Multi-level flexible resource cooperative regulation and control method and device for power distribution network

Abstract

The invention relates to the field of power distribution network operation control, and particularly provides a power distribution network multi-level flexible resource cooperative regulation and control method and device, and the method comprises the steps: converting a flexible resource total system optimization model composed of all flexible resource subsystem optimization models into a power distribution network system optimization model suitable for transaction control; substituting a given price vector into the power distribution network system optimization model and carrying out iterative solution to obtain an optimization result; and obtaining a regulation and control strategy by using the optimization result, and performing optimization regulation and control on the power distribution network by using the regulation and control strategy. According to the technical scheme provided by the invention, through cooperation of the flexible resource subsystem optimization model and the flexible resource total system optimization model, the calculation time is greatly shortened, the method can be suitable for power distribution network optimization regulation and control strategy calculation of large-scale multi-element flexible resource access, and the calculation result better fits the uncertainty characteristic of renewable energy sources.

Description

Technical Field

[0001] The present invention relates to the field of distribution network operation control, and in particular to a method and device for coordinated control of multi-level flexible resources in a distribution network. Background Art

[0002] With the widespread grid integration of flexible resources such as photovoltaics and wind power, the need to fully utilize these resources is urgent, and the coordinated regulation of flexible resources has received increasing attention. Currently, much research has been conducted on the coordinated regulation of flexible resources with multi-energy coupling. However, existing research has the following drawbacks:

[0003] (1) Existing flexible resource control methods are mostly concentrated in small-scale multi-element flexible resource systems. When applied to large-scale multi-element flexible resource systems, the amount of computation increases due to the increase in scale, and the computation time becomes longer. This makes it difficult to complete the control process within a limited time, resulting in poor collaborative control effects and an inability to respond to system changes and needs in a timely manner. For example, in large-scale systems, it is necessary to simultaneously consider the operating status, constraints, and coupling relationships of many flexible resources. The computational complexity grows exponentially. Traditional optimization algorithms are prone to falling into local optimal solutions during the solution process, making it difficult to find the global optimal solution, thus affecting the accuracy and effectiveness of the control strategy.

[0004] (2) Flexible resource control methods do not adequately consider the operating risks of equipment such as transformers. In actual distribution network operation, transformers are key equipment, and their operating status is crucial to the safety and stability of the system. However, existing flexible resource control methods often ignore the operating limitations and risk factors of transformers. For example, when scheduling and optimizing flexible resources, the load rate, temperature rise, insulation performance and other parameters of the transformer are not fully considered, which may cause the transformer to operate under high load or overload conditions, increase equipment loss and failure risks, and thus affect the safe operation and power supply reliability of the entire distribution network.

[0005] (3) Lack of consideration for the uncertainty of renewable energy output. Renewable energy sources such as photovoltaic and wind power are highly random and volatile. Their output is affected by many factors such as weather conditions and seasonal changes, and has great uncertainty. However, in existing research on coordinated regulation of flexible resources, it is often assumed that the output of renewable energy is certain, or only simple prediction errors are considered, without fully considering the impact of the randomness and volatility of output on system regulation. This will result in the inability of regulation strategies to effectively cope with the uncertainty of renewable energy output in practical applications, and will not achieve the optimal operation of the system and the reasonable allocation of resources, affecting the economy and stability of the system. For example, when the output of renewable energy suddenly increases or decreases, the system may not be able to adjust the operating status of other flexible resources in a timely manner, leading to problems such as supply and demand imbalance or equipment overload. Summary of the Invention

[0006] In order to overcome the above-mentioned defects, the present invention proposes a method and device for coordinated control of multi-level flexible resources in a distribution network.

[0007] In a first aspect, a method for coordinated control of multi-level flexible resources in a distribution network is provided, the method comprising:

[0008] The flexible resource overall system optimization model composed of the flexible resource subsystem optimization models is converted into a distribution network system optimization model under applicable transaction control;

[0009] Substituting a given price vector into the distribution network system optimization model and performing iterative solution to obtain an optimization result;

[0010] Utilizing the optimization results to obtain a control strategy, and utilizing the control strategy to optimize and control the distribution network;

[0011] The optimization results include at least one of the following: the power input from the main power grid to each flexible resource subsystem, the reduced renewable energy and heat, the charging and discharging power and price vector of the energy storage system, the charging and discharging capacity of the thermal energy system, and the input power of the main transformer of the distribution network system.

[0012] Preferably, substituting the given price vector into the distribution network system optimization model and performing iterative solution includes:

[0013] Step a. Substitute the given price vector into the distribution network system optimization model and solve it to obtain the initial optimization result;

[0014] Step b. Determine whether the convergence condition is met based on the initial optimization result. If so, output the initial optimization result. Otherwise, update the given price vector and return to step a.

[0015] Furthermore, the convergence conditions are as follows:

[0016] ΔP k <θ

[0017]

[0018] In the above formula, ΔP k is the power balance coefficient of the distribution network system in the initial optimization result obtained by the kth iteration, P Tr,k* is the input power of the main transformer of the distribution network system in the initial optimization result obtained by the k-th iteration, is the power input from the main grid to the nth flexible resource subsystem in the initial optimization result obtained by the kth iteration, and N is the number of flexible resource subsystems.

[0019] Furthermore, the given price vector is updated as follows:

[0020]

[0021] In the above formula, is the given price vector in the k+1th iteration solution process, is the given price vector in the kth iteration solution process, η k The preset step size for the kth iteration of the solution process.

[0022] Furthermore, the given price vector is updated using a bisection method.

[0023] Furthermore, the flexible resource overall system optimization model composed of the flexible resource subsystem optimization models is as follows:

[0024]

[0025] In the above formula, F t,n is the objective function value corresponding to the nth flexible resource subsystem at time t, N is the number of flexible resource subsystems included in the distribution network system, and t c is the starting time, t e is the end time, P t,n is the power input from the main grid to the nth flexible resource subsystem at time t, P t Tr is the input power of the main transformer of the distribution network system at time t, P t RES is the total renewable energy power shared at time t, is the maximum output power of the main transformer of the distribution network system, is the maximum input power of the main transformer of the distribution network system, W n is the constraint condition corresponding to the nth flexible resource subsystem.

[0026] Furthermore, the distribution network system optimization model is as follows:

[0027]

[0028] In the above formula, SP n is the optimization model corresponding to the nth flexible resource subsystem under transaction control, λ e,t is the local electricity price at time t, τ g,t is the natural gas price at time t, τ e,t is the real-time electricity price of the main power grid at time t, is the natural gas consumed by the cogeneration of the nth flexible resource subsystem at time t, is the natural gas consumed by the electric boiler of the nth flexible resource subsystem at time t.

[0029] Furthermore, the flexible resource subsystem optimization model includes an objective function with the goal of minimizing the purchase cost and corresponding constraints.

[0030] Furthermore, the objective function is as follows:

[0031]

[0032] In the above formula, F t is the objective function value, τ e,t and τ g,t are the real-time electricity price and natural gas price of the main power grid at time t; and are the natural gas consumed by cogeneration and electric boilers, P t is the power input from the main grid by the flexible resource subsystem at time t.

