Virtual power plant scheduling optimization method and device

Abstract

The invention provides a virtual power plant scheduling optimization method and device, and the method comprises the steps: building an adjustment cost function of an adjustable load according to the adjustable load and the electricity price of a virtual power plant; establishing a virtual power plant arbitrage model by taking the minimum total operation cost calculated according to the energy consumption cost of the virtual power plant and the peak regulation income as a target and taking the physical behavior characteristics of the energy storage system in the virtual power plant and the adjustment strategy of the adjustable load as constraints; wherein the energy consumption cost is determined according to the charging and discharging strategy of the energy storage system, the adjusting strategy of the adjustable load, the basic load of the virtual power plant, the electricity price and the adjusting cost of the adjustable load, and the peak regulation income is determined according to the charging and discharging strategy of the energy storage system, the adjusting strategy of the adjustable load and the power peak regulation subsidy price; inputting the electricity price, the electric power peak regulation subsidy price, the basic load of the virtual power plant, the energy storage energy of the energy storage system and the adjustable range of the adjustable load, and obtaining the charging and discharging strategy of the energy storage system, the adjustment strategy and the adjustment cost of the adjustable load and the total operation cost of the virtual power plant through the virtual power plant arbitrage model. Accurate scheduling of virtual power plant resources is achieved, and the requirements for flexibility and economy of a power system are met.

Description

Technical Field

[0001] This application relates to the technical field of virtual power plant operation optimization, and particularly to a virtual power plant scheduling optimization method and device. Background Art

[0002] With the development of the power system, virtual power plants have gradually become an important part of the power system. By aggregating resources such as distributed generation, energy storage systems, and adjustable loads, virtual power plants provide flexible power supply and ancillary services for the power system.

[0003] However, there are some limitations in the existing scheduling and control methods of virtual power plants. On the one hand, most virtual power plants mainly participate in the energy market or ancillary service market separately. The participation mode in a single market limits the space for improving the economic benefits of virtual power plants and cannot achieve the optimal allocation of resources. On the other hand, the existing technologies do not accurately characterize the regulation cost of virtual power plants. The regulation cost is an important consideration factor for the operation optimization of virtual power plants. Inaccurate cost characterization may lead to unreasonable scheduling decisions, affecting the economy and operation efficiency of virtual power plants.

[0004] Therefore, in view of the deficiencies in the scheduling and control methods of virtual power plants, this application provides a virtual power plant scheduling optimization method. Summary of the Invention

[0005] This application provides a virtual power plant scheduling optimization method and device, which can solve the technical problems in the existing technology that the virtual power plant scheduling cannot achieve the optimal allocation of resources, and inaccurate cost characterization may lead to unreasonable scheduling decisions, affecting the economy and operation efficiency of virtual power plants.

[0006] In the first aspect, an embodiment of this application provides a virtual power plant scheduling optimization method, and the virtual power plant scheduling optimization method includes:

[0007] Establish a regulation cost function for the adjustable load according to the adjustable load and electricity price of the virtual power plant;

[0008] With the goal of minimizing the total operation cost calculated based on the energy consumption cost and peak regulation revenue of the virtual power plant, and with the physical behavior characteristics of the energy storage system in the virtual power plant and the regulation strategies of the adjustable load located in the corresponding constraint intervals as constraints, establish a virtual power plant arbitrage model, where the energy consumption cost is determined according to the charge and discharge strategies of the energy storage system, the regulation strategies of the adjustable load, the base load of the virtual power plant, the electricity price, and the regulation cost of the adjustable load, and the peak regulation revenue is determined according to the charge and discharge strategies of the energy storage system, the regulation strategies of the adjustable load, and the power peak regulation subsidy price;

[0009] Input the electricity price, the electricity peak shaving subsidy price, the base load of the virtual power plant, the energy storage capacity of the energy storage system, and the adjustable range of the adjustable load. Through the virtual power plant arbitrage model, obtain the charge and discharge strategy of the energy storage system, the adjustment strategy and adjustment cost of the adjustable load, and the total operating cost of the virtual power plant.

[0010] Combined with the first aspect, in an implementation manner, establishing the adjustment cost function of the adjustable load according to the adjustable load and electricity price of the virtual power plant includes:

[0011] U load (L flex ) = αL flex 2 - price * L flex

[0012] Wherein, U load is the adjustment cost of the adjustable load, L flex is the adjustable load, price is the electricity price, α is the adjustable load cost coefficient, and the value of α is greater than 0.

[0013] In an implementation manner:

[0014] The charge and discharge strategy of the energy storage system includes the charging power and discharging power of the energy storage system;

[0015] The adjustment strategy of the adjustable load includes the power of the adjustable load.

