Method and device for optimizing energy consumption of prosumers considering energy storage life loss

Abstract

This application discloses a method and apparatus for optimizing energy consumption by prosumers that considers energy storage life loss, including: obtaining the electricity purchase price and electricity sales price of the prosumer from the power grid; obtaining the electricity cost of the prosumer based on the electricity purchase price, electricity sales price, and the power purchased and sold by the prosumer from the power grid; constructing a total operating cost function for the prosumer based on the electricity cost, the comfort loss cost caused by energy consumption changes, the energy storage life loss cost, and the photovoltaic system operation and maintenance cost; solving the total operating cost function for the prosumer based on total energy consumption optimization constraints, energy storage system constraints, interruptible load constraints, transferable load constraints, and power balance constraints to determine the prosumer's energy consumption strategy. By incorporating the energy storage life loss cost into the construction of the total operating cost function for the prosumer and solving it, the prosumer's energy consumption optimization strategy obtained is accurate and feasible while considering the full life cycle cost of the energy storage equipment.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a method and device for optimizing energy consumption of producers and consumers taking into account the loss of energy storage life. Background Art

[0002] With the continuous construction of new power systems and the gradual liberalization of the electricity sales market, the penetration rate of distributed energy on the demand side continues to increase, and traditional electricity users are gradually transformed into prosumers with dual "source-load" attributes. Prosumers refer to users who are both electricity consumers and electricity producers. The role of such users in the energy market is becoming increasingly prominent.

[0003] Prosumers use energy storage systems to balance supply and demand and improve energy efficiency. However, the lifespan of energy storage systems, particularly degradation caused by frequent charging and discharging, is a key factor affecting their economic efficiency and reliability. Traditional prosumer energy optimization strategies often focus on common factors such as electricity costs and supply-demand balance. The lifespan of energy storage systems is not considered when building models and formulating plans. As a key component in balancing electricity supply and demand for prosumers, energy storage systems inevitably lose lifespan due to frequent charging and discharging cycles. This neglect results in inaccurate prosumer energy optimization strategies, which fail to achieve the desired results in practical applications. Summary of the Invention

[0004] The embodiments of the present application provide a method and apparatus for optimizing energy consumption by prosumers that takes into account the life loss of energy storage. By adding the energy storage life loss cost and solving it when constructing the total operating cost function of the prosumer, the prosumer energy consumption optimization strategy obtained by the solution is accurate and feasible while taking into account the full life cycle cost of the energy storage equipment.

[0005] In a first aspect, an embodiment of the present application provides a method for optimizing energy consumption by prosumers taking into account energy storage life loss, the method comprising:

[0006] The electricity price information calculated by the power grid based on the output of new energy is used to determine the electricity purchase price of the prosumer from the power grid and the electricity sales price of the prosumer to the power grid; the electricity cost of the prosumer is obtained based on the electricity purchase price of the prosumer from the power grid, the electricity sales price of the prosumer to the power grid, the electricity purchase power of the prosumer from the power grid and the electricity sales power of the prosumer to the power grid; the comfort loss cost caused by the energy consumption change of the prosumer is obtained based on the demand response comfort loss coefficient of the prosumer, the optimized energy consumption power of the prosumer and the predicted electricity consumption power of the prosumer; the energy storage life of the prosumer is calculated based on the energy storage life of the prosumer. The energy storage life loss cost of the prosumer is obtained based on the loss cost coefficient, the charging power of the energy storage system and the discharging power of the energy storage system; the photovoltaic system operation and maintenance cost of the prosumer is obtained based on the photovoltaic cost coefficient and the photovoltaic predicted output of the prosumer; the total operation cost function of the prosumer is constructed based on the electricity cost, the comfort loss cost caused by energy consumption changes, the energy storage life loss cost and the photovoltaic system operation and maintenance cost; the total operation cost function of the prosumer is solved based on the total energy consumption optimization constraints, the energy storage system constraints, the interruptible load constraints, the transferable load constraints and the power balance constraints to determine the energy consumption strategy of the prosumer.

[0007] In one possible implementation, the electricity cost of the prosumer is obtained based on the electricity purchase price of the prosumer from the grid, the electricity sale price of the prosumer to the grid, the power purchased from the grid by the prosumer, and the power sold to the grid by the prosumer. The cost includes:

[0008] Obtain a first product of the electricity purchase price of the prosumer from the grid and the electricity power purchased by the prosumer from the grid; obtain a second product of the electricity sales price of the prosumer to the grid and the electricity power sold by the prosumer to the grid; obtain a first difference between the first product and the second product, the first difference being the electricity cost of the prosumer.

[0009] In one possible implementation, obtaining the comfort loss cost caused by the prosumer's energy consumption change based on the prosumer's demand response comfort loss coefficient, the prosumer's optimized energy consumption power, and the prosumer's predicted electricity consumption power includes:

[0010] Obtain a second difference between the optimized electricity consumption of the prosumer and the predicted electricity consumption of the prosumer; obtain a third product of the demand response comfort loss coefficient of the prosumer and the square of the second difference, wherein the third product is the comfort loss cost caused by the energy consumption change of the prosumer.

[0011] In one possible implementation, the energy storage life loss cost of the prosumer is obtained based on the energy storage life loss cost coefficient of the prosumer, the energy storage system charging power, and the energy storage system discharging power, including:

[0012] Determine the energy storage life loss cost coefficient of the prosumer based on the energy storage efficiency, the rated power of the energy storage, the unit operation and maintenance cost of the target time period, the estimated operating life of the energy storage system, the discount rate, the number of cycles in the target time period, the energy storage cost of the energy storage, the power storage cost of the energy storage and the rated service life of the energy storage; obtain a first sum of the charging power of the energy storage system and the discharging power of the energy storage system; obtain a fourth product of the first sum and the energy storage life loss cost coefficient of the prosumer, and the fourth product is the energy storage life loss cost coefficient of the prosumer.

[0013] In one possible implementation, the calculation formula for the prosumer's energy storage life loss cost coefficient is as follows:

[0014] ;

[0015] in, is the energy storage efficiency; is the rated power of the energy storage; The unit operation and maintenance cost for the target time period; The estimated operating life of the energy storage system; is the discount rate; is the number of cycles of the target time period; the energy storage cost for said stored energy; is the power storage cost of the energy storage; is the rated service life of the energy storage.

[0016] In one possible implementation, the photovoltaic system operation and maintenance cost of the prosumer is obtained based on the photovoltaic cost coefficient and the photovoltaic predicted output of the prosumer, including:

[0017] A fifth product of the photovoltaic cost coefficient and the photovoltaic predicted output of the prosumer is obtained, where the fifth product is the photovoltaic system operation and maintenance cost of the prosumer.

[0018] In one possible implementation, a total cost function for prosumers is constructed based on electricity costs, comfort loss costs due to energy consumption variations, energy storage life loss costs, and photovoltaic system operation and maintenance costs, including:

[0019] The total operating cost is obtained based on the sum of the electricity cost, the comfort loss cost caused by energy consumption changes, the energy storage life loss cost, and the photovoltaic system operation and maintenance cost; the total operating cost function of the prosumer is constructed by minimizing the total operating cost within the regulation cycle.

[0020] In one possible implementation, solving the total operating cost function of the prosumer based on the total energy optimization constraint, the energy storage system constraint, the interruptible load constraint, the transferable load constraint, and the power balance constraint includes:

[0021] Under the premise of satisfying the total energy consumption optimization constraints, energy storage system constraints, interruptible load constraints, transferable load constraints and power balance constraints, the total operating cost function of the prosumer is used as the particle fitness at each optimization in the improved particle swarm algorithm based on reverse learning. If the number of iterations exceeds the preset number of iterations, or the error of any iteration is less than the preset error, the solution is terminated, and the particle position corresponding to the swarm optimal value of the improved particle swarm algorithm based on reverse learning is determined as the solution to the total operating cost function of the prosumer.

