High-proportion new energy power distribution network flexibility resource optimization scheduling method and device

Abstract

The invention discloses a high-proportion new energy power distribution network flexibility resource optimization scheduling method and device, and the method comprises the steps: taking the maximization of the total revenue of a power distribution network as a target, and taking a new energy power generation constraint, a gas turbine generator constraint, an energy storage device constraint, a power user constraint for providing load reduction or load transfer, and a power distribution network and main network interaction constraint as constraint conditions; constructing a flexible resource optimization scheduling model; the total profit of the power distribution network is obtained by subtracting the total purchase cost from the profit obtained by selling the flexible resources to the main network by the power distribution network; the total purchase cost includes purchase of a flexible resource cost from a primary network, purchase of a flexible resource cost from new energy power generation, purchase of a flexible resource cost from a gas turbine generator, purchase of a flexible resource cost from an energy storage device, purchase of a flexible resource cost from a power consumer, and loss due to lack of flexible resources. According to the invention, the power supply with flexible resources and the flexible load are fully stimulated to participate in adjustment, so that the overall safety of the power system is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of power dispatching, and in particular to a method and device for optimizing the dispatching of flexible resources in a high-proportion new energy distribution network. Background Art

[0002] In recent years, the issue of carbon emissions has received great attention worldwide. In order to ensure the use of sustainable energy, new energy represented by wind power generation and photovoltaic power generation has developed rapidly, and the proportion of new energy has continued to grow rapidly. However, these renewable energy generation has the characteristics of uncertainty and intermittence, which has led to a sharp increase in the demand for flexibility regulation at various time scales. Distribution network dispatching agencies urgently need flexibility resources to cope with the operational challenges brought by renewable energy generation, and more flexibility resources are needed to participate in system balance regulation. The activation of flexibility resources within the distribution system is crucial to solving the operational problems faced by distribution and transmission networks. For distribution networks, flexibility can alleviate problems such as voltage fluctuations, line congestion and reliability. For transmission networks, distribution network flexibility resources can provide additional balancing resources and ancillary services to enhance the overall security of the power system. How to fully motivate flexibility resources to participate in regulation is an urgent problem that needs to be solved. Summary of the invention

[0003] The purpose of the present invention is to overcome the above-mentioned defects and problems existing in the prior art, and to provide a method and device for optimizing the scheduling of flexible resources in a high-proportion new energy distribution network. The method can fully encourage power sources and flexible loads with flexible resources to participate in the regulation, so as to enhance the overall safety of the power system.

[0004] To achieve the above objectives, the technical solution of the present invention is: a method for optimizing the dispatching of flexibility resources of a high-proportion new energy distribution network, comprising:

[0005] With the goal of maximizing the total revenue of the distribution network, a flexible resource optimization scheduling model is constructed with constraints on renewable energy generation, gas turbine generators, energy storage devices, power users who provide load reduction or load transfer, and interaction constraints between the distribution network and the main grid.

[0006] The total revenue of the distribution network is the revenue obtained by the distribution network from selling flexibility resources to the main grid minus the total purchase cost, and the total purchase cost includes the cost of purchasing flexibility resources from the main grid, the cost of purchasing flexibility resources from renewable energy power generation, the cost of purchasing flexibility resources from gas turbine generators, the cost of purchasing flexibility resources from energy storage devices, the cost of purchasing flexibility resources from power users, and the loss caused by the lack of flexibility resources;

[0007] Based on the flexibility resource optimization scheduling model, determine the flexibility resource optimization scheduling strategy.

