Multi-resource economic dispatching optimization method and device for virtual power plant, equipment and medium

Abstract

The invention relates to the technical field of virtual power plant optimization dispatching, and discloses a multi-resource economic dispatching optimization method, device and equipment for a virtual power plant and a medium, and the method comprises the steps: obtaining the power grid response invitation information and dispatching basic data of a power consumer when the power consumer in the virtual power plant is configured with an energy storage system; the power grid response invitation information comprises a power grid response type and an invitation mode; determining an optimization time length based on the invitation mode; and based on the power grid response type, scheduling basic data, optimization time duration and adjustable resources under the power grid response type, constructing an optimization model based on peak-valley arbitrage income and response compensation income maximization as targets under the corresponding power grid response type, and solving the optimal solution of the corresponding optimization model by adopting a preset solution algorithm. And a virtual power plant multi-resource economic optimal scheduling scheme is obtained. According to the method, collaborative optimization of response effectiveness and operation economy is realized, and the economy and flexibility of multi-resource scheduling of the virtual power plant are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of virtual power plant optimal scheduling, in particular to a multi-resource economic scheduling optimization method, device, equipment and medium of a virtual power plant. BACKGROUND

[0002] Currently, when participating in grid invitation response, the virtual power plant hopes to release more adjustment capacity of the aggregated resources. Considering the bidirectional adjustment characteristics of the energy storage system, the current artificial means is usually used to set the charging and discharging strategy of the energy storage system in advance, so that the energy storage system is fully charged before peak shaving or discharges when filling the valley, so as to ensure that the virtual power plant has the maximum adjustment capacity. Although this strategy helps to improve the response speed and stability of the system, it often ignores the time-of-use price signal, resulting in that the energy storage system fails to play its potential of peak-valley arbitrage during the response period.

[0003] The existing method focuses on the adjustment performance of the virtual power plant when participating in the grid invitation response, and optimizes the scheduling strategy of the multi-resource to achieve the target of effective response, ignoring the peak-valley arbitrage benefit of the energy storage during the response period, and failing to realize the collaborative improvement of flexibility and economy of the energy storage system participating in local peak-valley arbitrage and participating in grid response. Therefore, it is urgent to build a multi-resource economic optimization strategy considering the response frequency of the virtual power plant to overcome the defect of ignoring the peak-valley arbitrage benefit of the energy storage during the response period. SUMMARY

[0004] Therefore, the present application provides a multi-resource economic scheduling optimization method, device, equipment and medium of a virtual power plant to solve the problem of ignoring the peak-valley arbitrage benefit of the energy storage during the response period and failing to realize the collaborative improvement of flexibility and economy of the energy storage system participating in local peak-valley arbitrage and participating in grid response in the prior art.

[0005] In a first aspect, the present application provides a multi-resource economic scheduling optimization method of a virtual power plant, which comprises:

[0006] When the power users in the virtual power plant are configured with the energy storage system, the grid response invitation information and scheduling basic data of the power users are obtained; the grid response invitation information includes the grid response type and the invitation mode;

[0007] The optimization time length is determined based on the invitation mode;

[0008] Based on the grid response type, the scheduling basic data, the optimization time length and the adjustable resources under the grid response type, an optimization model corresponding to the grid response type is constructed, which takes the maximization of the peak-valley arbitrage benefit and the response compensation benefit as the target;

[0009] The preset solving algorithm is used to solve the optimal solution of the corresponding optimization model, and the multi-resource economic optimal scheduling scheme of the virtual power plant is obtained.

[0010] The virtual power plant multi-resource economic scheduling optimization method provided by the application can accurately adapt to and construct an optimization model according to different response types and different invitation modes of the power grid, can take into account the peak-valley arbitrage and resource benefits of photovoltaic resources under the premise of ensuring that the response rate of the virtual power plant stably meets the examination requirements, can quickly obtain an optimal solution through an efficient solving algorithm, and realizes the collaborative optimization of response effectiveness and operation economy, thereby significantly improving the economy and flexibility of the multi-resource scheduling of the virtual power plant, and solving the problem that the existing technology ignores the peak-valley arbitrage benefits of the energy storage during the response period and fails to realize the collaborative improvement of the flexibility and economy of the energy storage system participating in the local peak-valley arbitrage and participating in the power grid response.

[0011] In an optional implementation, the invitation mode includes a day-ahead invitation and an intra-day invitation; the optimization time length is determined based on the invitation mode, including:

[0012] When the invitation mode is the day-ahead invitation, the optimization time length is from 0 o'clock of the response day to 24 o'clock of the response day;

[0013] When the invitation mode is the intra-day invitation, the optimization time length is from the invitation time to 24 o'clock of the response day.

[0014] The virtual power plant multi-resource economic scheduling optimization method provided by the application determines the optimization time length by distinguishing between the day-ahead invitation and the intra-day invitation, provides sufficient time dimensions for the day-ahead invitation to cover the whole day, fully plans the long-period economy of the energy storage peak-valley arbitrage and the multi-resource collaboration, and gives the intra-day invitation a time range that fits the temporary scheduling demand, taking into account the real-time responsiveness and the resource optimization of the remaining period of the day, so that the virtual power plant can accurately adapt to the response invitation of the power grid with different time effectiveness, and improve the flexibility and efficiency of the scheduling.

[0015] In an optional implementation, the power grid response type includes a peak shaving scenario and a valley filling scenario; the scheduling basic data includes a time-of-use electricity price of a power user, a charging power of an energy storage system, and a discharging power of the energy storage system;

[0016] Based on the power grid response type, the scheduling basic data, the optimization time length, and the adjustable resources under the power grid response type, an optimization model corresponding to the power grid response type is constructed, with the maximum peak-valley arbitrage benefit and response compensation benefit as the target, including:

[0017] When the power grid response type is the peak shaving scenario, based on the time-of-use electricity price of the power user, the charging power of the energy storage system, the discharging power of the energy storage system, the optimization time length, and the adjustable resources under the peak shaving scenario, an optimization model of the peak shaving scenario is constructed with the maximum sum of the first peak-valley arbitrage benefit and the first response compensation benefit as the target under a first preset constraint condition set;

[0018] When the grid response type is the valley filling scenario, a response period length is acquired, based on the time-of-use electricity price of the power user, the charging power of the energy storage system, the discharging power of the energy storage system, the response period length, the length of time other than the response time, and the adjustable resources of the valley filling scenario, a second peak-valley arbitrage income and a second response compensation income are maximized as a target, and an optimization model of the valley filling scenario is constructed under a second preset constraint condition set.

[0019] The application provides a multi-resource economic scheduling optimization method of a virtual power plant, which constructs an optimization model for two different response scenarios of grid peak shaving and valley filling by combining the characteristics of respective adjustable resources: the peak shaving scenario focuses on the cooperation of energy storage charging and discharging, time-of-use electricity price, and response compensation, and the valley filling scenario further considers the time dimension of the response period and the non-response period and the resource income of photovoltaic resources, so that the characteristics of resources and the demand of the grid can be accurately adapted under different scenarios, the peak-valley arbitrage income and the response compensation income are maximized on the premise of meeting the response rate assessment requirement, and the cooperation optimization of response effectiveness and operation economy under each scenario is realized.

[0020] In an optional implementation, the optimization model of the peak shaving scenario is expressed by the following formula:

[0021]

[0022] In the formula, the first peak-valley arbitrage income is represented by the following formula: The first response compensation income is represented by the following formula: c t The time-of-use electricity price of the power user is represented by the following formula: P (t), and the unit is yuan / kWh; The discharging power of the energy storage system i at the t moment is represented by the following formula: P dis,i (t), and the unit is kW; The charging power of the energy storage system i at the t moment is represented by the following formula: P ch,i (t), and the unit is kW; t The flag bit of each moment is represented by the following formula: m n,t = 1 if the moment t is in the response period, otherwise m n,t = 0; F t profit The total adjustable income at the t moment is represented by the following formula: F (t) ; the time granularity is represented by the following formula: Δt, and the unit is h; and the optimization time length is represented by the following formula: T;

[0023] The optimization model of the valley filling scenario is expressed by the following formula:

[0024]

[0025] In the formula, the second peak-valley arbitrage income is represented by the following formula: The second response loss income is represented by the following formula: c t The time-of-use electricity price of the power user is represented by the following formula: P (t), and the unit is yuan / kWh; The discharging power of the energy storage system i at the t moment is represented by the following formula: P dis,i (t), and the unit is kW; ​​is the charging power of the energy storage system i at time t, in kW; Δt is the response period length, in h; T out is the time length outside the response period; T response is the response period time length; m t is the flag bit at each time, if the time t is within the response period m n,t = 1, otherwise m n,t = 0; F t = -F t ES -F t loss +F t profit , F t profit is the total adjustable income of the energy storage system at time t; F t loss is the loss income of the photovoltaic at time t; F t ES is the actual charging cost of the energy storage system within the response period at time t.