[0016] In an implementation manner, taking the physical behavior characteristics of the energy storage system in the virtual power plant and the adjustment strategy of the adjustable load being in the corresponding constraint intervals as constraints includes:

[0017] Constraining the energy storage capacity of the energy storage system to be greater than or equal to zero and less than or equal to the maximum preset energy storage capacity;

[0018] Constraining both the charging power and discharging power of the energy storage system to be greater than or equal to zero and less than or equal to the maximum preset charge and discharge power;

[0019] Constraining the power of the adjustable load to be greater than or equal to the corresponding minimum adjustable load and less than or equal to the corresponding maximum adjustable load, where the interval formed by the minimum adjustable load and the maximum adjustable load is the adjustable range of the adjustable load.

[0020] In one implementation, with the goal of minimizing the total operating cost calculated based on the energy consumption cost and peak shaving revenue of the virtual power plant, and with the physical behavior characteristics of the energy storage system in the virtual power plant and the adjustment strategies of the adjustable loads within the corresponding constraint intervals as constraints, a virtual power plant arbitrage model is established, including:

[0021] Establish the objective function of the virtual power plant arbitrage model:

[0022] min f = C - R r

[0023] Where:

[0024]

[0025] In the formula, f is the total operating cost of the virtual power plant, C is the energy consumption cost, and R r is the peak shaving revenue, is the total energy consumption power of the virtual power plant at time t, is the electricity price at time t, is the adjustment cost of the adjustable load, is the peak shaving service power of the virtual power plant at time t, is the electricity peak shaving subsidy price at time t, t is the time subscript, and T is the time set, is the electricity peak shaving subsidy price at time t;

[0026] Where:

[0027]

[0028] In the formula, is the output power of the energy storage system at time t, is the base load of the virtual power plant at time t, is the power of the adjustable load at time t;

[0029] The constraints of the virtual power plant arbitrage model are:

[0030]

[0031] Where, is the stored energy of the energy storage system at time t, η is the charge-discharge efficiency of the energy storage system, is the charging power of the energy storage system at time t, is the discharging power of the energy storage system at time t, E S ,max is the maximum stored energy capacity of the energy storage system, P S,max is the maximum charge-discharge power of the energy storage system, is the minimum value of the adjustable load, is the maximum value of the adjustable load, and α is the adjustable load cost coefficient.

[0032] In a second aspect, an embodiment of the present application provides a virtual power plant dispatching optimization device, where the virtual power plant dispatching optimization device includes:

[0033] A first establishment module, which is used to establish an adjustment cost function of the adjustable load according to the adjustable load and electricity price of the virtual power plant;

[0034] A second establishment module, which is used to take the minimum total operating cost calculated according to the energy consumption cost and peak shaving income of the virtual power plant as the goal, and take the physical behavior characteristics of the energy storage system in the virtual power plant and the adjustment strategy of the adjustable load located in the corresponding constraint intervals as constraints, and establish a virtual power plant arbitrage model, where the energy consumption cost is determined according to the charge and discharge strategy of the energy storage system, the adjustment strategy of the adjustable load, the base load of the virtual power plant, the electricity price and the adjustment cost of the adjustable load, and the peak shaving income is determined according to the charge and discharge strategy of the energy storage system, the adjustment strategy of the adjustable load and the power peak shaving subsidy price;

[0035] An optimization module, which is used to input the electricity price, the power peak shaving subsidy price, the base load of the virtual power plant, the stored energy of the energy storage system and the adjustable range of the adjustable load, and through the virtual power plant arbitrage model, obtain the charge and discharge strategy, the adjustment strategy and adjustment cost of the adjustable load of the energy storage system, and the total operating cost of the virtual power plant.

[0036] Combined with the second aspect, in an implementation manner, the first establishment module is further used to include:

[0037] U load (L flex ) = αL flex 2 - price * L flex

[0038] where, U load is the adjustment cost of the adjustable load, L flex is the adjustable load, price is the electricity price, α is the adjustable load cost coefficient, and the value of α is greater than 0.

[0039] In an implementation manner:

[0040] The charge and discharge strategy of the energy storage system includes the charging power and discharging power of the energy storage system;

[0041] The adjustment strategy of the adjustable load includes the power of the adjustable load.

[0042] In one implementation, the second establishment module is further configured to:

[0043] constrain the stored energy of the energy storage system to be greater than or equal to zero and less than or equal to the maximum value of the preset stored energy capacity;

[0044] constrain the charging power and discharging power of the energy storage system to be greater than or equal to zero and less than or equal to the maximum value of the preset charging and discharging power;

[0045] constrain the power of the adjustable load to be greater than or equal to the corresponding minimum value of the adjustable load and less than or equal to the corresponding maximum value of the adjustable load, where the interval formed by the minimum value of the adjustable load and the maximum value of the adjustable load is the adjustable range of the adjustable load.