[0022] In a second aspect, an embodiment of the present application provides a prosumer energy optimization device that takes into account energy storage life loss, the device comprising:

[0023] An acquisition module is used to determine the electricity purchase price of the prosumer from the grid and the electricity sale price of the prosumer to the grid based on the electricity price information calculated by the grid according to the output of renewable energy;

[0024] and for obtaining the electricity cost of the prosumer based on the electricity purchase price of the prosumer from the grid, the electricity sale price of the prosumer to the grid, the electricity power purchased by the prosumer from the grid, and the electricity power sold by the prosumer to the grid;

[0025] and for obtaining a comfort loss cost caused by a change in energy consumption of the prosumer based on a demand response comfort loss coefficient of the prosumer, the optimized energy consumption power of the prosumer, and the predicted electricity consumption power of the prosumer;

[0026] and for obtaining the energy storage life loss cost of the prosumer according to the energy storage life loss cost coefficient of the prosumer, the energy storage system charging power, and the energy storage system discharging power;

[0027] and for obtaining the photovoltaic system operation and maintenance cost of the prosumer based on the photovoltaic cost coefficient and photovoltaic predicted output of the prosumer;

[0028] A construction module is used to construct a total operating cost function for prosumers based on electricity costs, comfort loss costs caused by energy consumption changes, energy storage life loss costs, and photovoltaic system operation and maintenance costs;

[0029] A solution module is used to solve the total operating cost function of the prosumer according to the total energy consumption optimization constraint, the energy storage system constraint, the interruptible load constraint, the transferable load constraint and the power balance constraint, and determine the energy consumption strategy of the prosumer.

[0030] In a third aspect, an embodiment of the present application provides a computer, including:

[0031] A memory, a processor, and an executable program code stored in the memory and executable on the processor, wherein the executable program code is configured to implement part or all of the steps described in any method in the first aspect.

[0032] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which is stored a prosumer energy optimization program that takes into account the life loss of energy storage. When the prosumer energy optimization program that takes into account the life loss of energy storage is executed by a processor, some or all of the steps described in any method in the first aspect are implemented.

[0033] In a fifth aspect, embodiments of the present application provide a computer program product, wherein the computer program product includes a computer program operable to cause a computer to perform some or all of the steps described in any method of the first aspect of the embodiments of the present application. The computer program product may be a software installation package.

[0034] By implementing the embodiments of the present application, the electricity price information calculated by the power grid based on the output of new energy is first used to determine the electricity purchase price of the prosumer from the power grid and the electricity sales price of the prosumer to the power grid; then, based on the electricity purchase price of the prosumer from the power grid, the electricity sales price of the prosumer to the power grid, the electricity purchase power of the prosumer from the power grid and the electricity sales power of the prosumer to the power grid, the electricity cost of the prosumer is obtained; then, based on the demand response comfort loss coefficient of the prosumer, the optimized energy consumption power of the prosumer and the predicted electricity consumption power of the prosumer, the comfort loss cost caused by the energy consumption change of the prosumer is obtained; then, based on the prosumer's demand response comfort loss coefficient, the optimized energy consumption power of the prosumer and the predicted electricity consumption power of the prosumer, the comfort loss cost caused by the energy consumption change of the prosumer is obtained. The energy storage life loss cost coefficient, energy storage system charging power, and energy storage system discharging power of the prosumer are used to obtain the energy storage life loss cost of the prosumer. Then, based on the photovoltaic cost coefficient and photovoltaic predicted output of the prosumer, the photovoltaic system operation and maintenance cost of the prosumer is obtained. Then, based on the electricity cost, the comfort loss cost caused by energy consumption changes, the energy storage life loss cost, and the photovoltaic system operation and maintenance cost, the total operating cost function of the prosumer is constructed. Finally, based on the total energy consumption optimization constraints, energy storage system constraints, interruptible load constraints, transferable load constraints, and power balance constraints, the total operating cost function of the prosumer is solved to determine the energy consumption strategy of the prosumer. By incorporating the energy storage life loss cost into the construction of the total operating cost function of the prosumer and solving it, the energy consumption optimization strategy of the prosumer obtained is accurate and feasible while considering the full life cycle cost of the energy storage equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the drawings required for use in the embodiments of the present application or the background technology will be described below.

[0036] Figure 1 This is a schematic diagram of the architecture of a prosumer energy optimization system that takes into account energy storage life loss, provided in an embodiment of the present application;

[0037] Figure 2 This is a flow chart of a method for optimizing energy consumption by prosumers taking into account energy storage life loss, provided in an embodiment of the present application;

[0038] Figure 3 This is a flow chart of a method for obtaining the electricity cost of a prosumer provided in an embodiment of the present application;

[0039] Figure 4 This is a flow chart of a method for obtaining the comfort loss cost caused by energy consumption changes of a prosumer, provided in an embodiment of the present application;

[0040] Figure 5 This is a flow chart of a method for obtaining the energy storage life loss cost of a prosumer provided in an embodiment of the present application;

[0041] Figure 6 This is a flow chart of a method for constructing a total cost function for producers and consumers provided in an embodiment of the present application;

[0042] Figure 7 This is a schematic structural diagram of a prosumer energy optimization device that takes into account energy storage life loss, provided in an embodiment of the present application;

[0043] Figure 8 It is a structural diagram of a computer provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative work should fall within the scope of protection of the present invention.

[0045] The terms "first," "second," and "third," etc. in the specification, claims, and drawings of this application are used to distinguish between different objects, not to describe a particular order. In addition, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements, but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0046] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0047] With the continuous construction of new power systems and the gradual liberalization of the electricity sales market, the penetration rate of distributed energy on the demand side continues to increase, and traditional electricity users are gradually transformed into prosumers with dual "source-load" attributes. Prosumers refer to users who are both electricity consumers and electricity producers. The role of such users in the energy market is becoming increasingly prominent.

[0048] Prosumers use energy storage systems to balance supply and demand and improve energy efficiency. However, the lifespan of energy storage systems, particularly degradation caused by frequent charging and discharging, is a key factor affecting their economic efficiency and reliability. Traditional prosumer energy optimization strategies often focus on common factors such as electricity costs and supply-demand balance. The lifespan of energy storage systems is not considered when building models and formulating plans. As a key component in balancing electricity supply and demand for prosumers, energy storage systems inevitably lose lifespan due to frequent charging and discharging cycles. This neglect results in inaccurate prosumer energy optimization strategies, which fail to achieve the desired results in practical applications.

[0049] In the embodiment of the present application, the power grid first obtains the electricity price information calculated based on the output of new energy, and determines the electricity purchase price of the prosumer from the grid and the electricity sales price of the prosumer to the grid; then, the electricity cost of the prosumer is obtained based on the electricity purchase price of the prosumer from the grid, the electricity sales price of the prosumer to the grid, the electricity purchase power of the prosumer from the grid, and the electricity sales power of the prosumer to the grid; then, the comfort loss cost caused by the energy consumption change of the prosumer is obtained based on the demand response comfort loss coefficient of the prosumer, the optimized energy consumption power of the prosumer, and the predicted electricity consumption power of the prosumer; then, the comfort loss cost caused by the energy consumption change of the prosumer is obtained based on the storage power of the prosumer. The energy storage life loss cost coefficient, energy storage system charging power, and energy storage system discharging power are used to obtain the energy storage life loss cost of the prosumer. The photovoltaic system operation and maintenance cost of the prosumer is then obtained based on the photovoltaic cost coefficient and photovoltaic predicted output of the prosumer. The total operating cost function of the prosumer is then constructed based on the electricity cost, the comfort loss cost caused by energy consumption changes, the energy storage life loss cost, and the photovoltaic system operation and maintenance cost. Finally, the total operating cost function of the prosumer is solved based on the total energy consumption optimization constraints, energy storage system constraints, interruptible load constraints, transferable load constraints, and power balance constraints to determine the prosumer's energy consumption strategy. By incorporating the energy storage life loss cost into the construction of the total operating cost function and solving it, the prosumer's energy consumption optimization strategy is made accurate and feasible while considering the full life cycle cost of the energy storage equipment.