[0026] In an optional embodiment, the first preset constraint condition set includes: the energy storage system discharging power constraint, the energy storage system charging power constraint, the charging and discharging state constraint, the capacity constraint, the first energy storage system power constraint within the response period, the energy storage system discharging state constraint within the response period, and the target linearization introduction constraint;

[0027] The second preset constraint condition set includes the energy storage system discharging power constraint, the energy storage system charging power constraint, the charging and discharging state constraint, the capacity constraint, the linearization constraint, the energy storage system charging state constraint within the response period, and the second energy storage system power constraint within the response period;

[0028] Based on the response type, the dispatching basic data, the optimization time length, and the adjustable resource under the response type, an optimization model is constructed, which aims to maximize the peak-valley arbitrage income and the response compensation income under the corresponding type, and further includes:

[0029] Based on the grid response type and the dispatching basic data, the average power upper and lower limits in the first energy storage system power constraint within the response period and the second energy storage system power constraint within the response period are calculated.

[0030] The application provides a multi-resource economic scheduling optimization method of a virtual power plant.

[0031] In an optional embodiment, the scheduling basic data further comprises an effective response rate upper and lower limit, a total adjustable capacity of the power user, and a predicted average power of the energy storage system.

[0032] Based on the grid response type and the scheduling basic data, the average power upper and lower limit in the response period of the energy storage system is calculated, including:

[0033] According to the grid response type, it is determined whether the response rate when the energy storage system does not participate in the response is within the effective response rate upper and lower limit, and based on the determination result, the different calculation logics of the average power upper and lower limit are determined in combination with the total adjustable capacity of the power user, the effective response rate upper and lower limit, and the predicted average power of the energy storage system.

[0034] The corresponding average power upper and lower limit is calculated based on the different calculation logics.

[0035] The multi-resource economic scheduling optimization method of the virtual power plant provided by the application, when calculating the average power upper and lower limit in the response period of the energy storage system, first determines whether the response rate when the energy storage does not participate in the response is within the effective response rate upper and lower limit range according to the grid response type (peak shaving / valley filling), and then sets different calculation logics and derives the corresponding upper and lower limits in combination with the total adjustable capacity of the power user, the effective response rate upper and lower limit, and the predicted average power of the energy storage system in the scheduling basic data, which not only ensures the accurate adaptation of the average power constraint and the grid response type, but also avoids the subjective setting of the power upper and lower limit based on the key basic data, can reduce the influence of excessive adjustment on the economic targets such as energy storage peak-valley arbitrage while ensuring that the response rate of the virtual power plant is stable and meets the evaluation requirements, and significantly improves the scientificity of power constraint calculation and the reliability of virtual power plant scheduling.

[0036] In an optional embodiment, according to the grid response type, it is determined whether the response rate when the energy storage system does not participate in the response is within the effective response rate upper and lower limit, and based on the determination result, the different calculation logics of the average power upper and lower limit are determined in combination with the total adjustable capacity of the power user, the effective response rate upper and lower limit, and the predicted average power of the energy storage system, including:

[0037] When the grid response type is peak shaving or valley filling, in any one of the following cases: the response rate when the energy storage system does not participate in response is between the lower limit of the effective response rate and a preset value, the response rate when the energy storage system does not participate in response is less than or equal to the lower limit of the effective response rate, the response rate when the energy storage system does not participate in response is between the preset value and the upper limit of the effective response rate, and the response rate when the energy storage system does not participate in response is greater than the upper limit of the effective response rate, the different calculation logics of the average power upper and lower limits are determined based on the judgment result, the total adjustable capacity of the power users, the upper and lower limits of the effective response rate, and the predicted average power of the energy storage system.

[0038] The virtual power plant multi-resource economic scheduling optimization method provided by the application can cover four full-scene cases of "the response rate when the energy storage system does not participate in response is lower than the lower limit of the effective response rate, is between the lower limit and a preset value, is between the preset value and the upper limit, and is higher than the upper limit" for the two types of grid response types of peak shaving and valley filling, and different calculation logics are customized for each scene in combination with the total adjustable capacity of the power users, the upper and lower limits of the effective response rate, and the predicted average power of the energy storage system, so that the average power upper and lower limits are calculated more in line with actual scheduling requirements, the energy storage adjustment demand in different response rate states is accurately matched, the response rate of the virtual power plant is ensured to be stable and meet the evaluation standard, the influence of excessive adjustment or insufficient adjustment on the economic targets such as energy storage peak-valley arbitrage is effectively avoided, and the scientificity of power constraint calculation and the accuracy of virtual power plant multi-resource scheduling are significantly improved.

[0039] In a second aspect, the application provides a virtual power plant multi-resource economic scheduling optimization device, which comprises:

[0040] An invitation information and basic data acquisition module is configured to acquire grid response invitation information and scheduling basic data of the power users when the power users in the virtual power plant are configured with energy storage systems, wherein the grid response invitation information comprises a grid response type and an invitation mode.

[0041] An optimization time length determination module is configured to determine an optimization time length based on the invitation mode.

[0042] An optimization model construction module is configured to construct an optimization model based on the grid response type, the scheduling basic data, the optimization time length, and the adjustable resources under the grid response type, wherein the optimization model aims to maximize the peak-valley arbitrage income and the response compensation income.

[0043] An optimal solution solving module is configured to solve the optimal solution of the corresponding optimization model by using a solving algorithm to obtain a virtual power plant multi-resource economic optimal scheduling scheme.

[0044] In a third aspect, the present application provides a computer device, comprising a memory and a processor, which are connected with each other in communication, the memory stores computer instructions, and the processor executes the computer instructions to perform the method for multi-resource economic scheduling optimization of a virtual power plant according to the first aspect or any one of the corresponding embodiments thereof.

[0045] In a fourth aspect, the present application provides a computer readable storage medium, which stores computer instructions for making a computer perform the method for multi-resource economic scheduling optimization of a virtual power plant according to the first aspect or any one of the corresponding embodiments thereof.

[0046] In a fifth aspect, the present application provides a computer program product, which comprises computer instructions for making a computer perform the method for multi-resource economic scheduling optimization of a virtual power plant according to the first aspect or any one of the corresponding embodiments thereof. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed in the specific embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0048] Figure 1 is a flowchart of a method for multi-resource economic scheduling optimization of a virtual power plant according to an embodiment of the present application;

[0049] Figure 2 is a flowchart of another method for multi-resource economic scheduling optimization of a virtual power plant according to an embodiment of the present application;

[0050] Figure 3 is a flowchart of still another method for multi-resource economic scheduling optimization of a virtual power plant according to an embodiment of the present application;

[0051] Figure 4 is a flowchart of yet another method for multi-resource economic scheduling optimization of a virtual power plant according to an embodiment of the present application;

[0052] Figure 5 is a structural block diagram of a device for multi-resource economic scheduling optimization of a virtual power plant according to an embodiment of the present application;

[0053] Figure 6 is a hardware structure schematic diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0055] The existing method focuses on the regulation performance thereof when participating in the response to the invitation of the power grid, and optimizes the scheduling strategy of the multiple resources aiming at effective response, ignores the peak-valley arbitrage benefit of the energy storage during the response, and fails to realize the collaborative promotion of flexibility and economy of the energy storage system participating in local peak-valley arbitrage and participating in the response to the power grid.

[0056] The embodiments of the present application provide a multiple-resource economic scheduling optimization method of a virtual power plant, which achieves the effect of fully tapping the potential of the energy storage system in peak-valley arbitrage while guaranteeing response reliability through a collaborative optimization strategy taking into account the response regulation capability of the multiple resources and the overall economic benefit.

[0057] According to the embodiments of the present application, a multiple-resource economic scheduling optimization method embodiment of a virtual power plant is provided. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a group of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown herein.

[0058] A multiple-resource economic scheduling optimization method of a virtual power plant is provided in the embodiments of the present application, which can be used for a virtual power plant, Figure 1 is a flowchart of the multiple-resource economic scheduling optimization method of a virtual power plant according to the embodiments of the present application, as Figure 1 shown, the flowchart includes the following steps:

[0059] Step S101, when an energy storage system is configured for a power user in a virtual power plant, obtaining grid response invitation information and scheduling basic data of the power user; the grid response invitation information includes a grid response type and an invitation mode.

