A method for implementing demand response of heterogeneous temperature control load based on PID control

Abstract

The application discloses a PID control-based heterogeneous temperature control load demand response implementation method, and specifically comprises the following steps: 1. constructing temperature control load models containing different types and parameters, and collecting actual weather data; 2. designing a regional simulator containing 100 buildings, each building being equipped with different types of temperature control loads, and simulating the response of different buildings under the same temperature set point instruction; 3. adopting a PID control strategy to adjust the offset of indoor temperature set points and hot water storage tank temperature set points, and realizing the adjustment of regional power consumption; and 4. verifying through numerical experiments that the PID control strategy can realize effective adjustment of regional power consumption to respond to real-time demand of a power grid. The application realizes effective control of power consumption by adjusting the offset of indoor temperature set points and hot water storage tank temperature set points, and demonstrates that even in the presence of a large amount of heterogeneity, the temperature set points of temperature control loads can be adjusted to realize the adjustment of regional power consumption and effectively respond to the demand of a power grid.

Description

Technical Field

[0001] This invention belongs to the field of smart energy management, specifically a method for implementing heterogeneous temperature-controlled load demand response based on PID control. Background Technology

[0002] With the continuous development of the electricity market, demand response (DR), as an effective power load management strategy, is receiving increasing attention. Demand response adjusts the electricity consumption behavior of users to respond to changes in grid supply and demand, thereby balancing the power system load, reducing energy costs, and improving energy efficiency. Especially in situations with a large number of heterogeneous temperature-controlled loads, how to effectively implement demand response is a problem that urgently needs to be solved.

[0003] In existing technologies, demand response often relies on complex control algorithms and substantial computational resources. These methods suffer from low computational efficiency and poor real-time performance when dealing with large-scale heterogeneous loads. Furthermore, because temperature-controlled loads of different types and parameters exhibit varying dynamic characteristics, traditional demand response methods struggle to adapt to this heterogeneity, resulting in suboptimal control performance.

[0004] The differences between this application and the prior art are as follows:

[0005] Technical comparison with patent CN117366809A "A method and system for modeling temperature control load considering temperature coupling"

[0006] Patent CN117366809A establishes an accurate temperature-controlled load model to predict and control power load by simulating the temperature coupling effect between air conditioners and electric water heaters. Our approach, however, focuses on power demand response in practical applications, dynamically adjusting the temperature-controlled load setpoint through a PID control strategy to adapt to real-time changes in grid demand.

[0007] Patent CN117366809A describes the precise control of indoor temperature by constructing a detailed electro-thermal equivalent parameter model and a PID control model. Our approach employs a PID control strategy, combined with an optimization algorithm, to determine the optimal deviation from the setpoint, thereby effectively regulating power consumption while ensuring comfort and energy efficiency.

[0008] Comparison with patent CN117313396A "An environmental temperature energy-saving optimization method and system considering multi-subject demand response"

[0009] Patent CN117313396A describes a two-stage robust optimization model for central air conditioning to optimize energy consumption, emphasizing minimizing energy consumption under different seasons and load conditions. Our approach, however, employs real-time adjustment based on a PID control strategy. By dynamically adjusting the setpoint, it adapts to immediate changes in power demand, placing greater emphasis on system response speed and adjustment flexibility.

[0010] Patent CN117313396A focuses on solving an optimization model using the CCG algorithm to obtain temperature control results for the load's environmental requirements. Our approach goes beyond model building; it incorporates optimization algorithms to determine the optimal setpoint deviation and verifies this through numerical experiments on a regional simulator of 100 buildings, thus ensuring the practical applicability and effectiveness of the control strategy. Summary of the Invention

[0011] To address the aforementioned technical problems, this invention proposes a PID-based method for implementing demand response for heterogeneous temperature-controlled loads, specifically a demand response control method for temperature-controlled loads (TCL). This method utilizes a PID control strategy to regulate regional power consumption to adapt to changes in power demand.

[0012] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0013] A method for implementing heterogeneous temperature-controlled load demand response based on PID control includes the following steps:

[0014] Step (1): Construct temperature control load models with different types and parameters, and collect actual weather data;

[0015] Step (2): Design a regional simulator containing 100 buildings, each equipped with different types of temperature control loads, to simulate the response of different buildings under the same temperature setpoint command;

[0016] Step (3): Using a PID control strategy, the offset between the indoor temperature setpoint and the hot water storage tank temperature setpoint is adjusted to regulate the regional power consumption; and the offset adjustment of the setpoint is based on the average power consumption data of the past 5 minutes, and also includes an optimization algorithm to determine the optimal setpoint deviation under the premise of meeting comfort and energy efficiency.