[0050] The energy consumption optimization method and device for prosumers considering energy storage life loss provided in the embodiments of the present application can be applied to Figure 1 In the prosumer energy optimization system shown, see Figure 1 , Figure 1 This is a schematic diagram of the architecture of a prosumer energy optimization system provided in an embodiment of the present application. The prosumer energy optimization system 100 includes a terminal 101 and a server 102. Terminal 101 can communicate with server 102 via a network. Terminal 101 refers to a device used by a user, such as a smartphone or computer. In this solution, terminal 101 provides an interface for users to interact with the prosumer energy optimization system 100 that considers energy storage life loss. Through terminal 101, users can interact with the prosumer energy optimization system 100 that considers energy storage life loss, receive the prosumer energy optimization strategy obtained through solution from server 102, and display it on the user interface. Users can use terminal 101 to understand the prosumer energy optimization strategy being implemented in the current time period and can also use terminal 101 to set or modify multiple parameters required for determining the prosumer energy optimization strategy, such as the prosumer photovoltaic cost coefficient and the prosumer demand response comfort loss coefficient.

[0051] Server 102 is a remote computer used to process large amounts of computing tasks and store data. In this solution, server 102 is responsible for constructing electricity costs, comfort loss costs due to energy usage variations, energy storage lifespan loss costs, and photovoltaic system operation and maintenance costs based on the acquired data. Server 102 then constructs a total operating cost function for the prosumer based on electricity costs, comfort loss costs due to energy usage variations, energy storage lifespan loss costs, and photovoltaic system operation and maintenance costs. Server 102 then solves this total operating cost function based on total energy usage optimization constraints, energy storage system constraints, interruptible load constraints, transferable load constraints, and power balance constraints to determine the prosumer's energy usage strategy.

[0052] Based on this, the present application provides a method and device for optimizing energy consumption by producers and consumers taking into account the loss of energy storage life. The present application is described in detail below in conjunction with the accompanying drawings.

[0053] See also Figure 2 , Figure 2 This is a flow chart of a method for optimizing energy consumption by prosumers taking into account energy storage life loss, as provided in an embodiment of the present application. Figure 2 As shown, the method includes the following steps:

[0054] S201, determining the electricity purchase price of the prosumer from the grid and the electricity sales price of the prosumer to the grid through electricity price information calculated by the grid based on the output of new energy.

[0055] The power grid collects real-time data on renewable energy generation through various monitoring devices and sensors installed on wind power, photovoltaic power generation facilities, and other renewable energy generation facilities. This data includes, but is not limited to, power generation, power, voltage, and frequency. For example, in a large wind farm, each wind turbine is equipped with a monitoring device that transmits its operating data to a farm-level monitoring system, which then aggregates and uploads it to the power grid dispatch center.

[0056] The power grid uses specialized power market pricing models and algorithms to calculate the real-time electricity price based on real-time monitoring of renewable energy output, grid load forecasts, power system operating status, and other relevant factors. This price is dynamic, taking into account factors such as power supply and demand, generation costs, and grid operating efficiency at different times. The power grid then communicates this real-time price information to relevant prosumers and consumers through specific information dissemination channels, such as the power market trading platform, official websites, and text message notifications. These prosumers then use this information to determine the price they will purchase and sell electricity to the power grid.

[0057] Specifically, prosumers can determine the price they purchase and sell electricity to the grid based on factors such as their own renewable energy generation costs, expected revenue, market competition, and their assessment of future electricity market price trends. For example, if a prosumer anticipates future price increases and the current real-time price is relatively low, they will choose to purchase more electricity at the current real-time price. Conversely, if a prosumer believes the current price is too high and their electricity needs are not urgent, they will temporarily reduce their purchases.

[0058] When it comes to electricity prices sold to the grid, if a prosumer believes its own generation costs are low and anticipates high market prices, it will set a relatively high price. Conversely, if a prosumer wants to sell electricity quickly, it will reference real-time market prices and set a price below the market average.

[0059] S202 , obtaining the electricity cost of the prosumer based on the electricity purchase price of the prosumer from the grid, the electricity sale price of the prosumer to the grid, the electricity power purchased from the grid by the prosumer, and the electricity power sold to the grid by the prosumer.

[0060] The power purchased by a prosumer from the grid refers to the amount of power consumed per unit time when drawing electricity from the grid, typically measured in kilowatts (kW) or megawatts (MW). When a prosumer's electrical equipment runs, it draws energy from the grid to meet its electricity needs. This energy draw rate is the purchased power.

[0061] The power sold by a prosumer to the grid refers to the amount of power delivered per unit time when the prosumer transmits excess electricity generated by its own renewable energy generation equipment (such as solar panels and wind turbines) to the grid. This power is also measured in kilowatts (kW) or megawatts (MW). When a prosumer's renewable energy generation exceeds its own electricity needs, the excess electricity can be sold to the grid. The rate at which this power is delivered to the grid is the power sold.

[0062] In one possible implementation, see Figure 3 , Figure 3 This is a flow chart of a method for calculating the electricity cost of a prosumer provided in an embodiment of the present application. Figure 3As shown, according to the electricity purchase price of the prosumer from the grid, the electricity selling price of the prosumer to the grid, the electricity purchase power of the prosumer from the grid and the electricity selling power of the prosumer to the grid, the electricity cost of the prosumer is obtained, including: obtaining a first product of the electricity purchase price of the prosumer from the grid and the electricity purchase power of the prosumer from the grid; obtaining a second product of the electricity selling price of the prosumer to the grid and the electricity selling power of the prosumer to the grid; obtaining a first difference between the first product and the second product, the first difference being the electricity cost of the prosumer.

[0063] Among them, the electricity cost of the prosumer is equal to the difference between the electricity purchase fee and the electricity sales fee. At a certain moment, the electricity purchase fee is the product of the electricity purchase price and the purchased power of the prosumer from the power grid, and the electricity sales fee is the product of the electricity sales price and the sold power of the prosumer to the power grid. At this time, the electricity cost of the prosumer is equal to the product of the electricity purchase price and the purchased power of the prosumer from the power grid minus the product of the electricity sales price and the sold power of the prosumer to the power grid.

[0064] Specifically, the calculation formula for electricity cost at time t is as follows:

[0065] ;

[0066] in, is the electricity price that the prosumer purchases from the grid at time t, is the electricity price sold by the prosumer to the grid at time t, and They are respectively the power purchased by the prosumer from the grid and the power sold by the prosumer to the grid at time t.

[0067] It can be seen that in this example, the electricity cost of the prosumer is determined by the difference between the product of the electricity purchase price and the purchased power of the prosumer from the grid and the product of the electricity sales price and the sales power of the prosumer to the grid. This can accurately reflect the economic income and expenditure in its electricity transaction, thereby encouraging the prosumer to optimize energy allocation and flexibly adjust strategies according to electricity price fluctuations.

[0068] S203 : Obtaining the comfort loss cost caused by the energy consumption change of the prosumer according to the demand response comfort loss coefficient of the prosumer, the optimized energy consumption power of the prosumer, and the predicted electricity consumption power of the prosumer.

[0069] The prosumer's demand response comfort loss coefficient is a quantitative indicator of the degree of comfort loss caused by changes in a prosumer's electricity usage behavior when implementing demand response. For example, to respond to the grid's load adjustment requirements, a prosumer may adjust air conditioning temperatures or reduce appliance usage. These actions will have a certain impact on their living or work comfort. In this case, the prosumer's demand response comfort loss coefficient can be used to measure the impact of such actions on their living or work comfort.

[0070] Specifically, the demand response comfort loss coefficient of the prosumer can be configured or changed by the user at the terminal of the prosumer energy optimization system. The demand response comfort loss coefficient of the prosumer can also be established by the user to establish a mathematical model to classify and quantify the various electricity consumption behaviors of the prosumer and the corresponding user comfort. Then, combined with the comfort feedback data of the prosumer under different electricity consumption behaviors, a relationship model between the change in electricity consumption behavior and comfort loss is constructed to obtain the demand response comfort loss coefficient.