[0008] The objective function of the flexibility resource optimization scheduling model is:

[0009]

[0010] In the formula, π DSO is the total revenue of the distribution network in the operation period T; The proceeds from selling flexibility resources to the main network; and are the power of upward and downward flexibility resources sold to the main network, respectively; and Raise and lower the price of flexibility resources on the mainnet respectively; The cost of purchasing flexibility resources from the mainnet; and The power of the upward and downward flexibility resources purchased from the main network; To reduce the cost of purchasing flexibility resources from renewable energy generation; GR and Ω GD They are respectively curtailable and interruptible new energy generation electronic sets; and are the unit costs of reducing output and disconnecting renewable energy generation i in time period t, respectively; and are the power reduction and disconnection of renewable energy generation i in time period t respectively; Cost of purchasing flexibility resources for power generation from gas turbines; Ω GT For gas turbine generator set; and are the upward and downward adjustment prices of gas turbine generator j in period t, respectively; and are the power increase and decrease of gas turbine generator j in time period t, respectively; is the cost of purchasing flexibility resources from energy storage devices; Ω S For energy storage device set; and are the charging and discharging prices of energy storage device k in time period t, respectively; and are the charging and discharging powers of the energy storage device k in time period t, respectively; is the cost of purchasing flexibility resources from electricity users; CR and Ω SP are the subsets of loads that can be curtailed and shifted by electricity users, respectively; and are the prices for reducing and shifting loads by electricity user l in period t, respectively; and are the power curtailed and transferred by power user l in time period t, respectively; is the loss caused by the lack of flexibility resources; VoLL is the value of power loss load; κ UCPE is the severity factor of the consequences of not obtaining the required flexibility resources; and They are respectively the distribution network that does not obtain the upward and downward flexibility power in time period t.

[0011] The renewable energy generation constraints are:

[0012]

[0013] In the formula, and are the predicted power reduction and disconnection of renewable energy generation i in time period t, respectively; and are the power reduction and disconnection of renewable energy generation i in time period t respectively; It is the on or off state of the renewable energy generator i that can be disconnected during time period t.

[0014] The gas turbine generator constraints are:

[0015]

[0016] In the formula, and are the upper and lower limits of the power of gas turbine generator j respectively; is the operating state variable of gas turbine generator j; and are the down-climbing power and up-climbing power of gas turbine generator j respectively; and are the shutdown and startup state variables of gas turbine generator j respectively; and They are the minimum operating time and the minimum downtime respectively.

[0017] The energy storage device constraints are:

[0018]

[0019] In the formula, Ω S is the set of energy storage devices of k; is the charge level of the energy storage device k in time period t; is the rated capacity of the energy storage device k; is the maximum charge level of the energy storage device k; is the minimum charge level of the energy storage device k; and are the charging and discharging powers of the energy storage device k in time period t, respectively; and are the charging and discharging efficiencies of the energy storage device k, respectively; and are the maximum charging and discharging powers of energy storage device k, respectively; and are respectively the charging and discharging state quantities of the energy storage device k in time period t.

[0020] The power user constraints for providing load reduction or load transfer include load reduction constraints and load transfer constraints;

[0021] The curtailable load constraint is:

[0022]

[0023] In the formula, is the load shedding start state quantity of load l in period t; is the load reduction operation state quantity of load l in period t; is the load shedding end state quantity of load l in period t; The maximum number of load reductions for load l; D l and are the minimum and maximum time intervals between two consecutive load sheddings, respectively; is the predicted power reduction of load l in time period t; is the power cut by load l in time period t;

[0024] The transferable load constraint is:

[0025]

[0026] In the formula, Ω SP is a subset of the transferable loads; is the power transferred by load l in time period t; and are the power reduction and increase of the transferable load l in time period t, respectively; and are the increase load state quantity and decrease load state quantity of load l in time period t respectively; is the predicted transfer power of load l in time period t.

[0027] The interaction constraints between the distribution network and the main network are:

[0028]

[0029] In the formula, is the maximum power that the distribution network can adjust in time period t; is the maximum power that the distribution network can reduce in time period t.

[0030] A device for optimizing and dispatching flexibility resources of a high-proportion new energy distribution network, which is applied to the above-mentioned method, comprises:

[0031] A flexibility resource optimization scheduling model construction module is used to construct a flexibility resource optimization scheduling model with the goal of maximizing the total revenue of the distribution network, and with the constraints of renewable energy power generation, gas turbine generator constraints, energy storage device constraints, power user constraints that provide load reduction or load transfer, and distribution network and main grid interaction constraints as constraints; the total revenue of the distribution network is the revenue obtained by the distribution network from selling flexibility resources to the main grid minus the total purchase cost, and the total purchase cost includes the cost of purchasing flexibility resources from the main grid, the cost of purchasing flexibility resources from renewable energy power generation, the cost of purchasing flexibility resources from gas turbine generators, the cost of purchasing flexibility resources from energy storage devices, the cost of purchasing flexibility resources from power users, and the loss caused by the lack of flexibility resources;

[0032] The flexible resource optimization scheduling strategy solving module is used to determine the flexible resource optimization scheduling strategy based on the flexible resource optimization scheduling model.