[0060] Specifically, a virtual power plant (VPP) is a new energy management mode that integrates distributed energy resources (such as photovoltaic, energy storage, electric vehicles, and adjustable loads) through digital technology to form a new type of virtual machine group that can be scheduled, and does not rely on a physical power plant, but through advanced information communication technology and intelligent algorithms, the fragmented resources scattered on the user side are aggregated into a large-scale and controllable power system.

[0061] When the virtual power plant participates in the grid invitation response, the grid response invitation information and scheduling basic data of the power user are obtained, the grid response invitation information includes invitation mode, grid response type, response start and end time. The scheduling basic data includes the price data of each power user of the virtual power plant, the benchmark coal-fired power price data, the technical parameters of the energy storage system, the effective response rate range, the response compensation price, and the adjustable capacity and total adjustable capacity of each type of resource corresponding to each time period of the power user, and the predicted average power of the energy storage system.

[0062] Specifically, the price is the time-of-use price of the power user, which is the purchase electricity price of the State Grid or the contract price of the power user and the power selling company. The technical parameters of the energy storage system include the rated power, the rated capacity, the charging and discharging efficiency, the discharging depth, and the current state of charge; the invitation information includes the invitation mode, the response type, and the response start and end time. Among them, the invitation mode includes day-ahead invitation and intraday invitation; the response type includes peak shaving and valley filling. The adjustable resources of the power user in the peak shaving scenario include the energy storage system, the charging pile, and the air conditioner; the adjustable resources of the power user in the valley filling scenario include the energy storage system and the distributed photovoltaic.

[0063] Step S102, determining the optimization time length based on the invitation mode.

[0064] Specifically, the invitation mode refers to the time type of the response demand initiated by the grid to the virtual power plant, which is divided into two types:

[0065] Day-ahead invitation: the grid issues an invitation on the day before the response day (such as T-1 day), clearly defines the response demand (such as peak shaving / valley filling period, response rate requirement, etc.) on the next day (T day), and gives the virtual power plant sufficient preparation time;

[0066] Intraday invitation: the grid issues an invitation on the response day (T day), and the execution time of the response demand is usually within a few hours after the invitation is issued (such as in the morning, and the afternoon is required), which has higher real-time requirements for scheduling.

[0067] The optimization time length refers to the time range covered by the virtual power plant in the multi-resource scheduling optimization, i.e., how long the charging and discharging, load adjustment, and other strategies of the resources are planned, which is the time boundary of the scheduling model and directly determines the time granularity (such as hourly, minute) and coverage range of the optimization strategy.

[0068] In order to adapt the virtual power plant to the different timeliness requirements of the grid, the optimization time length is determined according to the day-ahead invitation and the intraday invitation.

[0069] Step S103, based on the grid response type, the scheduling basic data, the optimization time length, and the adjustable resources under the grid response type, an optimization model is constructed for maximizing the peak-valley arbitrage income and response compensation income under the corresponding grid response type.

[0070] Specifically, the peak-valley arbitrage income refers to the income obtained by the energy storage system (or other adjustable resources) through the price difference between low price period and high price period, which is a market-driven income and does not depend on the response demand of the power grid.

[0071] Specifically, the power market implements time-of-use electricity price (e.g. low price in valley period and high price in peak period), and the energy storage system can store energy at low price in valley period (e.g. at night) and discharge energy at high price in peak period (e.g. during the day), thereby realizing arbitrage through the price difference between charging cost and discharging income. For example, if the valley period price is 0.3 yuan / kWh and the peak period price is 0.8 yuan / kWh, the charging cost of the energy storage system is 30 yuan for 100 kWh, and the discharging income is 72 yuan for 90 kWh (considering efficiency loss), then the peak-valley arbitrage income is 42 yuan. The core driving force of such income is the time-of-use electricity price difference, and it is irrelevant to whether to participate in the response of the power grid, and it is a basic income source of the energy storage system itself.

[0072] The response compensation income refers to the reward income obtained by the virtual power plant for additional adjustment resources (exceeding the normal peak-valley arbitrage) when responding to the invitation of the power grid to cut peak and fill valley, which is essentially a service compensation for the virtual power plant participating in the regulation of the power grid.

[0073] For example, the power grid initiates a peak cutting invitation (requires reducing load in a certain period), and the virtual power plant needs to additionally increase the discharging capacity of the energy storage system or reduce the air conditioning load to meet the response rate requirement in addition to the normal peak discharging, and the power grid will compensate according to the additional adjusted electricity quantity x response compensation price; similarly, when filling valley, the virtual power plant additionally increases the charging capacity of the energy storage system, and will also obtain the corresponding compensation. The core driving force of such income is the response demand of the power grid, and it is directly related to the response type (peak cutting / filling valley) and the response quantity (exceeding the basic adjustment), which is an additional income of the virtual power plant participating in the cooperation of the power grid.

[0074] When constructing the optimization model, the response type (i.e. peak cutting / filling valley) in the invitation information of the power grid is taken as the core, and the optimization model in different scenarios is established respectively. If it is a peak cutting scenario, the adjustable resources focus on the energy storage system, charging pile and air conditioner (which need to reduce load or increase discharge to meet the demand of the power grid to reduce load); if it is a filling valley scenario, the adjustable resources focus on the energy storage system and distributed photovoltaic (which need to increase charging or consume photovoltaic to meet the demand of the power grid to increase load).

[0075] Based on the obtained dispatching basic data as the parameter input of the optimization model in the corresponding scenario, for example:

[0076] Economic parameters: time-of-use electricity price (calculate peak-valley arbitrage), response compensation price (calculate response compensation income), photovoltaic power generation income (filling valley scenario needs to deduct photovoltaic loss);

[0077] Technical parameters: energy storage rated power / capacity / charge-discharge efficiency / state of charge (constraint charge-discharge behavior), non-energy storage resource adjustable capacity (such as maximum reduction of charging pile, maximum output of photovoltaic), upper and lower limits of effective response rate (constraint response effectiveness);

[0078] Predictive parameters: average power of energy storage system prediction (baseline power reference).

[0079] The optimization time length is taken as the time dimension of the optimization model, the optimization period is divided into several time granularities (such as 1 hour / 15 minutes), and the model calculation is ensured to cover the whole time period to be regulated, avoiding invalid planning beyond the time range.

[0080] The core objective of the optimization model is to maximize the peak-valley arbitrage income + response compensation income (in the valley filling scenario, the photovoltaic loss income is additionally deducted to avoid excessive regulation at the expense of photovoltaic economy), while embedding scenario constraints to ensure that the response rate meets the standard and the resource is safely operated.

[0081] S104, an optimal solution of the corresponding optimization model is solved by using a preset solving algorithm, and an economic optimal dispatching scheme of the virtual power plant multi-resource is obtained.

[0082] Since the optimization model contains variables such as the charge-discharge state of the energy storage system, it belongs to a mixed integer linear programming model, and therefore a preset solving algorithm such as the branch and bound method or the cutting plane method is used for solving. The branch and bound method narrows down the optimal solution range by gradually decomposing the problem boundary, and the cutting plane method eliminates infeasible solutions by adding constraint planes, so as to efficiently find the global optimal solution.

[0083] The multi-resource economic dispatching optimization method of the virtual power plant provided in this embodiment can accurately adapt to and construct an optimization model according to different response types and different invitation modes of the power grid, take into account the energy storage peak-valley arbitrage, photovoltaic and other resource incomes on the premise of ensuring that the response rate of the virtual power plant meets the examination requirements, quickly obtain an optimal solution through an efficient solving algorithm, realize the collaborative optimization of response effectiveness and operation economy, significantly improve the economy and flexibility of the multi-resource dispatching of the virtual power plant, and solve the problem that the existing technology ignores the peak-valley arbitrage income of the energy storage during the response period and fails to realize the collaborative improvement of the flexibility and economy of the energy storage system participating in the local peak-valley arbitrage and participating in the power grid response.

[0084] In this embodiment, a multi-resource economic dispatching optimization method of a virtual power plant is provided, which can be used for a virtual power plant, Figure 2 The flowchart of the multi-resource economic dispatching optimization method of the virtual power plant according to the embodiment of the present application is shown in Figure 2 The flowchart includes the following steps:

[0085] Step S201, when the power user in the virtual power plant is configured with an energy storage system, obtaining grid response invitation information and scheduling basic data of the power user; the grid response invitation information includes a grid response type and an invitation mode. For details, please refer to Figure 1 Step S101 of the embodiment shown will not be described here.