[0071] The prosumer's optimized energy usage refers to the most reasonable and economical power usage achieved through optimization algorithms or strategies based on the prosumer's energy production capacity, equipment characteristics, electricity demand, and external environmental factors (such as electricity prices and grid load). This optimized power usage minimizes electricity costs while meeting the prosumer's basic electricity needs.

[0072] Specifically, the optimal energy consumption of prosumers can be determined by establishing an optimization model. First, the optimization objective is determined, such as minimizing electricity costs or maximizing energy efficiency. Then, considering constraints such as the prosumer's energy production capacity, the power range and operating time requirements of the electrical equipment, and the grid's power supply capacity and safety restrictions, an appropriate optimization algorithm, such as linear programming, nonlinear programming, or genetic algorithms, is selected to solve the model and determine the optimal energy consumption of the prosumer.

[0073] The predicted electricity consumption of a prosumer refers to the estimated value of the prosumer's electricity consumption within a certain time period in the future. Specifically, the predicted electricity consumption of a prosumer can be calculated using methods based on historical data and physical models. Methods based on historical data include time series analysis, neural networks, support vector machines, and others. By analyzing the prosumer's past electricity consumption data, exploring the patterns and trends therein, and establishing a forecasting model, future electricity consumption can be predicted. The physical model-based method establishes a corresponding electricity consumption calculation model based on the physical characteristics of the prosumer's electrical equipment, such as type, quantity, usage patterns, and production activities. It then combines information such as future production plans and living arrangements to predict electricity consumption.

[0074] In one possible implementation, see Figure 4 , Figure 4 This is a flow chart of a method for obtaining the comfort loss cost caused by the energy consumption change of the prosumer provided in an embodiment of the present application, such as Figure 4 As shown, based on the demand response comfort loss coefficient of the prosumer, the optimized energy consumption power of the prosumer, and the predicted electricity consumption power of the prosumer, the comfort loss cost caused by the energy consumption change of the prosumer is obtained, including:

[0075] Obtain a second difference between the optimized electricity consumption of the prosumer and the predicted electricity consumption of the prosumer; obtain a third product of the demand response comfort loss coefficient of the prosumer and the square of the second difference, wherein the third product is the comfort loss cost caused by the energy consumption change of the prosumer.

[0076] The difference between the optimized and predicted power consumption of a prosumer reflects the degree of change in actual power consumption behavior. The larger the difference, the greater the change in power consumption behavior, and the greater the potential impact on comfort. This difference is squared to highlight the impact of the change on comfort loss, as comfort loss often does not exhibit a simple linear relationship with power consumption changes. The square of this difference is then multiplied by the demand response comfort loss coefficient. Since the demand response comfort loss coefficient can be adjusted for different prosumers or different power consumption scenarios, the resulting comfort loss cost caused by the prosumer's energy consumption changes can comprehensively account for comfort losses caused by individual differences and power consumption changes under different power consumption scenarios.

[0077] Specifically, the calculation formula for the comfort loss cost caused by the energy consumption change of the prosumer at time t is as follows:

[0078] ;

[0079] in, is the comfort loss cost caused by the energy consumption change of the prosumer at time t, is the demand response comfort loss coefficient at time t, and are the optimized electricity consumption and predicted electricity consumption of the prosumer at time t respectively.

[0080] Optionally, since comfort loss is often not in a simple linear relationship with changes in electricity consumption, in different electricity consumption scenarios, in addition to directly multiplying the square of the difference by the demand response comfort loss coefficient to obtain the comfort loss cost caused by the energy consumption change of the prosumer, you can also use an exponential function (such as an exponential function with the natural constant e as the base) or a piecewise function (such as a linear function when the difference is small; a quadratic function or exponential function when the interpolation is large) on the difference, and then multiply the obtained value by the demand response comfort loss coefficient to obtain the comfort loss cost caused by the energy consumption change of the prosumer.

[0081] It can be seen that in this example, by multiplying the square of the difference between the optimized electricity consumption of the prosumer and the predicted electricity consumption by the demand response comfort loss coefficient, the comfort loss cost caused by the energy consumption change of the prosumer can be obtained. This can comprehensively consider the comfort loss caused by different individual differences and electricity consumption changes in electricity consumption scenarios.

[0082] S204 , obtaining the energy storage life loss cost of the prosumer according to the energy storage life loss cost coefficient of the prosumer, the energy storage system charging power, and the energy storage system discharging power.

[0083] Among them, the prosumer's energy storage life loss cost coefficient is used to measure the cost corresponding to the life loss of the energy storage equipment caused by the unit charge and discharge amount during the charging and discharging process of the energy storage system. Different types of energy storage technologies (such as lithium-ion batteries, lead-acid batteries, etc.) have different life loss characteristics. This coefficient can reflect the relationship between the value loss of the energy storage equipment caused by charging and discharging and the charge and discharge amount. Specifically, the prosumer's energy storage life loss cost coefficient can be obtained directly from the relevant information provided by the energy storage equipment manufacturer, and then set or changed by the user at the terminal of the prosumer's energy optimization system. Alternatively, the prosumer's energy storage life loss cost coefficient can also be obtained by conducting long-term experimental tests on the energy storage equipment, collecting data on the number of charge and discharge cycles and the performance degradation of the equipment, and then calculating the life loss cost corresponding to the unit charge and discharge amount based on the initial cost and expected life of the equipment, thereby obtaining this coefficient.

[0084] The energy storage system charging power refers to the amount of electrical power absorbed per unit time during the charging process. This reflects the rate at which the energy storage system receives electrical energy and is typically measured in kilowatts (kW) or megawatts (MW). Specifically, the energy storage system charging power can be directly measured using power measurement equipment installed in the energy storage system's charging circuit.

[0085] The energy storage system discharge power refers to the amount of electrical power released to an external load per unit time during the energy storage system's discharge process. This power reflects the system's ability to provide electrical energy to the outside world and is typically measured in kilowatts (kW) or megawatts (MW). Specifically, the energy storage system's charging power can be directly measured using power measurement equipment installed in the system's discharge circuit.

[0086] The prosumer's energy storage lifespan loss cost refers to the cost incurred by the prosumer due to the loss of energy storage equipment lifespan during the charging and discharging process. Specifically, the energy storage system's charge and discharge capacity over a specific time period can be calculated based on the charging and discharging power. This is then multiplied by the energy storage lifespan loss cost coefficient to obtain the energy storage lifespan loss cost for that period.

[0087] In one possible implementation, see Figure 5 , Figure 5 This is a flow chart of a method for obtaining the energy storage life loss cost of a prosumer provided in an embodiment of the present application, such as Figure 5 As shown, based on the energy storage life loss cost coefficient of the prosumer, the energy storage system charging power, and the energy storage system discharging power, the energy storage life loss cost of the prosumer is obtained, including:

[0088] Determine the energy storage life loss cost coefficient of the prosumer based on the energy storage efficiency, the rated power of the energy storage, the unit operation and maintenance cost of the target time period, the estimated operating life of the energy storage system, the discount rate, the number of cycles in the target time period, the energy storage cost of the energy storage, the power storage cost of the energy storage and the rated service life of the energy storage; obtain a first sum of the charging power of the energy storage system and the discharging power of the energy storage system; obtain a fourth product of the first sum and the energy storage life loss cost coefficient of the prosumer, and the fourth product is the energy storage life loss cost coefficient of the prosumer.

