Cloud energy storage optimization scheduling method and system considering demand side response

Abstract

The invention discloses a cloud energy storage optimization scheduling method and system considering demand side response. The method comprises the following steps: obtaining operation parameters of target cloud energy storage operation, and establishing a mathematical model of cloud energy storage operation according to the operation parameters; establishing constraint conditions of the mathematical model based on cloud energy storage operation, wherein the constraint conditions comprise energy storage equipment constraint, electric quantity constraint and electricity selling price constraint; solving of a mathematical model and constraint conditions of cloud energy storage operation is converted into solving of a nonlinear double-layer optimization problem, and solving of the nonlinear double-layer optimization problem is completed in combination with improved KKT conditions. Through optimal scheduling, efficient operation of the cloud energy storage system is realized, the power utilization cost of a user is reduced, and the stability and reliability of a power grid are improved at the same time. Besides, the charging and discharging strategy of the energy storage equipment can be flexibly adjusted according to the price fluctuation of the real-time market, so that the economic benefit of cloud energy storage operation is improved while the user benefit is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud energy storage optimization scheduling, and in particular to a cloud energy storage optimization scheduling method and system considering demand-side response. Background Art

[0002] With the continuous development and popularization of smart grid technology, more and more loads that can actively control or adjust energy consumption have entered people's lives, such as smart homes and electric vehicles. The characteristics of these loads are different from those of traditional loads. Users will actively reduce or transfer loads according to information about electricity price changes or incentive information. They have significant price sensitivity and price elasticity and can be called active loads. Active loads have become a load-side resource that promotes the balance of power supply and demand due to their demand-side response characteristics. Using optimized scheduling methods to guide or stimulate the power consumption strategy of active loads can help mitigate the peak load value of the power grid and improve the level of local consumption of distributed renewable energy. Energy storage technology can charge when power demand is low and discharge when power demand is peak, making up for the power supply gap and effectively alleviating the impact of active loads on the power grid during peak hours. The energy storage system can adjust frequency and power through rapid response. When a large number of active loads are connected to the power grid, the energy storage system can quickly provide or absorb power to cope with sudden load changes or power fluctuations. By considering demand-side response, the scheduling strategy of energy storage equipment is optimized to improve the operating efficiency of the power system. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a cloud energy storage optimization scheduling method and system taking into account demand-side response, which can solve the problems mentioned in the background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a cloud energy storage optimization scheduling method considering demand-side response, comprising:

[0008] Obtaining operating parameters of the target cloud energy storage operation, and establishing a mathematical model of the cloud energy storage operation according to the operating parameters;

[0009] Establishing constraint conditions based on the mathematical model of the cloud energy storage operation, wherein the constraint conditions include energy storage equipment constraint, power constraint and power selling price constraint;

[0010] The mathematical model of the cloud energy storage operation and the solution of the constraint conditions are converted into the solution of a nonlinear two-layer optimization problem, and the solution of the nonlinear two-layer optimization problem is completed by combining the improved KKT condition.

[0011] As a preferred solution of the cloud energy storage optimization scheduling method considering demand-side response described in the present invention, wherein: the acquisition of the operating parameters of the target cloud energy storage operation includes determining the power consumption period of the active load users, and determining the power purchase amount in different time periods according to the user's power demand.

[0012] As a preferred solution of the cloud energy storage optimization scheduling method considering demand-side response described in the present invention, wherein: the mathematical model of cloud energy storage operation established according to the operating parameters includes:

[0013] The mathematical model of cloud energy storage operation includes a cloud energy storage operation objective function, which is expressed as:

[0014]

[0015] Among them, c t The price of electricity sold to users. is the power consumption of the i-th load in period t; p - is the electricity selling price in the real-time market, is the amount of electricity sold in the real-time market during period t; p d is the day-ahead market electricity purchase price, E t is the amount of electricity purchased in the day-ahead market; + It represents the real-time market electricity purchase price. It is to buy electricity in the real-time market.

[0016] As a preferred solution of the cloud energy storage optimization scheduling method considering demand-side response described in the present invention, the power selling price constraints include:

[0017]

[0018] Among them, c tmin and c tmax is the lower and upper limits of the electricity price at time t, c av represents the average daily electricity price, T is the number of hours per day, which is 24.