[0033] A flexible resource optimization and dispatching device for a high-proportion new energy distribution network, comprising a memory and a processor;

[0034] The memory is used to store computer program code and transmit the computer program code to the processor;

[0035] The processor is used to execute the method according to the instructions in the computer program code.

[0036] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] In a method and device for optimizing and dispatching flexibility resources in a high-proportion new energy distribution network of the present invention, based on different types of flexibility resources in the distribution network, a flexibility resource optimization and dispatching model is constructed with the goal of maximizing the total revenue of the distribution network, and with renewable energy power generation constraints, gas turbine generator constraints, energy storage device constraints, power user constraints that provide load reduction or load transfer, and distribution network and main grid interaction constraints as constraints. The dispatching model fully encourages power sources and flexible loads with flexibility resources to participate in regulation, thereby enhancing the overall safety of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1It is a flow chart of a method for optimizing the scheduling of flexibility resources in a high-proportion new energy distribution network according to the present invention.

[0040] Figure 2 It is a schematic diagram of the flexible resource optimization scheduling framework mentioned in the embodiment of the present invention.

[0041] Figure 3 It is a schematic diagram of the flexibility adjustment parameters mentioned in the embodiment of the present invention.

[0042] Figure 4 It is a structural block diagram of a flexible resource optimization and dispatching device for a high-proportion new energy distribution network according to the present invention.

[0043] Figure 5 It is a structural block diagram of a flexible resource optimization and dispatching device for a high-proportion new energy distribution network according to the present invention. DETAILED DESCRIPTION

[0044] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0045] See also Figure 1 , a method for optimizing the dispatching of flexibility resources in a high-proportion renewable energy distribution network, comprising:

[0046] S1. With the goal of maximizing the total revenue of the distribution network, a flexible resource optimization scheduling model is constructed with constraints on renewable energy generation, gas turbine generators, energy storage devices, power users who provide load reduction or load transfer, and interaction constraints between the distribution network and the main grid as constraints;

[0047] The total revenue of the distribution network is the revenue obtained by the distribution network from selling flexibility resources to the main grid minus the total purchase cost, and the total purchase cost includes the cost of purchasing flexibility resources from the main grid, the cost of purchasing flexibility resources from renewable energy power generation, the cost of purchasing flexibility resources from gas turbine generators, the cost of purchasing flexibility resources from energy storage devices, the cost of purchasing flexibility resources from power users, and the loss caused by the lack of flexibility resources;

[0048] S2. Determine the flexibility resource optimization scheduling strategy based on the flexibility resource optimization scheduling model.

[0049] Based on the flexible resource optimization dispatch model, a dispatching system consisting of distribution network dispatching organizations, renewable energy power generation, energy storage devices, gas turbine generators and power users that can provide flexibility support is established for high-proportion renewable energy. Figure 2 The flexibility resource scheduling framework shown in FIG. 4 realizes the optimal scheduling of flexibility resources in the distribution network.

[0050] Based on the constructed flexibility resource optimization scheduling model and the data submitted by new energy power generation, gas turbine generators, energy storage devices and power users who can provide flexibility support, the flexibility resource optimization scheduling strategy is determined.

[0051] The flexibility resource optimization scheduling strategy described above is that the distribution network dispatching organization determines the adjustment amount of renewable energy power generation, energy storage devices, gas turbine generators and flexible loads, as well as the amount of flexibility interaction with the main grid, based on the constraint parameters of flexibility resource adjustment submitted by renewable energy power generation, energy storage devices, gas turbine generators and flexible loads, the amount of dispatchable flexibility resources and their bids, on the premise of satisfying the flexibility adjustment constraints and with the goal of maximizing the total revenue of the distribution network.