[0033] Furthermore, the constraints include: power balance constraints, interconnection line power constraints, cogeneration, electric furnace and electric boiler capacity constraints, cogeneration and electric boiler ramp limits, energy storage system and thermal energy system maximum charge and discharge power constraints, constraints related to movable loads, upper limit constraints on renewable energy reduction and heat reduction, upper and lower energy limits on energy storage system and thermal energy system, and target energy limits on batteries.

[0034] Furthermore, the power balance constraint conditions are as follows:

[0035]

[0036] The tie line power constraints are as follows:

[0037]

[0038] The capacity constraints for cogeneration, electric furnaces, and electric boilers are as follows:

[0039]

[0040] The ramp limits for cogeneration and electric boilers are as follows:

[0041]

[0042] The maximum charge and discharge power constraints of the energy storage system and thermal energy system are as follows:

[0043]

[0044] The constraints related to movable loads are as follows:

[0045]

[0046] The upper bound constraints for renewable energy curtailment and heat curtailment are as follows:

[0047]

[0048] The upper and lower energy limits of the energy storage system and thermal energy system are as follows:

[0049]

[0050]

[0051] The target energy limit of the battery is as follows:

[0052]

[0053] In the above formula, P t is the power input from the main grid by the flexible resource subsystem at time t, The power generated by on-site renewable energy in maximum power point tracking mode, and are the natural gas consumed by cogeneration and electric boiler at time t, and are the gas-electricity efficiency and gas-heat efficiency of cogeneration, is the power consumed by the boiler at time t, and are the charging and discharging power of the energy storage system at time t, and is the efficiency of natural gas furnaces and electric boilers, and are the charge and discharge of the thermal energy system at time t, and are the renewable energy and heat curtailed at time t, L e,t and L th,t are the fixed electric load and thermal load at time t, are the maximum input and output power through the connecting line, and are the installed capacities of cogeneration, electric furnaces and boilers, respectively. P CHP , P GF and P EB are the lower limits of the electric power of cogeneration, electric furnace and boiler, respectively, P t CHP , P t GF are the electricity consumed by cogeneration and electric furnace at time t, are the electricity consumed by the cogeneration and boiler at time t+1, ΔP CHP and ΔP EB are the hourly rise rates of the cogeneration and boiler, are the maximum charge and discharge power of the energy storage system, are the maximum charge and discharge capacity of the thermal energy system, are the charging and discharging power of the energy storage system, are the transferable electric load and thermal load at time t, and are the total power load and thermal load that can be transferred within the dispatching day, and are the upper limits of transferable electric load and thermal load, Ω th and Ω e are the feasible time intervals for shifting electric load and thermal load, E EES , are the minimum energy and maximum energy of the energy storage system, E TES , are the minimum energy and maximum energy of the thermal energy system, t c The energy of the energy storage system and thermal energy system at the moment, θ EES ,θ TES are the self-discharge rates of the energy storage system and thermal energy system, and are the net energy changes of the energy storage system and thermal energy system at time t, and are the target energies of the energy storage system and thermal energy system at the end of the optimization period, t c is the starting time, t e For the ending moment.

[0054] Furthermore, the net energy changes of the energy storage system and the thermal energy system at time t are as follows:

[0055]

[0056] In the above formula, ΔT is the unit optimization period, are the charging and discharging efficiency of the energy storage system, are the charging and discharging efficiencies of the thermal energy system, respectively.

[0057] Furthermore, when there is a reduction in renewable energy in the distribution network system, the charge and discharge power of the energy storage system in the optimization result is converted according to the following formula:

[0058]

[0059] In the above formula, are the charging and discharging powers of the energy storage system that meet the mutual exclusion constraints of the energy storage charging and discharging modes, and ΔT is the unit optimization period.

[0060] Furthermore, when there is a reduction in renewable energy in the distribution network system, the charge and discharge capacity of the thermal energy system in the optimization result is converted according to the following formula:

[0061]

[0062] In the above formula, are the charge and discharge amounts of the energy storage system that meet the mutual exclusion constraints of thermal energy charge and discharge modes, and ΔT is the unit optimization period.

[0063] In a second aspect, a multi-level flexible resource coordinated control device for a distribution network is provided, the multi-level flexible resource coordinated control device for a distribution network comprising:

[0064] A conversion module, used for converting the flexible resource overall system optimization model composed of the flexible resource subsystem optimization models into a distribution network system optimization model under applicable transaction control;

[0065] An analysis module, configured to substitute a given price vector into the distribution network system optimization model and perform iterative solution to obtain an optimization result;

[0066] A control module, configured to obtain a control strategy using the optimization result, and optimize and control the distribution network using the control strategy;

[0067] The optimization results include at least one of the following: the power input from the main power grid to each flexible resource subsystem, the reduced renewable energy and heat, the charging and discharging power and price vector of the energy storage system, the charging and discharging capacity of the thermal energy system, and the input power of the main transformer of the distribution network system.

[0068] Preferably, substituting the given price vector into the distribution network system optimization model and performing iterative solution includes:

[0069] Step a. Substitute the given price vector into the distribution network system optimization model and solve it to obtain the initial optimization result;

[0070] Step b. Determine whether the convergence condition is met based on the initial optimization result. If so, output the initial optimization result. Otherwise, update the given price vector and return to step a.

[0071] Furthermore, the convergence conditions are as follows:

[0072] ΔP k <θ

[0073]

[0074] In the above formula, ΔP k is the power balance coefficient of the distribution network system in the initial optimization result obtained by the kth iteration, P Tr,k* is the input power of the main transformer of the distribution network system in the initial optimization result obtained by the k-th iteration, is the power input from the main grid to the nth flexible resource subsystem in the initial optimization result obtained by the kth iteration, and N is the number of flexible resource subsystems.

[0075] Furthermore, the given price vector is updated as follows:

[0076]

[0077] In the above formula, is the given price vector in the k+1th iteration solution process, is the given price vector in the kth iteration solution process, η k The preset step size for the kth iteration of the solution process.

[0078] Furthermore, the given price vector is updated using a bisection method.

[0079] Furthermore, the flexible resource overall system optimization model composed of the flexible resource subsystem optimization models is as follows:

[0080]

[0081] In the above formula, F t,n is the objective function value corresponding to the nth flexible resource subsystem at time t, N is the number of flexible resource subsystems included in the distribution network system, and t c is the starting time, t e is the end time, P t,n is the power input from the main grid to the nth flexible resource subsystem at time t, P t Tr is the input power of the main transformer of the distribution network system at time t, P t RES is the total renewable energy power shared at time t, is the maximum output power of the main transformer of the distribution network system, is the maximum input power of the main transformer of the distribution network system, W n is the constraint condition corresponding to the nth flexible resource subsystem.

[0082] Furthermore, the distribution network system optimization model is as follows:

[0083]

[0084] In the above formula, SP n is the optimization model corresponding to the nth flexible resource subsystem under transaction control, λ e,t is the local electricity price at time t, τ g,t is the natural gas price at time t, τ e,t is the real-time electricity price of the main power grid at time t, is the natural gas consumed by the cogeneration of the nth flexible resource subsystem at time t, is the natural gas consumed by the electric boiler of the nth flexible resource subsystem at time t.