[0046] In one implementation, the second establishment module is further configured to:

[0047] establish the objective function of the virtual power plant arbitrage model:

[0048] min f=C - R r

[0049] Where:

[0050]

[0051] In the formula, f is the total operating cost of the virtual power plant, C is the energy consumption cost, and R r is the peak shaving revenue, is the total energy consumption power of the virtual power plant at time t, is the electricity price at time t, is the adjustment cost of the adjustable load, is the peak shaving service power of the virtual power plant at time t, is the electricity peak shaving subsidy price at time t, t is the time subscript, and T is the time set, is the electricity peak shaving subsidy price at time t;

[0052] Where:

[0053]

[0054] In the formula, is the output power of the energy storage system at time t, is the base load of the virtual power plant at time t, is the power of the adjustable load at time t;

[0055] The constraints of the virtual power plant arbitrage model are:

[0056]

[0057]

[0058] Among them, is the energy storage energy of the energy storage system at time t, η is the charge-discharge efficiency of the energy storage system, is the charging power of the energy storage system at time t, is the discharging power of the energy storage system at time t, E S ,max is the maximum value of the energy storage capacity of the energy storage system, P S,max is the maximum value of the charge-discharge power of the energy storage system, is the minimum value of the adjustable load, is the maximum value of the adjustable load, and α is the adjustable load cost coefficient.

[0059] The beneficial effects brought by the technical solution provided by the embodiment of the present application include:

[0060] By establishing a regulation cost function of the adjustable load according to the adjustable load and electricity price of the virtual power plant; aiming at minimizing the total operating cost calculated based on the energy consumption cost and peak shaving profit of the virtual power plant, and taking the physical behavior characteristics of the energy storage system in the virtual power plant and the regulation strategy of the adjustable load being within the corresponding constraint intervals as constraints, a virtual power plant arbitrage model is established, where the energy consumption cost is determined according to the charge-discharge strategy of the energy storage system, the regulation strategy of the adjustable load, the base load of the virtual power plant, the electricity price, and the regulation cost of the adjustable load, and the peak shaving profit is determined according to the charge-discharge strategy of the energy storage system, the regulation strategy of the adjustable load, and the power peak shaving subsidy price; inputting the electricity price, the power peak shaving subsidy price, the base load of the virtual power plant, the energy storage energy of the energy storage system, and the adjustable range of the adjustable load, through the virtual power plant arbitrage model, the charge-discharge strategy of the energy storage system, the regulation strategy of the adjustable load, the regulation cost, and the total operating cost of the virtual power plant are obtained, realizing the dynamic adjustment of the operating strategies of the energy storage system and the adjustable load according to the real-time electricity price and power peak shaving subsidy, realizing the precise scheduling of the virtual power plant, and at the same time enhancing its response capabilities in the peak shaving market and the electricity market, meeting the flexibility and economy requirements of the power system. Description of the Drawings

[0061] Figure 1 is a schematic flow chart of an embodiment of the virtual power plant scheduling optimization method of the present application;

[0062] Figure 2 is a schematic functional module diagram of an embodiment of the virtual power plant scheduling optimization device of the present application. Detailed Embodiment

[0063] To enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0064] To make the purpose, technical solution and advantages of this application clearer, the following will further describe the embodiments of this application in detail with reference to the drawings.

[0065] In a first aspect, an embodiment of this application provides a method for optimizing the dispatching of a virtual power plant.

[0066] In one embodiment, referring to Figure 1 , Figure 1 is a schematic flowchart of the first embodiment of the method for optimizing the dispatching of the virtual power plant of this application. As Figure 1 shown, the method for optimizing the dispatching of the virtual power plant includes:

[0067] Step S101: Establish an adjustment cost function for the adjustable load according to the adjustable load and electricity price of the virtual power plant.

[0068] Specifically, in the coordinated optimization of the virtual power plant, the dynamic adjustment cost of the adjustable load is an important index for evaluating the load flexibility. Based on the response characteristics of the load, the adjustment cost function can effectively reflect the economy of the load response and the cost trade-off relationship of the load adjustment. For different types of adjustable loads, such as temperature control loads and industrial loads, the time characteristics of the adjustable capacity and the adjustment depth affect their adjustment costs. Among them, the time characteristic means that different adjustment times of the adjustable load will result in different costs and adjustable capacities. For example, the adjustment capacity of the air-conditioning load is small and the cost is high at noon. The adjustment depth refers to the load adjustment amount of the adjustable load.

[0069] The load composition of the virtual power plant includes:

[0070] L total = L base + L flex

[0071] Among them, L total is the total load of the virtual power plant, L base is the basic load of the virtual power plant, and L flex is the adjustable load of the virtual power plant.

[0072] It should be noted that for different electricity prices, due to the active adjustment of electricity users themselves, the initial value of their adjustable load should be 0. In this embodiment, the time-of-use (TOU) electricity price is used. When the electricity price changes, the base load L base will automatically adjust to an appropriate value, so that for the user, the current L base is the optimal value of the utility considering the electricity price cost. Therefore, in the absence of external interference, there should be L flex = 0 at any time, and regardless of how L flex changes (increases or decreases), the total utility for the user will decrease.