[0019] As a preferred solution of the cloud energy storage optimization scheduling method considering demand-side response described in the present invention, the power constraint includes:

[0020]

[0021] Among them, z t is a Boolean variable that can only take values ​​of 0 or 1, which is used to ensure that it is impossible to buy and sell electricity at the same time; M is the sum of the electrical power of all loads; Refers to the discharge capacity of the energy storage device;

[0022]

[0023] in, Refers to the amount of charge in the energy storage device.

[0024] As a preferred solution of the cloud energy storage optimization scheduling method considering demand-side response described in the present invention, the energy storage device constraints include:

[0025]

[0026] Among them, r t + and r t - Refers to the efficiency of charging and discharging of energy storage equipment. The maximum charging and discharging efficiency is C + and C - , It refers to the power state of the energy storage device at time t, which satisfies the power constraint.

[0027] As a preferred solution of the cloud energy storage optimization scheduling method considering demand-side response described in the present invention, wherein: the mathematical model of the cloud energy storage operation and the solution of the constraint conditions are converted into the solution of a nonlinear two-layer optimization problem, and the solution of the nonlinear two-layer optimization problem is completed in combination with the improved KKT condition, including: the upper problem of the nonlinear two-layer optimization problem is to maximize the profit of the cloud energy storage operation, and the lower problem is to minimize the user's electricity cost.

[0028] In a second aspect, the present invention provides a cloud energy storage optimization scheduling system considering demand-side response, including:

[0029] A model building module, used to obtain operating parameters of the target cloud energy storage operation and establish a mathematical model of the cloud energy storage operation according to the operating parameters;

[0030] A constraint establishment module, used to establish constraint conditions based on the mathematical model of the cloud energy storage operation, wherein the constraint conditions include energy storage equipment constraints, power constraints, and power selling price constraints;

[0031] The solution module is used to convert the mathematical model of the cloud energy storage operation and the solution of the constraint conditions into the solution of the nonlinear two-layer optimization problem, and complete the solution of the nonlinear two-layer optimization problem in combination with the improved KKT condition.

[0032] In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned method when executing the computer program.

[0033] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the method described above when executed by a processor.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention proposes a cloud energy storage optimization scheduling method and system that considers demand-side response, obtains the operating parameters of the target cloud energy storage operation, and establishes a mathematical model of the cloud energy storage operation based on the operating parameters; establishes constraints based on the mathematical model of the cloud energy storage operation, and the constraints include energy storage equipment constraints, power constraints, and electricity price constraints; converts the mathematical model of the cloud energy storage operation and the solution of the constraints into the solution of a nonlinear two-layer optimization problem, and completes the solution of the nonlinear two-layer optimization problem in combination with the improved KKT condition. Through optimized scheduling, the efficient operation of the cloud energy storage system is achieved, the user's electricity cost is reduced, and the stability and reliability of the power grid are improved. In addition, the present invention can also flexibly adjust the charging and discharging strategies of the energy storage equipment according to the price fluctuations in the real-time market, thereby improving the economic benefits of cloud energy storage operations while ensuring the interests of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0036] Figure 1 A method flow chart of a cloud energy storage optimization scheduling method and system considering demand-side response provided by an embodiment of the present invention;

[0037] Figure 2 A schematic diagram of electricity demand of three typical users in different time periods of a cloud energy storage optimization scheduling method and system considering demand-side response provided by an embodiment of the present invention;

[0038] Figure 3 A schematic diagram of electricity demand of three typical users in different time periods of a cloud energy storage optimization scheduling method and system considering demand-side response provided by an embodiment of the present invention;

[0039] Figure 4A schematic diagram of power consumption of different types of users in different time periods in a cloud energy storage optimization scheduling method and system considering demand-side response provided by an embodiment of the present invention;

[0040] Figure 5 A schematic diagram of the operation process of an energy storage power station of a cloud energy storage optimization scheduling method and system considering demand-side response provided by an embodiment of the present invention;

[0041] Figure 6 A schematic diagram of a cloud energy storage optimization scheduling method and system for purchasing and selling electricity in real time in a market taking into account demand-side response provided by an embodiment of the present invention;

[0042] Figure 7 A schematic diagram of day-ahead electricity purchase of a cloud energy storage optimization scheduling method and system considering demand-side response provided by an embodiment of the present invention;

[0043] Figure 8 A schematic diagram of the results of electricity price optimization of a cloud energy storage optimization scheduling method and system considering demand-side response provided by an embodiment of the present invention;

[0044] Fig. 9 An internal structural diagram of a computer device of a cloud energy storage optimization scheduling method and system considering demand-side response provided in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0046] Example 1

[0047] Reference Figure 1-Figure 9 , which is the first embodiment of the present invention, and provides a cloud energy storage optimization scheduling method and system considering demand-side response, including:

[0048] Before describing the embodiments of the present application in detail, some related concepts are first explained for the sake of clarity.