[0085] Furthermore, the flexible resource subsystem optimization model includes an objective function with the goal of minimizing the purchase cost and corresponding constraints.

[0086] Furthermore, the objective function is as follows:

[0087]

[0088] In the above formula, F t is the objective function value, τ e,t and τ g,t are the real-time electricity price and natural gas price of the main power grid at time t; and are the natural gas consumed by cogeneration and electric boilers, P t is the power input from the main grid by the flexible resource subsystem at time t.

[0089] Furthermore, the constraints include: power balance constraints, interconnection line power constraints, cogeneration, electric furnace and electric boiler capacity constraints, cogeneration and electric boiler ramp limits, energy storage system and thermal energy system maximum charge and discharge power constraints, constraints related to movable loads, upper limit constraints on renewable energy reduction and heat reduction, upper and lower energy limits on energy storage system and thermal energy system, and target energy limits on batteries.

[0090] Furthermore, the power balance constraint conditions are as follows:

[0091]

[0092] The tie line power constraints are as follows:

[0093]

[0094] The capacity constraints for cogeneration, electric furnaces, and electric boilers are as follows:

[0095]

[0096] The ramp limits for cogeneration and electric boilers are as follows:

[0097]

[0098] The maximum charge and discharge power constraints of the energy storage system and thermal energy system are as follows:

[0099]

[0100] The constraints related to movable loads are as follows:

[0101]

[0102] The upper bound constraints for renewable energy curtailment and heat curtailment are as follows:

[0103]

[0104] The upper and lower energy limits of the energy storage system and thermal energy system are as follows:

[0105]

[0106]

[0107] The target energy limit of the battery is as follows:

[0108]

[0109] In the above formula, P t is the power input from the main grid by the flexible resource subsystem at time t, The power generated by on-site renewable energy in maximum power point tracking mode, and are the natural gas consumed by cogeneration and electric boiler at time t, and are the gas-electricity efficiency and gas-heat efficiency of cogeneration, is the power consumed by the boiler at time t, and are the charging and discharging power of the energy storage system at time t, and is the efficiency of natural gas furnaces and electric boilers, and are the charge and discharge of the thermal energy system at time t, and are the renewable energy and heat curtailed at time t, L e,t and L th,t are the fixed electric load and thermal load at time t, are the maximum input and output power through the connecting line, and are the installed capacities of cogeneration, electric furnaces and boilers, respectively. P CHP , P GF and P EB are the lower limits of the electric power of cogeneration, electric furnace and boiler, respectively, P t CHP , P t GF are the electricity consumed by cogeneration and electric furnace at time t, are the electricity consumed by the cogeneration and boiler at time t+1, ΔP CHP and ΔP EB are the hourly rise rates of the cogeneration and boiler, are the maximum charge and discharge power of the energy storage system, are the maximum charge and discharge capacity of the thermal energy system, are the charging and discharging power of the energy storage system, are the transferable electric load and thermal load at time t, and are the total power load and thermal load that can be transferred within the dispatching day, and are the upper limits of transferable electric load and thermal load, Ω th and Ω e are the feasible time intervals for shifting electric load and thermal load, E EES , are the minimum energy and maximum energy of the energy storage system, E TES , are the minimum energy and maximum energy of the thermal energy system, t c The energy of the energy storage system and thermal energy system at the moment, θ EES ,θ TES are the self-discharge rates of the energy storage system and thermal energy system, and are the net energy changes of the energy storage system and thermal energy system at time t, and are the target energies of the energy storage system and thermal energy system at the end of the optimization period, t c is the starting time, t e For the ending moment.

[0110] Furthermore, the net energy changes of the energy storage system and the thermal energy system at time t are as follows:

[0111]

[0112] In the above formula, ΔT is the unit optimization period, are the charging and discharging efficiency of the energy storage system, are the charging and discharging efficiencies of the thermal energy system, respectively.

[0113] Furthermore, when there is a reduction in renewable energy in the distribution network system, the charge and discharge power of the energy storage system in the optimization result is converted according to the following formula:

[0114]

[0115] In the above formula, are the charging and discharging powers of the energy storage system that meet the mutual exclusion constraints of the energy storage charging and discharging modes, and ΔT is the unit optimization period.

[0116] Furthermore, when there is a reduction in renewable energy in the distribution network system, the charge and discharge capacity of the thermal energy system in the optimization result is converted according to the following formula:

[0117]

[0118] In the above formula, are the charge and discharge amounts of the energy storage system that meet the mutual exclusion constraints of thermal energy charge and discharge modes, and ΔT is the unit optimization period.

[0119] In a third aspect, a computer device is provided, comprising: one or more processors;

[0120] The processor is configured to store one or more programs;

[0121] When the one or more programs are executed by the one or more processors, the method for coordinated control of multi-level flexible resources in the distribution network is implemented.

[0122] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed, the method for coordinated control of multi-level flexible resources in the distribution network is implemented.

[0123] The above one or more technical solutions of the present invention have at least one or more of the following beneficial effects:

[0124] The present invention provides a method and device for coordinated control of multi-level flexible resources in a distribution network, comprising: converting a flexible resource overall system optimization model composed of optimization models of each flexible resource subsystem into a distribution network system optimization model applicable to transaction control; substituting a given price vector into the distribution network system optimization model and performing iterative solution to obtain an optimization result; utilizing the optimization result to obtain a control strategy, and utilizing the control strategy to optimize and control the distribution network; wherein the optimization result includes at least one of the following: the power input from the main grid by each flexible resource subsystem, the renewable energy and heat curtailed, the charge and discharge power and price vector of the energy storage system, the charge and discharge capacity of the thermal energy system, and the input power of the main transformer of the distribution network system. The technical solution provided by the present invention significantly reduces the calculation time through the coordination of the flexible resource subsystem optimization model and the flexible resource overall system optimization model, and can be applied to the calculation of distribution network optimization control strategies with large-scale multi-flexible resource access, and the calculation results are more in line with the uncertainty characteristics of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0125] Figure 1 This is a flow chart of the main steps of the method for coordinated control of multi-level flexible resources in a distribution network according to an embodiment of the present invention;

[0126] Figure 2 is a load curve diagram of the flexible resource subsystem 1 according to an embodiment of the present invention;

[0127] Figure 3 is a load curve diagram of the flexible resource subsystem 2 according to an embodiment of the present invention;

[0128] Figure 4 is a load curve diagram of the flexible resource subsystem 3 according to an embodiment of the present invention;

[0129] Figure 5 This is a diagram showing the collaborative control results of multiple multi-element flexible resource systems according to an embodiment of the present invention;

[0130] Figure 6 is a real-time electricity price curve diagram according to an embodiment of the present invention;

[0131] Figure 7 is a scheduling result diagram of the flexible resource subsystem 1 according to an embodiment of the present invention;