[0073] The total electricity consumption utility is based on the sum of the electricity consumption effects and accumulations:

[0074] U total (L flex ) = U load (L flex ) + U cost (L flex )

[0075] In the formula, U total is the total utility, U load is the user experience utility brought by the adjusted load, and U cost is the direct economic utility of the electricity price cost caused by the adjusted load. When L flex ≥0, obviously the increase in electricity consumption will increase the user experience utility, that is, U load (L flex )≥0. When L flex ≤0, the reduction in electricity consumption will affect the user's life, that is, U load (L flex )≤0. Therefore, it can be concluded that U load (L flex ) is a monotonically increasing function of L flex . Under a fixed electricity price, U cost (L flex ) is a linear function of electricity consumption, that is, U cost (L flex ) = -price * L flex . Where price is the electricity price, that is, for each additional unit of extra electric energy used, the economic cost will increase by price, thus reducing the overall utility.

[0076] Considering that after the adjustment of L base , L flex = 0 is the optimal situation of the user's utility, that is, regardless of whether L flex increases or decreases, the overall user utility U cost (L flex) ≤ 0. From the optimality condition, the derivative of the adjustment cost function with respect to L at this time is 0. flxe The derivative is 0.

[0077] Taking the derivative of the total electricity consumption utility formula, we can obtain U′ cost (L flex ) = -price. That is, the function image of U cost is tangent to the function image of -price * L at 0. On this basis, considering the derivative, for most electricity users, increasing additional electricity consumption will not bring much of an electricity consumption experience, while reducing electricity consumption will have a greater impact on normal production activities and life. Therefore, the function image of U flex should be a convex function. In summary, according to the adjustable load and electricity price of the virtual power plant, the adjustment cost function of the adjustable load is established as follows: cost U

[0078] U load (L flex ) = αL flex 2 - price * L flex

[0079] In the formula, U load is the adjustment cost of the adjustable load, L flex is the adjustable load, price is the electricity price, α is the adjustable load cost coefficient, and the value of α is greater than 0. The size of α mainly depends on the sensitivity of the adjustable load to the adjustment amount. If it is more sensitive, then α is larger. Therefore, α can be adjusted according to the actual situation. Among them, the electricity price is the energy price in the electricity market.

[0080] It should be noted that in this embodiment, an adjustment cost function for the adjustable load is established, and the adjustment cost functions of the energy storage system and the adjustable load in the virtual power plant are defined. In this embodiment, by constructing quantitative indicators of flexibility and economy, considering the adjustment costs and dynamic response capabilities of different adjustable loads, it supports the effective resource management of the virtual power plant in the peak shaving market. The setting of the adjustment cost function comprehensively considers factors such as real-time electricity price and adjustment capacity, providing basic data support for subsequent optimization decisions.

[0081] Step S102: Taking the minimum of the total operating cost calculated based on the energy consumption cost and peak shaving revenue of the virtual power plant as the goal, and taking the physical behavior characteristics of the energy storage system in the virtual power plant and the adjustment strategy of the adjustable load being within the corresponding constraint intervals as constraints, establish a virtual power plant arbitrage model, where the energy consumption cost is determined according to the charge and discharge strategy of the energy storage system, the adjustment strategy of the adjustable load, the base load of the virtual power plant, the electricity price, and the adjustment cost of the adjustable load, and the peak shaving revenue is determined according to the charge and discharge strategy of the energy storage system, the adjustment strategy of the adjustable load, and the power peak shaving subsidy price.

[0082] It should be noted that the virtual power plant arbitrage model aims to establish constraints for the energy storage and ordinary users in the virtual power plant based on their physical and behavioral characteristics, characterize the characteristics of the electricity energy market and the peak shaving service market in the power market, establish corresponding constraints, optimize the coordinated operation of the virtual power plant in the spot market and the peak shaving market, and maximize the market revenue by flexibly managing the energy storage system and the adjustable load. The core of the virtual power plant arbitrage model is to design a dynamic decision-making mechanism based on the market price fluctuations and peak shaving market subsidies. First, the energy storage system makes charge and discharge decisions according to the real-time electricity price in the spot market and the electricity price for peak shaving subsidies to obtain electricity price arbitrage benefits. At the same time, the adjustable load adjusts timely according to the peak shaving demand and the electricity price to balance the resource allocation and improve the economic benefits of the system. Through the prediction and rolling optimization of the multi-period load demand, the virtual power plant arbitrage model realizes the efficient scheduling of the virtual power plant in the market, not only reducing the operating cost but also enhancing the load balancing ability of the power system.

[0083] Specifically, in this embodiment, the charge and discharge strategy of the energy storage system includes the charging power and the discharging power of the energy storage system; the adjustment strategy of the adjustable load includes the power of the adjustable load.