[0049] Cloud energy storage operation: Cloud energy storage operation refers to a business model that uses cloud computing technology to centrally manage and optimize the scheduling of energy storage facilities. In this model, data from energy storage systems (such as battery energy storage stations) are collected and transmitted to the cloud via the Internet, where data analysis, optimized decisions and remote control are performed. The goal of cloud energy storage operation is to improve the efficiency and economy of energy storage systems and maximize the utilization of energy storage resources through intelligent scheduling. For example, charging when electricity demand is low and discharging when demand is high can balance the load on the grid and participate in trading activities in the electricity market.

[0050] KKT conditions (Carlos-Kuhn-Tucker conditions): KKT conditions are a set of necessary conditions named after three mathematicians for determining local optimal solutions in nonlinear programming problems. These conditions are applicable to optimization problems with equality constraints and / or inequality constraints. KKT conditions usually include the following parts: Gradient conditions: The gradient of the objective function forms a linear combination with the gradient of the constraint conditions. Constraint qualification conditions: Ensure that the constraint set has a certain "regularity" at the optimal solution. Lagrange multiplier rule: For inequality constraints, the Lagrange multiplier must be non-negative. Complementary relaxation conditions: The product between the multiplier of the inequality constraint and its corresponding constraint is zero. KKT conditions are used to solve nonlinear two-level optimization problems. The upper-level problem is to maximize the profit of the cloud energy storage operator, while the lower-level problem is to minimize the cost of electricity for users. By setting the optimization of the lower-level problem as the inner-level problem in the KKT system, the two-level optimization problem can eventually be transformed into a single-level optimization problem for solution.

[0051] There are some challenges and limitations in related technologies, such as the charging and discharging efficiency of energy storage equipment, the stability of the power grid, and the volatility of real-time market electricity prices.

[0052] This application provides a method that can effectively solve the above-mentioned problems. Next, we will combine multiple embodiments to explain in detail how to implement the cloud energy storage optimization scheduling method that takes into account the demand-side response.

[0053] Figure 1 A method flow chart of a cloud energy storage optimization scheduling method and system considering demand-side response is shown, including:

[0054] S101, obtaining operating parameters of a target cloud energy storage operation, and establishing a mathematical model of the cloud energy storage operation according to the operating parameters;

[0055] In the embodiment of the present application, it is first necessary to determine the power consumption period of the active load users. According to the power demand, the cloud energy storage operator can determine the power purchase amount in different time periods in the day-ahead market, and publish the day-ahead power price information of the next day to these active load users. It is worth noting that the power price provided by the cloud energy storage must be lower than the power price in the real-time market, and the upper and lower limits of the power selling price can also be stipulated to ensure the interests of users.

[0056] During operation, it is necessary to install smart terminals on the charging devices to ensure intelligent control. Cloud energy storage operators use smart terminals to control the automatic charging of energy storage equipment and pay the operators for electricity usage.

[0057] Cloud energy storage operators can also sell electricity in the real-time market, while considering the purchase of electricity in the real-time market to cope with the temporary change of electricity consumption plans by users on the demand side. The profitability of cloud energy storage is affected by the electricity consumption of active loads. If cloud energy storage operators change the pricing for a certain period of time, the electricity consumption strategy on the demand side will be changed, thus bringing changes in the costs and benefits of multiple parties.

[0058] In the embodiment of the present application, the mathematical model of cloud energy storage operation is established according to the operating parameters, including:

[0059] The mathematical model of cloud energy storage operation includes the cloud energy storage operation objective function, which is expressed as:

[0060]

[0061] Among them, c t The price of electricity sold to users. is the power consumption of the i-th load in period t; p - is the electricity selling price in the real-time market, is the amount of electricity sold in the real-time market during period t; p d is the day-ahead market electricity purchase price, E t is the amount of electricity purchased in the day-ahead market; + It represents the real-time market electricity purchase price. It is the purchase of electricity in the real-time market. The first item is the income from selling electricity to users, the second item is the income from selling excess electricity in the real-time market, the third item is the cost of purchasing electricity in the day-ahead market, and the fourth item is the cost of purchasing electricity on behalf of the real-time market.

