Power generation and energy storage scheduling method, device and equipment based on dynamic programming

Abstract

A power generation and energy storage scheduling method, device and equipment based on dynamic programming, the method comprising: constructing a standby energy storage power generation vector equation according to a daily average time period total power consumption vector, a user power consumption fluctuation parameter and a user power supply demand response parameter; constructing a power supply demand inequality according to a daily time period power generation vector, the standby energy storage power generation vector and the daily average time period total power consumption vector; calculating a daily total energy storage consumption cost according to the daily time period power generation vector, an initial energy storage amount and an upper limit of energy storage; constructing an optimization scheduling model with the minimum sum of the daily total energy storage consumption cost and a daily total power generation cost as a target and the power supply demand inequality as a constraint condition, and solving to obtain a real-time power generation and energy storage planning scheme. The present application considers the loss brought by high power in short-time production and energy storage to the greatest extent on the premise of meeting the basic power demand of users, thereby achieving the purpose of balancing production and control cost.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power generation planning, in particular to a power generation and energy storage scheduling method, device and equipment based on dynamic programming. BACKGROUND

[0002] The power demand of users has the characteristics of instability, randomness and intermittence in time and space, and the power consumption in different time periods is significantly different. By connecting users to a large power grid and relying on its large energy storage system for power supply, the continuous and stable supply of user power can be effectively guaranteed. In order to maintain the stability of the energy storage level of the power grid, while supplying power to users, the large power grid can supplement power by adjusting the generation capacity in real time. For the power grid, by planning an efficient generation capacity scheme, not only can the power demand of users be guaranteed, but also the energy storage level of the power grid can be effectively stabilized.

[0003] However, there is a natural loss that cannot be ignored in the process of power generation and energy storage. The efficiency of power generation is limited by hardware conditions, and when a power shortage event occurs and results in higher power supply demand, too high generation efficiency can cause equipment overload, thereby exacerbating the natural loss of power generation and increasing the risk of hardware damage. On the other hand, too high power storage not only increases storage loss, but also increases the risk of high-voltage leakage, thereby increasing equipment maintenance costs. Therefore, how to reasonably plan the power generation scheme while minimizing the natural loss of power generation and energy storage has become a key and challenge. SUMMARY

[0004] The purpose of the present application is to overcome the above-mentioned defects and problems existing in the prior art, and to provide a power generation and energy storage scheduling method, device and equipment based on dynamic programming, which maximizes the consideration of the loss caused by high power in short-time production and energy storage under the premise of meeting the basic power demand of users, thereby achieving the purpose of balancing production and control costs.

[0005] To achieve the above purpose, the technical solution of the present application is: a power generation and energy storage scheduling method based on dynamic programming, comprising:

[0006] According to the daily average user time period power consumption matrix, a daily average time period total power consumption vector is calculated; according to the daily average time period total power consumption vector, a user power consumption shock parameter and a user power supply demand response parameter, a standby energy storage power generation vector equation is constructed;

[0007] According to the number of daily time periods, a daily time period power generation vector is defined, and according to the daily time period power generation vector, a daily total power generation cost is calculated;

[0008] According to the daily time period power generation vector, the standby energy storage power generation vector and the daily average time period total power consumption vector, a power supply demand inequality is constructed;

[0009] Calculate the daily time period energy storage consumption according to the daily time period generation vector, initial energy storage, and upper limit of energy storage, and calculate the total daily energy storage consumption cost according to the daily time period energy storage consumption;

[0010] Build an optimal scheduling model with the minimum sum of total daily energy storage consumption cost and total daily generation cost as the target and the energy supply demand inequality as the constraint condition, and solve to obtain the real-time power generation and energy storage planning scheme.

[0011] The daily average time period total power consumption vector is D, the vector length is the number of daily time periods A, and the a-th element is:

[0012]

[0013] In the formula, D a is the total power consumption of all users in the a-th time period; B is the number of users; C a，b is the daily average power consumption of the b-th user in the a-th time period.