[0132] Figure 8 is a scheduling result diagram of the flexible resource subsystem 2 according to an embodiment of the present invention;

[0133] Figure 9 This is a diagram showing the scheduling results of the flexible resource subsystem 3 according to an embodiment of the present invention. DETAILED DESCRIPTION

[0134] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0135] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0136] As disclosed in the background technology, with the widespread grid integration of flexible resources such as photovoltaics and wind power, the need to fully utilize these resources is urgent, and the coordinated regulation of flexible resources has received increasing attention. Currently, much research has been conducted on the coordinated regulation of flexible resources with multi-energy coupling. However, existing research has the following drawbacks:

[0137] (1) Existing flexible resource control methods are mostly concentrated in small-scale multi-element flexible resource systems. When applied to large-scale multi-element flexible resource systems, the amount of computation increases due to the increase in scale, and the computation time becomes longer. This makes it difficult to complete the control process within a limited time, resulting in poor collaborative control effects and an inability to respond to system changes and needs in a timely manner. For example, in large-scale systems, it is necessary to simultaneously consider the operating status, constraints, and coupling relationships of many flexible resources. The computational complexity grows exponentially. Traditional optimization algorithms are prone to falling into local optimal solutions during the solution process, making it difficult to find the global optimal solution, thus affecting the accuracy and effectiveness of the control strategy.

[0138] (2) Flexible resource control methods do not adequately consider the operating risks of equipment such as transformers. In actual distribution network operation, transformers are key equipment, and their operating status is crucial to the safety and stability of the system. However, existing flexible resource control methods often ignore the operating limitations and risk factors of transformers. For example, when scheduling and optimizing flexible resources, the load rate, temperature rise, insulation performance and other parameters of the transformer are not fully considered, which may cause the transformer to operate under high load or overload conditions, increase equipment loss and failure risks, and thus affect the safe operation and power supply reliability of the entire distribution network.

[0139] (3) Lack of consideration for the uncertainty of renewable energy output. Renewable energy sources such as photovoltaic and wind power are highly random and volatile. Their output is affected by many factors such as weather conditions and seasonal changes, and has great uncertainty. However, in existing research on coordinated regulation of flexible resources, it is often assumed that the output of renewable energy is certain, or only simple prediction errors are considered, without fully considering the impact of the randomness and volatility of output on system regulation. This will result in the inability of regulation strategies to effectively cope with the uncertainty of renewable energy output in practical applications, and will not achieve the optimal operation of the system and the reasonable allocation of resources, affecting the economy and stability of the system. For example, when the output of renewable energy suddenly increases or decreases, the system may not be able to adjust the operating status of other flexible resources in a timely manner, leading to problems such as supply and demand imbalance or equipment overload.

[0140] In order to improve the above-mentioned problems, the present invention provides a method and device for coordinated control of multi-level flexible resources in a distribution network, comprising: converting a flexible resource total system optimization model composed of optimization models of each flexible resource subsystem into a distribution network system optimization model applicable to transaction control; substituting a given price vector into the distribution network system optimization model and performing iterative solution to obtain an optimization result; utilizing the optimization result to obtain a control strategy, and utilizing the control strategy to optimize and control the distribution network; wherein the optimization result includes at least one of the following: the power input from the main grid by each flexible resource subsystem, the renewable energy and heat curtailed, the charge and discharge power and price vector of the energy storage system, the charge and discharge capacity of the thermal energy system, and the input power of the main transformer of the distribution network system. The technical solution provided by the present invention significantly reduces the calculation time through the coordination of the flexible resource subsystem optimization model and the flexible resource total system optimization model, and can be applied to the calculation of distribution network optimization control strategies with large-scale multi-flexible resource access, and the calculation results are more in line with the uncertainty characteristics of renewable energy.

[0141] The above scheme is described in detail below.

[0142] Example 1

[0143] See attached Figure 1 , Figure 1 This is a flow chart of the main steps of the method for coordinated control of multi-level flexible resources in a distribution network according to an embodiment of the present invention. Figure 1 As shown, the method for coordinated control of multi-level flexible resources in a distribution network in an embodiment of the present invention mainly includes the following steps:

[0144] Step S101: converting the flexible resource overall system optimization model composed of the flexible resource subsystem optimization models into a distribution network system optimization model applicable to transaction control;

[0145] Step S102: Substituting a given price vector into the distribution network system optimization model and performing iterative solution to obtain an optimization result;

[0146] Step S103: obtaining a control strategy using the optimization result, and optimizing and controlling the distribution network using the control strategy;

[0147] The optimization results include at least one of the following: the power input from the main power grid to each flexible resource subsystem, the reduced renewable energy and heat, the charging and discharging power and price vector of the energy storage system, the charging and discharging capacity of the thermal energy system, and the input power of the main transformer of the distribution network system.

[0148] In this embodiment, substituting the given price vector into the distribution network system optimization model and performing iterative solution includes:

[0149] Step a. Substitute the given price vector into the distribution network system optimization model and solve it to obtain the initial optimization result;

[0150] Step b. Determine whether the convergence condition is met based on the initial optimization result. If so, output the initial optimization result. Otherwise, update the given price vector and return to step a.

[0151] In one embodiment, the convergence condition is as follows:

[0152] ΔP k <θ

[0153]

[0154] In the above formula, ΔP k is the power balance coefficient of the distribution network system in the initial optimization result obtained by the kth iteration, P Tr,k* is the input power of the main transformer of the distribution network system in the initial optimization result obtained by the k-th iteration, is the power input from the main grid to the nth flexible resource subsystem in the initial optimization result obtained by the kth iteration, and N is the number of flexible resource subsystems.

[0155] In one embodiment, the given price vector is updated as follows:

[0156]

[0157] In the above formula, is the given price vector in the k+1th iteration solution process, is the given price vector in the kth iteration solution process, η k The preset step size for the kth iteration of the solution process.

[0158] In one embodiment, a binary search method is used to update the given price vector.

[0159] In one embodiment, the flexible resource overall system optimization model composed of the flexible resource subsystem optimization models is as follows:

[0160]

[0161] In the above formula, F t,n is the objective function value corresponding to the nth flexible resource subsystem at time t, N is the number of flexible resource subsystems included in the distribution network system, and t c is the starting time, t e is the end time, P t,n is the power input from the main grid to the nth flexible resource subsystem at time t, P t Tr is the input power of the main transformer of the distribution network system at time t, P t RES is the total renewable energy power shared at time t, is the maximum output power of the main transformer of the distribution network system, is the maximum input power of the main transformer of the distribution network system, W n is the constraint condition corresponding to the nth flexible resource subsystem.

[0162] In one embodiment, the distribution network system optimization model is as follows:

[0163]

[0164] In the above formula, SP n is the optimization model corresponding to the nth flexible resource subsystem under transaction control, λ e,t is the local electricity price at time t, τ g,t is the natural gas price at time t, τ e,t is the real-time electricity price of the main power grid at time t, is the natural gas consumed by the cogeneration of the nth flexible resource subsystem at time t, is the natural gas consumed by the electric boiler of the nth flexible resource subsystem at time t.