[0052] The distribution network flexibility resource optimization and scheduling method for high-proportion new energy described in the present invention, the distribution network flexibility resource optimization and scheduling model includes new energy power generation, energy storage devices, gas turbine generators and power users with flexibility resources, as well as distribution network scheduling agencies and main networks. Distribution network flexibility resources can be obtained from power sources, energy storage devices and power users with flexibility adjustment capabilities, specifically including new energy power generation, energy storage devices, gas turbine generators and flexible loads that can provide flexibility support, and can also be obtained from the transmission network. The distribution network and the main network can interact with flexibility resources. The distribution network can sell abundant flexibility resources to the main network to support the stable operation of the transmission network, and can also purchase flexibility resources from the main network. Flexibility adjustment participants need to meet certain conditions when participating in the adjustment, such as the limitation of adjustment capacity, so participants need to submit dispatchable flexibility resources, constraint parameters for providing flexibility resources, adjustable quantities and response bids to the distribution network dispatching agency. The distribution network dispatching agency implements flexibility resource optimization scheduling with the goal of maximizing total revenue.

[0053] Furthermore, the objective function of the flexibility resource optimization scheduling model is:

[0054]

[0055] In the formula, π DSO is the total revenue of the distribution network in the operation period T; The proceeds from selling flexibility resources to the main network; and are the power of upward and downward flexibility resources sold to the main network, respectively; and Raise and lower the price of flexibility resources on the mainnet respectively; The cost of purchasing flexibility resources from the mainnet; and The power of the upward and downward flexibility resources purchased from the main network; To reduce the cost of purchasing flexibility resources from renewable energy generation; GR and Ω GD They are respectively curtailable and interruptible new energy generation electronic sets; and are the unit costs of reducing output and disconnecting renewable energy generation i in time period t, respectively; and are the power reduction and disconnection of renewable energy generation i in time period t respectively; Cost of purchasing flexibility resources for power generation from gas turbines; Ω GT For gas turbine generator set; and are the upward and downward adjustment prices of gas turbine generator j in period t, respectively; and are the power increase and decrease of gas turbine generator j in time period t, respectively; is the cost of purchasing flexibility resources from energy storage devices; Ω S For energy storage device set; and are the charging and discharging prices of energy storage device k in time period t, respectively; and are the charging and discharging powers of the energy storage device k in time period t, respectively; The cost of purchasing flexibility resources from electricity users. Flexible loads can provide flexibility support through load reduction or load shifting; Ω CR and Ω SP are the subsets of loads that can be curtailed and shifted by electricity users, respectively; and are the prices for reducing and shifting loads by electricity user l in period t, respectively; and are the power curtailed and transferred by power user l in time period t, respectively; is the loss caused by the lack of flexibility resources; VoLL is the value of power loss load; κ UCPE is the severity coefficient of the consequences caused by not obtaining the required flexibility resources. This coefficient is evaluated and determined by the distribution network dispatching agency and can be taken as 1 without special circumstances; and They are respectively the distribution network that does not obtain the upward and downward flexibility power in time period t.

[0056] The objective function introduces the loss caused by the lack of flexibility resources, so that the distribution network dispatching organization is allowed not to purchase all the flexibility resources required by the system, and there can be a certain reserve margin. The loss value caused by the lack of flexibility resources can be calculated offline by considering the influencing factors such as offline network flexibility resources and power outage probability.

[0057] To facilitate understanding of the flexibility adjustment constraints, Figure 3 A schematic diagram of flexibility adjustment parameters is given.

[0058] Furthermore, the present invention considers two types of renewable energy generation with flexible supply capacity, namely disconnectable and curtailable renewable energy generation. Disconnectable renewable energy generation can only be shut down, and its power generation cannot be adjusted to a specific set value. Curtailable renewable energy generation can adjust its power generation within a specified time according to the received signal. These two types of renewable energy generation participate in the downward flexibility regulation, and their regulation capacity is limited by their maximum available capacity. The renewable energy generation constraints are:

[0059]

[0060] In the formula, and are the predicted power reduction and disconnection of renewable energy generation i in time period t, respectively; and are the power reduction and disconnection of renewable energy generation i in time period t respectively; It is the on or off state of the renewable energy generator i that can be disconnected during time period t.