[0086] Step S202, determining the optimization time length based on the invitation mode.

[0087] Specifically, the invitation mode includes day-ahead invitation and intraday invitation, and the above step S202 includes:

[0088] Step S2021, when the invitation mode is day-ahead invitation, the optimization time length is from 0 o'clock of the response day to 24 o'clock of the response day.

[0089] Specifically, if it is day-ahead invitation, the optimization time length is set to 0 o'clock to 24 o'clock of the response day (i.e. 24 hours a day). The reason is that day-ahead invitation has sufficient preparation time, and needs to plan the whole day's peak-valley electricity price, resource state (such as energy storage state of charge), to achieve whole-day economic optimization (such as arbitrage of energy storage charging at valley time and discharging at peak time, while meeting the next day's response demand).

[0090] Step S2022, when the invitation mode is intraday invitation, the optimization time length is from the invitation time to 24 o'clock of the response day.

[0091] Specifically, if it is intraday invitation, the optimization time length is set to the invitation time to 24 o'clock of the response day. The reason is that intraday invitation is more urgent, and there is no need to backtrack the time that has passed in the day (which cannot be adjusted), but only to focus on the remaining period from the invitation to the end of the day, to optimize the resource utilization in the remaining time (such as avoiding excessive adjustment to affect the economy of the remaining period of the day) while meeting the immediate response demand.

[0092] The above setting mode makes the scheduling optimization of the virtual power plant not only fit the time characteristics of the grid invitation (advance planning and real-time response), but also accurately cover the time range that needs to be regulated, balancing the forward-looking and flexibility of the scheduling.

[0093] Step S203, based on the grid response type, the scheduling basic data, the optimization time length, and the adjustable resources under the grid response type, an optimization model is constructed under the corresponding grid response type, with the goal of maximizing the peak-valley arbitrage income and response compensation income. For details, please refer to Figure 1 Step S103 of the embodiment shown will not be described here.

[0094] Step S204, the optimal solution of the corresponding optimization model is solved by using a preset solving algorithm, to obtain a multi-resource economic optimal scheduling scheme of the virtual power plant. For details, please refer to Figure 1 Step S104 of the embodiment shown will not be described here.

[0095] The virtual power plant multi-resource economic scheduling optimization method provided by the embodiment determines the optimization time length by distinguishing between day-ahead invitations and intra-day invitations, provides sufficient time dimensions for day-ahead invitations to fully plan long-period economicities of energy storage peak-valley arbitrage and multi-resource cooperation, and gives intra-day invitations a time range that fits temporary scheduling needs, taking into account real-time responsiveness and resource optimization in the remaining period of the day, so that the virtual power plant can accurately adapt to power grid response invitations with different time efficiencies and improve the flexibility and efficiency of scheduling.

[0096] In the embodiment, a virtual power plant multi-resource economic scheduling optimization method is provided, which can be used for a virtual power plant, Figure 3 The flowchart of the virtual power plant multi-resource economic scheduling optimization method according to the embodiment of the application is shown in Figure 3 The flowchart includes the following steps:

[0097] In step S301, when an energy storage system is configured for a power user in the virtual power plant, the power grid response invitation information and scheduling basic data of the power user are obtained. The power grid response invitation information includes the power grid response type and the invitation mode. For details, see step S201 of the embodiment shown in Figure 2 which will not be repeated here.

[0098] In step S302, the optimization time length is determined based on the invitation mode. For details, see step S202 of the embodiment shown in Figure 2 which will not be repeated here.

[0099] In step S303, based on the power grid response type, the scheduling basic data, the optimization time length, and the adjustable resources under the power grid response type, an optimization model corresponding to the power grid response type is constructed, with the maximum peak-valley arbitrage income and response compensation income as the target.

[0100] Specifically, the power grid response type includes a peak shaving scenario and a valley filling scenario; the scheduling basic data includes the time-of-use electricity price of the power user, the charging power of the energy storage system, and the discharging power of the energy storage system; and the above step S303 includes:

[0101] In step S3031, when the power grid response type is the peak shaving scenario, based on the time-of-use electricity price of the power user, the charging power of the energy storage system, the discharging power of the energy storage system, the optimization time length, and the adjustable resources under the peak shaving scenario, an optimization model for the peak shaving scenario is constructed with the maximum sum of the first peak-valley arbitrage income and the first response compensation income as the target, and under a first preset constraint condition set.

[0102] In an optional implementation, the optimization model for the peak shaving scenario is represented by the following formula:

[0103]

[0104] wherein, is the first peak-valley arbitrage revenue, is the first response compensation revenue, c t is the time-of-use price of electricity for power users, with the unit of yuan / kWh; is the discharging power of the energy storage system i at time t, with the unit of kW; is the charging power of the energy storage system i at time t, with the unit of kW; m t is the flag at each time, if the time t is within the response period, then m n,t = 1, otherwise m n,t = 0; F t profit is the total adjustable revenue at time t; Δt is the time granularity, with the unit of h; T is the optimization time length.

[0105] wherein:

[0106]

[0107] wherein, zΔP t ES is a nonlinear term, which needs to be linearized, and let N t = zΔP t ES .

[0108] At this time F t profit The calculation formula is updated as:

[0109]

[0110] Meanwhile, the target linearization of the peak-cut scenario needs to be introduced as a constraint.

[0111] wherein: z is a 0-1 variable indicating whether to participate in the response, z = 0 indicates not to participate in the response, and z = 1 indicates to participate in the response; is the compensation price per unit of electricity for participating in the response, with the unit of yuan / kWh; ΔP other is the adjustable capacity of other resources except the energy storage system in the peak-cut scenario, with the unit of kW; ΔP t ES is the adjustable capacity of the energy storage system at time t, with the unit of kW; P t es-pre is the predicted power of the energy storage system at time t, with the unit of kW.

[0112] In an alternative embodiment, the peak shaving scenario constraints, i.e. the first preset constraint set, include the energy storage system discharging power constraint, the energy storage system charging power constraint, the charging and discharging state constraint, the capacity constraint, the first energy storage system power constraint within the response period, the energy storage system discharging state constraint within the response period, and the target linearization introduction constraint, each of which is described in detail as follows:

[0113] 1) Energy storage system discharging power constraint:

[0114]

[0115] wherein: is the discharging state of the energy storage system i at time t, represents discharging at time t, represents not discharging at time t. i,N is the rated charging / discharging power of the energy storage system i, with the unit of kW.

[0116] 2) Energy storage system charging power constraint:

[0117]

[0118] wherein: is the charging state of the energy storage system i at time t, represents charging at time t, represents not charging at time t.

[0119] 3) Charging and discharging state constraint: The constraint prevents the energy storage system from being charged and discharged at the same time, and the charging and discharging state constraint formula is as follows:

[0120]

[0121] 4) Capacity constraint:

[0122] (1-SOC i,DOD )S i,N ≤S i,t ≤S i,N (8);

[0123]

[0124] wherein: S i,t is the capacity of the energy storage system i at time t, with the unit of kWh; SOC i,DOD is the depth of discharge of the energy storage system i; S i,N is the rated capacity of the energy storage system i, with the unit of kWh; is the charging efficiency of the energy storage system i; is the discharging efficiency of the energy storage system i.

[0125] 5) In response to the power constraint of the first energy storage system in the response period, to ensure that the response rate meets the expected requirements, the power of the energy storage system in the response period needs to be limited, and the formula is as follows:

[0126]

[0127] In the formula, P min is the minimum average power of the energy storage system in the response time; P max is the maximum average power of the energy storage system in the response time; and M is a large constant.

[0128] 6) Energy storage system discharge state constraint in the response period: in the peak shaving scenario, the energy storage system needs to be in the discharge state in the response period.

[0129] q t =zm t (11);

[0130]

[0131] In the formula, q t is the minimum discharge power reference of the energy storage system.

[0132] 7) Target linearization introduces constraints:

[0133] M(z-1)≤N t -ΔP t ES ≤M(1-z) (13);

[0134] -Mz≤N t ≤Mz (14).

[0135] In step S3032, when the grid response type is the valley filling scenario, the length of the response period is obtained, and based on the time-of-use electricity price of the power user, the charging power of the energy storage system, the discharging power of the energy storage system, the length of the response period, the length of time other than the response time, and the adjustable resources of the valley filling scenario, a second peak-valley arbitrage income and a second response compensation income are maximized as the target, and an optimization model of the valley filling scenario is constructed under a second preset constraint condition set.