[0089] Energy storage efficiency refers to the energy conversion efficiency of the energy storage system during the charging and discharging process, typically expressed as a percentage. The rated power of energy storage refers to the power that the energy storage system can continuously and stably output or input, measured in units such as watts (W), kilowatts (kW), or megawatts (MW). The unit operation and maintenance cost for a target time period refers to the maintenance and operating costs required per unit time or per unit charge and discharge volume of the energy storage system within a specific target time period. Maintenance and operating costs include equipment maintenance, overhaul, component replacement, and labor management expenses. The estimated operating life of an energy storage system is the estimated time period over which the energy storage system will operate normally and maintain a certain performance level, based on factors such as energy storage technology, equipment quality, and operating conditions. The discount rate refers to the ratio used to convert future costs or benefits into current values ​​and can be set or modified by the user directly on the prosumer energy optimization system terminal. The number of cycles in a target time period refers to the number of complete charge and discharge cycles the energy storage system completes within the target time period. The energy storage cost of energy storage refers to the cost required to store a unit of energy (e.g., per kilowatt-hour), including the purchase and installation costs of the energy storage equipment, as well as other costs related to energy storage. The power storage cost of energy storage refers to the cost of the equipment required to achieve the rated power. The rated service life of energy storage refers to the expected service life of the energy storage system, which can be directly obtained from the technical data provided by the energy storage equipment manufacturer.

[0090] Among them, energy storage efficiency, rated power of energy storage, unit operation and maintenance cost of the target time period, estimated operating life of the energy storage system, number of cycles in the target time period, energy storage cost of energy storage, power storage cost of energy storage and rated service life of energy storage can all be directly obtained.

[0091] Among them, the charging and discharging amount of the energy storage system in a certain time period is calculated through the charging power and discharging power of the energy storage system, and then multiplied by the energy storage life loss cost coefficient to obtain the energy storage life loss cost in that time period.

[0092] Specifically, the calculation formula for energy storage life loss cost is as follows:

[0093] ;

[0094] in, is the energy storage life loss cost of the prosumer at time t, is the energy storage life loss cost coefficient at time t, and are the charging power and discharging power of the energy storage system at time t respectively.

[0095] It can be seen that in this example, the charge and discharge amount of the energy storage system within a certain time period is calculated using the energy storage system charging power and the energy storage system discharging power, and then multiplied by the energy storage life loss cost coefficient to obtain the energy storage life loss cost within the time period. This can accurately reflect the energy storage life loss cost under different situations.

[0096] In one possible implementation, when determining the energy storage life loss cost coefficient of the prosumer based on energy storage efficiency, rated power of energy storage, unit operation and maintenance cost in the target time period, estimated operating life of the energy storage system, discount rate, number of cycles in the target time period, energy storage cost of energy storage, power storage cost of energy storage, and rated service life of energy storage, the calculation formula of the energy storage life loss cost coefficient of the prosumer is as follows:

[0097] ;

[0098] in, is the energy storage efficiency; is the rated power of the energy storage; The unit operation and maintenance cost for the target time period; The estimated operating life of the energy storage system; is the discount rate; is the number of cycles of the target time period; the energy storage cost for said stored energy; is the power storage cost of the energy storage; is the rated service life of the energy storage.

[0099] It can be seen that in this example, by considering technical indicators such as energy storage efficiency and power, as well as economic factors such as operation and maintenance costs and estimated lifespan, and then reflecting the time value of money through the discount rate, all factors affecting the energy storage life loss cost are taken into consideration. This is conducive to ensuring the accuracy of the calculated energy storage life loss cost coefficient, and facilitating the subsequent accurate assessment of energy storage life loss costs for prosumers.

[0100] S205 , obtaining the photovoltaic system operation and maintenance cost of the prosumer according to the photovoltaic cost coefficient and the photovoltaic predicted output of the prosumer.

[0101] The photovoltaic cost coefficient for prosumers measures the proportion of costs incurred per unit of electricity generated during photovoltaic power generation. PV forecast output is an estimate of the power generation of a photovoltaic system over a specific time period in the future. PV forecasting can be based on physical models, calculated based on solar radiation models and photovoltaic cell electrical characteristics models. Alternatively, data-driven approaches, such as using machine learning algorithms like neural networks and support vector machines, can be used to learn and analyze large amounts of historical data to develop forecast models to estimate PV output.

[0102] The O&M costs of a prosumer's PV system refer to the maintenance and operating expenses incurred by the prosumer throughout the system's lifecycle to ensure proper operation and power generation efficiency. These expenses include regular equipment inspections, cleaning, repairs, parts replacement, personnel training, and the cost of monitoring system operations.

[0103] In one possible implementation, the photovoltaic system operation and maintenance cost of the prosumer is obtained based on the photovoltaic cost coefficient and the photovoltaic predicted output of the prosumer, including:

[0104] A fifth product of the photovoltaic cost coefficient and the photovoltaic predicted output of the prosumer is obtained, where the fifth product is the photovoltaic system operation and maintenance cost of the prosumer.

[0105] The calculation formula for the operation and maintenance costs of a prosumer’s photovoltaic system is as follows:

[0106] ;

[0107] in, is the photovoltaic system operation and maintenance cost of the prosumer at time t, is the photovoltaic cost coefficient of the prosumer at time t, Contribute to photovoltaic prediction output.

[0108] It can be seen that in this example, the photovoltaic cost coefficient reflects the comprehensive cost per unit of power generation, and the photovoltaic predicted output reflects the power generation. The photovoltaic system operation and maintenance cost of the prosumer is obtained by multiplying the photovoltaic cost coefficient of the prosumer and the photovoltaic predicted output, which is conducive to quickly and accurately obtaining the photovoltaic system operation and maintenance cost of the prosumer.

[0109] S206 , constructing a total operating cost function for the prosumer based on the electricity cost, the comfort loss cost caused by energy consumption changes, the energy storage life loss cost, and the photovoltaic system operation and maintenance cost.

[0110] In one possible implementation, see Figure 6 , Figure 6 This is a flow chart of a method for constructing a total cost function for producers and consumers provided in an embodiment of the present application. Figure 6 As shown in FIG, based on the electricity cost, the comfort loss cost caused by energy consumption changes, the energy storage life loss cost, and the photovoltaic system operation and maintenance cost, the total operating cost function of the prosumer is constructed, including:

[0111] The total operating cost is obtained based on the sum of the electricity cost, the comfort loss cost caused by energy consumption changes, the energy storage life loss cost, and the photovoltaic system operation and maintenance cost; the total operating cost function of the prosumer is constructed by minimizing the total operating cost within the regulation cycle.

[0112] Specifically, the formula for the total operating cost function of the prosumer is as follows:

[0113] ;

[0114] Where C is the total operating cost of the prosumer, To schedule time slots, and are the photovoltaic system operation and maintenance costs and energy storage life loss costs of the prosumer at time t, respectively; The cost of comfort loss caused by changes in energy consumption; is the electricity interaction cost between prosumers and the grid.

[0115] It can be seen that in this example, by comprehensively considering the electricity cost, comfort loss cost, energy storage life loss cost and photovoltaic system operation and maintenance cost, we avoid focusing on a single cost and ignoring other related costs. Then, constructing a function with the goal of minimizing the total operating cost is conducive to the subsequent solution of the function and accurately obtaining the energy optimization strategy for producers and consumers.

[0116] S207 , solving the total operating cost function of the prosumer based on the total energy consumption optimization constraint, the energy storage system constraint, the interruptible load constraint, the transferable load constraint, and the power balance constraint, and determining the prosumer's energy consumption strategy.

[0117] The specific constraints are as follows:

[0118] (1) Total energy consumption constraints:

[0119] In order to meet the basic electricity needs of prosumers, the total energy consumption needs to be within the set range:

[0120] ;

[0121] ;

[0122] in, and Optimize the minimum and maximum values ​​of total power for the load within the cycle; and The minimum and maximum values ​​of power are optimized for the load within the time slot.

[0123] (2) Energy storage system constraints

[0124] Assuming that the energy storage system of each prosumer is mainly composed of batteries, the charging and discharging constraints it should meet are as follows:

[0125] ;

[0126] ;

[0127] ;

[0128] ;

[0129] in, and are the maximum values ​​of charging and discharging power of the energy storage system respectively; is the state of charge of the energy storage system at time t; and are the maximum and minimum values ​​of the energy storage system capacity.

