application diagram of the load-side resource collaborative scheduling method of the present invention;

[0050] Figure 6 A line chart showing changes in the coordinated charging and discharging strategy of an exemplary energy storage system in the present invention;

[0051] Figure 7 A line graph showing the energy storage status of an exemplary distributed energy storage system in the present invention;

[0052] Figure 8 This is an exemplary comparison diagram of power load demand response before and after the present invention;

[0053] Figure 9This is a line graph showing hourly node voltage changes of an exemplary distributed resource in the present invention. DETAILED DESCRIPTION

[0054] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0055] Example 1

[0056] like Figure 1 As shown, this embodiment provides a user demand response modeling and load-side resource coordinated scheduling method, proposes a distribution network, distributed photovoltaic, distributed energy storage and flexible load coordinated scheduling model, coordinates the load-side distributed resources through the distribution network, and proposes a user demand response modeling and load-side resource coordinated scheduling method under the consideration of power flow constraints to minimize the total operating cost of the distribution network (grid), integrated distributed photovoltaic (source), distributed energy storage (storage) and flexible load (load), which can be referred to as the "grid-source-storage-load" minimum total operating cost; specifically, the following steps are included:

[0057] S1. Analyze the objective function of the distribution network: The distribution network integrates load-side resources, including distributed photovoltaics, distributed energy storage, and demand-response flexible power loads. Its objective function is to minimize the total operating cost.

[0058] In this step, the objective function of the distribution network is:

[0059] minC=C DN +C PV +C ES +C LA (1)

[0060] Where minC is the minimum total operating cost of the distribution network, C DN 、C PV 、C ES and C LA They are the operating costs of distribution network, distributed photovoltaic, distributed energy storage and flexible load respectively.

[0061] Among them, the operating cost of the distribution network mainly consists of three parts: power loss cost, power purchase cost and power sales revenue, as shown in the following formula:

[0062]

[0063] Where, π loss is the power loss cost coefficient; is the power loss of distribution network branch ij; The power exchanged with the upper-level power grid, with positive values ​​representing purchasing power and negative values ​​representing selling power; and are the electricity sales and purchase prices of the upper-level power grid in time period t, T is the operation cycle, and B is the branch set of the distribution network.

[0064] Furthermore, the operating cost of distributed photovoltaics is composed of curtailment penalty costs and maintenance costs, as shown in the following formula:

[0065]

[0066] Where, ε pv is the penalty cost coefficient, τ pv is the maintenance cost coefficient, and are the actual power and predicted power of distributed photovoltaic connected to busbar i, and N is the busbar set of the distribution network.

[0067] Furthermore, in the operating costs of distributed energy storage, the discharge and charging of distributed energy storage will generate maintenance costs, as shown in the following formula:

[0068]

[0069] Where minC ES is the minimum operating cost of distributed energy storage, τ es is the distributed energy storage maintenance cost coefficient; and are the charging and discharging power of distributed energy storage at bus i respectively.

[0070] Furthermore, the flexible load cost is the operating cost of the demand-response flexible load. The load aggregator is responsible for the aggregation and demand response of the flexible load, but the demand response and adjustment of the load will generate discomfort costs. Therefore, the operating cost of the aggregated flexible load is shown as follows:

[0071]

[0072] Where minC LA is the minimum flexible load cost, β la is the discomfort cost coefficient, and are the actual load and predicted load of bus i respectively.

[0073] S2. Analyze operational constraints and establish a user demand response modeling and load-side resource coordinated scheduling method. These operational constraints include distribution network operational constraints, distributed photovoltaic (PV) operational constraints, distributed energy storage operational constraints, and flexible load operational constraints. Combined with the distribution network's objective function and operational constraints, an algorithmic equation for user demand response modeling and load-side resource coordinated scheduling is constructed.

[0074] In this step, the distribution network operation constraints are:

[0075]

[0076] Where δ(j) is the set of nodes whose parent node is j; r ij and x ij are the resistance and reactance of branch ij respectively; and are the active power and reactive power of branch ij respectively; is the square value of the bus voltage i, is the square value of the current in branch ij; and Represent the square values ​​of the maximum and minimum values ​​of the i-th bus voltage respectively; and Respectively represent the maximum and minimum reactive power of the static VAR compensator (SVC); is the reactive power of SVC at bus i.

