Demand side resource management method and system based on price response and medium

Abstract

The invention relates to a demand side resource management method and system based on price response and a medium, and belongs to the technical field of power systems. The power supply and demand condition in the future time period is analyzed according to the collected data, a corresponding price strategy is formulated, and the power supply and demand condition is evaluated by maximizing the sum of supply and demand coordination indexes in all time periods; sending the price strategy to an intelligent electric meter or mobile equipment of a user through a communication network; receiving response information from the user, wherein the response information is feedback for the user to adjust the power utilization mode; and adjusting load distribution in the power grid according to the response information of the user. The power supply and demand condition in the future can be effectively predicted, the coordination index of the supply and demand parties can be maximized through the optimization algorithm, the user is guided to actively change the power consumption mode and the power consumption habit based on the electricity price, the power consumption load in the peak period is reduced, and the load stability is improved, so that the load curve becomes smoother.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular, to a method, system, and medium for demand-side resource management based on price response. Background Art

[0002] With the increasing global attention to sustainable development and environmental protection, the proportion of renewable energy (such as wind energy, solar energy, etc.) in power supply has been increasing year by year. However, the intermittency and uncertainty of such energy sources pose challenges to the stable operation of the power grid. At the same time, due to environmental impacts and resource limitations, traditional power generation methods are gradually reducing their share in total power generation. Therefore, how to effectively manage power demand-side resources and adapt to unstable power supply situations by flexibly adjusting users' electricity consumption patterns has become an important issue faced by modern power systems.

[0003] Traditional power supply and demand management models mainly rely on supply-side regulation, that is, by increasing or decreasing power generation to match changes in users' demand. This method has several obvious limitations:

[0004] 1. High cost: To meet the demand during peak hours, power companies need to maintain a large amount of standby power generation capacity, which increases operating costs;

[0005] 2. Environmental pollution: Enabling fossil fuel power plants during peak hours is not only costly but also generates more greenhouse gas and other pollutant emissions;

[0006] 3. Unsustainability: With the increasing proportion of renewable energy, traditional power generation scheduling methods based on fixed schedules are difficult to adapt to the intermittent and volatile characteristics of these energy sources.

[0007] To address the above problems, demand-side resource management technologies based on price response have been developed in recent years. This technology encourages users to adjust their electricity consumption behavior according to market price signals through real-time electricity price mechanisms, thereby achieving the purpose of peak shaving and valley filling. Specifically, when the power supply is tight (such as during high-temperature summers), increasing the electricity price can prompt users to reduce unnecessary electricity consumption; while during periods of sufficient supply and low demand, the electricity price is reduced to stimulate consumption.

[0008] However, there is still room for improvement in existing demand-side resource management systems based on price response. Regarding how to quantify the coordination between the supply and demand sides and formulate optimal price strategies accordingly, current research is not deep enough. In practical applications, there is a lack of effective means to collect and process response information from users, which limits the flexibility and response speed of the system. Secondly, most existing systems fail to fully consider the discomfort costs of users, that is, the inconvenience or discomfort that users may suffer due to changing their electricity consumption habits.

[0009] Based on this, this case is thus proposed. Summary of the Invention

[0010] One of the objectives of the present invention is to provide a demand-side resource management method based on price response, aiming to optimize the electricity consumption behavior on the user side through the price response mechanism, thereby improving the coordination between the load curve and the power generation output curve of the entire system.

[0011] To achieve the above objective, the technical solution of the present invention is as follows:

[0012] A demand-side resource management method based on price response, comprising the following steps:

[0013] S01. Collect power generation output prediction data, user electricity consumption historical data, real-time monitoring data, and meteorological data;

[0014] S02. Analyze the power supply and demand situation in the future period based on the collected data, and formulate corresponding price strategies, where the power supply and demand situation is evaluated by maximizing the sum of the supply-demand coordination indicators in all time periods;

[0015] S03. Send the price strategy to the smart meter or mobile device of the user through the communication network;

[0016] S04. Receive the response information from the user, and this response information is the feedback of the user adjusting their electricity consumption pattern;

[0017] S05. Adjust the load distribution in the power grid according to the response information of the user.

[0018] Further, the sum of maximizing the supply-demand coordination indicator C t is calculated by the formula:

[0019]

[0020] In the formula, T represents the set of time periods; G t represents the predicted value of power generation output in time period t; D t (p t , x t ) represents the electricity demand function under the given electricity price p t and the user behavior variable x t in time period t; m is a constant used to avoid the denominator being zero

[0021] Further, the price strategy includes, but is not limited to, one or more combinations of time-of-use electricity price, real-time electricity price, and peak electricity price.

[0022] Further, the price strategy includes suggestions for electricity consumption patterns for different electricity prices.