[0130] Furthermore, the energy storage system can only be charged or discharged at any given moment:

[0131] ;

[0132] This section uses the large M method to linearize the above nonlinear constraints. It is a binary state variable and M is assumed to be an infinite constant, as shown in the following formula:

[0133] .

[0134] (3) Interruptible load constraints

[0135] Interruptible loads mainly refer to temperature-controlled loads such as air conditioners and water heaters. Temperature-controlled loads can be uniformly modeled as follows:

[0136] ;

[0137] ;

[0138] ;

[0139] in, is the temperature of the temperature-controlled load r at time t; is the equivalent thermal resistance; is the equivalent thermal capacitance; is the equivalent heat ratio.

[0140] in, is the on / off state of the temperature control load r at time t (on is 1, off is 0); is the outdoor temperature; Set the temperature for the load, is the preset fluctuation value; is the overall power consumption of the temperature control load; is the rated power of the temperature-controlled load r in the on / off state; is the number of temperature control loads.

[0141] (4) Transferable load constraints

[0142] Transferable loads can be operated at a time when electricity prices are relatively low within the set timeframe for completing tasks. Once work begins, it cannot be suspended. The power consumption model for this type of load can be expressed as:

[0143] ;

[0144] in, is the total power consumption of the transferable load at time t, is the power of the transferable load k at time t, is the starting state variable of the transferable load k at time t, is the number of transferable loads.

[0145] The constraints that the transferable load needs to meet are:

[0146] ;

[0147] ;

[0148] in, and They are the earliest start time and the latest start time of the transferable load k, and are the start and end working time of the transferable load k respectively. Among them, the second formula indicates whether the transferable load k is in the starting state at time t within the working interval. If it is in the starting state, it is 1, and if it is not in the starting state, it is 0. Indicates from The minimum continuous working time of the transferable load k from the time.

[0149] (5) Power balance constraints

[0150] ;

[0151] ;

[0152] Since each prosumer can only participate in transactions as a buyer or seller during the same period, the power it purchases and sells should meet the following constraints (linearized using the Big M method):

[0153] .

[0154] In one possible implementation, solving the total operating cost function of the prosumer based on the total energy optimization constraint, the energy storage system constraint, the interruptible load constraint, the transferable load constraint, and the power balance constraint includes:

[0155] Under the premise of satisfying the total energy consumption optimization constraints, energy storage system constraints, interruptible load constraints, transferable load constraints and power balance constraints, the total operating cost function of the prosumer is used as the particle fitness at each optimization in the improved particle swarm algorithm based on reverse learning. If the number of iterations exceeds the preset number of iterations, or the error of any iteration is less than the preset error, the solution is terminated, and the particle position corresponding to the swarm optimal value of the improved particle swarm algorithm based on reverse learning is determined as the solution to the total operating cost function of the prosumer.

[0156] The preset number of iterations and the preset error can be set or changed by the user at the terminal of the prosumer energy optimization system.

[0157] Among them, the improved particle swarm algorithm based on reverse learning is used to solve the constructed total operating cost function C of the prosumer. The specific steps include:

[0158] (1) Initialize the speed and position of the particle (initialize the power generation and consumption data of the prosumer, the initial purchase and sale electricity price and other parameters).

[0159] (2) In the process of iterative optimization of the particle swarm, the energy optimization strategy of the prosumer is generated according to the particle position of each optimization.

[0160] (3) Calculate the particle fitness F (i.e., objective function C) under this optimization, update the speed and position of the particle swarm according to the speed and position update formula, and perform reverse learning on the particle swarm. Assuming that there is a feasible solution x in the search space, its reverse solution is The calculation formula is:

[0161] ;

[0162] ;

[0163] Optional, reverse solution The calculation formula can also be:

[0164] ;

[0165] ;

[0166] Among them, rand() is a random number between 0 and 1. is the j-th dimension position of the i-th particle at the m-th iteration optimization; is the historical minimum value of the i-th particle in the j-th dimension during the m-th optimization; is the historical maximum value of the i-th particle in the j-th dimension during the m-th optimization.

[0167] (4) The adaptive weight w that changes with the particle fitness F is calculated according to the following formula.

[0168] ;

[0169] in, is the current fitness value, is the average fitness value, is the minimum fitness value.

[0170] (5) Determine the number of iterations and the error range. Within the range of the number of iterations, perform multiple optimization searches to obtain the optimal values ​​of the individual particles and the optimal values ​​of the group. The particle position corresponding to the final optimal value of the group is the optimal solution for the energy optimization of the prosumer.

[0171] As can be seen, in this example, under the premise of multiple constraints (total energy optimization, energy storage system, interruptible load, transferable load, and power balance), the total operating cost function of the prosumer is set as particle fitness. Using an improved particle swarm algorithm based on reverse learning, the optimal solution can be effectively searched. Using a preset number of iterations and a preset error as termination conditions, the algorithm avoids ineffective excessive iterations and stops promptly when the accuracy requirements are met. The particle position corresponding to the final swarm optimal value is used as the function solution to optimize the total operating cost of the prosumer.

[0172] See also Figure 7 , Figure 7 This is a schematic diagram of the structure of a prosumer energy optimization device that takes into account the energy storage life loss provided in an embodiment of the present application. Figure 7 As shown, the prosumer energy optimization device 700 considering energy storage life loss includes:

[0173] An acquisition module 701 is configured to determine the electricity price at which a prosumer purchases electricity from the grid and the electricity price at which the prosumer sells electricity to the grid using electricity price information calculated by the grid based on the output of renewable energy;

[0174] and for obtaining the electricity cost of the prosumer based on the electricity purchase price of the prosumer from the grid, the electricity sale price of the prosumer to the grid, the electricity power purchased by the prosumer from the grid, and the electricity power sold by the prosumer to the grid;

[0175] and for obtaining a comfort loss cost caused by a change in energy consumption of the prosumer based on a demand response comfort loss coefficient of the prosumer, the optimized energy consumption power of the prosumer, and the predicted electricity consumption power of the prosumer;

[0176] and for obtaining the energy storage life loss cost of the prosumer according to the energy storage life loss cost coefficient of the prosumer, the energy storage system charging power, and the energy storage system discharging power;

[0177] and for obtaining the photovoltaic system operation and maintenance cost of the prosumer based on the photovoltaic cost coefficient and photovoltaic predicted output of the prosumer;

[0178] A construction module 702 is used to construct a total operating cost function for the prosumer based on electricity costs, comfort loss costs caused by energy consumption changes, energy storage life loss costs, and photovoltaic system operation and maintenance costs;

[0179] The solution module 703 is used to solve the total operating cost function of the prosumer according to the total energy consumption optimization constraint, the energy storage system constraint, the interruptible load constraint, the transferable load constraint and the power balance constraint, and determine the energy consumption strategy of the prosumer.

[0180] In one possible implementation, in terms of obtaining the electricity usage cost of the prosumer based on the electricity purchase price of the prosumer from the grid, the electricity sales price of the prosumer to the grid, the electricity purchase power of the prosumer from the grid, and the electricity sales power of the prosumer to the grid, the acquisition module 701 is specifically used to: obtain a first product of the electricity purchase price of the prosumer from the grid and the electricity purchase power of the prosumer from the grid; obtain a second product of the electricity sales price of the prosumer to the grid and the electricity sales power of the prosumer to the grid; obtain a first difference between the first product and the second product, the first difference being the electricity usage cost of the prosumer.

[0181] In one possible implementation, in terms of obtaining the comfort loss cost caused by the energy consumption change of the prosumer based on the demand response comfort loss coefficient of the prosumer, the optimized energy consumption power of the prosumer, and the predicted electricity consumption power of the prosumer, the acquisition module 701 is specifically used to: obtain the second difference between the optimized electricity consumption power of the prosumer and the predicted electricity consumption power of the prosumer; obtain the third product of the demand response comfort loss coefficient of the prosumer and the square of the second difference, and the third product is the comfort loss cost caused by the energy consumption change of the prosumer.