[0136] In an optional implementation, the optimization model of the valley filling scenario is represented by the following formula:

[0137]

[0138] In the formula, is the second peak-valley arbitrage income, is the second response loss income, c t is the time-of-use electricity price of the power user, in yuan / kWh; is the discharging power of the energy storage system i at time t, with the unit of kW; is the charging power of the energy storage system i at time t, with the unit of kW; Δt is the length of the response period, with the unit of h; T out is the length of time outside the response period; T response is the length of the response period; m t is the flag bit at each time, if the time t is within the response period m n,t = 1, otherwise m n,t = 0; wherein F t = -F t ES -F t loss +F t profit , F t profit is the total adjustable income at time t; F t loss is the loss income of photovoltaic at time t; F t ES is the actual charging cost of the energy storage system participating in the response at time t, that is, the actual charging cost of the energy storage system within the response period at time t.

[0139] wherein, the total adjustable income F t profit at time t; the loss income of photovoltaic F t loss at time t; the actual charging and discharging income F t ES of the energy storage system participating in the response at time t.

[0140] A, the total adjustable income F t profit at time t:

[0141]

[0142] In the formula, zΔP t ES is nonlinear, which needs to be linearized, and N t = zΔP t ES .

[0143] At this time F t profit The calculation formula is updated as: At the same time, the valley filling scene linearization constraint-A, that is, formula (20) needs to be added.

[0144] In the formula: z is a 0-1 variable of whether to participate in the response, z = 0 represents not participating in the response, and z = 1 represents participating in the response; The compensation price for participating in the response unit electric quantity is yuan / kWh; ΔP other The adjustable capacity of other resources in the valley filling scene except the energy storage system is kW; ΔP t ES The adjustable capacity of the energy storage system at t is kW; P t es-pre The predicted power of the energy storage system at t is kW.

[0145] B, the loss benefit F of photovoltaic at t t loss Calculation logic:

[0146] F t loss = zc t ΔP t Pv Δt (18);

[0147] In the formula, ΔP t Pv The adjustable capacity of photovoltaic at t is kW.

[0148] C, the actual charging cost F of the energy storage at t in the participation period t ES :

[0149]

[0150] In the formula, is nonlinear and needs to be linearized, that is, At this time, F t ES The calculation formula is updated as: Meanwhile, the linearization constraint-B, that is, formula (21) needs to be added.

[0151] The valley filling scene constraint condition, that is, the second preset constraint condition set includes the constraints of the energy storage system in the valley filling scene, which are consistent with the constraints 1) to 4) in the peak shaving scene, which will not be repeated here. The differentiated constraints of the peak shaving scene are as follows:

[0152] (1) Linearization constraint:

[0153]

[0154] In the formula, M is a larger number.

[0155] (2) Energy storage system charging state constraint in the response period: in the valley filling scene, the energy storage system needs to be in the charging state in the response period.

[0156] q t = zm t(22);

[0157]

[0158] (3) The second energy storage system power constraint in the response period: to ensure that the response rate meets the expected requirements, the energy storage system power of the energy storage system in the response period needs to be limited.

[0159]

[0160] In the formula: P min is the minimum average power of the energy storage system in the response time; P max is the maximum average power of the energy storage system in the response time.

[0161] It should be noted that when calculating the upper and lower limits of the average power in the first energy storage system power constraint in the response period and the second energy storage system power constraint in the response period, the following also includes:

[0162] Based on the grid response type and the scheduling basic data, the upper and lower limits of the average power in the first energy storage system power constraint in the response period and the second energy storage system power constraint in the response period are calculated.

[0163] The scheduling basic data further includes the effective response rate upper and lower limits, the total adjustable capacity of the power users, and the predicted average power of the energy storage system; based on the grid response type and the scheduling basic data, the upper and lower limits of the average power in the first energy storage system power constraint in the response period and the second energy storage system power constraint in the response period are calculated, including:

[0164] According to the grid response type, it is determined whether the response rate when the energy storage system does not participate in the response is within the effective response rate upper and lower limits, and based on the determination result, the total adjustable capacity of the power users, the effective response rate upper and lower limits, and the predicted average power of the energy storage system, different calculation logics of the upper and lower limits of the average power are determined respectively; and based on the different calculation logics, the corresponding upper and lower limits of the average power are calculated.

[0165] Specifically, based on the grid response type, it is determined whether the response rate when the energy storage system does not participate in the response is within the upper and lower limits of the effective response rate. Based on the judgment result, combined with the total adjustable capacity of the power user, the upper and lower limits of the effective response rate, and the predicted average power of the energy storage system, different calculation logics are used to determine the upper and lower limits of the average power. This includes: when the grid response type is peak shaving or valley filling, under any of the following conditions when the response rate when the energy storage system does not participate in the response is between the lower limit of the effective response rate and a preset value, when the response rate when the energy storage system does not participate in the response is less than or equal to the lower limit of the effective response rate, when the response rate when the energy storage system does not participate in the response is between the preset value and the upper limit of the effective response rate, and when the response rate when the energy storage system does not participate in the response is greater than the upper limit of the effective response rate, different calculation logics are used to determine the upper and lower limits of the average power based on the judgment result, combined with the total adjustable capacity of the power user, the upper and lower limits of the effective response rate, and the predicted average power of the energy storage system.

[0166] Specifically, the preset value is set to 100%.

[0167] In four scenarios, explain the minimum average power P of the energy storage system during the response period while meeting the response rate requirement. min Maximum average power P max The calculation logic:

[0168] I. When Time: Indicates the response rate of other resources when the energy storage system does not participate in the response. Between the lower limit of the effective response rate and 100%, the minimum average power P min Maximum average power P max The calculation formulas are expressed as follows:

[0169]

[0170] In the formula: ΔP represents the adjustable capacity of all resources, including the energy storage system, during the response time period, in kW; ΔP other α represents the adjustable capacity of resources other than energy storage systems in peak shaving scenarios, expressed in kW; min α is the lower limit of the effective response rate. max P represents the upper limit of the effective response rate. es-pre-avg The predicted average power of all energy storage systems within the response time, i.e., the predicted average power of the energy storage system, is expressed in kW; P i,N The rated discharge power of energy storage system i is expressed in kW.

[0171] II. When Time: Indicates the response rate of other resources when the energy storage system does not participate in the response. If the discharge power of the energy storage system is below the effective response rate lower limit, the discharge power needs to be increased to meet the response rate requirement, with a minimum average power P. min Maximum average power Pmax The calculation formulas are expressed as follows:

[0172] III. When Time: Indicates the response rate of other resources when the energy storage system does not participate in the response. Between 100% and the upper limit of the effective response rate; it should be noted that when the energy storage system does not participate in the response, if This indicates that the adjustability of the energy storage system is negative, and the minimum average power P min Maximum average power P max The calculation formulas are expressed as follows:

[0173]

[0174] IV. When Time: Indicates the response rate of other resources when the energy storage system does not participate in the response. The minimum average power P is greater than the upper limit of the effective response rate. min Maximum average power P max The calculation formulas are expressed as follows:

[0175]

[0176] Under four scenarios, the minimum average power P of the energy storage system during the response time in the valley filling scenario is... min Maximum average power P max The calculation logic is as follows:

[0177] one, This indicates that when the energy storage system does not participate in the response, the response rate of other resources is between the lower limit of the effective response rate and 100%, and the minimum average power P is... min Maximum average power P max The calculation formulas are expressed as follows:

[0178]

[0179] II. When Time: Indicates that when the energy storage system is not participating in the response, the response rate of other resources is below the lower limit. In order to achieve an effective response, the charging power of the energy storage system needs to be increased. Minimum average power P min Maximum average power P max The calculation formulas are expressed as follows:

[0180]

[0181]

[0182] III. When When: indicates that the response rate of other resources is between 100% and the upper limit of the effective response rate when the energy storage system does not participate in response. It should be noted that when the energy storage system does not participate in response, if The calculation formulas of the minimum average power P min and the maximum average power P max are as follows:

[0183]

[0184] IV. When When: indicates that the response rate of other resources is greater than the upper limit of the effective response rate when the energy storage system does not participate in response. The calculation formulas of the minimum average power P min and the maximum average power P max are as follows:

[0185] The multi-resource economic scheduling optimization method of the virtual power plant provided in the embodiment, in calculating the upper and lower limits of the average power in the response period of the energy storage system, first determines whether the response rate when the energy storage does not participate in response is in the effective response rate upper and lower limit interval according to the grid response type (peak shaving / valley filling), and then sets different calculation logics and derives the corresponding upper and lower limits in combination with the total adjustable capacity of the power user, the upper and lower limits of the effective response rate and the predicted average power of the energy storage system, which not only ensures the accurate adaptation of the average power constraint and the grid response type, but also avoids the subjective setting of the power upper and lower limits relying on the key basic data, can reduce the influence of excessive adjustment on the economic targets such as energy storage peak-valley arbitrage while ensuring that the response rate of the virtual power plant is stable and meets the examination requirements, and significantly improves the scientificity of power constraint calculation and the reliability of virtual power plant scheduling.