[0023] Furthermore, the supply-demand coordination index C t considers the discomfort cost of users, and this index is defined as:

[0024]

[0025] In the formula, λ and μ are weight parameters used to balance the relative importance between supply-demand coordination and user discomfort; is the discomfort cost function.

[0026] Furthermore, the discomfort cost function includes a linear form, a quadratic form, or an exponential form, where:

[0027] Discomfort cost linear function:

[0028] In the formula, k is a direct proportionality coefficient;

[0029] Discomfort cost quadratic function:

[0030] Discomfort cost exponential function:

[0031] In the formula, n is a constant used to control the speed at which discomfort increases with the degree of deviation.

[0032] Furthermore, the electricity demand function includes a linear form, a quadratic form, or an elastic demand form; where:

[0033] Electricity demand linear function: D t (p t ,x t ) = a - b·p t + c·x t ;

[0034] In the formula, a represents the electricity consumption without price changes or user behavior adjustments; b is the price elasticity coefficient indicating the impact of electricity price changes on electricity consumption; c represents the user response coefficient reflecting the contribution of user response behavior to electricity consumption;

[0035] Electricity demand quadratic function: D t (p t ,x t ) = a - b·p t - d·pt 2 + c·x t ;

[0036] In the formula, d represents the coefficient of the square term of the electricity price, used to capture the accelerating or decelerating effect of the electricity price on electricity consumption;

[0037] Elastic demand function:

[0038] Wherein, P0 represents the benchmark price.

[0039] Furthermore, the supply-demand coordination index needs to satisfy the supply-demand balance constraint and the electricity price limit, where:

[0040] Supply-demand balance constraint:

[0041] ∣G t -D t (p t ,x t )∣≤Δ, where Δ represents the maximum allowable supply-demand gap, and G t represents the actual generated electricity during time period t;

[0042] Electricity price limit:

[0043] P min ≤p t ≤P max ,P min and P max respectively represent the lower limit and the upper limit of the electricity price.

[0044] The second object of the present invention is to provide a demand-side resource management system based on price response for implementing the above-mentioned demand-side resource management method based on price response, including:

[0045] A data acquisition module for collecting power generation output prediction data;

[0046] Historical user electricity consumption data, real-time monitoring data and meteorological data;

[0047] An analysis and decision-making module for calculating the supply-demand coordination index according to the data and formulating a price strategy;

[0048] A communication module for sending the price strategy to users and receiving the response information of users;

[0049] A scheduling module for adjusting the load distribution in the power grid according to the response information of users.

[0050] Furthermore, it includes a user interaction interface for displaying current and future price information and submitting response information.

[0051] The third object of the present invention is a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it can implement the above-mentioned demand-side resource management method based on price response.

[0052] The advantages of the present invention are as follows: It can not only effectively predict the future power supply and demand situation, but also maximize the coordination index between the supply and demand sides through an optimization algorithm, guide users to actively change their electricity consumption patterns and habits based on electricity prices, reduce the electricity load during peak hours and improve load stability, thereby making the load curve smoother and the electricity consumption behavior of users more controllable; at the same time, the present application also introduces a user discomfort cost function to ensure that user experience is taken into account while pursuing supply-demand balance. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of the demand-side resource management method based on price response in the embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] The present invention will be further described in detail below in conjunction with the embodiments. It should be understood that the orientation or positional relationships indicated by the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc. in the text are based on the orientation or positional relationships shown in the coordinate system of the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.

[0055] As Figure 1 shown, the present embodiment proposes a demand-side resource management method based on price response, including the following steps:

[0056] S01. Collect power generation output prediction data, user electricity consumption historical data, real-time monitoring data and meteorological data. Here, the power generation output prediction data includes the power generation predictions of renewable energy (such as wind energy, solar energy) and traditional energy, and the user electricity consumption historical data contains information such as the average electricity consumption of users in different time periods and peak electricity consumption periods. The real-time monitoring data comes from the current electricity consumption situation of smart meters or sensors, and the meteorological data is used to predict the influencing factors of renewable energy power generation, such as solar radiation intensity, wind speed, etc. Of course, market information, such as fuel costs, policy changes, etc., can also be collected;

[0057] These data need to be cleaned, transformed and integrated to ensure their accuracy and applicability;

[0058] S02. Analyze the power supply and demand situation in the future period based on the collected data and formulate corresponding price strategies. Among them, the power supply and demand situation is evaluated by maximizing the sum of the supply-demand coordination index C t in all time periods, aiming to optimize the electricity consumption behavior on the user side through the price response mechanism, so as to improve the coordination between the load curve and the power generation output curve of the entire system;

[0059] This index is defined as:

[0060]

[0061] wherein, T represents a set of time periods; G t represents the predicted power generation output at time period t; D t (p t , x t ) represents the electricity demand function at time period t given the electricity price p t and the user behavior variable x t ; m is a constant used to avoid a zero denominator;