[0165] In one embodiment, the flexible resource subsystem optimization model includes an objective function with the goal of minimizing purchase cost and corresponding constraints.

[0166] In one embodiment, the objective function is as follows:

[0167]

[0168] In the above formula, F t is the objective function value, τe,t and τ g,t are the real-time electricity price and natural gas price of the main power grid at time t; and are the natural gas consumed by cogeneration and electric boilers, P t is the power input from the main grid by the flexible resource subsystem at time t.

[0169] In one embodiment, the constraints include: power balance constraints, tie line power constraints, cogeneration, electric furnace and electric boiler capacity constraints, cogeneration and electric boiler ramp limits, energy storage system and thermal energy system maximum charge and discharge power constraints, constraints related to movable loads, upper limits on renewable energy curtailment and heat curtailment, upper and lower energy limits on the energy storage system and thermal energy system, and target energy limits on batteries.

[0170] In one embodiment, the power balance constraint is as follows:

[0171]

[0172] The tie line power constraints are as follows:

[0173]

[0174] The capacity constraints for cogeneration, electric furnaces, and electric boilers are as follows:

[0175]

[0176] The ramp limits for cogeneration and electric boilers are as follows:

[0177]

[0178] The maximum charge and discharge power constraints of the energy storage system and thermal energy system are as follows:

[0179]

[0180] The constraints related to movable loads are as follows:

[0181]

[0182] The upper bound constraints for renewable energy curtailment and heat curtailment are as follows:

[0183]

[0184] The upper and lower energy limits of the energy storage system and thermal energy system are as follows:

[0185]

[0186] The target energy limit of the battery is as follows:

[0187]

[0188] In the above formula, P t is the power input from the main grid by the flexible resource subsystem at time t, The power generated by on-site renewable energy in maximum power point tracking mode, and are the natural gas consumed by cogeneration and electric boiler at time t, and are the gas-electricity efficiency and gas-heat efficiency of cogeneration, is the power consumed by the boiler at time t, and are the charging and discharging power of the energy storage system at time t, and is the efficiency of natural gas furnaces and electric boilers, and are the charge and discharge of the thermal energy system at time t, and are the renewable energy and heat curtailed at time t, L e,t and L th,t are the fixed electric load and thermal load at time t, are the maximum input and output power through the connecting line, and are the installed capacities of cogeneration, electric furnaces and boilers, respectively. P CHP , P GF and P EB are the lower limits of the electric power of cogeneration, electric furnace and boiler, respectively, P t CHP , P t GF are the electricity consumed by cogeneration and electric furnace at time t, are the electricity consumed by the cogeneration and boiler at time t+1, ΔP CHP and ΔP EB are the hourly rise rates of the cogeneration and boiler, are the maximum charge and discharge power of the energy storage system, are the maximum charge and discharge capacity of the thermal energy system, are the charging and discharging power of the energy storage system, are the transferable electric load and thermal load at time t, and are the total power load and thermal load that can be transferred within the dispatching day, and are the upper limits of transferable electric load and thermal load, Ω th and Ω e are the feasible time intervals for shifting electric load and thermal load, W EES , are the minimum energy and maximum energy of the energy storage system, W TES , are the minimum energy and maximum energy of the thermal energy system, t c The energy of the energy storage system and thermal energy system at the moment, θ EES ,θ TES are the self-discharge rates of the energy storage system and thermal energy system, and are the net energy changes of the energy storage system and thermal energy system at time t, and are the target energies of the energy storage system and thermal energy system at the end of the optimization period, t c is the starting time, t e For the ending moment.

[0189] In one embodiment, the net energy change of the energy storage system and the thermal energy system at time t is as follows:

[0190]

[0191] In the above formula, ΔT is the unit optimization period, are the charging and discharging efficiency of the energy storage system, are the charging and discharging efficiencies of the thermal energy system, respectively.

[0192] In one embodiment, when there is a reduction in renewable energy in the distribution network system, the charge and discharge power of the energy storage system in the optimization result is converted as follows:

[0193]

[0194] In the above formula, are the charging and discharging powers of the energy storage system that meet the mutual exclusion constraints of the energy storage charging and discharging modes, and ΔT is the unit optimization period.

[0195] In one embodiment, when there is a reduction in renewable energy in the distribution network system, the charge and discharge capacity of the thermal energy system in the optimization result is converted as follows:

[0196]

[0197] In the above formula, are the charge and discharge amounts of the energy storage system that meet the mutual exclusion constraints of thermal energy charge and discharge modes, and ΔT is the unit optimization period.

[0198] In a specific implementation, the embodiment consists of three flexible resource subsystems, namely flexible resource subsystem 1, flexible resource subsystem 2, and flexible resource subsystem 3. Figure 2 、 Figure 3 、 Figure 4 The fixed and transferable load curves for the three flexible resource subsystems are shown for a day. The flexible loads of flexible resource subsystems 2 and 3 primarily include electric vehicles, washing machines, and dishwashers, while the flexible loads of multi-flexible resource system 1 primarily include air conditioners, water heaters, sterilizers, and refrigerators.

[0199] Figure 5 It is the result of coordinated regulation of multiple flexible resource subsystems, and coordinated energy allocation of multiple flexible resource subsystems to maximize the overall benefits of the system. Figure 6 For the real-time electricity price. When a single flexible resource subsystem is independently scheduled, the main focus is on its own energy management and cost issues. Figure 7 , Figure 8 , Figure 9 These are the scheduling results of flexible resource subsystem 1, flexible resource subsystem 2, and flexible resource subsystem 3 respectively.

[0200] Example 2

[0201] Based on the same inventive concept, the present invention also provides a distribution network multi-level flexible resource coordinated control device, the distribution network multi-level flexible resource coordinated control device comprising:

[0202] A conversion module, used for converting the flexible resource overall system optimization model composed of the flexible resource subsystem optimization models into a distribution network system optimization model under applicable transaction control;

[0203] An analysis module, configured to substitute a given price vector into the distribution network system optimization model and perform iterative solution to obtain an optimization result;

[0204] A control module, configured to obtain a control strategy using the optimization result, and optimize and control the distribution network using the control strategy;

[0205] The optimization results include at least one of the following: the power input from the main power grid to each flexible resource subsystem, the reduced renewable energy and heat, the charging and discharging power and price vector of the energy storage system, the charging and discharging capacity of the thermal energy system, and the input power of the main transformer of the distribution network system.

[0206] Preferably, substituting the given price vector into the distribution network system optimization model and performing iterative solution includes:

[0207] Step a. Substitute the given price vector into the distribution network system optimization model and solve it to obtain the initial optimization result;

[0208] Step b. Determine whether the convergence condition is met based on the initial optimization result. If so, output the initial optimization result. Otherwise, update the given price vector and return to step a.

[0209] Furthermore, the convergence conditions are as follows:

[0210] ΔP k <θ

[0211]

[0212] In the above formula, ΔP k is the power balance coefficient of the distribution network system in the initial optimization result obtained by the kth iteration, P Tr,k* is the input power of the main transformer of the distribution network system in the initial optimization result obtained by the k-th iteration, is the power input from the main grid to the nth flexible resource subsystem in the initial optimization result obtained by the kth iteration, and N is the number of flexible resource subsystems.