[0017] Step (4): Through numerical experiments, verify that the PID control strategy can effectively regulate regional power consumption in order to respond to the real-time needs of the power grid.

[0018] As a further improvement of the present invention, in step (1), the temperature control load model includes a heating unit, an air conditioning unit and a boiler unit, and takes into account the thermal inertia and energy consumption characteristics of different building types.

[0019] As a further improvement of the present invention, in step (2), the regional simulator contains 100 buildings, each equipped with different types of temperature control loads, including lightweight buildings, near-zero energy buildings and large passive buildings, to simulate the response of different buildings under the same temperature setpoint command;

[0020] The following formula is used to calculate normalized power consumption during the simulation of actual heterogeneity:

[0021]

[0022] Among them, P normalized This is the normalized electricity consumption, Q. ER，i Q GSHP，i and Q EH，i These are the power ratings of the electric radiator, ground source heat pump, and electric heater, respectively, in S. ER，i S GSHP，i and S EH，i It corresponds to the switch state.

[0023] As a further improvement of the present invention, in step (3), the PID control strategy includes proportional control, integral control and derivative control, and each control parameter is dynamically adjusted according to real-time power consumption data;

[0024] The PID control strategy can be described using the following formula:

[0025]

[0026] Where, e(k) = P(k) - P d The deviation between the current power and the expected power is represented by Δe(k) = e(k) - e(k-1), which represents the rate of change of the deviation. and These are the proportional and derivative control parameters, and These are the offsets of the indoor temperature setpoint and the hot water storage tank temperature setpoint, respectively.

[0027] As a further improvement of the present invention, step (3) is as follows: 100 building simulators are tested to evaluate how the building's power consumption responds to changes in the indoor temperature setpoint and the hot water tank temperature setpoint after the implementation of the PID control strategy. In the experiment, the power consumption data before and after the setpoint adjustment are compared to verify the accurate tracking of aggregated power by the PID control strategy. The experimental results are used to evaluate the applicability and effectiveness of the PID control strategy in actual power grid demand response.

[0028] The beneficial effects of this invention are as follows: 1. By constructing a refined temperature control load model, this invention can accurately capture the thermal inertia and energy consumption characteristics of different building types, providing a precise control basis for demand response; 2. Utilizing a regional simulator, this invention can simulate building responses under actual heterogeneity, enhancing the applicability and effectiveness of demand response strategies; 3. Employing a PID control strategy, this invention achieves real-time adjustment of power consumption, improving the flexibility and accuracy of demand response; 4. Through algorithm optimization, this invention can optimize energy efficiency while ensuring comfort, achieving a balance between energy and comfort. Attached Figure Description

[0029] Figure 1 This is the overall flowchart of the present invention;

[0030] Figure 2 This is a diagram of the heterogeneous temperature-controlled load cluster control structure in an embodiment of the present invention. Detailed Implementation

[0031] The technical solution of the application will be further described in detail below with reference to the accompanying drawings. The described embodiments are only a part of the embodiments involved in this patent. All non-innovative embodiments based on this embodiment by other researchers in the art are within the protection scope of this patent.

[0032] like Figure 1 The heterogeneous temperature-controlled load demand response control technology described in this embodiment, such as... Figure 1 As shown, it includes the following steps:

[0033] Step 1: Construct temperature control load models with different types and parameters, and collect actual weather data;

[0034] In this embodiment, the temperature control load model includes heating units, air conditioning units, and boiler units, and considers the thermal inertia and energy consumption characteristics of different building types. A weather dataset from Helsinki, Finland, including outdoor temperature and solar radiation data, is used.

[0035] Step 2: Design a regional simulator containing 100 buildings, each equipped with different types of temperature control loads, to simulate the response of different buildings under the same temperature setpoint command;

[0036] In this embodiment, consider as follows Figure 2 The area simulator shown contains 100 buildings, each equipped with different types of temperature control loads. The building types include lightweight buildings, near-zero energy buildings, and large passive buildings. It simulates the response of different buildings with randomly distributed parameters to the same temperature setpoint command to simulate real heterogeneity.