[0084] Furthermore, with the physical behavior characteristics of the energy storage system in the virtual power plant and the adjustment strategy of the adjustable load being within the corresponding constraint intervals as the constraints, it includes constraining that the stored energy of the energy storage system is greater than or equal to zero and less than or equal to the maximum preset energy storage capacity; constraining that both the charging power and the discharging power of the energy storage system are greater than or equal to zero and less than or equal to the maximum preset charge and discharge power; constraining that the power of the adjustable load is greater than or equal to the corresponding minimum value of the adjustable load and less than or equal to the corresponding maximum value of the adjustable load, where the interval formed by the minimum value of the adjustable load and the maximum value of the adjustable load is the adjustable range of the adjustable load.

[0085] Exemplarily, with the goal of minimizing the total operating cost calculated based on the energy consumption cost and the peak shaving revenue of the virtual power plant, the objective function of the virtual power plant arbitrage model is established as:

[0086] min f = C - R r

[0087] The model goal is to minimize the total operating cost of the virtual power plant, where the expressions for the energy consumption cost and the peak shaving revenue are:

[0088]

[0089] In the formula, f is the total operating cost of the virtual power plant, C is the energy consumption cost, and R r is the peak shaving revenue, is the total energy consumption power of the virtual power plant at time t, is the electricity price at time t, is the adjustment cost of the adjustable load, is the peak shaving service power of the virtual power plant at time t, is the electricity peak shaving subsidy price at time t, where t is the time subscript and T is the time set, is the electricity peak shaving subsidy price at time t.

[0090] The adjustment cost of the adjustable load in the virtual power plant arbitrage model In the model, the U load (L flex 0 form constructed above can also be used.

[0091] The peak shaving service power of the virtual power plant at time t is composed of:

[0092]

[0093] The total energy consumption power of the virtual power plant at time t is composed of the output power of the energy storage system at time t, the base load of the virtual power plant at time t, and the power of the adjustable load at time t:

[0094]

[0095] In the formula, is the output power of the energy storage system at time t, is the base load of the virtual power plant at time t, is the power of the adjustable load at time t. Among them, the base load of the virtual power plant is a given value, determined by the user himself, generally the value after considering subtracting distributed resources.

[0096] The operating constraints of the virtual power plant arbitrage model are:

[0097]

[0098] Among them, is the stored energy of the energy storage system at time t (state of charge, SOC), which is affected by the energy at the previous moment and the current charge and discharge decision, and η is the charge and discharge efficiency of the energy storage system, is the charging power of the energy storage system at time t, is the discharging power of the energy storage system at time t. Considering the loss caused by the charge and discharge efficiency, and there is no forced energy consumption limit such as new energy utilization rate in this model, so there will be no simultaneous charge and discharge behavior during operation, otherwise it will bring unnecessary economic costs. E S,maxis the maximum energy storage capacity of the energy storage system, P S,max is the maximum charge-discharge power of the energy storage system, is the minimum value of the adjustable load, is the maximum value of the adjustable load, and α is the cost coefficient of the adjustable load. In the virtual power plant arbitrage model, the electricity price (energy price) and the power peak shaving subsidy price in the power market are both given values. The virtual power plant optimizes the control of its own resources based on the adjustment cost and the market price.

[0099] It should be noted that in this embodiment, an operation model for the virtual power plant to arbitrage in the spot market and the peak shaving market is constructed. The virtual power plant arbitrage model coordinates the optimal dispatching of the energy storage system and the adjustable load, uses the energy storage to participate in the arbitrage of the electricity energy market, and at the same time improves the flexibility through the adjustable load to further participate in the peak shaving market, with the goal of minimizing the total operating cost of the virtual power plant, so as to maximize the revenue of the virtual power plant. The core of the virtual power plant arbitrage model lies in the coordination of the power source and the load, considering the fluctuations in the spot market electricity price and the subsidy policy (power peak shaving subsidy price) in the peak shaving market, so as to achieve the optimal balance between the electricity quantity and the peak shaving revenue, and provide sustainable economic guarantee for the virtual power plant.

[0100] Step S103: Input the electricity price, the power peak shaving subsidy price, the base load of the virtual power plant, the stored energy of the energy storage system, and the adjustable range of the adjustable load. Through the virtual power plant arbitrage model, obtain the charge-discharge strategy of the energy storage system, the adjustment strategy and adjustment cost of the adjustable load, and the total operating cost of the virtual power plant.

[0101] In this embodiment, the constructed virtual power plant arbitrage model is used to dynamically adjust the energy output strategy of the virtual power plant by real-time tracking the power market price and the power peak shaving subsidy price, so as to achieve the maximization of economic benefits and the efficient response to the power grid peak shaving demand. The virtual power plant arbitrage model is an adaptive optimization model, which updates the control strategy in real time according to the fluctuations in the electricity price and the changes in the peak shaving subsidy, and combines intelligent algorithms to realize the coordinated dispatching of multiple energy resources, ensuring that the virtual power plant is always in the optimal operating state when participating in the power market transactions and ancillary services.