[0062] Solve the above model using an appropriate optimization algorithm to find the optimal electricity price strategy p t and the user behavior variable x t to maximize the objective function. Once the optimal solution is found, a specific price strategy can be formulated based on this. The price strategy includes time-of-use electricity prices (setting different electricity prices according to different time periods to encourage users to increase electricity consumption during low-demand periods and reduce it during peak periods), peak electricity prices (setting higher electricity prices during time periods when the expected electricity load will exceed the supply capacity to curb unnecessary electricity consumption), real-time electricity prices, etc. When presenting the price strategy, suggestions for electricity consumption patterns for different electricity prices can also be attached;

[0063] S03. Send the said price strategy to the user's smart meter or mobile device through a communication network;

[0064] S04. Receive response information from the user, which is the feedback of the user adjusting their electricity consumption pattern, such as the intention to adjust electricity consumption (the user decides whether to adjust their electricity consumption behavior according to the received price signal. For example, reduce the use of high-power electrical appliances (such as air conditioners, washing machines, etc.) during high electricity price periods, or increase electricity consumption during low electricity price periods), specific adjustment measures (in addition to indicating the intention, the user can also provide specific adjustment details, such as planning to reduce how much electricity during which time period, or using smart devices to automatically perform certain energy-saving operations), feedback and satisfaction (the user may provide feedback on the impact of the current price strategy on their life, including but not limited to changes in comfort, feelings of economic burden, etc.);

[0065] S05. According to the user's response information, conduct power dispatching. For example, balance the grid load by dispatching standby power generation resources, starting the discharge or charge of energy storage devices, etc. to ensure the stable operation of the entire system.

[0066] It is crucial to understand the user's response to different price incentives as it directly affects whether the user will adjust their electricity consumption pattern according to the changes in electricity prices. To consider this factor, this embodiment adds a user discomfort cost function to quantify the degree of inconvenience or discomfort felt by the user due to adjusting their electricity consumption behavior. The improved model is as follows:

[0067]

[0068] In the formula, λ and μ are weight parameters used to balance the relative importance between supply-demand coordination and user discomfort; is the discomfort cost function.

[0069] In the above model, the discomfort cost function and the electricity demand function have different calculation forms in different scenarios. Which form to choose depends on the specific application scenario and the user's tolerance for deviating from their ideal electricity consumption.

[0070] For the discomfort cost function, it includes a linear form, a quadratic form, or an exponential form, where:

[0071] Discomfort cost linear function: In the formula, k is a proportionality coefficient, applicable to the situation where the user is less sensitive to electricity consumption changes. For example, adjusting the usage time or power of lighting equipment usually does not significantly affect the user's daily life;

[0072] Discomfort cost quadratic function: It is applicable to those situations where deviating from the ideal electricity consumption will bring relatively obvious but not extreme inconvenience. For example, adjusting the air conditioner temperature setting value, a moderate change may only cause slight inconvenience;

[0073] Discomfort cost exponential function: In the formula, n is a constant used to control the rate at which discomfort increases with the degree of deviation, applicable to situations where the user is extremely sensitive to electricity consumption changes. For example, for a family that depends on specific electrical appliances to maintain health or safety conditions (such as medical equipment that needs to run continuously), any deviation may cause serious inconvenience or even danger.

[0074] For the electricity demand function, it includes a linear form, a quadratic form, or an elastic demand form, where:

[0075] Electricity demand linear function: D t (p t ,x t )=a - b·p t + c·x t; where a represents the electricity consumption without price changes or user behavior adjustments; b is the price elasticity coefficient, indicating the degree of impact of electricity price changes on electricity consumption; c represents the user response coefficient, reflecting the contribution of user response behavior to electricity consumption; Applicable scenario: when it is considered that the user's electricity consumption behavior responds linearly to price changes and the user response behavior can be simply quantified;

[0076] Quadratic function of electricity demand: D t (p t ,x t ) = a - b·p t - d·pt 2 + c·x t ; where d represents the coefficient of the electricity price squared term, used to capture the accelerating or decelerating effect of electricity price on electricity consumption; Applicable scenario: when it is considered that the impact of electricity price on electricity consumption is not constant, but decreases rapidly as the electricity price increases;

[0077] Elastic demand function: In the formula, P0 represents the benchmark price; Applicable scenario: applicable to situations where it is necessary to accurately measure the impact of price elasticity on electricity consumption, especially in economic analysis.

[0078] The above model needs to satisfy the supply-demand balance constraint and electricity price limit, where:

[0079] Supply-demand balance constraint, ensuring that the power generation is at least sufficient to meet the electricity demand, but allowing a certain deviation to reflect the incomplete matching in the actual situation:

[0080] ∣G t - D t (p t ,x t )∣ ≤ Δ, where Δ represents the maximum allowable supply-demand gap, and G t represents the actual power generation in time period t;

[0081] Electricity price limit: P min ≤ p t ≤ P max , P min and P max represent the lower and upper limits of the electricity price respectively.