[0213] Furthermore, the given price vector is updated as follows:

[0214]

[0215] In the above formula, is the given price vector in the k+1th iteration solution process, is the given price vector in the kth iteration solution process, η k The preset step size for the kth iteration of the solution process.

[0216] Furthermore, the given price vector is updated using a bisection method.

[0217] Furthermore, the flexible resource overall system optimization model composed of the flexible resource subsystem optimization models is as follows:

[0218]

[0219] In the above formula, F t,n is the objective function value corresponding to the nth flexible resource subsystem at time t, N is the number of flexible resource subsystems included in the distribution network system, and t c is the starting time, t e is the end time, P t,nis the power input from the main grid to the nth flexible resource subsystem at time t, P t Tr is the input power of the main transformer of the distribution network system at time t, P t RES is the total renewable energy power shared at time t, is the maximum output power of the main transformer of the distribution network system, is the maximum input power of the main transformer of the distribution network system, W n is the constraint condition corresponding to the nth flexible resource subsystem.

[0220] Furthermore, the distribution network system optimization model is as follows:

[0221]

[0222] In the above formula, SP n is the optimization model corresponding to the nth flexible resource subsystem under transaction control, λ e,t is the local electricity price at time t, τ g,t is the natural gas price at time t, τ e,t is the real-time electricity price of the main power grid at time t, is the natural gas consumed by the cogeneration of the nth flexible resource subsystem at time t, is the natural gas consumed by the electric boiler of the nth flexible resource subsystem at time t.

[0223] Furthermore, the flexible resource subsystem optimization model includes an objective function with the goal of minimizing the purchase cost and corresponding constraints.

[0224] Furthermore, the objective function is as follows:

[0225]

[0226] In the above formula, F t is the objective function value, η e,t and τ g,t are the real-time electricity price and natural gas price of the main power grid at time t; and are the natural gas consumed by cogeneration and electric boilers, P t is the power input from the main grid by the flexible resource subsystem at time t.

[0227] Furthermore, the constraints include: power balance constraints, interconnection line power constraints, cogeneration, electric furnace and electric boiler capacity constraints, cogeneration and electric boiler ramp limits, energy storage system and thermal energy system maximum charge and discharge power constraints, constraints related to movable loads, upper limit constraints on renewable energy reduction and heat reduction, upper and lower energy limits on energy storage system and thermal energy system, and target energy limits on batteries.

[0228] Furthermore, the power balance constraint conditions are as follows:

[0229]

[0230] The tie line power constraints are as follows:

[0231]

[0232] The capacity constraints for cogeneration, electric furnaces, and electric boilers are as follows:

[0233]

[0234] The ramp limits for cogeneration and electric boilers are as follows:

[0235]

[0236] The maximum charge and discharge power constraints of the energy storage system and thermal energy system are as follows:

[0237]

[0238] The constraints related to movable loads are as follows:

[0239]

[0240] The upper bound constraints for renewable energy curtailment and heat curtailment are as follows:

[0241]

[0242] The upper and lower energy limits of the energy storage system and thermal energy system are as follows:

[0243]

[0244] The target energy limit of the battery is as follows:

[0245]

[0246] In the above formula, P t is the power input from the main grid by the flexible resource subsystem at time t, The power generated by on-site renewable energy in maximum power point tracking mode, and are the natural gas consumed by cogeneration and electric boiler at time t, and are the gas-electricity efficiency and gas-heat efficiency of cogeneration, is the power consumed by the boiler at time t, and are the charging and discharging power of the energy storage system at time t, and is the efficiency of natural gas furnaces and electric boilers, and are the charge and discharge of the thermal energy system at time t, and are the renewable energy and heat curtailed at time t, L e,t and L th,t are the fixed electric load and thermal load at time t, are the maximum input and output power through the connecting line, and are the installed capacities of cogeneration, electric furnace and boiler respectively, P CHP ,P GF and P EB are the lower limits of the electric power of cogeneration, electric furnace and boiler, respectively, P t CHP , P t GF are the electricity consumed by cogeneration and electric furnace at time t, are the electricity consumed by the cogeneration and boiler at time t+1, ΔP CHP and ΔP EB are the hourly rise rates of the cogeneration and boiler, are the maximum charge and discharge power of the energy storage system, are the maximum charge and discharge capacity of the thermal energy system, are the charging and discharging power of the energy storage system, are the transferable electric load and thermal load at time t, and are the total power load and thermal load that can be transferred within the dispatching day, and are the upper limits of transferable electric load and thermal load, Ω th and Ω e are the feasible time intervals for transferring electric load and thermal load, E EES , are the minimum energy and maximum energy of the energy storage system, E TES , are the minimum energy and maximum energy of the thermal energy system, t c The energy of the energy storage system and thermal energy system at the moment, θ EES ,θ TES are the self-discharge rates of the energy storage system and thermal energy system, and are the net energy changes of the energy storage system and thermal energy system at time t, and are the target energies of the energy storage system and thermal energy system at the end of the optimization period, t c is the starting time, t e For the ending moment.

[0247] Furthermore, the net energy changes of the energy storage system and the thermal energy system at time t are as follows:

[0248]

[0249] In the above formula, ΔT is the unit optimization period, are the charging and discharging efficiency of the energy storage system, are the charging and discharging efficiencies of the thermal energy system, respectively.

[0250] Furthermore, when there is a reduction in renewable energy in the distribution network system, the charge and discharge power of the energy storage system in the optimization result is converted according to the following formula:

[0251]

[0252] In the above formula, are the charging and discharging powers of the energy storage system that meet the mutual exclusion constraints of the energy storage charging and discharging modes, and ΔT is the unit optimization period.

[0253] Furthermore, when there is a reduction in renewable energy in the distribution network system, the charge and discharge capacity of the thermal energy system in the optimization result is converted according to the following formula:

[0254]

[0255] In the above formula, are the charge and discharge amounts of the energy storage system that meet the mutual exclusion constraints of thermal energy charge and discharge modes, and ΔT is the unit optimization period.

[0256] Example 3

[0257] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a method for coordinated control of multi-level flexible resources in a distribution network in the above embodiment.

[0258] Example 4

[0259] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space that stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of a method for coordinated control of multi-level flexible resources in a distribution network in the above embodiment.

[0260] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0261] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0262] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0263] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0264] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for coordinated control of multi-level flexible resources in a distribution network, characterized in that: The method comprises: The flexible resource overall system optimization model composed of the flexible resource subsystem optimization models is converted into a distribution network system optimization model under applicable transaction control; Substituting a given price vector into the distribution network system optimization model and performing iterative solution to obtain an optimization result; Utilizing the optimization results to obtain a control strategy, and utilizing the control strategy to optimize and control the distribution network; The optimization results include at least one of the following: the power input from the main power grid to each flexible resource subsystem, the reduced renewable energy and heat, the charging and discharging power and price vector of the energy storage system, the charging and discharging capacity of the thermal energy system, and the input power of the main transformer of the distribution network system.