[0014] The standby energy storage generation vector equation is:

[0015]

[0016] In the formula, L a is the standby energy storage generation in the a-th time period; J is the user power supply demand response parameter; D a is the total power consumption of all users in the a-th time period; I is the user power consumption fluctuation parameter.

[0017] The energy supply demand inequality is:

[0018] E a -L a -D a ≥ 0; a ∈ [1, A];

[0019] In the formula, E a is the generation in the a-th time period; L a is the standby energy storage generation in the a-th time period; D a is the total power consumption of all users in the a-th time period; A is the number of daily time periods.

[0020] The calculation method of the total daily energy storage consumption cost is:

[0021] Calculate the original energy storage N a in the a=1 time period as:

[0022] N a = G;

[0023] In the formula, G is the initial energy storage;

[0024] Calculate the energy storage consumption J of the a-th period a is:

[0025] J a = βN a + χ;

[0026] In the formula, β is the energy consumption coefficient parameter; χ is the energy consumption constant parameter;

[0027] Calculate the energy storage N of the a+1-th period a+1 is:

[0028] N a+1 = max{min{N a + E a - J a , H}, 0};

[0029] In the formula, E a is the power generation of the a-th period; H is the upper limit of energy storage;

[0030] Iterate according to the above steps, and stop when the number of periods reaches the upper limit A of daily periods, and calculate the energy storage consumption J of the A-th period A is:

[0031] J A = βN A + χ;

[0032] In the formula, N A is the energy storage of the A-th period;

[0033] The total daily energy storage consumption cost K is calculated as:

[0034]

[0035] In the formula, A is the number of daily periods.

[0036] The total daily power generation cost F is:

[0037]

[0038] In the formula, E a is the power generation of the a-th period; A is the number of daily periods; α is the power generation cost parameter.

[0039] The optimization scheduling model is:

[0040]

[0041] In the formula, K is the total daily energy storage consumption cost; F is the total daily power generation cost; E is the daily time period power generation vector, A is the number of daily periods; E ais the power generation of the a-th time period; L a is the standby energy storage power generation of the a-th time period; D a is the total power consumption of all users in the a-th time period.

[0042] A power generation and energy storage scheduling device based on dynamic programming, which is applied to the above-mentioned method, and the device comprises:

[0043] A standby energy storage power generation calculation module is configured to calculate a daily average time period total power consumption vector according to a daily average user time period power consumption matrix, and construct a standby energy storage power generation vector equation according to the daily average time period total power consumption vector, a user power consumption fluctuation parameter and a user power supply demand response parameter;

[0044] A daily total power generation cost calculation module is configured to define a daily time period power generation vector according to the number of daily time periods, and calculate a daily total power generation cost according to the daily time period power generation vector;

[0045] A power supply demand inequality construction module is configured to construct a power supply demand inequality according to the daily time period power generation vector, the standby energy storage power generation vector and the daily average time period total power consumption vector;

[0046] A daily total energy storage consumption cost calculation module is configured to calculate a daily time period energy storage consumption according to the daily time period power generation vector, an initial energy storage amount and an upper limit of energy storage, and calculate a daily total energy storage consumption cost according to the daily time period energy storage consumption;

[0047] A power generation and energy storage planning scheme acquisition module is configured to construct an optimization scheduling model with the sum of the daily total energy storage consumption cost and the daily total power generation cost as a target and the power supply demand inequality as a constraint condition, and obtain a real-time power generation and energy storage planning scheme by solving the optimization scheduling model.

[0048] A power generation and energy storage scheduling device based on dynamic programming, which comprises a memory and a processor;

[0049] The memory is configured to store computer program codes and transmit the computer program codes to the processor;

[0050] The processor is configured to execute the above-mentioned method according to instructions in the computer program codes.

[0051] A computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the above-mentioned method.