[0037] We use the following formula to calculate normalized power consumption:

[0038]

[0039] Among them, P normalized This is the normalized electricity consumption, Q. ER，i Q GSHP，i and Q EH，i These are the power ratings of the electric radiator, ground source heat pump, and electric heater, respectively, in S. ER，i S GSHP，i and S EH，i It corresponds to the switch state.

[0040] Step 3: Using a PID control strategy, the offset between the indoor temperature setpoint and the hot water storage tank temperature setpoint is adjusted to regulate the regional power consumption. The setpoint offset adjustment is based on the average power consumption data of the past 5 minutes and includes an optimization algorithm to determine the optimal setpoint deviation while meeting comfort and energy efficiency requirements.

[0041] In this embodiment, the PID control strategy includes proportional control, integral control, and derivative control, and each control parameter is dynamically adjusted based on real-time power consumption data. Specifically, we use the following formula to describe the PID control strategy:

[0042]

[0043] Where, e(k) = P(k) - P d The deviation between the current power and the expected power is represented by Δe(k) = e(k) - e(k-1), which represents the rate of change of the deviation. and These are the proportional and derivative control parameters, and These are the offsets of the indoor temperature setpoint and the hot water storage tank temperature setpoint, respectively.

[0044] Step 4: Through numerical experiments, verify that the PID control strategy can effectively regulate regional power consumption to respond to the real-time needs of the power grid.

[0045] In this embodiment, 100 building simulators were tested to evaluate how the building's power consumption responded to changes in the indoor temperature setpoint and the hot water tank temperature setpoint after implementing the PID control strategy. The experiment compared power consumption data before and after setpoint adjustments to verify the PID control strategy's ability to accurately track aggregated power. The experimental results were used to evaluate the applicability and effectiveness of the PID control strategy in actual power grid demand response. Through the above steps, this invention can achieve effective demand response control for heterogeneous thermally controlled loads, improving the flexibility and efficiency of the power system, reducing energy costs, and improving energy utilization efficiency.

[0046] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any modifications or equivalent changes made based on the technical essence of the present invention shall still fall within the scope of protection claimed by the present invention.

Claims

1. A PID control-based heterogeneous temperature-controlled load demand response implementation method, characterized in that, The method comprises the following steps: Step (1), constructing temperature-controlled load models of different types and parameters, and collecting actual weather data; Step (2), designing a regional simulator containing 100 buildings, each equipped with different types of temperature-controlled loads, to simulate the response of different buildings under the same temperature set point command; In step (2), the regional simulator contains 100 buildings, each equipped with different types of temperature-controlled loads, including lightweight buildings, near-zero energy buildings and large passive buildings, to simulate the response of different buildings under the same temperature set point command; The following formula is used to calculate the normalized power consumption in the simulation of actual heterogeneity: ； wherein, is the normalized power consumption, , and are the powers of the electric radiator, the ground source heat pump and the electric heater, respectively, , and are the corresponding switching states; Step (3), using a PID control strategy to adjust the offset of the indoor temperature set point and the hot water tank temperature set point to achieve the regulation of regional power consumption; and the set point offset adjustment is based on the average power consumption data of the past 5 minutes, and also includes an optimization algorithm to determine the optimal set point deviation under the premise of meeting comfort and energy efficiency; The step (3) is specifically as follows: the 100 building simulators are tested to evaluate how the building's power consumption responds to changes in the indoor temperature set point and the hot water tank temperature set point after implementing the PID control strategy; in the experiment, the power consumption data before and after the set point adjustment are compared to verify the accuracy of the PID control strategy in tracking aggregated power; the experimental results are used to evaluate the applicability and effectiveness of the PID control strategy in actual grid demand response; Step (4), through numerical experiments, it is verified that the PID control strategy can effectively regulate the regional power consumption to respond to the real-time demand of the power grid. 2.The PID control based heterogeneous temperature-controlled load demand response implementation method according to claim 1, characterized in that: In step (1), the temperature-controlled load model includes heating units, air conditioning units and boiler units, and considers the thermal inertia and energy consumption characteristics of different building types. 3.The PID control based heterogeneous temperature-controlled load demand response implementation method according to claim 1, characterized in that: In step (3), the PID control strategy includes proportional control, integral control and derivative control, and each control parameter is dynamically adjusted according to real-time power consumption data; The following formula is used to describe the PID control strategy: ； ； wherein, represents the deviation between the current power and the desired power, represents the rate of change of the deviation, , and , are proportional and derivative control parameters, respectively, and are offsets of the indoor temperature setpoint and the hot water storage tank temperature setpoint, respectively.

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