[0077] Furthermore, the operating constraints of distributed photovoltaics are:

[0078]

[0079] Where, and are the actual power and predicted power of distributed photovoltaic connected to busbar i respectively.

[0080] Furthermore, the operating constraints of distributed energy storage are:

[0081]

[0082] Where, and are respectively the charging and discharging power of the i bus distributed energy storage, E i,t is the energy storage state at time slot t, and are the minimum and maximum values ​​of the i-bus distributed energy storage state, and are the maximum charge / discharge power of distributed energy storage at the i-th bus, w i,t It is a binary variable with values ​​of 0 or 1.

[0083] Finally, among the operational constraints of flexible loads, assuming that they can participate in price-based demand response programs and a certain proportion can be adjusted, reduced, or transferred to other time periods, we have:

[0084]

[0085] Where, γ i is the flexible load adjustment ratio of the i-th busbar;

[0086] To this end, the algorithm for user demand response modeling and load-side resource collaborative scheduling is proposed as follows:

[0087]

[0088] Among them, st(2)-(9) is the set of formulas of the above formulas (2)-(9).

[0089] Case Study Examples

[0090] like Figure 2-Figure 9 This embodiment also applies the above-mentioned user demand response modeling and load-side resource coordinated scheduling method to conduct a case analysis on the improved IEEE-33 bus distribution system. The specific steps are as follows:

[0091] 1) Simulation scene setting

[0092] like Figure 2 As shown, the above-mentioned user demand response modeling and load-side resource collaborative scheduling method are partially carried out on the distribution network improved by the IEEE-33 bus distribution system. Two distributed photovoltaics are installed on bus 8 (installed capacity of 2MW) and bus 20 (installed capacity of 3MW). Two distributed energy storages are installed on bus 16 (installed capacity of 6MWh) and bus 28 (installed capacity of 4MWh). In addition, two static VAR compensators are considered to provide reactive compensation, which are installed on bus 5 (installed capacity of 1Mvar) and bus 20 (installed capacity of 1Mvar). It is assumed that the two aggregated flexible loads on bus 13 and bus 23 can participate in the demand response scheme. Figure 3 Given the predicted output power of two distributed photovoltaic systems, Figure 4 The predicted flexible load power is given. In the modified IEEE-33 node test network, the original load on each bus is ignored. Specific parameters for each bus and branch are given. The node voltage constraint is set within [0.95, 1.05] pu. Table I shows the parameters of the distributed power storage and distribution network lines. Table II shows the industrial electricity price.

[0093] Table I SVC, distributed photovoltaic, distribution network, and distributed energy storage user parameters

[0094]

[0095] Table II Industrial Time-of-Use Electricity Prices

[0096]

[0097] 2) Load-side resource collaborative scheduling method

[0098] Figure 5 The load-side resource coordinated scheduling method is described. The following coordinated scheduling results are observed. During off-peak hours (1-7 hours), when electricity prices are low, distribution network operators primarily purchase electricity from the upstream grid to meet demand. Most of the supplied electricity is consumed by the loads on bus routes 13 and 23, and by energy storage charging on bus routes 16 and 28. The remainder is compensated for power losses. During flat hours (8-15 hours), the output power of distributed photovoltaic systems is surplus. Distributed photovoltaic power is used to meet the power demand of bus routes 13 and 23 and compensate for losses. The remainder is stored and charged on bus route 16 or sold to the upstream grid. During peak hours (16-24 hours), when electricity prices are higher, to reduce operating costs, energy storage on bus routes 16 and 28 is discharged to meet demand, with the shortfall purchased from the upstream grid. This power is then used to meet the load demand on bus routes 13 and 23 and to compensate for power losses. Therefore, the load-side resource coordinated scheduling method responds to time-of-use electricity prices and can achieve economical operation.