[0061] Furthermore, the gas turbine generator constraints include upper and lower power limit constraints, ramp constraints, minimum operating time constraints, and minimum downtime constraints. Specifically:

[0062] The unit is subject to the constraints of minimum and maximum output during operation:

[0063]

[0064] Unit climbing power constraint:

[0065]

[0066] Minimum operating time constraints and minimum downtime constraints for units:

[0067]

[0068] In the formula, and are the upper and lower limits of the power of gas turbine generator j respectively; is the operating state variable of gas turbine generator j; and are the down-climbing power and up-climbing power of gas turbine generator j respectively; and are the shutdown and startup state variables of gas turbine generator j respectively; and They are the minimum operating time and the minimum downtime respectively.

[0069] Furthermore, the energy storage device can provide flexible adjustment services of power up and power down through its discharge and charging mechanisms. The constraints of the energy storage device include the power balance constraint of the energy storage device during charging and discharging, the charge level constraint, the maximum power constraint of charging or discharging in each cycle, the charging and discharging state constraint, etc., specifically:

[0070] The power balance constraints of the energy storage device k during charging and discharging, as well as the constraints on the charge level of the energy storage device:

[0071]

[0072] The maximum power constraint for charging or discharging the energy storage device k in each cycle is:

[0073]

[0074] The charging and discharging state constraints prohibit the simultaneous charging and discharging of a certain energy storage device:

[0075]

[0076] The energy storage device remains at the same charge level at the beginning and end of the time period:

[0077]

[0078] In the formula, Ω S For energy storage device set; is the charge level of the energy storage device k in time period t; is the rated capacity of the energy storage device k; is the maximum charge level of the energy storage device k; is the minimum charge level of the energy storage device k; and are the charging and discharging powers of the energy storage device k in time period t, respectively; and are the charging and discharging efficiencies of the energy storage device k, respectively; and are the maximum charging and discharging powers of energy storage device k, respectively; and are respectively the charging and discharging state quantities of the energy storage device k in time period t.

[0079] Further, the power user constraints providing load reduction or load transfer include load reduction constraints and load transfer constraints;

[0080] If the power user responds to the flexibility demand by reducing the load, the original power consumption plan will not be changed after the response. Specifically, the load constraints that can be reduced include two types of constraints, one is the constraints on the load state that can be reduced, and the other is the constraints on the load regulation power that can be reduced, that is, the restrictions on the load's ability to participate in flexibility regulation:

[0081] The constraints that the states of the cuttable loads are mutually exclusive, and the constraints that the cuttable loads remain in the same state until the response ends:

[0082]

[0083] The maximum load reduction constraint for user l in one operation cycle is:

[0084]

[0085] The time constraints between load shedding response and load restoration, as well as the constraints for setting the maximum shedding duration:

[0086]

[0087] The constraints on load reduction, that is, the limitation of flexible load's ability to participate in flexibility regulation:

[0088]

[0089] In the formula, is the load shedding start state quantity of load l in period t; is the load reduction operation state quantity of load l in period t; is the load shedding end state quantity of load l in period t; The maximum number of load reductions for load l can be set as agreed; D l and are the minimum and maximum time intervals between two consecutive load sheddings, respectively; is the predicted power reduction of load l in time period t; is the power cut by load l in time period t;

[0090] Specifically, transferable load refers to the power user adjusting the power consumption mode according to the characteristics of the load and the demand for flexible regulation of the distribution network, and advancing or delaying the required load. The total power consumption of the transferable load user remains unchanged during the optimization period, and the distribution network dispatching agency can optimize the power load of the user in each period according to the need for flexible regulation. Typical loads are electric vehicle charging and heat storage loads. The constraints of transferable loads include balance constraints on power consumption, constraints on load increase and decrease, etc.