[0186] S304, the preset solving algorithm is used to solve the optimal solution of the corresponding optimization model to obtain a multi-resource economic optimal scheduling scheme of the virtual power plant.

[0187] Specifically, after the calculation of the minimum average power P min and the maximum average power P max in each response type scenario is completed, the branch and bound method or the cut plane method is used to solve the model, and the output result is output.

[0188] Wherein: the branch and bound method is a classical algorithm for solving integer programming and mixed integer programming problems, which is applied in the scene of virtual power plant multi-resource economic dispatch optimization which needs to deal with discrete variables (such as 0-1 variable of whether the energy storage system participates in response) and continuous variables (such as charging and discharging power of the energy storage system). The basic idea of the branch and bound method is to decompose the original problem (integer programming or mixed integer programming problem) into smaller sub-problems (branching process) constantly, and exclude those sub-problems that cannot contain the optimal solution by calculating the boundary value (bounding process) of the sub-problems, so as to narrow the search range and improve the solving efficiency. Based on the fact that the optimal solution of the integer programming problem must be in the solution space of the relaxation problem (linear programming problem after removing the integer constraint), and the optimal solution of the integer programming will not be better than the optimal solution of the relaxation problem.

[0189] The core idea of the cutting plane method is to start from the solution of the relaxation linear programming problem (linear programming problem after removing the integer constraint) of the integer programming problem, and gradually narrow the feasible region by adding additional linear constraints (i.e. cutting plane), so that the optimal solution of the relaxation problem gradually approaches the optimal solution of the integer programming problem. These cutting planes do not cut off any integer feasible solution, but only cut off non-integer solutions that do not meet the integer requirement, so that the optimal solution of the relaxation problem becomes an integer solution, and thus the optimal solution of the integer programming problem is obtained.

[0190] The multi-resource economic dispatch optimization method of the virtual power plant provided by the embodiment focuses on the coordination of energy storage charging and discharging, time-of-use electricity price and response compensation in the peak shaving scenario, and further considers the time dimension of response period and non-response period and the income of resources such as photovoltaic in the valley filling scenario, so that the resource characteristics and grid demand can be accurately adapted in different scenarios, the peak-valley arbitrage income and response compensation income are maximized on the premise of meeting the response rate assessment requirement, and the coordination optimization of response effectiveness and operation economy in each scenario is realized.

[0191] As one or more specific application embodiments of the embodiment of the application, the accompanying drawings are combined to describe the specific application embodiments of the application. Figure 4 The multi-resource economic dispatch optimization method of the virtual power plant provided by the embodiment is further described in detail as follows. Figure 4 As shown in the figure, the specific steps are as follows:

[0192] Step 1: Determine whether the virtual power plant power user has an energy storage system, if yes, execute step 2, otherwise end.

[0193] Step 2: Obtain the time-of-use electricity price of the power user, the benchmark coal-fired electricity price, the technical parameters of the energy storage system, the invitation information, the effective response rate range, the response compensation price, the adjustable capacity of various resources of the power user, the total adjustable capacity, and the predicted power value of the energy storage system.

[0194] The response type includes a peak clipping scenario and a valley filling scenario. The adjustable resources of the power user in the peak clipping scenario include the energy storage system, the charging pile, and the air conditioner. The adjustable resources of the power user in the valley filling scenario include the energy storage system and the distributed photovoltaic.

[0195] Step 3: Calculate the upper and lower limits of the average power of the energy storage system in the response period according to the response type. In the peak clipping scenario, the minimum average power P min and the maximum average power P max of the energy storage system in the response period are calculated under the premise of meeting the response rate. The calculation formulas are shown in formulas (25) to (32). In the valley filling scenario, the minimum average power P min and the maximum average power P max of the energy storage system in the response period are calculated. The calculation formulas are shown in formulas (33) to (40), which are not described here.

[0196] Step 4: Determine the optimization time length of the algorithm according to the invitation mode. If it is day-ahead invitation, the optimization period is from 0 o'clock of the response day to 24 o'clock of the response day. If it is intra-day invitation, the optimization period is from the invitation time to 24 o'clock of the response day.

[0197] Step 5: Construct a mixed integer linear optimization model under different response type scenarios.

[0198] The optimization model of the peak clipping scenario is represented by the following formula:

[0199]

[0200] In the formula, is the first peak-valley arbitrage income, is the first response compensation income, c t is the time-of-use electricity price of the power user, with a unit of yuan / kWh; is the discharging power of the energy storage system i at time t, with a unit of kW; is the charging power of the energy storage system i at time t, with a unit of kW; m t is the flag at each time. If the time t is within the response period, m n,t = 1, otherwise m n,t = 0; F t profit is the total adjustable income at time t; Δt is the time granularity, with a unit of h; T is the optimization time length.

[0201] Among them:

[0202]

[0203] wherein zΔP t ES is a nonlinear term, which needs to be linearized, and let N t =zΔP t ES .

[0204] At this time, F t profit The calculation formula is updated as:

[0205]

[0206] Meanwhile, the target linearization constraint of the peak clipping scenario needs to be added.

[0207] wherein z is a 0-1 variable indicating whether to participate in response, z=0 indicates not to participate in response, and z=1 indicates to participate in response; is the compensation price of participating in response per unit of electricity, in yuan / kWh; ΔP other is the adjustable capacity of other resources except the energy storage system in the peak clipping scenario, in kW; ΔP t ES is the adjustable capacity of the energy storage system at time t, in kW; P t es-pre is the predicted power of the energy storage system at time t, in kW.

[0208] The constraint condition in the peak clipping scenario is formula (5) to formula (14).

[0209] In an optional embodiment, the optimization model of the valley filling scenario is represented by the following formula:

[0210]

[0211] wherein, is the second peak-valley arbitrage income, is the second response loss income, c t is the time-of-use electricity price of the power user, in yuan / kWh; is the discharging power of the energy storage system i at time t, in kW; is the charging power of the energy storage system i at time t, in kW; Δt is the length of the response period, in h; T out is the length of time outside the response period; T response is the length of the response period; m t is the flag bit at each time, m n,t =1 if the time t is within the response period, otherwise m n,t =0; F t =-Ft ES - F t loss + F t profit , F t profit Ft is the total adjustable income at time t; F t loss Ft is the loss income of photovoltaic at time t; F t ES Ft is the actual charging and discharging income of the energy storage system participating in response at time t, i.e., the actual charging cost of the energy storage system within the response period at time t.

[0212] Ft is the total adjustable income at time t; F t profit Ft is the loss income of photovoltaic at time t; F t loss Ft is the actual charging and discharging income of the energy storage system participating in response at time t, i.e., the actual charging cost of the energy storage system within the response period at time t. t ES The calculation logic is as follows:

[0213] A, Ft is the total adjustable income at time t; F t profit :

[0214]

[0215]

[0216] In the formula, zAP t ES is nonlinear and needs to be linearized, and N t = zAP t ES .

[0217] Ft is the total adjustable income at time t; F t profit The calculation formula is updated as: At the same time, the valley filling scene linearization constraint-A needs to be added, i.e., formula (20).

[0218] In the formula: z is a 0-1 variable of whether to participate in response, z = 0 represents not participating in response, and z = 1 represents participating in response; is the compensation price of participating in response per unit of electricity, with a unit of yuan / kWh; AP other is the adjustable capacity of other resources except the energy storage system in the valley filling scene, with a unit of kW; AP t ES is the adjustable capacity of the energy storage system at time t, with a unit of kW; P t es-pre is the predicted power of the energy storage system at time t, with a unit of kW.

[0219] B, loss benefit of photovoltaic at time t t loss Calculation logic:

[0220] F t loss = zc t ΔP t Pv Δt (18) ;

[0221] In the formula: ΔP t Pv is the adjustable capacity of photovoltaic at time t, with the unit of kW.