[0102] In this embodiment, through the virtual power plant operation optimization technology based on the given peak shaving subsidy and real-time electricity price prediction on a daily basis, by adjusting appropriate parameters through the virtual power plant arbitrage model, the charge-discharge strategy of the virtual power plant energy storage and the adjustment optimization of the adjustable load can be realized. The operation efficiency is optimized through the real-time feedback mechanism, the economic benefits of the virtual power plant are maximized, and at the same time its response capabilities in the peak shaving market and the electricity quantity market are enhanced, meeting the flexibility and economic requirements of the power system.

[0103] A virtual power plant scheduling optimization method provided by the present invention addresses the collaborative optimization requirements of a virtual power plant including an energy storage system and adjustable loads in the spot market and the peak shaving market, and proposes an innovative solution to improve the economic benefits of the virtual power plant, enhance the stability of the power system, and meet the real-time response requirements of the spot and peak shaving markets. First, by establishing an optimization strategy based on real-time electricity prices and peak shaving subsidies, a quantitative evaluation index system adjustment cost function covering the operating characteristics of the energy storage system and adjustable loads is constructed to accurately evaluate the multi-resource operating characteristics of the virtual power plant. On this basis, an arbitrage model of a virtual power plant with energy storage as the core for collaborative operation is proposed, systematically considering the interaction characteristics and constraint conditions of energy storage and load resources, aiming to optimize resource utilization efficiency and achieve dynamic balance between supply and demand. Finally, an intelligent algorithm is applied for load optimization control to maximize the operating benefits of energy storage and adjustable loads in the virtual power plant by improving the response speed and flexibility of the system, while ensuring flexible participation and economic maximization in the spot market and the peak shaving market.

[0104] In a second aspect, an embodiment of the present application further provides a virtual power plant scheduling optimization device.

[0105] In one embodiment, with reference to Figure 2 , Figure 2 is a schematic diagram of the functional modules of an embodiment of the virtual power plant scheduling optimization device of the present application. As shown in Figure 2 , the virtual power plant scheduling optimization device includes:

[0106] A first establishment module, which is used to establish an adjustment cost function of the adjustable load according to the adjustable load and electricity price of the virtual power plant;

[0107] A second establishment module, which is used to establish a virtual power plant arbitrage model with the goal of minimizing the total operating cost calculated based on the energy consumption cost and peak shaving revenue of the virtual power plant, and with the physical behavior characteristics of the energy storage system in the virtual power plant and the adjustment strategy of the adjustable load being within the corresponding constraint intervals as constraints, where the energy consumption cost is determined according to the charge and discharge strategy of the energy storage system, the adjustment strategy of the adjustable load, the base load of the virtual power plant, the electricity price, and the adjustment cost of the adjustable load, and the peak shaving revenue is determined according to the charge and discharge strategy of the energy storage system, the adjustment strategy of the adjustable load, and the power peak shaving subsidy price;

[0108] An optimization module, which is used to input the electricity price, the power peak shaving subsidy price, the base load of the virtual power plant, the stored energy of the energy storage system, and the adjustable range of the adjustable load, and through the virtual power plant arbitrage model, obtain the charge and discharge strategy, the adjustment strategy and adjustment cost of the adjustable load, and the total operating cost of the virtual power plant.

[0109] Further, in one embodiment, the first establishment module is further configured to:

[0110] U load (L flex ) = αL flex 2 -price*L flex

[0111] Wherein, U load is the adjustment cost of the adjustable load, L flex is the adjustable load, price is the electricity price, α is the adjustable load cost coefficient, and the value of α is greater than 0.

[0112] Further, in one embodiment:

[0113] The charge-discharge strategy of the energy storage system includes the charging power and the discharging power of the energy storage system;

[0114] The adjustment strategy of the adjustable load includes the power of the adjustable load.

[0115] Further, in one embodiment, the second establishment module is further configured to:

[0116] Constrain the stored energy of the energy storage system to be greater than or equal to zero and less than or equal to the preset maximum energy storage capacity;

[0117] Constrain both the charging power and the discharging power of the energy storage system to be greater than or equal to zero and less than or equal to the preset maximum charge-discharge power;

[0118] Constrain the power of the adjustable load to be greater than or equal to the corresponding minimum adjustable load and less than or equal to the corresponding maximum adjustable load, wherein the interval formed by the minimum adjustable load and the maximum adjustable load is the adjustable range of the adjustable load.