[0091] The balance constraint of the power consumption of the transferable load is:

[0092]

[0093] State constraints for transferable loads:

[0094]

[0095] This constraint prevents simultaneous increases and decreases in load during a period;

[0096] Constraints on load increase and decrease for transferable loads:

[0097]

[0098] In the formula, Ω SP is a subset of the transferable loads; is the power transferred by load l in time period t; and are the power reduction and increase of the transferable load l in time period t, respectively; and are the increase load state quantity and decrease load state quantity of load l in time period t respectively; is the predicted transfer power of load l in time period t; the load increase is limited by the large parameter M.

[0099] Furthermore, the interaction constraints between the distribution network and the main grid are:

[0100]

[0101] In the formula, is the maximum power that the distribution network can adjust in time period t; is the maximum power that the distribution network can reduce in time period t.

[0102] The distribution network can interact with the main grid for flexibility, including power up and power down, according to the pre-specified price of the main grid's flexibility products. Interacting with the main grid for flexibility increases the margin of the distribution network's flexibility regulation. When the distribution network has sufficient flexibility resources, it can sell flexibility resources to the main grid. When the distribution network lacks flexibility resources, it can purchase flexibility resources from the main grid. The main grid's flexibility adjustment price on the previous trading day can be used as a reference for optimizing the initial value selection. The exchange power between the distribution network and the main grid is limited by the transmission power of the tie line.

[0103] See also Figure 4 , a device for optimizing and dispatching flexibility resources of a high-proportion new energy distribution network, which is applied to the above-mentioned method for optimizing and dispatching flexibility resources of a high-proportion new energy distribution network, and the device comprises:

[0104] A flexibility resource optimization scheduling model construction module is used to construct a flexibility resource optimization scheduling model with the goal of maximizing the total revenue of the distribution network, and with the constraints of renewable energy power generation, gas turbine generator constraints, energy storage device constraints, power user constraints that provide load reduction or load transfer, and distribution network and main grid interaction constraints as constraints; the total revenue of the distribution network is the revenue obtained by the distribution network from selling flexibility resources to the main grid minus the total purchase cost, and the total purchase cost includes the cost of purchasing flexibility resources from the main grid, the cost of purchasing flexibility resources from renewable energy power generation, the cost of purchasing flexibility resources from gas turbine generators, the cost of purchasing flexibility resources from energy storage devices, the cost of purchasing flexibility resources from power users, and the loss caused by the lack of flexibility resources;

[0105] The flexible resource optimization scheduling strategy solving module is used to determine the flexible resource optimization scheduling strategy based on the flexible resource optimization scheduling model.

[0106] The device also includes an energy management subsystem. The energy management subsystem of renewable energy power generation, energy storage device, gas turbine generator and power user is used to regulate the flexibility resources of this unit and to exchange information with the distribution network dispatching organization (including the flexibility resource optimization dispatching model construction module and the flexibility resource optimization dispatching strategy solution module).

[0107] The device also includes an information interaction module, which is used for the distribution network dispatching organization to interact with the management subsystems of renewable energy power generation, energy storage devices, gas turbine generators and power users to adjust constraint parameters, adjustable quantities, bids and other information.

[0108] Renewable energy power generation, energy storage devices, gas turbine generators, etc. submit available flexibility resource parameters and bids to the distribution network dispatching agency through the energy management subsystem based on the technical performance of their own electrical equipment; power users who can provide flexibility support submit flexibility resource parameters and response bids for load migration and load reduction to the distribution network dispatching agency based on their electricity consumption characteristics.

[0109] The parameters submitted to the distribution network dispatching agency include parameters of operating constraints such as renewable energy power generation, energy storage devices, and gas turbine generators, such as the limits on the upward and downward adjustment of power, the limits on the ramp rate, the limits on the number of adjustments, and the amount and bid of flexible resources that can be dispatched in each period; power users who can provide flexibility support, etc., submit to the distribution network dispatching agency the amount of flexible resources that can migrate and reduce loads, the limits on the adjustment power, and the bids based on their electricity consumption characteristics. Appendix 1 to Appendix 3 are examples of parameters submitted by renewable energy power generation, gas turbine generators, and energy storage devices.