[0052] Compared with the prior art, the power generation and energy storage scheduling device based on dynamic programming has the following beneficial effects:

[0053] In the power production and energy storage scheduling method, device and equipment based on dynamic programming, on the one hand, based on the historical data of real-time user power consumption, the user power consumption expectation is obtained in combination with the mathematical distribution of user power consumption, and the system power generation expectation is further obtained by considering the power generation response demand restriction, so as to obtain the system power generation quantity restriction condition. On the other hand, the iterative relationship among real-time power generation quantity, real-time energy storage quantity and real-time consumption quantity is constructed, the real-time power generation cost and loss quantity are calculated in combination with the initial energy storage quantity, so as to output the daily power generation cost and loss comprehensive, and finally the optimization problem is constructed, the optimization problem takes the guarantee of user response power generation quantity as the restriction condition, takes the minimum daily total energy storage consumption cost and daily total power generation quantity cost as the target, solves the problem to obtain the optimal system real-time production and energy storage planning scheme, the real-time production and energy storage planning scheme maximally considers the loss brought by high power quantity in short-time production and energy storage on the premise of meeting the basic power demand of users, so as to balance the production and control cost. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 is a flow chart of the power production and energy storage scheduling method based on dynamic programming.

[0055] Figure 2 is a structural block diagram of the power production and energy storage scheduling device based on dynamic programming.

[0056] Figure 3 is a structural block diagram of the power production and energy storage scheduling device based on dynamic programming. DETAILED DESCRIPTION

[0057] The application will be further described in detail in combination with the description of the drawings and specific embodiments.

[0058] Referring to Figure 1 A power production and energy storage scheduling method based on dynamic programming, comprising:

[0059] S1, calculating a daily average time period total power consumption vector according to a daily average user time period power consumption matrix, and constructing a standby energy storage power generation vector equation according to the daily average time period total power consumption vector, a user power consumption fluctuation parameter and a user power supply demand response parameter;

[0060] S2, defining a daily time period power generation vector according to the number of daily time periods, and calculating a daily total power generation cost according to the daily time period power generation vector;

[0061] S3, constructing a power supply demand inequality according to the daily time period power generation vector, the standby energy storage power generation vector and the daily average time period total power consumption vector;

[0062] S4, calculate daily time period energy storage consumption according to daily time period power generation vector, initial energy storage amount and upper limit of energy storage, and calculate total daily energy storage consumption cost according to daily time period energy storage consumption;

[0063] S5, construct an optimization scheduling model with the minimum sum of total daily energy storage consumption cost and total daily power generation cost as the target and with the energy supply demand inequality as the constraint condition, and solve to obtain a real-time power production and energy storage planning scheme.

[0064] In view of the deficiencies of the existing large power grid energy storage and production balancing strategy, in view of the uncertainty and randomness existing in the real-time power supply demand of users, and fully considering the time and space differences between power demand and power supply system, the application provides a power production and energy storage scheduling method based on dynamic programming, which aims to reduce the consumption in the process of power production and storage as much as possible. Under the premise of meeting the random and concentrated power supply demand of users, the power loss caused by high power storage and short-term production is minimized.

[0065] Further, the daily average time period total power consumption vector is D, the vector length is the number of daily time periods A, and the a-th element is:

[0066]

[0067] In the formula, D a is the total power consumption of all users in the a-th time period; B is the number of users; C is the daily average user time period power consumption matrix, the matrix size is A*B, C a，b is the element of the a-th row and b-th column of the daily average user time period power consumption matrix C, which means the daily average power consumption of the b-th user in the a-th time period.

[0068] Further, the standby energy storage power generation vector equation is:

[0069]

[0070] The above equation is solved by normalization, and the normalized equation is:

[0071]

[0072] In the formula, L is the standby energy storage power generation vector, the vector length is A, L a is the a-th element of the standby energy storage power generation vector L, which means the standby energy storage power generation in the a-th time period; J is the user power supply demand response parameter, the value size satisfies J∈(0, 1), which means that at least a percentage J of users meet the power demand at any time; D aLet be the total electricity consumption of all users in the 'a'-th time period; I is the user electricity consumption oscillation parameter, which represents the standard deviation of a normal distribution of user electricity consumption. The normalization equation can be solved by directly consulting the standard normal distribution table.

[0073] Furthermore, the daily time-segmented power generation vector E is:

[0074] E = (E1, E2, ..., E a , ..., E A );

[0075] In the formula, E is the independent variable, and its vector length is A. a Let be the a-th element of the daily time-segmented power generation vector E, which represents the power generation during the a-th time segment.