2. The method according to claim 1, wherein Substituting the given price vector into the distribution network system optimization model and performing iterative solution includes: Step a. Substitute the given price vector into the distribution network system optimization model and solve it to obtain the initial optimization result; Step b. Determine whether the convergence condition is met based on the initial optimization result. If so, output the initial optimization result. Otherwise, update the given price vector and return to step a.

3. The method according to claim 2, wherein The convergence conditions are as follows: ΔP k <θ In the above formula, ΔP k is the power balance coefficient of the distribution network system in the initial optimization result obtained by the kth iteration, P Tr,k* is the input power of the main transformer of the distribution network system in the initial optimization result obtained by the k-th iteration, is the power input from the main grid to the nth flexible resource subsystem in the initial optimization result obtained by the kth iteration, and N is the number of flexible resource subsystems.

4. The method according to claim 3, wherein Update the given price vector as follows: In the above formula, is the given price vector in the k+1th iteration solution process, is the given price vector in the kth iteration solution process, η k The preset step size for the kth iteration of the solution process.

5. The method according to claim 3, wherein The given price vector is updated using a binary search method.

6. The method according to claim 3, wherein The flexible resource overall system optimization model composed of the flexible resource subsystem optimization models is as follows: In the above formula, F t,n is the objective function value corresponding to the nth flexible resource subsystem at time t, N is the number of flexible resource subsystems included in the distribution network system, and t c is the starting time, t e is the end time, P t,n is the power input from the main grid to the nth flexible resource subsystem at time t, P t Tr is the input power of the main transformer of the distribution network system at time t, P t RES is the total renewable energy power shared at time t, is the maximum output power of the main transformer of the distribution network system, is the maximum input power of the main transformer of the distribution network system, W n is the constraint condition corresponding to the nth flexible resource subsystem.

7. The method according to claim 6, wherein The distribution network system optimization model is as follows: In the above formula, SP n is the optimization model corresponding to the nth flexible resource subsystem under transaction control, λ e,t is the local electricity price at time t, τ g,t is the natural gas price at time t, τ e,t is the real-time electricity price of the main power grid at time t, is the natural gas consumed by the cogeneration of the nth flexible resource subsystem at time t, is the natural gas consumed by the electric boiler of the nth flexible resource subsystem at time t.

8. The method according to claim 2, wherein The flexible resource subsystem optimization model includes an objective function with the goal of minimizing purchase cost and corresponding constraints.

9. The method according to claim 8, wherein The objective function is as follows: In the above formula, F t is the objective function value, τ e,t and τ g,t are the real-time electricity price and natural gas price of the main power grid at time t; and are the natural gas consumed by cogeneration and electric boilers, P t is the power input from the main grid by the flexible resource subsystem at time t.

10. The method according to claim 8, wherein The constraints include: power balance constraints, interconnection line power constraints, cogeneration, electric furnace and electric boiler capacity constraints, cogeneration and electric boiler ramp limits, energy storage system and thermal energy system maximum charge and discharge power constraints, constraints related to movable loads, upper limits on renewable energy reduction and heat reduction, upper and lower limits on the energy of the energy storage system and thermal energy system, and target energy limits on batteries.

11. The method according to claim 10, wherein The power balance constraints are as follows: The tie line power constraints are as follows: The capacity constraints for cogeneration, electric furnaces, and electric boilers are as follows: The ramp limits for cogeneration and electric boilers are as follows: The maximum charge and discharge power constraints of the energy storage system and thermal energy system are as follows: The constraints related to movable loads are as follows: The upper bound constraints for renewable energy curtailment and heat curtailment are as follows: The upper and lower energy limits of the energy storage system and thermal energy system are as follows: The target energy limit of the battery is as follows: In the above formula, P t is the power input from the main grid by the flexible resource subsystem at time t, The power generated by on-site renewable energy in maximum power point tracking mode, and are the natural gas consumed by cogeneration and electric boiler at time t, and are the gas-electricity efficiency and gas-heat efficiency of cogeneration, is the power consumed by the boiler at time t, and are the charging and discharging power of the energy storage system at time t, and is the efficiency of natural gas furnaces and electric boilers, and are the charge and discharge of the thermal energy system at time t, and are the renewable energy and heat curtailed at time t, L e,t and L th,t are the fixed electric load and thermal load at time t, are the maximum input and output power through the connecting line, and are the installed capacities of cogeneration, electric furnaces and boilers, respectively. P CHP , P GF and P EB are the lower limits of the electric power of cogeneration, electric furnace and boiler, respectively, P t CHP , P t GF are the electricity consumed by cogeneration and electric furnace at time t, are the electricity consumed by the cogeneration and boiler at time t+1, ΔP CHP and ΔP EB are the hourly rise rates of the cogeneration and boiler, are the maximum charge and discharge power of the energy storage system, are the maximum charge and discharge capacity of the thermal energy system, are the charging and discharging power of the energy storage system, are the transferable electric load and thermal load at time t, and are the total power load and thermal load that can be transferred within the dispatching day, and are the upper limits of transferable electric load and thermal load, Ω th and Ω e are the feasible time intervals for shifting electric load and thermal load, E EES , are the minimum energy and maximum energy of the energy storage system, E TES , are the minimum energy and maximum energy of the thermal energy system, t c The energy of the energy storage system and thermal energy system at the moment, θ EES ,θ TES are the self-discharge rates of the energy storage system and thermal energy system, and are the net energy changes of the energy storage system and thermal energy system at time t, and are the target energies of the energy storage system and thermal energy system at the end of the optimization period, t c is the starting time, t e For the ending moment.

12. The method according to claim 11, wherein The net energy changes of the energy storage system and the thermal energy system at time t are as follows: In the above formula, ΔT is the unit optimization period, are the charging and discharging efficiency of the energy storage system, are the charging and discharging efficiencies of the thermal energy system, respectively.

13. The method according to claim 12, wherein: When there is a reduction in renewable energy in the distribution network system, the charging and discharging power of the energy storage system in the optimization result is converted as follows: In the above formula, are the charging and discharging powers of the energy storage system that meet the mutual exclusion constraints of the energy storage charging and discharging modes, and ΔT is the unit optimization period.

14. The method according to claim 12, wherein: When there is a reduction in renewable energy in the distribution network system, the charge and discharge capacity of the thermal energy system in the optimization result is converted as follows: In the above formula, are the charge and discharge amounts of the energy storage system that meet the mutual exclusion constraints of thermal energy charge and discharge modes, and ΔT is the unit optimization period.

15. A multi-level flexible resource coordinated control device for a distribution network, characterized in that: The device comprises: A conversion module, used for converting the flexible resource overall system optimization model composed of the flexible resource subsystem optimization models into a distribution network system optimization model under applicable transaction control; An analysis module, configured to substitute a given price vector into the distribution network system optimization model and perform iterative solution to obtain an optimization result; A control module, configured to obtain a control strategy using the optimization result, and optimize and control the distribution network using the control strategy; The optimization results include at least one of the following: the power input from the main power grid to each flexible resource subsystem, the reduced renewable energy and heat, the charging and discharging power and price vector of the energy storage system, the charging and discharging capacity of the thermal energy system, and the input power of the main transformer of the distribution network system.

