Power grid power supply method based on demand response

Abstract

The invention discloses a power grid power supply method based on demand response. The method comprises the following steps: S1, data receipt and analysis; s2, establishing a demand prediction model; s3, formulating a scheduling strategy; s4, establishing a dynamic electricity price model; s5, establishing a reward model for the user to participate in the demand response; and S6, the user participates in implementation. According to the invention, scientific decision is supported by using real-time data and a prediction model; flexible scheduling is realized through dynamic electricity price and demand response; the user participation degree is improved through an economic compensation incentive mechanism; the reliability and economical efficiency of power grid operation are improved, and the enthusiasm of energy conservation and emission reduction of users is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution box monitoring, and specifically to a power grid power supply method based on demand response. Background Art

[0002] With the growth of global energy demand and the enhancement of environmental protection awareness, traditional power systems are facing unprecedented challenges. On the one hand, fossil fuel resources are becoming increasingly scarce, leading to an increase in power generation costs; on the other hand, climate change has prompted governments around the world to increase their investment in renewable energy. Existing power systems mainly rely on centralized power stations and one-way transmission modes, which are difficult to adapt to the modern complex and variable electricity consumption demands. Especially during peak load periods, traditional scheduling strategies often fail to effectively relieve the power grid pressure, easily causing problems such as insufficient power supply or equipment overload. In addition, due to the lack of an effective user interaction mechanism, consumers are in a passive position in power consumption decisions, which is not conducive to forming a good atmosphere for the whole society to jointly participate in energy conservation and emission reduction. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to overcome the existing defects, and provide a power grid power supply method based on demand response, which uses real-time data and prediction models to support scientific decision-making; realizes flexible scheduling through dynamic electricity prices and demand response; improves user participation through an economic compensation incentive mechanism; not only improves the reliability and economy of power grid operation, but also promotes the enthusiasm of users for energy conservation and emission reduction, and can effectively solve the problems in the background art.

[0004] To achieve the above object, the present invention provides the following technical solution: A power grid power supply method based on demand response, including the following steps:

[0005] Step S1, data collection and analysis: The background system collects the electricity consumption of users in real time through smart meters ; Step S2, establish a demand prediction model: Power consumption demand prediction model: The background system uses a regression model to predict future power consumption demand based on historical data and external factors, expressed as , where is the predicted power consumption demand, is the actual power consumption at time point , is the time point wind speed, the wind speed indirectly affects the power consumption demand, , , are regression coefficients, is the error term; Step S3, formulate a scheduling strategy: The cost minimization model is , the total cost includes the power generation cost , the transmission cost and the demand response cost compensation ; Step S4, establish a dynamic electricity price model: The real-time electricity price is dynamically adjusted according to the grid load condition, expressed as follows: , where is the real-time electricity price, is the benchmark electricity price, is the dynamic adjustment coefficient; Step S5, establish a reward model for users to participate in demand response: Economic compensation model: Users can obtain economic compensation for reducing electricity consumption during peak hours, expressed as , where is the compensation amount, is the compensation unit price, is the user's benchmark electricity consumption, is the user's actual electricity consumption Step S6, user participation implementation: Users register to participate in the demand response program through smart meters or mobile applications. The background system publishes real-time electricity prices, grid loads, and incentive information to users through the APP or text messages. Users adjust their electricity consumption behaviors according to the incentive policies, and the background system collects users' electricity consumption data and compensation amounts in real time.

[0006] Preferably, in the said step S1, is a function that changes with time, used to describe the electricity consumption of users at any given time point t, and a value is taken every 15 minutes or one hour.

[0007] Preferably, in the said step S2, represents the difference between the model prediction value and the actual observation value. The error term is randomly distributed and its mean value is close to zero.

[0008] Preferably, in the said step S3, the constraint conditions are: Power balance constraint, , where is the total power generation at time point , is the transmission loss at the same time point , is the electricity demand at time point ; Maximum and minimum power generation limits, , where The minimum power generation required for the stable operation of the power grid The maximum power generation that the power grid can safely handle within a specific time

[0009] Compared with the prior art, the beneficial effects of the present invention are as follows: using real-time data and prediction models to support scientific decision-making; achieving flexible scheduling through dynamic electricity prices and demand response; improving user participation through economic compensation incentive mechanisms; not only enhancing the reliability and economy of power grid operation, but also promoting the enthusiasm of users for energy conservation and emission reduction BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a schematic flow chart of the present invention DETAILED DESCRIPTION OF THE INVENTION

[0011] The present invention can be explained in detail through the following embodiments. The purpose of disclosing the present invention is to protect all technical improvements within the scope of the present invention. In the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "front", "rear", "left", "right", etc. indicating the orientation or positional relationship, they are only corresponding to the drawings of the present application. For the convenience of describing the present invention, rather than indicating or implying that the device or element referred to must have a specific orientation

[0012] Please refer to Figure 1 , the present invention provides a technical solution: a power grid power supply method based on demand response, including the following steps

[0013] Step S1, data collection and analysis The background system collects the electricity consumption of users in real time through smart meters ; is a function that changes over time and is used to describe the power consumption of users at any given time point t, taking a value every 15 minutes or one hour is not only a key indicator for measuring the electricity consumption of individual users, but also an important basis for realizing the efficient operation of the power grid and promoting the goal of energy conservation and emission reduction. Through the accurate measurement and analysis of , it can help power grid operators better understand and manage power resources, and at the same time provide more flexible and economical electricity usage options for users