[0076] Furthermore, the energy supply-demand inequality is as follows:

[0077] E a -L a -D a ≥0; a∈[1,A];

[0078] In the formula, E a L represents the power generation during the a-th time period; a D represents the backup energy storage power generation during the a-th time period; a Let A be the total electricity consumption of all users in the a-th time period; A is the number of time periods per day.

[0079] Furthermore, the method for calculating the total daily energy storage consumption cost is as follows:

[0080] Calculate the initial stored energy N for the a=1th time period. a for:

[0081] N a =G;

[0082] In the formula, G is the initial stored energy;

[0083] Calculate the energy storage consumption J in the a-th time period. a for:

[0084] J a =βN a +χ;

[0085] In the formula, β is a parameter of energy consumption coefficient; χ is a parameter of energy consumption constant; the two parameters exist because of the self-discharge phenomenon of the power storage device, i.e., the phenomenon that the stored power is spontaneously consumed in the device; the self-discharge phenomenon of different performances and materials needs to be analyzed in specific backgrounds, such as device environmental temperature, device material, voltage, etc. (only material, 1% for lithium battery, 12% to 14% for lead-acid battery). Generally, the self-discharge function in a steady state environment is close to linear, and the energy consumption coefficient parameter β and the energy consumption constant parameter χ can be looked up on the power storage device or can be measured by professional instruments. For some power storage devices with better performance or devices with lower power storage capacity, the energy consumption constant parameter χ is close to 0.

[0086] Calculate the energy storage N of the a+1th period a+1 is:

[0087] N a+1 = max{min{N a +E a -J a , H}, 0};

[0088] In the formula, E a is the power generation of the a th period; H is the upper limit of energy storage; min{N a +E a -J a , H} is a minimum value operation, and the output is the smaller value between N a +E a -J a and H; max{min{N a +E a -J a , H}, 0} is a maximum value operation, and the output is the larger value between min{N a +E a -J a , H} and 0;

[0089] Iterate according to the above steps, and stop when the number of periods reaches the upper limit A of daily periods, and calculate the energy storage consumption J of the A th period A is:

[0090] J A = βN A + χ;

[0091] In the formula, N A is the energy storage of the A th period;

[0092] Calculate the total daily energy storage consumption cost K as:

[0093]

[0094] In the formula, A is the number of daily periods.

[0095] Further, the total daily power generation cost F is:

[0096]

[0097] In the formula, E a is the power generation of the a-th period; A is the number of daily periods; and a is a power generation cost parameter, which is obtained based on grid historical data, and the power generation and the power generation cost are not a simple linear relationship; when the power generation is too large, the cost will rise exponentially due to factors such as equipment hardware, coal prices, and temperature loss, and the basic power generation cost parameter can be obtained according to grid historical data.

[0098] Further, the optimization scheduling model is:

[0099]

[0100] In the formula, K is the total daily energy storage consumption cost; F is the total daily power generation cost; E is the daily time period power generation vector, and A is the number of daily periods; E a is the power generation of the a-th period; L a is the standby energy storage power generation of the a-th period; D a is the total power consumption of all users in the a-th period.

[0101] The grid has multiple high power supply demand periods in a day, but the average total daily power supply is basically periodic, and a large increase in power generation in a short time will cause the cost to rise exponentially, therefore, the power supply pressure can be relieved by time period power generation throughout the day, and the total power supply demand is always limited, and the corresponding power generation demand is also limited, and advance power generation will also cause the energy storage consumption to increase linearly, and the purpose of the present application is to find a balance between the two, so as to balance the short-time increase in power generation cost (power generation price) and the advance power generation energy storage cost (self-discharge amount) under the condition of guaranteeing the basic power supply demand, both of which are better the lower the better. The optimization scheduling model can be efficiently solved by the cvx module in the matlab program.