[0182] In one possible implementation, in terms of obtaining the energy storage life loss cost of the prosumer based on the energy storage life loss cost coefficient, the energy storage system charging power and the energy storage system discharging power of the prosumer, the acquisition module 701 is specifically used to: determine the energy storage life loss cost coefficient of the prosumer based on the energy storage efficiency, the rated power of the energy storage, the unit operation and maintenance cost of the target time period, the estimated operating life of the energy storage system, the discount rate, the number of cycles in the target time period, the energy storage cost of the energy storage, the power storage cost of the energy storage and the rated service life of the energy storage; obtain a first sum of the charging power of the energy storage system and the discharging power of the energy storage system; obtain a fourth product of the first sum and the energy storage life loss cost coefficient of the prosumer, and the fourth product is the energy storage life loss cost coefficient of the prosumer.

[0183] In one possible implementation, the calculation formula for the prosumer's energy storage life loss cost coefficient is as follows:

[0184] ;

[0185] in, is the energy storage efficiency; is the rated power of the energy storage; The unit operation and maintenance cost for the target time period; The estimated operating life of the energy storage system; is the discount rate; is the number of cycles of the target time period; the energy storage cost for said stored energy; is the power storage cost of the energy storage; is the rated service life of the energy storage.

[0186] In one possible implementation, in terms of obtaining the photovoltaic system operation and maintenance cost of the prosumer based on the photovoltaic cost coefficient and photovoltaic predicted output of the prosumer, the acquisition module 701 is specifically used to: obtain the fifth product of the photovoltaic cost coefficient and the photovoltaic predicted output of the prosumer, and the fifth product is the photovoltaic system operation and maintenance cost of the prosumer.

[0187] In one possible implementation, in terms of constructing a total cost function for prosumers based on electricity costs, comfort loss costs caused by changes in energy consumption, energy storage life loss costs, and photovoltaic system operation and maintenance costs, construction module 702 is specifically used to: obtain the total operating cost based on the sum of the electricity costs, comfort loss costs caused by changes in energy consumption, energy storage life loss costs, and photovoltaic system operation and maintenance costs; and construct the total operating cost function for prosumers by minimizing the total operating cost within the control cycle.

[0188] In one possible implementation, in terms of solving the total operating cost function of the prosumer based on the total energy consumption optimization constraint, the energy storage system constraint, the interruptible load constraint, the transferable load constraint and the power balance constraint, the solution module 703 is specifically used to: on the premise of satisfying the total energy consumption optimization constraint, the energy storage system constraint, the interruptible load constraint, the transferable load constraint and the power balance constraint, use the total operating cost function of the prosumer as the particle fitness under each optimization in the improved particle swarm algorithm based on reverse learning; if the number of iterations exceeds the preset number of iterations, or the error of any iteration is less than the preset error, the solution is terminated, and the particle position corresponding to the swarm optimal value of the improved particle swarm algorithm based on reverse learning is determined to be the solution of the total operating cost function of the prosumer.

[0189] It is worth noting that the specific functional implementation of the prosumer energy optimization device 700 considering the energy storage life loss is shown in the above Figure 2 The description of the prosumer energy optimization method considering energy storage lifespan loss is shown. For example, acquisition module 701 is used to implement the relevant contents of S201-S205, construction module 702 is used to implement the relevant contents of S206, and solution module 703 is used to implement the relevant contents of S207. The various units or modules in the prosumer energy optimization device considering energy storage lifespan loss 700 can be individually or completely combined into one or more additional units or modules, or some of the units or modules can be further divided into multiple functionally smaller units or modules to achieve the same operation without affecting the technical effects of the embodiments of the present invention. The above-mentioned units or modules are divided according to logical functions. In actual applications, the functions of one unit (or module) are implemented by multiple units (or modules), or the functions of multiple units (or modules) are implemented by one unit (or module).

[0190] According to the description of the above method embodiment and related device embodiment, please refer to Figure 8 , Figure 8 It is a structural diagram of a computer provided in an embodiment of the present application. Figure 8 The computer 800 shown includes a processor 801 , a memory 802 , a communication interface 803 , and a bus 804 . The processor 801 , the memory 802 , and the communication interface 803 are communicatively connected to each other via the bus 804 .

[0191] Optionally, the memory 802 is a ROM, a static storage device, a dynamic storage device or a RAM.

[0192] The memory 802 can store executable program codes. When the executable program codes stored in the memory 802 are executed by the processor 801, the processor 801 and the communication interface 803 are used to execute the program codes. Figure 2 The illustrated embodiment shows various steps of a method for optimizing energy consumption of a prosumer by considering energy storage life loss.

[0193] The processor 801 uses a general-purpose CPU, a microprocessor, an application-specific integrated circuit ASIC, a GPU or one or more integrated circuits to execute relevant programs to implement the energy optimization method for prosumers taking into account the energy storage life loss of the method embodiment of the present application.

[0194] Processor 801 can also be an integrated circuit chip with signal processing capabilities. During implementation, the various steps of the prosumer energy optimization method considering energy storage lifespan loss described herein can be completed via hardware integrated logic circuits or software instructions within processor 801. Optionally, processor 801 can be a general-purpose processor, DSP, ASIC, FPGA, or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component. The processor can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of this application. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in conjunction with the embodiments of this application can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules within the decoding processor. The optional software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other storage media well-known in the art. The storage medium is located in the memory 802, and the processor 801 reads the information in the memory 802, and combines its hardware to complete the functions required to be executed by the modules included in the prosumer energy optimization device 700 considering the energy storage life loss in an embodiment of the present application, or executes the prosumer energy optimization method considering the energy storage life loss in the method embodiment of the present application.

[0195] The communication interface 803 uses, for example but not limited to, a transceiver or other transceiver-related device.

[0196] The bus 804 may include a path for transmitting information between various components of the computer 800 (eg, the memory 802 , the processor 801 , and the communication interface 803 ).

[0197] It should be noted that although Figure 8 The computer 800 shown only shows a memory, a processor, and a communication interface. However, in the specific implementation process, those skilled in the art should understand that the computer 800 also includes other devices necessary for normal operation. At the same time, according to specific needs, those skilled in the art should understand that the computer 800 may also include hardware devices that implement other additional functions. In addition, those skilled in the art should understand that the computer 800 may also include only the devices necessary to implement the embodiments of the present application, and does not necessarily include Figure 8 All devices shown in .

[0198] An embodiment of the present application provides a computer-readable storage medium, in which a computer program for electronic data exchange is stored. The computer program includes execution instructions, and the execution instructions are used to execute part or all of the steps of any one of the methods for optimizing energy consumption by prosumers considering energy storage life loss as described in the above-mentioned embodiments of the method for optimizing energy consumption by prosumers considering energy storage life loss. The above-mentioned computer includes an electronic terminal device.

[0199] An embodiment of the present application provides a computer program product, wherein the computer program product includes a computer program, and the computer program is operable to enable a computer to perform part or all of the steps of any of the methods for optimizing energy consumption by producers and consumers taking into account the loss of energy storage life as described in the above method embodiments. The computer program product can be a software installation package.

[0200] It should be noted that for any of the aforementioned embodiments of the method for optimizing energy consumption by prosumers taking into account the loss of energy storage life, for the sake of simplicity, they are all expressed as a series of action combinations. However, those skilled in the art should be aware that this application is not limited to the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by this application.

[0201] The above is a detailed introduction to the embodiments of the present application. This article uses specific examples to illustrate the principles and implementation methods of a method and device for optimizing energy consumption by producers and consumers that takes into account the life loss of energy storage. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for general technical personnel in this field, based on the idea of ​​a method and device for optimizing energy consumption by producers and consumers that takes into account the life loss of energy storage, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

[0202] The present application is described with reference to the flowcharts and / or block diagrams of the methods, hardware products, and computer program products of the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 produce 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.

[0203] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1The memory may include a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0204] Although the present application is described herein in conjunction with various embodiments, in the process of implementing the claimed application, those skilled in the art may understand and implement other variations of the disclosed embodiments by reviewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality of components or steps. The fact that certain measures are recited in different dependent claims does not mean that these measures cannot be combined to produce good results.