[0062] It should be noted that the benefit of the above step S101 is that it can provide a flexible and dynamic cloud energy storage operation strategy, so that energy storage operators can adjust the day-ahead power purchase and sales strategy according to changes in real-time market electricity prices and user electricity demand, thereby maximizing profits and meeting user needs.

[0063] S102, establishing constraint conditions of a mathematical model based on cloud energy storage operation, wherein the constraint conditions include energy storage equipment constraint, power constraint, and power selling price constraint;

[0064] In the embodiment of the present application, the electricity selling price constraints include:

[0065]

[0066] Among them, c tmin and c tmax is the lower and upper limits of the electricity price at time t, c av represents the average daily electricity price, T is the number of hours per day, which is 24.

[0067] Furthermore, power constraints include:

[0068]

[0069] Among them, z t is a Boolean variable that can only take values ​​of 0 or 1, which is used to ensure that it is impossible to buy and sell electricity at the same time; M is the sum of the electrical power of all loads; Refers to the discharge capacity of the energy storage device;

[0070]

[0071] in, Refers to the amount of charge in the energy storage device.

[0072] Furthermore, energy storage device constraints include:

[0073]

[0074] Among them, r t + and r t - Refers to the efficiency of charging and discharging of energy storage equipment. The maximum charging and discharging efficiency is C + and C - , It refers to the power state of the energy storage device at time t, which satisfies the power constraint.

[0075] In an optional embodiment, other constraints may also be established, such as power balance constraints, load power constraints, user satisfaction constraints, etc.

[0076] Exemplarily, the power balance constraint is used to ensure that the power change of the energy storage device in each period matches the purchased and sold power:

[0077]

[0078] in, and are the charging and discharging amounts of the energy storage device during period t, C t Indicates the charging status of the energy storage device during time period t. The value is 0 or 1, indicating whether it is in the charging or discharging state. It represents the efficiency of charging and discharging of energy storage equipment during period t;

[0079] Load power constraints are used to ensure that the user's total power consumption meets actual demand:

[0080]

[0081] Among them, Total Demand represents the total electricity demand. Represents the power load at time t.

[0082] The user satisfaction constraint ensures that the user's electricity cost does not exceed a certain threshold to maintain user satisfaction:

[0083]

[0084] in, It represents the electricity selling price at time t, and Cost Threshold represents the preset cost threshold.

[0085] It should be noted that the selection and establishment of constraint conditions can be carried out according to the following standards:

[0086] In the scenario of pursuing maximum profit, energy storage operators will give priority to electricity price constraints. They will set lower and upper limits of the electricity price to ensure that they can obtain higher prices when selling electricity in the real-time market and the day-ahead market. At the same time, they will also pay attention to the daily average electricity price to ensure the stability of overall revenue. Or, in the scenario where user demand is prioritized, electricity price constraints may be relaxed to ensure that user electricity needs are met. Operators may set a lower lower limit of the electricity price to avoid affecting user satisfaction due to excessively high prices during peak hours, and then select the electricity price constraint;

[0087] If the emphasis is on system stability, power constraints are particularly important. Operators will strictly control the charging and discharging status of energy storage equipment to ensure that there is no simultaneous purchase and sale of electricity to avoid system overload or equipment damage. Or, in cost control scenarios, power constraints may be set more loosely to allow more power to be purchased when electricity prices are lower, thereby reducing overall operating costs. In this case, power constraints are selected;

[0088] If the equipment life is prioritized, the energy storage equipment constraints will be strictly set to ensure that the charging and discharging efficiency and status of the equipment are within a safe range. Operators will set a lower upper limit for the charging and discharging efficiency to avoid damage to the equipment caused by frequent charging and discharging. Or, in scenarios where rapid response to market changes is required, the energy storage equipment constraints may be appropriately relaxed to allow the equipment to perform efficient charging and discharging operations in a short period of time, thereby seizing market opportunities, and then the energy storage equipment constraints will be selected;

[0089] In the system stability scenario, the power balance constraint is crucial. The operator will ensure that the power changes of the energy storage equipment in each period match the power purchase and sale to avoid system instability caused by power imbalance. Or, in the cost control scenario, the power balance constraint may be appropriately adjusted to allow more power to be purchased when the electricity price is low, thereby reducing the overall operating cost. In this case, the power balance constraint is selected.