[0102] Referring to Figure 2 , the present application also provides a power generation and energy storage scheduling device based on dynamic programming, which is applied to the power generation and energy storage scheduling method based on dynamic programming, and the device comprises:

[0103] a standby energy storage power generation amount calculation module, configured to calculate a daily average time period total power consumption vector according to a daily average user time period power consumption matrix, and construct a standby energy storage power generation vector equation according to the daily average time period total power consumption vector, a user power consumption fluctuation parameter, and a user power supply demand response parameter;

[0104] The daily total power generation cost calculation module is configured to define a daily sub-period power generation vector according to the number of daily time periods, and calculate a daily total power generation cost according to the daily sub-period power generation vector;

[0105] The energy supply demand inequality construction module is configured to construct an energy supply demand inequality according to the daily sub-period power generation vector, the standby energy storage power generation vector, and a daily average sub-period total power consumption vector;

[0106] The daily total energy storage consumption cost calculation module is configured to calculate a daily sub-period energy storage consumption amount according to the daily sub-period power generation vector, an initial energy storage amount, and an upper limit of the energy storage, and calculate a daily total energy storage consumption cost according to the daily sub-period energy storage consumption amount;

[0107] The power production and energy storage planning scheme acquisition module is configured to construct an optimization scheduling model with a minimum sum of the daily total energy storage consumption cost and the daily total power generation cost as a target, and the energy supply demand inequality as a constraint condition, and obtain a real-time power production and energy storage planning scheme by solving the optimization scheduling model.

[0108] Referring to Figure 3 , the application further provides a power production and energy storage scheduling device based on dynamic programming, which comprises a memory and a processor.

[0109] The memory is configured to store computer program codes and transmit the computer program codes to the processor.

[0110] The processor is configured to execute the above-mentioned power production and energy storage scheduling method based on dynamic programming according to instructions in the computer program codes.

[0111] The application further provides a computer readable storage medium, wherein the computer readable storage medium stores computer programs, and the computer programs are executed by a processor to implement the above-mentioned power production and energy storage scheduling method based on dynamic programming.

[0112] Generally, computer instructions used to implement the method of the application can be carried by any combination of one or more computer readable storage media. Non-transitory computer readable storage media can include any computer readable medium except a signal propagating transitorily, per se.

[0113] The computer readable storage medium may, for example, be tangible and non-transitory. A non-exhaustive list of examples of a computer readable storage medium include magnetic, optical, or semiconductor storages. Examples of magnetic storage include a magnetic or optical disk, e.g., a computer disk. Examples of optical storage include an optical disk, such as a compact disc (CD), a digital versatile disc (DVD), or a Blu-ray disc. Examples of semiconductor storage include a solid-state drive (SSD) or a flash drive. The computer readable storage medium can store data which is readable. For example, the computer readable storage medium can include a machine- readable storage that is encoded with one or more programs that, when executed, can instruct a machine or system to perform a method. The computer readable storage medium can be used to store data which is readable by a machine or system. For example, the computer readable storage medium can include a machine- readable storage that is encoded with data which can be read by a machine or system when executing one or more programs.

[0114] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages, particularly Python language and platform frameworks based on TensorFlow, PyTorch, etc. suitable for neural network computing. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0115] The above apparatus and non-transitory computer readable storage medium can refer to the specific description of a power generation and energy storage scheduling method based on dynamic programming and its beneficial effects, which will not be repeated here.