[0014] Step S2, establishing a demand prediction model Power consumption demand prediction model: The background system uses a regression model to predict future power consumption demand based on historical data and external factors, expressed as , where is the predicted power consumption demand is the actual power consumption at the time point ​ is the time point of the wind speed. The wind speed indirectly affects the electricity demand. The wind speed may indirectly affect the electricity demand. For example, in cold or hot weather, stronger winds may lead to higher heating or cooling demands, thus increasing power consumption. , , are the regression coefficients, is the error term, represents the difference between the model predicted value and the actual observed value. The error term is randomly distributed and its mean is close to zero; This load forecasting model aims to provide a reasonable estimate of future electricity demand by combining historical electricity consumption data and external environmental information, which is of great significance for optimizing power resource allocation, formulating effective power generation plans, and implementing demand response strategies.

[0015] Step S3, formulate a scheduling strategy: The cost minimization model is , and the total cost includes the generation cost , the transmission cost and the demand response cost compensation ; By accurately calculating and managing the costs in the above three aspects, grid operators can better plan resources, improve system efficiency, and ensure power supply security and economy at the same time. It not only pays attention to technical issues but also considers the balance between economic and social benefits; The constraint conditions are: Power balance constraint, , where is the total generation at the time point , is the transmission loss at the same time point , is the electricity demand at the time point ; Maximum and minimum generation limits, , where is the minimum generation required for the stable operation of the power grid, is the maximum generation that the power grid can safely handle within a specific time.

[0016] Step S4, establish a dynamic electricity price model: The real-time electricity price is dynamically adjusted according to the grid load situation, which is expressed as follows: , where is the real-time electricity price, is the benchmark electricity price, is the dynamic adjustment coefficient.

[0017] Step S5, establish a reward model for users to participate in demand response: Economic compensation model: Users can obtain economic compensation for reducing power consumption during peak hours, expressed as , where is the compensation amount, is the compensation unit price, is the user's benchmark power consumption, is the user's actual power consumption, encouraging users to adjust their power consumption patterns and reduce the load during peak hours.

[0018] Step S6, user participation implementation:

[0019] Users register to participate in the demand response program through smart meters or mobile applications. The background system publishes real-time electricity prices, grid loads, and incentive information to users through the APP or text messages. Users adjust their power consumption behaviors according to the incentive policies, and the background system collects the users' power consumption data and compensation amounts in real time.

[0020] By adopting this method, scientific decision-making can be supported by real-time data and prediction models; flexible scheduling can be achieved through dynamic electricity prices and demand response; the user participation rate can be improved through the economic compensation incentive mechanism; not only the reliability and economy of power grid operation are improved, but also the enthusiasm of users for energy conservation and emission reduction is promoted.

[0021] The parts not detailed in the present invention are prior art. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention; therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, aiming to include all changes falling within the meaning and scope of the equivalent elements in the content of the present invention.

Claims

1. A power grid power supply method based on demand response, characterized in that: It includes the following steps: Step S1, data receipt and analysis: The back-end system collects the electricity consumption of users in real time through smart meters ; Step S2, establish a demand forecasting model: Electricity demand forecasting model: The back-end system uses a regression model to predict future electricity demand based on historical data and external factors, expressed as , where is the predicted electricity demand, is the actual electricity consumption at time point , is the time point 's wind speed, and the wind speed indirectly affects the electricity demand, , , are the regression coefficients, is the error term; Step S3, formulate a scheduling strategy: The cost minimization model is , the total cost includes the power generation cost , the transmission cost and the demand response cost compensation ; Step S4, establish a dynamic electricity price model: The real-time electricity price is dynamically adjusted according to the grid load conditions, expressed as follows: , where is the real-time electricity price, is the benchmark electricity price, is the dynamic adjustment coefficient; Step S5, establish a reward model for users to participate in demand response: Economic compensation model: Users can obtain economic compensation for reducing power consumption during peak hours, expressed as , where is the compensation amount, is the compensation unit price, is the user's baseline power consumption, is the user's actual power consumption; Step S6, user participation implementation: Users register to participate in the demand response program through smart meters or mobile applications. The background system publishes real-time electricity prices, grid loads, and incentive information to users through the APP or text messages. Users adjust their electricity consumption behaviors according to the incentive policies, and the background system collects users' electricity consumption data and compensation amounts in real time.

2. The grid power supply method based on demand response according to claim 1, characterized in that: In the step S1, is a function that changes over time and is used to describe the power consumption of a user at any given time point t, with a value taken every 15 minutes or one hour.

3. A power grid power supply method based on demand response according to claim 1, characterized in that: In the said step S2, represents the difference between the model predicted value and the actual observed value. The error term is randomly distributed and its mean value is close to zero.

4. A grid power supply method based on demand response according to claim 1, characterized in that: In the said Step S3, the constraint conditions are: Power balance constraint, , where, is the total power generation at time point , is the transmission loss at the same time point , is the electricity demand at time point . Maximum and minimum power generation limits, , where is the minimum power generation required for the stable operation of the power grid, is the maximum power generation that the power grid can safely handle within a specific time.

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