[0090] If the user demand is prioritized, the load power constraint will be strictly set to ensure that the user's total power consumption meets the actual demand. The operator will pay attention to the power load in each period to avoid affecting the normal power consumption of users due to insufficient power. Or, in the cost control scenario, the load power constraint may be appropriately relaxed to allow more power to be purchased when the electricity price is low, thereby reducing the overall operating cost. In this case, the load power constraint is selected;

[0091] If the user satisfaction is prioritized, the user satisfaction constraint will be strictly set to ensure that the user's electricity cost does not exceed the preset threshold. Operators will pay attention to the relationship between the electricity price and the user's electricity consumption to avoid affecting user satisfaction due to excessively high prices. Or, in the scenario of maximizing revenue, the user satisfaction constraint may be appropriately adjusted to allow more electricity to be sold when the electricity price is higher, thereby increasing overall revenue. In this case, the user satisfaction constraint is selected;

[0092] In summary, the criteria for selecting constraints in different scenarios mainly depend on the specific needs and priorities of energy storage operators. By flexibly adjusting constraints, operators can find the best balance between ensuring system stability, meeting user needs and maximizing revenue.

[0093] It should be noted that the benefit of step S102 is that it can ensure that cloud energy storage operations comply with the actual limitations of equipment and the market while meeting user needs. By establishing reasonable constraints, it is possible to avoid excessive charging and discharging of energy storage equipment, ensure the stable operation of the power grid, and ensure that operators set reasonable prices in the market, thereby achieving their own profit goals while ensuring user interests.

[0094] S103, converting the mathematical model of cloud energy storage operation and the solution of constraint conditions into the solution of nonlinear two-layer optimization problem, and combining with improved KKT conditions to complete the solution of nonlinear two-layer optimization problem.

[0095] In the implementation of this application, the mathematical model of cloud energy storage operation and the solution of constraints are converted into the solution of a nonlinear two-layer optimization problem, and the solution of the nonlinear two-layer optimization problem is completed in combination with the improved KKT condition, including: the upper problem of the nonlinear two-layer optimization problem is to maximize the profit of cloud energy storage operation, and the lower problem is to minimize the user's electricity cost.

[0096] In the embodiment of the present application, the objective function of the upper layer problem is expressed as:

[0097]

[0098] in, Represents the price of electricity sold to users at time t. Represents the user's electricity consumption at time t. Represents the price of electricity sold in the real-time market at time t. Represents the amount of electricity sold in the real-time market at time t. It represents the price of electricity purchased in the day-ahead market at time t. It represents the amount of electricity purchased in the day-ahead market at time t. Represents the price of purchasing electricity in the real-time market at time t. represents the amount of electricity purchased in the real-time market at time t. Each term in this expression represents the revenue or cost of the cloud energy storage operator at time t. It represents the income that cloud energy storage operators get from selling electricity to users. It represents the revenue that cloud energy storage operators earn from selling electricity in the real-time market. It represents the cost of purchasing electricity in the day-ahead market for cloud energy storage operators. Represents the cost of cloud energy storage operators purchasing electricity in the real-time market.

[0099] The objective function of the underlying problem is expressed as:

[0100]

[0101] In the embodiment of the present application, the constraints of the upper-level problem and the lower-level problem are selected according to the above-mentioned scene selection principle.

[0102] It should be noted that by converting the mathematical model and constraints of cloud energy storage operation into a nonlinear two-level optimization problem, the complex interactions and decision-making processes in actual operation can be simulated more accurately. The upper-level problem focuses on maximizing the profits of cloud energy storage operators, which involves optimizing the electricity price, charging and discharging strategies, and the efficiency of energy storage equipment. The lower-level problem is dedicated to minimizing the user's electricity cost, ensuring that while meeting user needs, the user's cost will not be too high due to electricity price fluctuations or unreasonable operation strategies.

[0103] In order to solve this nonlinear two-level optimization problem, the improved Karush-Kuhn-Tucker (KKT) condition is used. The KKT condition is an important method for solving nonlinear programming problems, which can provide the necessary conditions for the optimal solution of the problem. In the two-level optimization problem, the KKT conditions of the upper and lower problems need to be satisfied at the same time, which makes the solution of the problem more complicated. By introducing appropriate slack variables and penalty terms, the two-level problem can be converted into a single-level problem, so that it can be solved using the existing optimization algorithm.