[0116] Although the embodiments of the present application have been shown and described above, it should be understood by those having ordinary skill in the art that the above embodiments are exemplary and cannot be interpreted as limiting the present application, and those having ordinary skill in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A method for power generation and energy storage dispatch based on dynamic programming, characterized in that, include: Calculate the daily average total electricity consumption vector based on the daily average user electricity consumption matrix for different time periods. A vector equation for backup energy storage power generation is constructed based on the daily average total electricity consumption vector, user electricity consumption oscillation parameters, and user power demand response parameters. Define the daily time-segment power generation vector based on the number of time segments per day, and calculate the total daily power generation cost based on the daily time-segment power generation vector; Construct an energy supply-demand inequality based on the daily time-of-day power generation vector, the backup energy storage power generation vector, and the daily average time-of-day total electricity consumption vector. The daily time-of-use energy consumption is calculated based on the daily time-of-use power generation vector, initial energy storage, and energy storage limit, and the daily total energy storage consumption cost is calculated based on the daily time-of-use energy consumption. With the goal of minimizing the sum of daily total energy storage consumption cost and daily total power generation cost, an optimal scheduling model is constructed under the constraint of the energy supply-demand inequality, and the real-time power capacity and energy storage planning scheme are obtained by solving the model. The vector equation for the backup energy storage power generation is: ； In the formula, For the first Backup energy storage power generation for each time period; Parameters for responding to user power supply needs; For all users in the first Total electricity consumption for each time period; For user power consumption fluctuation parameters; The energy supply and demand inequality is as follows: ； ； In the formula, For the first Power generation in a given time period; For the first Backup energy storage power generation for each time period; For all users Total electricity consumption for each time period; Number of time slots per day; The method for calculating the total daily energy storage consumption cost is as follows: Calculate the first Original energy storage for each time period for: ； In the formula, For initial energy storage; Calculate the first Energy storage consumption during each time period for: ； In the formula, This refers to the energy consumption coefficient parameter; This is the energy consumption constant parameter; Calculate the first Energy storage for each time period for: ； In the formula, For the first Power generation in a given time period; This is the upper limit of energy storage; Iterate using the steps described above until the number of segments reaches the daily time slot limit. Stop at time, calculate the first Energy storage consumption during each time period for: ； In the formula, For the first Energy storage for a given period of time; Calculate the total daily energy storage cost for: ； In the formula, This represents the number of time periods per day.

2. The method for power generation and energy storage dispatch based on dynamic programming according to claim 1, characterized in that, The daily average total electricity consumption vector by time period is: Its vector length is the number of time periods per day. , its first The elements are: ； In the formula, For all users in the first Total electricity consumption for each time period; For the number of users; For the first The user Average daily electricity consumption for each time period.

3. The method for power generation and energy storage dispatch based on dynamic programming according to claim 1, characterized in that, The total daily electricity generation cost for: ； In the formula, For the first Power generation in a given time period; Number of time slots per day; These are parameters related to power generation costs.

4. The method for power generation and energy storage dispatch based on dynamic programming according to claim 1, characterized in that, The optimized scheduling model is as follows: ； In the formula, This represents the total daily energy storage consumption cost. Cost of total daily power generation; This represents the daily time-segmented power generation vector. Number of time slots per day; For the first Power generation in a given time period; For the first Backup energy storage power generation for each time period; For all users Total electricity consumption during each time period.

5. A power generation and energy storage dispatching device based on dynamic programming, characterized in that, The device is used in the method according to any one of claims 1-4, the device comprising: The backup energy storage power generation calculation module is used to calculate the daily average time-period total electricity consumption vector based on the daily average user time-period electricity consumption matrix; and to construct the backup energy storage power generation vector equation based on the daily average time-period total electricity consumption vector, user electricity consumption oscillation parameters, and user power supply demand response parameters. The daily total power generation cost calculation module is used to define the daily time-segment power generation vector based on the number of daily time periods, and to calculate the daily total power generation cost based on the daily time-segment power generation vector. The energy supply-demand inequality construction module is used to construct energy supply-demand inequalities based on the daily time-of-day power generation vector, the backup energy storage power generation vector, and the daily average time-of-day total electricity consumption vector. The daily total energy storage consumption cost calculation module is used to calculate the daily time-period energy storage consumption based on the daily time-period power generation vector, initial energy storage capacity, and energy storage upper limit, and to calculate the daily total energy storage consumption cost based on the daily time-period energy storage consumption. The module for obtaining power capacity and energy storage planning schemes is used to construct an optimal scheduling model with the goal of minimizing the sum of daily total energy storage consumption cost and daily total power generation cost, and with the energy supply-demand inequality as a constraint, and solve for the real-time power capacity and energy storage planning schemes.

6. A power generation and energy storage dispatching device based on dynamic programming, characterized in that, Including memory and processor; The memory is used to store computer program code and transmit the computer program code to the processor; The processor is configured to execute the method as described in any one of claims 1 to 4 according to instructions in the computer program code.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 4.

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