[0119] Further, in one embodiment, the second establishment module is further configured to:

[0120] Establish the objective function of the virtual power plant arbitrage model:

[0121] min f = C - R r

[0122] Where:

[0123]

[0124] In the formula, f is the total operating cost of the virtual power plant, C is the energy consumption cost, R r is the peak shaving revenue, is the total energy consumption power of the virtual power plant at time t, is the electricity price at time t, is the adjustment cost of the adjustable load, is the peak shaving service power of the virtual power plant at time t, is the electricity peak shaving subsidy price at time t, where t is the time subscript and T is the time set, is the electricity peak shaving subsidy price at time t;

[0125] Among them:

[0126]

[0127] In the formula, is the output power of the energy storage system at time t, is the base load of the virtual power plant at time t, is the power of the adjustable load at time t;

[0128] The constraints of the virtual power plant arbitrage model are:

[0129]

[0130]

[0131] Among them, is the stored energy of the energy storage system at time t, and η is the charge-discharge efficiency of the energy storage system, is the charging power of the energy storage system at time t, is the discharging power of the energy storage system at time t, E S ,max is the maximum stored energy capacity of the energy storage system, P S,max is the maximum charge-discharge power of the energy storage system, is the minimum value of the adjustable load, is the maximum value of the adjustable load, and α is the adjustable load cost coefficient.

[0132] It should be noted that the above serial numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0133] In the description of the specification, claims and the above drawings of this application, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally further include steps or units not listed, or may optionally further include other steps or units inherent to these processes, methods, products or devices. Descriptions such as "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit that "first", "second" and "third" are different types.

[0134] In the description of the embodiments of this application, words such as "exemplary", "for example" or "for illustration purposes" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary", "for example" or "for illustration purposes" in the embodiments of this application should not be construed as being more preferred or having more advantages than other embodiments or designs. Rather, the use of words such as "exemplary", "for example" or "for illustration purposes" is intended to present related concepts in a specific manner.

[0135] In the description of the embodiments of this application, unless otherwise specified, " / " means "or". For example, A / B may mean A or B; "and / or" in the text is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "a plurality of" means two or more than two.

[0136] In some processes described in the embodiments of this application, there are a plurality of operations or steps that appear in a specific order. However, it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of this application or may be executed in parallel. The serial numbers of the operations are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0137] Through the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions for causing a terminal device to execute the methods described in the various embodiments of this application.

[0138] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present application.

Claims

1. A virtual power plant scheduling optimization method, characterized in that The virtual power plant scheduling optimization method includes: Based on the adjustable load and electricity price of the virtual power plant, establish the regulation cost function of the adjustable load; Aiming at minimizing the total operation cost calculated according to the energy consumption cost and peak shaving revenue of the virtual power plant, and taking the physical behavior characteristics of the energy storage system in the virtual power plant and the regulation strategies of the adjustable loads within the corresponding constraint intervals as constraints, establish a virtual power plant arbitrage model, where the energy consumption cost is determined according to the charge and discharge strategies of the energy storage system, the regulation strategies of the adjustable loads, the base load of the virtual power plant, the electricity price, and the regulation cost of the adjustable loads, and the peak shaving revenue is determined according to the charge and discharge strategies of the energy storage system, the regulation strategies of the adjustable loads, and the electricity peak shaving subsidy price; Input the electricity price, the electricity peak shaving subsidy price, the base load of the virtual power plant, the stored energy of the energy storage system, and the adjustable range of the adjustable load, and through the virtual power plant arbitrage model, obtain the charge and discharge strategies, the regulation strategies and regulation costs of the adjustable loads, and the total operation cost of the virtual power plant.

2. The virtual power plant scheduling optimization method according to claim 1, characterized in that The establishment of the regulation cost function of the adjustable load according to the adjustable load and electricity price of the virtual power plant includes: U load (L flex ) = αL flex 2 -price * L flex Among them, U load is the adjustment cost of the adjustable load, L flex is the adjustable load, price is the electricity price, α is the adjustable load cost coefficient, and the value of α is greater than 0.

3. The virtual power plant scheduling optimization method according to claim 1, characterized in that: The charge and discharge strategies of the energy storage system include the charging power and discharging power of the energy storage system; The regulation strategies of the adjustable loads include the power of the adjustable loads.

4. The virtual power plant scheduling optimization method according to claim 3, wherein The taking the physical behavior characteristics of the energy storage system in the virtual power plant and the regulation strategies of the adjustable loads within the corresponding constraint intervals as constraints includes: Constraining the stored energy of the energy storage system to be greater than or equal to zero and less than or equal to the preset maximum energy storage capacity; Constraining both the charging power and discharging power of the energy storage system to be greater than or equal to zero and less than or equal to the preset maximum charge and discharge power; Constraining the power of the adjustable load to be greater than or equal to the corresponding minimum adjustable load and less than or equal to the corresponding maximum adjustable load, where the interval formed by the minimum adjustable load and the maximum adjustable load is the adjustable range of the adjustable load.