[0205] A person skilled in the art can understand that all or part of the steps in the various methods of any of the above-mentioned embodiments of the method for optimizing energy consumption of producers and consumers taking into account the loss of energy storage life can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (English: Read-Only Memory, abbreviated: ROM), a random access memory (English: Random Access Memory, abbreviated: RAM), a magnetic disk or an optical disk, etc.

[0206] It can be understood that any product that is controlled or configured to execute the processing method of the flowchart described in an embodiment of the method for optimizing energy consumption of producers and consumers taking into account the life loss of energy storage, such as the device and computer program product of the above flowchart, falls within the scope of the related products described in this application.

[0207] Obviously, those skilled in the art may make various modifications and variations to the method and apparatus for optimizing prosumer energy use that takes into account energy storage life loss provided herein without departing from the spirit and scope of this application. Thus, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application is intended to include such modifications and variations.

Claims

1. A method for optimizing energy consumption by prosumers taking into account energy storage life loss, characterized in that: The method comprises: Determine the electricity price at which prosumers purchase electricity from the grid and the electricity price at which they sell electricity to the grid using electricity price information calculated by the grid based on the output of renewable energy; Obtaining the electricity cost of the prosumer based on the electricity purchase price of the prosumer from the grid, the electricity sale price of the prosumer to the grid, the power purchased by the prosumer from the grid, and the power sold by the prosumer to the grid; Obtaining a comfort loss cost caused by a change in the prosumer's energy consumption based on a preset demand response comfort loss coefficient of the prosumer, the prosumer's optimized energy consumption power, and the prosumer's predicted electricity consumption power, wherein the optimized energy consumption power and the predicted electricity consumption power are obtained by solving a model; Obtaining the energy storage life loss cost of the prosumer based on the preset energy storage life loss cost coefficient of the prosumer, the energy storage system charging power, and the energy storage system discharging power; Obtaining the photovoltaic system operation and maintenance cost of the prosumer based on the photovoltaic cost coefficient and photovoltaic predicted output of the prosumer, wherein the photovoltaic prediction process is obtained by solving a model; Constructing a total operating cost function for the prosumer based on the electricity cost, the comfort loss cost caused by energy consumption changes, the energy storage life loss cost, and the photovoltaic system operation and maintenance cost; Under the premise of satisfying the total energy consumption optimization constraints, energy storage system constraints, interruptible load constraints, transferable load constraints and power balance constraints, the total operating cost function of the prosumer is used as the particle fitness at each optimization in the improved particle swarm algorithm based on reverse learning. If the number of iterations exceeds the preset number of iterations, or the error of any iteration is less than the preset error, the solution is terminated, and the particle position corresponding to the swarm optimal value of the improved particle swarm algorithm based on reverse learning is determined as the solution to the total operating cost function of the prosumer.

2. The method according to claim 1, wherein The obtaining of the electricity cost of the prosumer according to the electricity purchase price of the prosumer from the grid, the electricity sale price of the prosumer to the grid, the power purchased by the prosumer from the grid, and the power sold by the prosumer to the grid comprises: Obtaining a first product of the electricity price purchased by the prosumer from the power grid and the power purchased by the prosumer from the power grid; Obtaining a second product of the electricity price sold by the prosumer to the power grid and the power sold by the prosumer to the power grid; A first difference between the first product and the second product is obtained, where the first difference is the electricity cost of the prosumer.

3. The method according to claim 1, wherein Obtaining the comfort loss cost caused by the prosumer's energy consumption change according to the prosumer's demand response comfort loss coefficient, the prosumer's optimized energy consumption power, and the prosumer's predicted electricity consumption power includes: Obtaining a second difference between the optimized power consumption of the prosumer and the predicted power consumption of the prosumer; A third product of the demand response comfort loss coefficient of the prosumer and the square of the second difference is obtained, where the third product is the comfort loss cost caused by the energy consumption change of the prosumer.

4. The method according to claim 1, wherein The energy storage life loss cost of the prosumer is obtained according to the energy storage life loss cost coefficient of the prosumer, the energy storage system charging power, and the energy storage system discharging power, including: Determining the energy storage life loss cost coefficient for the prosumer based on the energy storage efficiency, the rated power of the energy storage, the unit operation and maintenance cost during the target time period, the estimated operating life of the energy storage system, the discount rate, the number of cycles during the target time period, the energy storage cost of the energy storage, the power storage cost of the energy storage, and the rated service life of the energy storage; Obtaining a first sum of the energy storage system charging power and the energy storage system discharging power; A fourth product of the first sum and the energy storage life loss cost coefficient of the prosumer is obtained, where the fourth product is the energy storage life loss cost coefficient of the prosumer.

5. The method according to claim 4, wherein The calculation formula for the energy storage life loss cost coefficient of the prosumer is as follows: ； in, is the energy storage efficiency; is the rated power of the energy storage; The unit operation and maintenance cost for the target time period; The estimated operating life of the energy storage system; is the discount rate; is the number of cycles of the target time period; the energy storage cost for said stored energy; is the power storage cost of the energy storage; is the rated service life of the energy storage.

6. The method according to claim 1, wherein The photovoltaic system operation and maintenance cost of the prosumer is obtained based on the photovoltaic cost coefficient and the photovoltaic predicted output of the prosumer, including: A fifth product of the photovoltaic cost coefficient and the photovoltaic predicted output of the prosumer is obtained, where the fifth product is the photovoltaic system operation and maintenance cost of the prosumer.

7. The method according to any one of claims 1 to 6, wherein: The total cost function for prosumers is constructed based on electricity costs, comfort loss costs caused by energy consumption changes, energy storage life loss costs, and photovoltaic system operation and maintenance costs, including: Obtaining a total operating cost based on the sum of the electricity cost, the comfort loss cost caused by energy consumption changes, the energy storage life loss cost, and the photovoltaic system operation and maintenance cost; The total operating cost function of the prosumer is constructed by minimizing the total operating cost within the regulation cycle.

8. A prosumer energy optimization device taking into account energy storage life loss, characterized in that: The device comprises: An acquisition module is used to determine the electricity purchase price of the prosumer from the grid and the electricity sale price of the prosumer to the grid based on the electricity price information calculated by the grid according to the output of renewable energy; and for obtaining the electricity cost of the prosumer based on the electricity purchase price of the prosumer from the grid, the electricity sale price of the prosumer to the grid, the electricity power purchased by the prosumer from the grid, and the electricity power sold by the prosumer to the grid; and for obtaining a comfort loss cost caused by a change in energy consumption of the prosumer based on a demand response comfort loss coefficient of the prosumer, the optimized energy consumption power of the prosumer, and the predicted electricity consumption power of the prosumer; and for obtaining the energy storage life loss cost of the prosumer according to the energy storage life loss cost coefficient of the prosumer, the energy storage system charging power, and the energy storage system discharging power; and for obtaining the photovoltaic system operation and maintenance cost of the prosumer based on the photovoltaic cost coefficient and photovoltaic predicted output of the prosumer; A construction module is used to construct a total operating cost function for prosumers based on electricity costs, comfort loss costs caused by energy consumption changes, energy storage life loss costs, and photovoltaic system operation and maintenance costs; A solution module is used to use the total operating cost function of the prosumer as the particle fitness of each optimization in the improved particle swarm algorithm based on reverse learning, under the premise of satisfying the total energy optimization constraint, the energy storage system constraint, the interruptible load constraint, the transferable load constraint and the power balance constraint. If the number of iterations exceeds the preset number of iterations, or the error of any iteration is less than the preset error, the solution is terminated, and the particle position corresponding to the swarm optimal value of the improved particle swarm algorithm based on reverse learning is determined as the solution of the total operating cost function of the prosumer.

9. A computer, characterized in that: include: A memory, a processor, and an executable program code stored in the memory and executable on the processor, wherein when the processor executes the executable program code, the steps of the method for optimizing energy consumption of prosumers taking into account the loss of energy storage life as described in any one of claims 1 to 7 are performed.

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