[0110] Appendix 1 Data on load reduction and load cutoff of new energy power generation

[0111]

[0112] Appendix 2 Parameters of small gas turbine generators

[0113]

[0114] Appendix 3 Parameters and quotation information of energy storage devices

[0115]

[0116] See also Figure 5 , the present invention also provides a high-proportion new energy distribution network flexibility resource optimization and scheduling device, including a memory and a processor;

[0117] The memory is used to store computer program code and transmit the computer program code to the processor;

[0118] The processor is used to execute the above-mentioned method for optimizing and scheduling flexibility resources of a high-proportion new energy distribution network according to the instructions in the computer program code.

[0119] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned method for optimizing and scheduling flexibility resources of a high-proportion new energy distribution network is implemented.

[0120] Generally speaking, the computer instructions for implementing the method of the present invention may be carried in any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media may include any computer-readable media, except for the signal itself that is temporarily propagating.

[0121] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EKROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0122] Computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages, in particular, Python suitable for neural network computing and platform frameworks based on TensorFlow, PyTorch, etc. can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer or to an external computer (for example, using an Internet service provider to connect via the Internet) through any type of network, including a local area network (LAN) or a wide area network (WAN).

[0123] The above-mentioned devices and non-temporary computer-readable storage media can be found in the specific description of a method for optimizing the scheduling of flexibility resources in a high-proportion new energy distribution network and its beneficial effects, which will not be repeated here.

[0124] Although the embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for optimizing the dispatching of flexible resources in a high-proportion renewable energy distribution network, characterized in that: include: With the goal of maximizing the total revenue of the distribution network, a flexible resource optimization scheduling model is constructed with constraints on renewable energy generation, gas turbine generators, energy storage devices, power users who provide load reduction or load transfer, and interaction constraints between the distribution network and the main grid. The total revenue of the distribution network is the revenue obtained by the distribution network from selling flexibility resources to the main grid minus the total purchase cost, and the total purchase cost includes the cost of purchasing flexibility resources from the main grid, the cost of purchasing flexibility resources from renewable energy power generation, the cost of purchasing flexibility resources from gas turbine generators, the cost of purchasing flexibility resources from energy storage devices, the cost of purchasing flexibility resources from power users, and the loss caused by the lack of flexibility resources; Based on the flexibility resource optimization scheduling model, determine the flexibility resource optimization scheduling strategy.

2. According to claim 1, a method for optimizing the dispatching of flexibility resources of a high-proportion new energy distribution network is characterized in that: The objective function of the flexibility resource optimization scheduling model is: In the formula, π DSO is the total revenue of the distribution network in the operation period T; The proceeds from selling flexibility resources to the main network; and are the power of upward and downward flexibility resources sold to the main network, respectively; and Raise and lower the price of flexibility resources on the mainnet respectively; The cost of purchasing flexibility resources from the mainnet; and The power of the upward and downward flexibility resources purchased from the main network; To reduce the cost of purchasing flexibility resources from renewable energy generation; GR and Ω GD They are respectively curtailable and interruptible new energy generation electronic sets; and are the unit costs of reducing output and disconnecting renewable energy generation i in time period t, respectively; and are the power reduction and disconnection of renewable energy generation i in time period t respectively; Cost of purchasing flexibility resources for power generation from gas turbines; Ω GT For gas turbine generator set; and are the upward and downward adjustment prices of gas turbine generator j in period t, respectively; and are the power increase and decrease of gas turbine generator j in time period t, respectively; is the cost of purchasing flexibility resources from energy storage devices; Ω S For energy storage device set; and are the charging and discharging prices of energy storage device k in time period t, respectively; and are the charging and discharging powers of the energy storage device k in time period t, respectively; is the cost of purchasing flexibility resources from electricity users; CR and Ω SP are the subsets of loads that can be curtailed and shifted by electricity users, respectively; and are the prices for reducing and shifting loads by electricity user l in period t, respectively; and are the power curtailed and transferred by power user l in time period t, respectively; is the loss caused by the lack of flexibility resources; VoLL is the value of power loss load; κ UCPE is the severity factor of the consequences of not obtaining the required flexibility resources; and They are respectively the distribution network that does not obtain the upward and downward flexibility power in time period t.