[0222] C, actual charging cost of energy storage at time t in the participation period t ES :

[0223]

[0224] In the formula, is nonlinear, which needs to be linearized, and let At this time, F t ES The calculation formula is updated as: At the same time, linearization constraint-B, that is, formula (21) needs to be added.

[0225] The constraint conditions in the valley filling scenario are formula (5) to formula (8) and formula (20) to (24).

[0226] Step 6: solve the model by using the branch and bound method or the cutting plane method, and output the result.

[0227] The multi-resource economic dispatch optimization method of the virtual power plant provided in the embodiment can dynamically evaluate the overall benefit of participating in the response day according to the response compensation price issued by the power grid, combined with the current regional electricity price trend, and adaptively decide whether to participate in the response. The application combines the peak-valley arbitrage strategy of the response day energy storage system with the response rate, maximizes the peak-valley arbitrage potential of the energy storage system in the response period and the non-response period under the premise of preferentially guaranteeing that the response rate of the virtual power plant meets the specified range, and takes into account the benefits and response performance.

[0228] It should be noted that, due to the differences in policies of various provinces and cities, the method proposed based on the application can be customized and adjusted according to the specific regional policy requirements, and if it does not deviate from the essential idea, it should be included in the protection scope of the application.

[0229] The embodiment also provides a multi-resource economic scheduling optimization device of a virtual power plant, which is used for implementing the above-mentioned embodiment and preferred embodiment, and details are not repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiment is preferably implemented in software, implementation of hardware, or a combination of software and hardware, is also possible and contemplated.

[0230] The embodiment provides a multi-resource economic scheduling optimization device of a virtual power plant, which comprises: Figure 5

[0231] An invitation information and basic data acquisition module 501 is configured to acquire grid response invitation information and scheduling basic data of a power user when the power user in the virtual power plant is configured with an energy storage system; the grid response invitation information comprises a grid response type and an invitation mode.

[0232] An optimization time length determination module 502 is configured to determine an optimization time length based on the invitation mode.

[0233] An optimization model construction module 503 is configured to construct an optimization model based on the grid response type, the scheduling basic data, the optimization time length, and an adjustable resource under the grid response type, the optimization model being aimed at maximizing peak-valley arbitrage income and response compensation income.

[0234] An optimal solution solving module 504 is configured to solve an optimal solution of the corresponding optimization model by using a solving algorithm, to obtain a multi-resource economic optimal scheduling scheme of the virtual power plant.

[0235] In some optional embodiments, the invitation mode comprises a day-ahead invitation and an intra-day invitation; the optimization time length determination module 502 comprises:

[0236] A first determination unit is configured to, when the invitation mode is the day-ahead invitation, set the optimization time length as from 0 o'clock of a response day to 24 o'clock of the response day.

[0237] A second determination unit is configured to, when the invitation mode is the intra-day invitation, set the optimization time length as from an invitation time to 24 o'clock of the response day.

[0238] In some optional embodiments, the grid response type comprises a peak-cut scenario and a valley-fill scenario; the scheduling basic data comprises a time-of-use price of the power user, a charging power of the energy storage system, and a discharging power of the energy storage system; and the optimization model construction module 503 comprises:

[0239] ​The peak shaving scenario optimization model construction unit is configured to, when the grid response type is a peak shaving scenario, maximize a sum of a first peak-valley arbitrage income and a first response compensation income based on a time-of-use electricity price of a power user, a charging power of an energy storage system, a discharging power of the energy storage system, an optimization time length, and an adjustable resource in the peak shaving scenario, and construct an optimization model of the peak shaving scenario under a first preset constraint condition set.

[0240] The valley filling scenario optimization model construction unit is configured to, when the grid response type is a valley filling scenario, obtain a response time period length, maximize a sum of a second peak-valley arbitrage income and a second response compensation income based on a time-of-use electricity price of a power user, a charging power of an energy storage system, a discharging power of the energy storage system, the response time period length, a time length other than a response time, and an adjustable resource in the valley filling scenario, and construct an optimization model of the valley filling scenario under a second preset constraint condition set.

[0241] In an optional implementation, the optimization model of the peak shaving scenario is expressed by the following formula:

[0242]

[0243] In the formula, the first peak-valley arbitrage income is The first response compensation income is c t The time-of-use electricity price of the power user is in yuan / kWh; The discharging power of the energy storage system i at the t time is in kW; The charging power of the energy storage system i at the t time is in kW; t The flag bit at each time is m n,t = 1 if the time t is in the response time period, otherwise m n,t = 0; t profit The total adjustable income at the t time is F

[0244] The optimization model of the valley filling scenario is expressed by the following formula:

[0245]

[0246] In the formula, the second peak-valley arbitrage income is The second response loss income is c t The time-of-use electricity price of the power user is in yuan / kWh; The discharging power of the energy storage system i at the t time is in kW; The charging power of the energy storage system i at the t time is in kW; out ​​T is a length of the response period out of time; response m is a length of the response period; t F is a flag of each time, if the time t is in the response period m n,t = 1, otherwise m n,t = 0; F t = -F t ES -F t loss +F t profit , F t profit F is a total adjustable income of the time t; F t loss F is a loss income of the photovoltaic at the time t; F t ES F is an actual charging cost of the energy storage in the response period at the time t.

[0247] In an optional implementation, the first preset constraint condition set includes: the energy storage system discharging power constraint, the energy storage system charging power constraint, the charging and discharging state constraint, the capacity constraint, the first energy storage system power constraint in the response period, the energy storage system discharging state constraint in the response period, and the target linearization introduction constraint; the second preset constraint condition set includes the energy storage system discharging power constraint, the energy storage system charging power constraint, the charging and discharging state constraint, the capacity constraint, the linearization constraint, the energy storage system charging state constraint in the response period, and the second energy storage system power constraint in the response period; the optimization model construction module 503 further includes:

[0248] The average power upper and lower limit calculation sub-unit is configured to calculate the average power upper and lower limit in the first energy storage system power constraint in the response period and the second energy storage system power constraint in the response period based on the grid response type and the dispatching basic data.

[0249] In an optional implementation, the dispatching basic data further includes the effective response rate upper and lower limit, the total adjustable capacity of the power user, and the predicted average power of the energy storage system;

[0250] The average power upper and lower limit calculation sub-unit is further configured to:

[0251] determine different calculation logics of the average power upper and lower limit based on the judgment result in combination with the total adjustable capacity of the power user, the effective response rate upper and lower limit, and the predicted average power of the energy storage system according to whether the response rate when the energy storage system does not participate in the response is within the effective response rate upper and lower limit according to the grid response type; and calculate the corresponding average power upper and lower limit based on the different calculation logics.

[0252] Wherein, whether the response rate when the energy storage system does not participate in response is within the upper and lower limits of the effective response rate is judged according to the power grid response type, and different calculation logics of the upper and lower limits of the average power are determined based on the judgment result, the total adjustable capacity of the power user, the upper and lower limits of the effective response rate and the predicted average power of the energy storage system, including:

[0253] When the power grid response type is peak shaving or valley filling, in any one of the following situations: the response rate when the energy storage system does not participate in response is between the lower limit of the effective response rate and a preset value, the response rate when the energy storage system does not participate in response is less than or equal to the lower limit of the effective response rate, the response rate when the energy storage system does not participate in response is between the preset value and the upper limit of the effective response rate, and the response rate when the energy storage system does not participate in response is greater than the upper limit of the effective response rate, different calculation logics of the upper and lower limits of the average power are determined based on the judgment result, the total adjustable capacity of the power user, the upper and lower limits of the effective response rate and the predicted average power of the energy storage system.

[0254] Further function description of each module and unit above is the same as the corresponding embodiment above, and will not be repeated here.

[0255] The multi-resource economic dispatching optimization device of the virtual power plant in the embodiment is presented in the form of a functional unit, wherein the unit refers to an ASIC (Application Specific Integrated Circuit, Application Specific Integrated Circuit) circuit, a processor and a memory executing one or more software or fixed programs, and / or other devices that can provide the above functions.

[0256] The embodiment of the present application also provides a computer device with the above Figure 5 multi-resource economic dispatching optimization device of the virtual power plant shown in the figure.

[0257] Please refer to Figure 6 , Figure 6 is a structural schematic diagram of a computer device provided by an optional embodiment of the present application, as Figure 6As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 6 Take a processor 10 as an example.

[0258] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0259] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0260] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0261] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0262] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 can be connected by a bus or other means, Figure 6 The bus connection is taken as an example.