16. The device according to claim 15, characterized in that Substituting the given price vector into the distribution network system optimization model and performing iterative solution includes: Step a. Substitute the given price vector into the distribution network system optimization model and solve it to obtain the initial optimization result; Step b. Determine whether the convergence condition is met based on the initial optimization result. If so, output the initial optimization result. Otherwise, update the given price vector and return to step a.

17. The device according to claim 16, wherein The convergence conditions are as follows: ΔP k <θ In the above formula, ΔP k is the power balance coefficient of the distribution network system in the initial optimization result obtained by the kth iteration, P Tr,k* is the input power of the main transformer of the distribution network system in the initial optimization result obtained by the k-th iteration, is the power input from the main grid to the nth flexible resource subsystem in the initial optimization result obtained by the kth iteration, and N is the number of flexible resource subsystems.

18. The device according to claim 17, wherein Update the given price vector as follows: In the above formula, is the given price vector in the k+1th iteration solution process, is the given price vector in the kth iteration solution process, η k The preset step size for the kth iteration of the solution process.

19. The device according to claim 17, wherein The given price vector is updated using a binary search method.

20. The device according to claim 17, wherein The flexible resource overall system optimization model composed of the flexible resource subsystem optimization models is as follows: In the above formula, F t,n is the objective function value corresponding to the nth flexible resource subsystem at time t, N is the number of flexible resource subsystems included in the distribution network system, and t c is the starting time, t e is the end time, P t,n is the power input from the main grid to the nth flexible resource subsystem at time t, P t Tr is the input power of the main transformer of the distribution network system at time t, P t RES is the total renewable energy power shared at time t, is the maximum output power of the main transformer of the distribution network system, is the maximum input power of the main transformer of the distribution network system, W n is the constraint condition corresponding to the nth flexible resource subsystem.

21. The device according to claim 20, characterized in that The distribution network system optimization model is as follows: In the above formula, SP n is the optimization model corresponding to the nth flexible resource subsystem under transaction control, λ e,t is the local electricity price at time t, τ g,t is the natural gas price at time t, τ e,t is the real-time electricity price of the main power grid at time t, is the natural gas consumed by the cogeneration of the nth flexible resource subsystem at time t, is the natural gas consumed by the electric boiler of the nth flexible resource subsystem at time t.

22. The device according to claim 16, wherein The flexible resource subsystem optimization model includes an objective function with the goal of minimizing purchase cost and corresponding constraints.

23. The device according to claim 22, wherein The objective function is as follows: In the above formula, F t is the objective function value, τ e,t and τ g,t are the real-time electricity price and natural gas price of the main power grid at time t; and are the natural gas consumed by cogeneration and electric boilers, P t is the power input from the main grid by the flexible resource subsystem at time t.

24. The device according to claim 22, wherein The constraints include: power balance constraints, interconnection line power constraints, cogeneration, electric furnace and electric boiler capacity constraints, cogeneration and electric boiler ramp limits, energy storage system and thermal energy system maximum charge and discharge power constraints, constraints related to movable loads, upper limits on renewable energy reduction and heat reduction, upper and lower limits on the energy of the energy storage system and thermal energy system, and target energy limits on batteries.

25. The device according to claim 24, wherein The power balance constraints are as follows: The tie line power constraints are as follows: The capacity constraints for cogeneration, electric furnaces, and electric boilers are as follows: The ramp limits for cogeneration and electric boilers are as follows: The maximum charge and discharge power constraints of the energy storage system and thermal energy system are as follows: The constraints related to movable loads are as follows: The upper bound constraints for renewable energy curtailment and heat curtailment are as follows: The upper and lower energy limits of the energy storage system and thermal energy system are as follows: The target energy limit of the battery is as follows: In the above formula, P t is the power input from the main grid by the flexible resource subsystem at time t, The power generated by on-site renewable energy in maximum power point tracking mode, and are the natural gas consumed by cogeneration and electric boiler at time t, and are the gas-electricity efficiency and gas-heat efficiency of cogeneration, is the power consumed by the boiler at time t, and are the charging and discharging power of the energy storage system at time t, and is the efficiency of natural gas furnaces and electric boilers, and are the charge and discharge of the thermal energy system at time t, and are the renewable energy and heat curtailed at time t, L e,t and L th,t are the fixed electric load and thermal load at time t, are the maximum input and output power through the connecting line, and are the installed capacities of cogeneration, electric furnaces and boilers, respectively. P CHP , P GF and P EB are the lower limits of the electric power of cogeneration, electric furnace and boiler, respectively, P t CHP , P t GF are the electricity consumed by cogeneration and electric furnace at time t, are the electricity consumed by the cogeneration and boiler at time t+1, ΔP CHP and ΔP EB are the hourly rise rates of the cogeneration and boiler, are the maximum charge and discharge power of the energy storage system, are the maximum charge and discharge capacity of the thermal energy system, are the charging and discharging power of the energy storage system, are the transferable electric load and thermal load at time t, and are the total power load and thermal load that can be transferred within the dispatching day, and are the upper limits of transferable electric load and thermal load, Ω th and Ω e are the feasible time intervals for shifting electric load and thermal load, E EES , are the minimum energy and maximum energy of the energy storage system, E TES , are the minimum energy and maximum energy of the thermal energy system, t c The energy of the energy storage system and thermal energy system at the moment, θ EES ,θ TES are the self-discharge rates of the energy storage system and thermal energy system, and are the net energy changes of the energy storage system and thermal energy system at time t, and are the target energies of the energy storage system and thermal energy system at the end of the optimization period, t c is the starting time, t e For the ending moment.

26. The device according to claim 25, characterized in that The net energy changes of the energy storage system and the thermal energy system at time t are as follows: In the above formula, ΔT is the unit optimization period, are the charging and discharging efficiency of the energy storage system, are the charging and discharging efficiencies of the thermal energy system, respectively.

27. The device according to claim 26, wherein When there is a reduction in renewable energy in the distribution network system, the charging and discharging power of the energy storage system in the optimization result is converted as follows: In the above formula, are the charging and discharging powers of the energy storage system that meet the mutual exclusion constraints of the energy storage charging and discharging modes, and ΔT is the unit optimization period.

28. The device according to claim 26, wherein When there is a reduction in renewable energy in the distribution network system, the charge and discharge capacity of the thermal energy system in the optimization result is converted as follows: In the above formula, are the charge and discharge amounts of the energy storage system that meet the mutual exclusion constraints of thermal energy charge and discharge modes, and ΔT is the unit optimization period.

29. A computer device, characterized in that: include: one or more processors; The processor is configured to execute one or more programs; When the one or more programs are executed by the one or more processors, the method for coordinated control of multi-level flexible resources in a distribution network as described in any one of claims 1 to 14 is implemented.

30. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed, the method for coordinated control of multi-level flexible resources in a distribution network as described in any one of claims 1 to 14 is implemented.

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