3. The method for optimizing the dispatching of flexibility resources of a high-proportion new energy distribution network according to claim 1 is characterized in that: The renewable energy generation constraints are: In the formula, and are the predicted power reduction and disconnection of renewable energy generation i in time period t, respectively; and are the power reduction and disconnection of renewable energy generation i in time period t respectively; It is the on or off state of the renewable energy generator i that can be disconnected during time period t.

4. The method for optimizing the dispatching of flexibility resources of a high-proportion new energy distribution network according to claim 1 is characterized in that: The gas turbine generator constraints are: In the formula, and are the upper and lower limits of the power of gas turbine generator j respectively; is the operating state variable of gas turbine generator j; and are the down-climbing power and up-climbing power of gas turbine generator j respectively; and are the shutdown and startup state variables of gas turbine generator j respectively; and They are the minimum operating time and the minimum downtime respectively.

5. The method for optimizing the dispatching of flexibility resources of a high-proportion new energy distribution network according to claim 1 is characterized in that: The energy storage device constraints are: In the formula, Ω S For energy storage device set; is the charge level of the energy storage device k in time period t; is the rated capacity of the energy storage device k; is the maximum charge level of the energy storage device k; is the minimum charge level of the energy storage device k; and are the charging and discharging powers of the energy storage device k in time period t, respectively; Hehe are the charging and discharging efficiencies of the energy storage device k, respectively; and are the maximum charging and discharging powers of energy storage device k, respectively; and are respectively the charging and discharging state quantities of the energy storage device k in time period t.

6. A method for optimizing the dispatching of flexibility resources of a high-proportion new energy distribution network according to claim 1, characterized in that: The power user constraints for providing load reduction or load transfer include load reduction constraints and load transfer constraints; The curtailable load constraint is: In the formula, is the load shedding start state quantity of load l in period t; is the load reduction operation state quantity of load l in period t; is the load shedding end state quantity of load l in period t; The maximum number of load reductions for load l; D l and are the minimum and maximum time intervals between two consecutive load sheddings, respectively; is the predicted power reduction of load l in time period t; is the power cut by load l in time period t; The transferable load constraint is: In the formula, Ω SP is a subset of the transferable loads; is the power transferred by load l in time period t; and are the power reduction and increase of the transferable load l in time period t, respectively; and are the increase load state quantity and decrease load state quantity of load l in time period t respectively; is the predicted transfer power of load l in time period t.

7. The method for optimizing the dispatching of flexibility resources of a high-proportion new energy distribution network according to claim 1 is characterized in that: The interaction constraints between the distribution network and the main network are: In the formula, is the maximum power that the distribution network can adjust in time period t; is the maximum power that the distribution network can reduce in time period t.

8. A flexible resource optimization and dispatching device for a high-proportion new energy distribution network, characterized in that: The device is applied to the method described in any one of claims 1 to 7, and the device comprises: A flexibility resource optimization scheduling model construction module is used to construct a flexibility resource optimization scheduling model with the goal of maximizing the total revenue of the distribution network, and with the constraints of renewable energy power generation, gas turbine generator constraints, energy storage device constraints, power user constraints that provide load reduction or load transfer, and distribution network and main grid interaction constraints as constraints; the total revenue of the distribution network is the revenue obtained by the distribution network from selling flexibility resources to the main grid minus the total purchase cost, and the total purchase cost includes the cost of purchasing flexibility resources from the main grid, the cost of purchasing flexibility resources from renewable energy power generation, the cost of purchasing flexibility resources from gas turbine generators, the cost of purchasing flexibility resources from energy storage devices, the cost of purchasing flexibility resources from power users, and the loss caused by the lack of flexibility resources; The flexible resource optimization scheduling strategy solving module is used to determine the flexible resource optimization scheduling strategy based on the flexible resource optimization scheduling model.

9. A high-proportion new energy distribution network flexibility resource optimization and dispatching device, characterized in that: including memory and processor; The memory is used to store computer program code and transmit the computer program code to the processor; The processor is configured to execute the method according to any one of claims 1 to 7 according to instructions in the computer program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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