[0104] In the solution process, the initial charging and discharging strategies and electricity selling prices are first set, and then these decision variables are gradually adjusted through an iterative process until the improved KKT conditions are met. In each iteration, it is necessary to check whether the KKT conditions of the upper and lower problems are met at the same time. If the conditions are not met, the current solution will be improved by adjusting the decision variables until the optimal solution that meets all constraints is found.

[0105] In summary, the present invention proposes a cloud energy storage optimization scheduling method and system that considers demand-side response, obtains the operating parameters of the target cloud energy storage operation, and establishes a mathematical model of the cloud energy storage operation based on the operating parameters; establishes constraints based on the mathematical model of the cloud energy storage operation, and the constraints include energy storage equipment constraints, power constraints, and electricity price constraints; the mathematical model of the cloud energy storage operation and the solution of the constraints are converted into the solution of a nonlinear two-layer optimization problem, and the solution of the nonlinear two-layer optimization problem is completed in combination with the improved KKT condition. Through optimized scheduling, the efficient operation of the cloud energy storage system is achieved, the user's electricity cost is reduced, and the stability and reliability of the power grid are improved. In addition, the present invention can also flexibly adjust the charging and discharging strategies of the energy storage equipment according to the price fluctuations in the real-time market, thereby improving the economic benefits of cloud energy storage operations while ensuring the interests of users.

[0106] This embodiment also provides a cloud energy storage optimization scheduling system considering demand-side response, including:

[0107] A model building module, used to obtain the operating parameters of the target cloud energy storage operation and establish a mathematical model of the cloud energy storage operation according to the operating parameters;

[0108] The constraint establishment module is used to establish the constraint conditions of the mathematical model based on cloud energy storage operation. The constraint conditions include energy storage equipment constraints, power constraints and power selling price constraints.

[0109] The solution module is used to convert the mathematical model of cloud energy storage operation and the solution of constraint conditions into the solution of nonlinear two-layer optimization problem, and complete the solution of nonlinear two-layer optimization problem by combining the improved KKT condition.

[0110] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0111] This embodiment also provides a computer device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Fig. 9 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a cloud energy storage optimization scheduling method considering demand-side response is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse.

[0112] This embodiment further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0113] Obtaining the operating parameters of the target cloud energy storage operation, and establishing a mathematical model of the cloud energy storage operation according to the operating parameters;

[0114] Establish the constraints of the mathematical model based on cloud energy storage operation, including energy storage equipment constraints, power constraints and electricity price constraints;

[0115] The mathematical model of cloud energy storage operation and the solution of constraint conditions are converted into the solution of nonlinear two-layer optimization problem, and the solution of nonlinear two-layer optimization problem is completed by combining with improved KKT conditions.

[0116] Example 2

[0117] Reference Figure 2-Figure 8 , which is an embodiment of the present invention, provides a cloud energy storage optimization scheduling method and system considering demand-side response. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0118] Assume that the maximum charging and discharging power of the cloud energy storage configuration is 1MW, the maximum capacity is 5MW·h, the initial capacity is 2MW·h, and the energy storage capacity during operation is 0.1-0.9 of the maximum capacity. Assume that the price of buying and selling electricity in the real-time market is 1.2 times that of the day-ahead market at the same time, the electricity price sold by the cloud energy storage operator to users cannot exceed the price of the real-time market, and the lower limit of the electricity price is 0.8 times the day-ahead market, and the average daily electricity price is set at 0.5 yuan / kw·h.

[0119] Select electric vehicles as active loads. Figure 2 Represents the electricity demand of three typical users at different time periods, and 1 represents charging.

[0120] Assume that there are 30 users in the first category, 50 users in the second category, and 10 users in the third category. After optimization, the results are as follows. Figure 3 represents the day-ahead electricity price set by the power grid, Figure 4 It is the power consumption of different types of users in different time periods. Figure 5 It is the operation process of the energy storage power station. Figure 6 It is the situation of buying and selling electricity in the real-time market. Figure 7 The electricity purchase situation on the previous day. Figure 8 Shown are the results of electricity price optimization.