5. The virtual power plant scheduling optimization method according to claim 4, wherein The establishment of the virtual power plant arbitrage model with the goal of minimizing the total operation cost calculated according to the energy consumption cost and peak shaving revenue of the virtual power plant and taking the physical behavior characteristics of the energy storage system in the virtual power plant and the regulation strategies of the adjustable loads within the corresponding constraint intervals as constraints includes: Establish the objective function of the virtual power plant arbitrage model: min f = C - R r Where: where f is the total operating cost of the virtual power plant, C is the energy consumption cost, and R r is the peak shaving revenue, is the total energy consumption power of the virtual power plant at time t, is the electricity price at time t, is the adjustment cost of the adjustable load, is the peak shaving service power of the virtual power plant at time t, is the electricity peak shaving subsidy price at time t, t is the time subscript, and T is the time set, is the electricity peak shaving subsidy price at time t; Where: In the formula, is the output power of the energy storage system at time t, is the base load of the virtual power plant at time t, is the power of the adjustable load at time t; The constraints of the virtual power plant arbitrage model are: Among them, is the energy storage energy of the energy storage system at time t, η is the charge-discharge efficiency of the energy storage system, is the charging power of the energy storage system at time t, is the discharging power of the energy storage system at time t, E S,max is the maximum value of the energy storage capacity of the energy storage system, P S,max is the maximum value of the charge-discharge power of the energy storage system, is the minimum value of the adjustable load, is the maximum value of the adjustable load, and α is the adjustable load cost coefficient.

6. A virtual power plant dispatching optimization device, characterized in that, The virtual power plant scheduling optimization device includes: A first establishment module, which is used to establish the regulation cost function of the adjustable load according to the adjustable load and electricity price of the virtual power plant; A second establishment module, which is used to establish a virtual power plant arbitrage model with the goal of minimizing the total operating cost calculated based on the energy consumption cost and peak shaving revenue of the virtual power plant, and with the physical behavior characteristics of the energy storage system in the virtual power plant and the adjustment strategies of the adjustable loads being within the corresponding constraint intervals as constraints, where the energy consumption cost is determined based on the charge-discharge strategy of the energy storage system, the adjustment strategy of the adjustable loads, the base load of the virtual power plant, the electricity price, and the adjustment cost of the adjustable loads, and the peak shaving revenue is determined based on the charge-discharge strategy of the energy storage system, the adjustment strategy of the adjustable loads, and the electricity peak shaving subsidy price; An optimization module, which is used to input the electricity price, the electricity peak shaving subsidy price, the base load of the virtual power plant, the stored energy of the energy storage system, and the adjustable range of the adjustable loads, and through the virtual power plant arbitrage model, obtain the charge-discharge strategy of the energy storage system, the adjustment strategy of the adjustable loads, the adjustment cost, and the total operating cost of the virtual power plant.

7. The virtual power plant dispatching optimization device according to claim 6, wherein The first establishment module is further used for: U load (L flex ) = αL flex 2 - price * L flex Among them, U load is the adjustment cost of the adjustable load, L flex is the adjustable load, price is the electricity price, α is the adjustable load cost coefficient, and the value of α is greater than 0.

8. The virtual power plant scheduling optimization device according to claim 6, wherein: The charge-discharge strategy of the energy storage system includes the charging power and discharging power of the energy storage system; The adjustment strategy of the adjustable loads includes the power of the adjustable loads.

9. The virtual power plant dispatching optimization device according to claim 8, wherein The second establishment module is further used for: Constraining the stored energy of the energy storage system to be greater than or equal to zero and less than or equal to a preset maximum energy storage capacity; Constraining both the charging power and discharging power of the energy storage system to be greater than or equal to zero and less than or equal to a preset maximum charge-discharge power; Constraining the power of the adjustable loads to be greater than or equal to a corresponding minimum adjustable load value and less than or equal to a corresponding maximum adjustable load value, where the interval formed by the minimum adjustable load value and the maximum adjustable load value is the adjustable range of the adjustable loads.

10. The virtual power plant scheduling optimization device according to claim 9, characterized in that, The second establishment module is further used for: Establishing the objective function of the virtual power plant arbitrage model: min f = C - R r Where: Wherein, f is the total operating cost of the virtual power plant, C is the energy consumption cost, and R r is the peak shaving revenue, is the total energy consumption power of the virtual power plant at time t, is the electricity price at time t, is the regulation cost of the adjustable load, is the peak shaving service power of the virtual power plant at time t, is the electricity peak shaving subsidy price at time t, t is the time subscript, and T is the time set, is the electricity peak shaving subsidy price at time t; Where: In the formula, is the output power of the energy storage system at time t, is the base load of the virtual power plant at time t, is the power of the adjustable load at time t; The constraints of the virtual power plant arbitrage model are: Among them, is the energy storage energy of the energy storage system at time t, η is the charge-discharge efficiency of the energy storage system, is the charging power of the energy storage system at time t, is the discharging power of the energy storage system at time t, E S,max is the maximum value of the energy storage capacity of the energy storage system, P S,max is the maximum value of the charge-discharge power of the energy storage system, is the minimum value of the adjustable load, is the maximum value of the adjustable load, and α is the cost coefficient of the adjustable load.

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