[0099] Figure 6 This demonstrates the coordinated charging and discharging strategy of distributed energy storage. Positive and negative values ​​represent charging and discharging, respectively. Figure 7 The energy storage status of distributed energy storage is shown. Simulation results show that distributed energy storage primarily charges during off-peak hours and discharges during peak hours, achieving arbitrage and reducing operating costs. Due to the large storage capacity of the 16-bus system, it can also store some excess photovoltaic power during off-peak hours, reducing operating costs and promoting the consumption of distributed photovoltaic power.

[0100] Figure 8 The loads on busbars 13 and 23 before and after demand response are shown. This shifts a certain percentage of peak load demand to off-peak and normal hours, consuming more cheap electricity or distributed photovoltaics and reducing operating costs. Therefore, the coordinated dispatch strategy demonstrates excellent performance in shifting peak loads to valley loads.

[0101] Figure 9 is the hourly node voltage amplitude of the distributed resource. Since the proposed load-side resource collaborative scheduling method takes power flow constraints into account, all node bus amplitudes meet the voltage constraints and are within the constraint range of [0.95, 1.05] pu, which can maintain node voltage stability and safe operation of the entire system.

[0102] 3) Scene comparison

[0103] Three scenarios will be compared to test and demonstrate the application and effectiveness of the proposed user demand response modeling and load-side resource coordinated scheduling method. As shown in Table III, Scenario 1 illustrates the proposed user demand response modeling and load-side resource coordinated scheduling method. Scenario 2 is a source-grid-load coordinated scheduling scenario that does not include energy storage. Scenario 3 is similar to Scenario 1, but ignores the demand response of the loads on Buses 13 and 23.

[0104] Table III Simulation scenario settings

[0105]

[0106] Table IV shows the overall operating costs and power losses under the three scenarios. The results show that scenario 1 has the lowest operating cost, which is reduced by approximately 32.72% and 17.86% compared to scenarios 2 and 3, respectively. Therefore, the proposed user demand response modeling and load-side resource collaborative scheduling method can significantly reduce operating costs. In addition, the power losses in scenarios 1 and 3 are both smaller than those in scenario 2. Therefore, the use of energy storage can reduce power losses. However, the power loss of the distribution network in scenario 3 is smaller than that in scenario 1. This is because demand response changes the load curve, which may result in greater power loss. However, load demand response can shift part of the peak load to the valley time slot, which can significantly reduce operating costs.

[0107] Table IV Total cost and power consumption under different scenarios

[0108]

[0109] This case study proposes a user demand response modeling and load-side resource collaborative scheduling method to coordinate the operation of distributed resources on the load side. The following conclusions can be drawn.

[0110] 1) The proposed collaborative dispatch model is formulated from a social welfare perspective and can coordinate the operation of distributed renewable power, flexible loads, distributed power storage, and distribution networks to minimize overall operating costs;

[0111] 2) User demand response modeling and load-side resource collaborative scheduling methods can efficiently coordinate the operation of distributed resources and significantly reduce operating costs. Power storage and demand response for flexible power loads can play an important role in collaborative scheduling and reducing operating costs.

[0112] Those skilled in the art will appreciate that the process of implementing all or part of the steps of the above embodiments may be accomplished by hardware, or by instructing related hardware through a program.

[0113] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for user demand response modeling and load-side resource coordinated scheduling, characterized by: A coordinated dispatch model for distribution networks, distributed photovoltaics, distributed energy storage, and flexible loads is proposed. This model coordinates distributed resources on the load side through the distribution network. Taking power flow constraints into account, a user demand response modeling and load-side resource coordinated dispatching method are proposed to minimize the total operating cost of the distribution network, integrated distributed photovoltaics, distributed energy storage, and flexible loads. The model includes the following steps: S1. Analyze the objective function of the distribution network: The distribution network integrates load-side resources, including distributed photovoltaics, distributed energy storage, and demand-response flexible power loads. Its objective function is to minimize the total operating cost. S2. Analyze the operating constraints and establish a user demand response modeling and load-side resource coordinated scheduling method; among them, the operating constraints include distribution network operating constraints, distributed photovoltaic operating constraints, distributed energy storage operating constraints and flexible load operating constraints; combined with the objective function and operating constraints of the distribution network, construct the algorithm equation of the user demand response modeling and load-side resource coordinated scheduling method.