[0263] The input device 30 can receive inputted digital or character information, and generate key signal input related to user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touchpad, a pointing stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 can include a display device, an auxiliary lighting device (such as an LED), a tactile feedback device (such as a vibration motor), etc. The display device includes but is not limited to a liquid crystal display, a light-emitting diode, a display, and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0264] The embodiments of the present application also provide a computer readable storage medium, and the method according to the embodiments of the present application can be implemented in hardware, firmware, or recorded in a storage medium, or stored in a remote storage medium or a non-transitory machine readable storage medium and downloaded from a network and stored in a local storage medium, so that the method described herein can be processed by such software on a storage medium using a general purpose computer, a special purpose processor, or programmable or special hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk or a solid state disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that the computer, processor, microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code, which, when accessed and executed by the computer, processor or hardware, implements the method shown in the above embodiments.

[0265] Part of the present application can be applied as a computer program product, for example, computer program instructions, when executed by a computer, the operation of the computer can invoke or provide the method and / or technical solutions according to the present application. Those skilled in the art should understand that the form of computer program instructions in computer readable medium includes but is not limited to source file, executable file, installation package file, etc., and accordingly, the way of computer program instructions executed by computer includes but is not limited to: the computer directly executes the instructions, or the computer compiles the instructions and then executes the corresponding compiled program, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installed program. Here, the computer readable medium can be any available computer readable storage medium or communication medium accessible to the computer.

[0266] While embodiments of the application have been described in connection with the preferred embodiments of the various figures, those of ordinary skill in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the application, and that such modifications and changes fall within the scope of the appended claims.

Claims

1. A multi-resource economic dispatch optimization method for a virtual power plant, characterized in that, The method includes: When a power user in a virtual power plant is equipped with an energy storage system, the grid response invitation information and dispatch basic data of the power user are obtained; the grid response invitation information includes the grid response type and invitation method; The optimal time length is determined based on the invitation method described above; Based on grid response type, dispatching basic data, optimization time length, and adjustable resources under grid response type, an optimization model is constructed with the goal of maximizing peak-valley arbitrage revenue and response compensation revenue under the corresponding type. The optimal solution of the corresponding optimization model is obtained by using a preset solution algorithm, and the optimal scheduling scheme for multiple resources of the virtual power plant is obtained.

2. The method according to claim 1, characterized in that, The invitation methods include day-ahead invitations and day-to-day invitations; determining the optimized time length based on the invitation methods includes: When the invitation method is a day-ahead invitation, the optimization time length is from 0:00 to 24:00 on the response date; When the invitation method is intraday invitation, the optimized time length is from the invitation time to 24:00 on the response day.

3. The method according to claim 1, characterized in that, The grid response types include peak shaving scenarios and valley filling scenarios; the basic dispatch data includes time-of-use electricity prices for electricity users, charging power of energy storage systems, and discharging power of energy storage systems. The aforementioned optimization model, based on grid response type, scheduling basic data, optimization time length, and adjustable resources under the grid response type, aims to maximize peak-valley arbitrage revenue and response compensation revenue under the corresponding grid response type, including: When the grid response type is peak shaving scenario, based on the time-of-use electricity price of electricity users, the charging power of the energy storage system, the discharging power of the energy storage system, the optimization time length and the adjustable resources under the peak shaving scenario, the goal is to maximize the sum of the first peak-valley arbitrage income and the first response compensation income, and an optimization model for the peak shaving scenario is constructed under the first set of preset constraints. When the grid response type is valley filling scenario, the response time length is obtained. Based on the time-of-use electricity price of electricity users, the charging power of energy storage system, the discharging power of energy storage system, the response time length, the time length outside the response time, and the adjustable resources of valley filling scenario, the goal is to maximize the sum of the second peak-valley arbitrage income and the second response compensation income, and an optimization model for valley filling scenario is constructed under the second preset constraint condition set.

4. The method according to claim 3, characterized in that, The optimization model for the peak shaving scenario is expressed by the following formula: In the formula, For the first peak-to-valley arbitrage profit, For the first response compensation benefit, c t Time-of-use pricing for electricity users, in yuan / kWh; Let be the discharge power of energy storage system i at time t, in kW; The charging power of energy storage system i at time t, expressed in kW; m t For each time point, there is a flag bit. If time t is within the response time period, then m... n,t =1, otherwise m n,t =0;F t profit Δt represents the total adjustable revenue at time t; Δt represents the time granularity in hours; and T represents the optimization time length. The optimization model for the valley filling scenario is expressed by the following formula: In the formula, For the second peak-to-trough arbitrage profit, For the second response loss gain, c t Time-of-use pricing for electricity users, in yuan / kWh; Let be the discharge power of energy storage system i at time t, in kW; The charging power of energy storage system i at time t is expressed in kW; Δt is the response time duration in hours; T out The duration outside the response period; T response The duration of the response period; m t These are the flag bits for each time point. If time t falls within the response time interval m... n,t =1, otherwise m n,t =0; where F t profit F is the total adjustable revenue at time t; t loss Let F be the photovoltaic loss and gain at time t; t ES Let t be the actual charging cost of energy storage during the response period.

5. The method according to claim 3, characterized in that, The first set of preset constraints includes: energy storage system discharge power constraint, energy storage system charging power constraint, charge / discharge state constraint, capacity constraint, first energy storage system power constraint during the response period, energy storage system discharge state constraint during the response period, and target linearization introduced constraint. The second set of preset constraints includes the energy storage system discharge power constraint, energy storage system charging power constraint, charge / discharge state constraint, capacity constraint, linearization constraint, energy storage system charging state constraint during the response period, and second energy storage system power constraint during the response period. Based on grid response type, dispatching basic data, optimization time length, and adjustable resources under each grid response type, an optimization model is constructed with the objective of maximizing peak-valley arbitrage revenue and response compensation revenue under the corresponding type. This also includes: Based on the grid response type and dispatching data, the upper and lower limits of the average power in the power constraints of the first energy storage system and the power constraints of the second energy storage system during the response period are calculated.

6. The method according to claim 5, characterized in that, The basic dispatch data also includes upper and lower limits of effective response rate, total adjustable capacity of power users, and predicted average power of energy storage systems. Based on the grid response type and dispatching data, the average power upper and lower limits of the power constraints of the first energy storage system and the second energy storage system within the response period are calculated, including: Based on the grid response type, determine whether the response rate when the energy storage system does not participate in the response is within the effective response rate upper and lower limits. Based on the judgment result, combined with the total adjustable capacity of the power user, the effective response rate upper and lower limits, and the predicted average power of the energy storage system, determine the different calculation logics for the upper and lower limits of the average power. The upper and lower limits of the average power are calculated based on the different calculation logics.

7. The method according to claim 6, characterized in that, The step of determining whether the response rate of the energy storage system when it does not participate in the response is within the effective response rate upper and lower limits based on the grid response type, and determining the upper and lower limits of the average power based on the judgment result in combination with the total adjustable capacity of the power user, the effective response rate upper and lower limits, and the predicted average power of the energy storage system, includes different calculation logics for determining the upper and lower limits of the average power, respectively: When the grid response type is peak shaving or valley filling, the following situations are considered: when the response rate is between the lower limit of the effective response rate and a preset value when the energy storage system does not participate in the response; when the response rate is less than or equal to the lower limit of the effective response rate when the energy storage system does not participate in the response; when the response rate is between the preset value and the upper limit of the effective response rate when the energy storage system does not participate in the response; and when the response rate is greater than the upper limit of the effective response rate when the energy storage system does not participate in the response. Based on the judgment result and combined with the total adjustable capacity of the power user, the upper and lower limits of the effective response rate, and the predicted average power of the energy storage system, different calculation logics for the upper and lower limits of the average power are determined respectively.

8. A multi-resource economic dispatch optimization device for a virtual power plant, characterized in that, The device includes: The invitation information and basic data acquisition module is used to acquire the grid response invitation information and dispatch basic data of power users when power users in the virtual power plant are equipped with energy storage systems; the grid response invitation information includes the grid response type and invitation method; An optimization time length determination module is used to determine the optimization time length based on the invitation method; The optimization model building module is used to construct an optimization model based on the grid response type, scheduling basic data, optimization time length, and adjustable resources under the response type, with the goal of maximizing peak-valley arbitrage revenue and response compensation revenue under the corresponding type. The optimal solution module is used to solve the corresponding optimization model using a solution algorithm to obtain the optimal scheduling scheme for multiple resources of the virtual power plant.

9. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the multi-resource economic scheduling optimization method for a virtual power plant as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the multi-resource economic dispatch optimization method for the virtual power plant as described in any one of claims 1 to 7.

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