[0082] This embodiment also proposes a price-responsive demand-side resource management system for implementing the price-responsive demand-side resource management method described above, including:

[0083] Data acquisition module, used to collect power generation output prediction data;

[0084] Historical user electricity consumption data, real-time monitoring data, and meteorological data;

[0085] An analysis and decision-making module, configured to calculate a supply-demand coordination index based on the data and formulate a price strategy;

[0086] A communication module, configured to send the price strategy to a user and receive response information from the user;

[0087] A scheduling module, configured to perform power scheduling according to the response information of the user;

[0088] A user interface, configured to display current and future price information and submit response information.

[0089] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the above-described method for demand-side resource management based on price response can be implemented. The above embodiments are only used to explain the concept of the present invention, rather than limiting the protection scope of the present invention. Any non-substantive modification of the present invention using this concept shall fall within the protection scope of the present invention.

Claims

1. A demand-side resource management method based on price response, characterized in that: The following steps are involved: S01. Collect power generation output forecast data, user power consumption history data, real-time monitoring data and meteorological data; S02. Analyze the electricity supply and demand situation in the future period based on the collected data and formulate corresponding pricing strategies, wherein the electricity supply and demand situation is evaluated by maximizing the sum of the supply and demand coordination indicators in all time periods; S03. Sending the pricing strategy to the user's smart meter or mobile device via a communication network; S04. Receive response information from the user, the response information being feedback from the user to adjust his / her power usage mode; S05. Perform power dispatch according to the user's response information.

2. A demand-side resource management method based on price response as claimed in claim 1, characterized in that: The maximum supply and demand coordination index C in all time periods t The formula for the sum of is: In the formula, T represents the time period set; G t represents the predicted value of power generation output in time period t; D t (p t ,x t ) represents a given electricity price p in time period t t and user behavior variable x t The electricity demand function under ; m is a constant used to avoid the denominator being zero.

3. A demand-side resource management method based on price response as claimed in claim 1, characterized in that: The price strategy includes electricity usage pattern recommendations for different electricity prices.

4. A demand-side resource management method based on price response as claimed in claim 2, characterized in that: The supply-demand coordination index C t Considering the user's discomfort cost, the indicator is defined as: Where λ and μ are weight parameters used to balance the relative importance between supply-demand coordination and user discomfort; is the discomfort cost function.

5. A demand-side resource management method based on price response as claimed in claim 4, characterized in that: The discomfort cost function includes a linear form, a quadratic form or an exponential form, wherein: Discomfort cost linear function: In the formula, k is a positive proportional coefficient; Discomfort cost quadratic function: Discomfort cost index function: Where n is a constant used to control the speed at which discomfort increases with the degree of deviation.

6. A demand-side resource management method based on price response as claimed in claim 4, characterized in that: The electricity demand function includes a linear form, a quadratic form or an elastic demand form; wherein: Electricity demand linear function: D t (p t ,x t )=ab·p t +c·x t ; In the formula, a represents the electricity consumption without price changes or user behavior adjustments; b is the price elasticity coefficient, which represents the degree of influence of electricity price changes on electricity consumption; c represents the user response coefficient, which reflects the contribution of user response behavior to electricity consumption; Electricity demand quadratic function: D t (p t ,x t )=ab·p t -d·pt 2 +c·x t ; In the formula, d represents the coefficient of the square term of electricity price, which is used to capture the acceleration or deceleration effect of electricity price on electricity consumption; Elastic demand function: Where P0 represents the base price.

7. A demand-side resource management method based on price response as claimed in claim 2, characterized in that: The supply and demand coordination indicators must meet the supply and demand balance constraints and electricity price restrictions, where: Supply and demand balance constraint: |G t -D t (p t ,x t )∣≤Δ, Δ represents the maximum allowable gap between supply and demand, G t represents the actual amount of electricity generated in time period t; Electricity price limit: P min ≤p t ≤P max , P min and P max They represent the lower and upper limits of electricity prices respectively.

8. A demand-side resource management system based on price response, used to execute the demand-side resource management method based on price response according to any one of claims 1 to 7, characterized in that: include: Data acquisition module, used to collect power generation output forecast data; User electricity consumption history data, real-time monitoring data and meteorological data; An analysis and decision-making module, used to calculate supply and demand coordination indicators and formulate pricing strategies based on the data; The communication module is used to send the price strategy to the user and receive the user's response information; The dispatching module is used to dispatch electricity according to the user's response information.

9. A demand-side resource management system based on price response as claimed in claim 1, characterized in that: Includes a user interface for displaying current and future price information and submitting response information.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the price-response-based demand-side resource management method according to any one of claims 1 to 7 can be implemented.

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