2. The method for user demand response modeling and load-side resource coordinated scheduling according to claim 1 is characterized by: In S1, the objective function of the distribution network is: minC=C DN +C PV +C ES +C LA (1) Where minC is the minimum total operating cost of the distribution network, C DN 、C PV 、C ES and C LA They are the operating costs of distribution network, distributed photovoltaic, distributed energy storage and flexible load respectively.

3. The method for user demand response modeling and load-side resource coordinated scheduling according to claim 2 is characterized by: The distribution network operating cost is mainly composed of three parts: power loss cost, power purchase cost and power sales income, as shown in the following formula: Where, π loss is the power loss cost coefficient; is the power loss of distribution network branch ij; The power exchanged with the upper-level power grid, with positive values ​​representing purchasing power and negative values ​​representing selling power; and are the electricity sales and purchase prices of the upper-level power grid in time period t, T is the operation cycle, and B is the branch set of the distribution network.

4. The method for user demand response modeling and load-side resource coordinated scheduling according to claim 3 is characterized by: The distributed photovoltaic operation cost is composed of the power abandonment penalty cost and maintenance cost, as shown in the following formula: Where, ε pv is the penalty cost coefficient, τ pv is the maintenance cost coefficient, and are the actual power and predicted power of distributed photovoltaic connected to busbar i, and N is the busbar set of the distribution network.

5. The method for user demand response modeling and load-side resource coordinated scheduling according to claim 4 is characterized by: Among the operating costs of the distributed energy storage, the discharging and charging of the distributed energy storage will incur maintenance costs, as shown in the following formula: Where minC ES is the minimum operating cost of distributed energy storage, τ es is the distributed energy storage maintenance cost coefficient; and are the charging and discharging power of distributed energy storage at bus i respectively.

6. The method for user demand response modeling and load-side resource coordinated scheduling according to claim 5 is characterized by: The flexible load cost is the operating cost of the demand-response flexible load. The load aggregator is responsible for the aggregation and demand response of the flexible load. However, the demand response and adjustment of the load will generate discomfort costs. Therefore, the operating cost of the aggregated flexible load is shown as follows: Where minC LA is the minimum flexible load cost, β la is the discomfort cost coefficient, and are the actual load and predicted load of bus i respectively.

7. The method for user demand response modeling and load-side resource coordinated scheduling according to claim 6 is characterized by: In S2, the distribution network operation constraints are: Where δ(j) is the set of nodes whose parent node is j; r ij and x ij are the resistance and reactance of branch ij respectively; and are the active power and reactive power of branch ij respectively; is the square value of the bus voltage i, is the square value of the current in branch ij; and Represent the square values ​​of the maximum and minimum values ​​of the i-th bus voltage respectively; and Respectively represent the maximum and minimum reactive power of the static VAR compensator SVC; is the reactive power of the static VAR compensator SVC at bus i.

8. The method for user demand response modeling and load-side resource coordinated scheduling according to claim 7 is characterized by: The operating constraints of the distributed photovoltaic system are: Where, and are the actual power and predicted power of distributed photovoltaic connected to busbar i respectively.

9. The method for user demand response modeling and load-side resource coordinated scheduling according to claim 8, characterized in that: The operating constraints of the distributed energy storage are: Where, and are respectively the charging and discharging power of the i bus distributed energy storage, E i,t is the energy storage state at time slot t, and are the minimum and maximum values ​​of the i-bus distributed energy storage state, and are the maximum charge / discharge power of distributed energy storage at the i-th bus, w i,t It is a binary variable with values ​​of 0 or 1.

10. The method for user demand response modeling and load-side resource coordinated scheduling according to claim 9 is characterized in that: In the operational constraints of flexible loads, assuming that they can participate in price-based demand response programs and a certain proportion can be adjusted, reduced, or transferred to other time periods, the following are true: Where, γ i is the flexible load adjustment ratio of the i-th busbar; Then, the algorithm of user demand response modeling and load-side resource collaborative scheduling method is formulated as follows: Among them, st(2)-(9) is the set of formulas of formula (2)-formula (9).

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