[0121] from Figure 5 , 6 ,7 It can be seen that Cloud Energy Storage will choose to purchase electricity in the early morning and evening when the day-ahead electricity price is lower, and at the same time sell the excess electricity in the real-time market during the day to obtain profits. Figure 8 It can be seen that since the second type of users are the largest, they play a leading role in the scheduling and optimization of power consumption strategies. After optimization, cloud energy storage operators always raise the electricity price to the upper limit when the second type of users use electricity, and reduce the electricity price to the lower limit when the second type of users are idle, in order to meet the prerequisite of ensuring that the average daily electricity price remains unchanged. From the perspective of demand-side response, users of active loads can have lower electricity costs themselves, and the charging and discharging behaviors that may be full of randomness can become regular.

[0122] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0123] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0124] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0125] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0126] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1The steps for the functions specified in one or more boxes.

[0127] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0128] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A cloud energy storage optimization scheduling method considering demand-side response, characterized in that: include: Obtaining operating parameters of the target cloud energy storage operation, and establishing a mathematical model of the cloud energy storage operation according to the operating parameters; Establishing constraint conditions based on the mathematical model of the cloud energy storage operation, wherein the constraint conditions include energy storage equipment constraint, power constraint and power selling price constraint; The mathematical model of the cloud energy storage operation and the solution of the constraint conditions are converted into the solution of a nonlinear two-layer optimization problem, and the solution of the nonlinear two-layer optimization problem is completed by combining the improved KKT condition.

2. The cloud energy storage optimization scheduling method considering demand-side response according to claim 1 is characterized in that: The obtaining of the operating parameters of the target cloud energy storage operation includes determining the power consumption period of active load users, and determining the power purchase amount in different time periods according to the power consumption demand of users.

3. The cloud energy storage optimization scheduling method considering demand-side response according to claim 2 is characterized in that: The mathematical model of cloud energy storage operation established according to the operating parameters includes: The mathematical model of cloud energy storage operation includes a cloud energy storage operation objective function, which is expressed as: Among them, c t The price of electricity sold to users. is the power consumption of the i-th load in period t; p - is the electricity selling price in the real-time market, is the amount of electricity sold in the real-time market during period t; p d is the day-ahead market electricity purchase price, E t is the amount of electricity purchased in the day-ahead market; + It represents the real-time market electricity purchase price. It is to buy electricity in the real-time market.

4. The cloud energy storage optimization scheduling method considering demand-side response according to claim 3 is characterized in that: The electricity price constraints include: Among them, c tmin and c tmax is the lower and upper limits of the electricity price at time t, c av represents the average daily electricity price, T is the number of hours per day, which is 24.

5. The cloud energy storage optimization scheduling method considering demand-side response according to claim 4 is characterized in that: The power constraints include: Among them, z t is a Boolean variable that can only take values ​​of 0 or 1, which is used to ensure that it is impossible to buy and sell electricity at the same time; M is the sum of the electrical power of all loads; Refers to the discharge capacity of the energy storage device; in, Refers to the amount of charge in the energy storage device.

6. The cloud energy storage optimization scheduling method considering demand-side response according to claim 5, characterized in that: The energy storage device constraints include: Among them, r t + and r t - Refers to the efficiency of charging and discharging of energy storage equipment. The maximum charging and discharging efficiency is C + and C - , It refers to the power state of the energy storage device at time t, which satisfies the power constraint.

7. The cloud energy storage optimization scheduling method considering demand-side response according to claim 6 is characterized in that: The method of converting the mathematical model of the cloud energy storage operation and the solution of the constraint conditions into the solution of a nonlinear two-layer optimization problem, and combining the improved KKT conditions to complete the solution of the nonlinear two-layer optimization problem includes: the upper problem of the nonlinear two-layer optimization problem is to maximize the profit of the cloud energy storage operation, and the lower problem is to minimize the user's electricity cost.

8. A cloud energy storage optimization scheduling system considering demand-side response, characterized in that: include: A model building module, used to obtain operating parameters of the target cloud energy storage operation and establish a mathematical model of the cloud energy storage operation according to the operating parameters; A constraint establishment module, used to establish constraint conditions based on the mathematical model of the cloud energy storage operation, wherein the constraint conditions include energy storage equipment constraints, power constraints, and power selling price constraints; The solution module is used to convert the mathematical model of the cloud energy storage operation and the solution of the constraint conditions into the solution of the nonlinear two-layer optimization problem, and complete the solution of the nonlinear two-layer optimization problem in combination with the improved KKT condition.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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