Heterogeneous temperature control load demand response implementation method based on PID (Proportion Integration Differentiation) control

Abstract

A heterogeneous temperature control load demand response implementation method based on PID control comprises the specific steps that 1, temperature control load models containing different types and parameters are constructed, and actual weather data are collected; 2, a region simulator comprising 100 buildings is designed, each building is provided with different types of temperature control loads, and responses of different buildings under the same temperature set point instruction are simulated; and 3, a PID (Proportion Integration Differentiation) control strategy is adopted to adjust the deviation of an indoor temperature set point and a hot water storage tank temperature set point so as to realize the adjustment of regional power consumption. And 4, through a numerical experiment, the PID control strategy is verified to realize effective adjustment of regional power consumption so as to respond to the real-time demand of a power grid. By adjusting the deviation of the indoor temperature set point and the temperature set point of the hot water storage tank, effective control over power consumption is achieved, it is shown that even under the condition that a large amount of isomerism exists, adjustment over regional power consumption can be achieved by adjusting the temperature set point of the temperature control load, and the requirement of a power grid is effectively responded.

Description

Technical Field

[0001] The present invention belongs to the field of smart energy management, and specifically is a method for implementing heterogeneous temperature control load demand response based on PID control. Background Art

[0002] With the continuous development of the power market, demand response (DR) as an effective power load management strategy has received more and more attention. Demand response adjusts the power consumption behavior of power users to respond to changes in the supply and demand of the power grid, so as to balance the load of the power system, reduce energy costs and improve energy efficiency. Especially in the case of a large number of heterogeneous temperature control loads, how to effectively implement demand response is an urgent problem to be solved.

[0003] In existing technologies, the implementation of demand response often relies on complex control algorithms and a large amount of computing resources. These methods face the problems of low computing efficiency and poor real-time performance when dealing with large-scale heterogeneous loads. In addition, since temperature control loads of different types and parameters have different dynamic characteristics, traditional demand response methods are difficult to adapt to this heterogeneity, resulting in unsatisfactory control effects.

[0004] The present application is different from the prior art as follows:

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

[0006] In patent CN117366809A, an accurate temperature control load model is established to predict and control the power load by simulating the temperature coupling effect between air conditioners and electric water heaters. We focus on the power demand response in practical applications and dynamically adjust the temperature control load set point through PID control strategy to adapt to the real-time demand changes of the power grid.

[0007] In patent CN117366809A, a detailed electric-thermal equivalent parameter model and PID control model are constructed to achieve precise control of indoor temperature. We use PID control strategy and combine it with optimization algorithm to determine the optimal deviation of the set point to achieve effective regulation of power consumption while ensuring comfort and energy efficiency.

[0008] Technical comparison with patent CN117313396A "A method and system for optimizing ambient temperature energy saving taking into account multi-subject demand response"

[0009] Patent CN117313396A achieves energy-saving optimization of central air conditioning by building a two-stage robust optimization model, emphasizing the minimization of energy consumption under different seasons and load conditions. We adopt real-time regulation based on PID control strategy, dynamically adjust the set point to adapt to the immediate changes in power demand, and pay more attention to the system's response speed and adjustment flexibility.

[0010] Patent CN117313396A focuses on solving the optimization model through the CCG algorithm to obtain the temperature control results of the load subject's environmental requirements. However, we not only include model construction, but also combine the optimization algorithm to determine the optimal set point deviation, and conduct numerical experimental verification in a regional simulator of 100 buildings, thereby ensuring the actual applicability and effectiveness of the control strategy. Summary of the invention

[0011] To solve the above technical problems, the present invention proposes a method for implementing heterogeneous temperature-controlled load demand response based on PID control, and in particular, a demand response control method for temperature-controlled load (TCL). The method uses a PID control strategy to adjust regional power consumption to adapt to changes in power demand.

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

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

[0014] Step (1), constructing a temperature control load model including different types and parameters, and collecting actual weather data;

[0015] Step (2), designing a regional simulator containing 100 buildings, each building is equipped with different types of temperature control loads, and simulating the response of different buildings under the same temperature set point command;

[0016] 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 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 for determining the optimal set point deviation under the premise of satisfying comfort and energy efficiency;

[0017] 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.

[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 includes 100 buildings, each of which is equipped with different types of temperature control loads, and the building types include lightweight buildings, near-zero energy buildings, and large passive buildings, to simulate the responses of different buildings under the same temperature set point instruction;

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

[0021]

[0022] Among them, P normalized is the normalized power consumption, Q ER，i , Q GSHP，i and Q EH，i are the power of electric radiator, ground source heat pump and electric heater respectively, S ER，i , S GSHP，i and S EH，i is the corresponding switch state.

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

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

[0025]

[0026] Where, e(k) = P(k) - P d represents the deviation between the current power and the expected power, Δe(k)=e(k)-e(k-1) represents the rate of change of the deviation, and are the proportional and derivative control parameters, and are the offsets of the indoor temperature set point and the hot water tank temperature set point, respectively.

[0027] As a further improvement of the present invention, the step (3) is specifically as follows: 100 building simulators are tested to evaluate how the building's power consumption responds to changes in indoor temperature set points and hot water tank temperature set points after the implementation of the PID control strategy. In the experiment, the PID control strategy is verified to accurately track the aggregated power by comparing the power consumption data before and after the set point adjustment. 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 the present invention are as follows: The features of the present invention are as follows: 1. By constructing a refined temperature control load model, the present invention can accurately capture the thermal inertia and energy consumption characteristics of different building types, and provide a precise control basis for demand response; 2. By using a regional simulator, the present invention can simulate the building response under actual heterogeneity, and enhance the applicability and effectiveness of the demand response strategy; 3. By adopting a PID control strategy, the present invention realizes real-time adjustment of power consumption, and improves the flexibility and accuracy of demand response; 4. Through the optimization algorithm, the present invention can optimize energy consumption efficiency while ensuring comfort, and achieve a balance between energy and comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is the overall flow chart of the present invention;

[0030] Figure 2 It is a control structure diagram of a heterogeneous temperature control load cluster in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The following is a detailed description of the technical solution of the application in conjunction with the accompanying drawings. The described embodiments are only part of the embodiments involved in this patent. All non-innovative embodiments of other researchers in this field based on this embodiment are within the scope of protection of this patent.

[0032] like Figure 1 As described above, a heterogeneous temperature control load demand response control technology of this embodiment is as follows: Figure 1 As shown, the following steps are included:

[0033] Step 1: Build a temperature control load model 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 is used, including outdoor temperature and solar radiation data.

[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 set point command;

[0036] In this embodiment, consider Figure 2 The regional simulator shown contains 100 buildings, each equipped with different types of temperature control loads. The building types include lightweight buildings, nearly zero-energy buildings and large passive buildings. The response of different buildings with randomly distributed parameters to the same temperature set point command is simulated to simulate actual heterogeneity.

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

[0038]

[0039] Among them, P normalized is the normalized power consumption, Q ER，i , Q GSHP，i and Q EH，i are the power of electric radiator, ground source heat pump and electric heater respectively, S ER，i , S GSHP，i and S EH，i is the corresponding switch state.

[0040] Step 3: Use PID control strategy to adjust the offset of indoor temperature set point and hot water tank temperature set point to adjust the 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;

[0041] In this embodiment, the PID control strategy includes proportional control, integral control and differential control, and each control parameter is dynamically adjusted according to the 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 represents the deviation between the current power and the expected power, Δe(k)=e(k)-e(k-1) represents the rate of change of the deviation, and are the proportional and derivative control parameters, and are the offsets of the indoor temperature set point and the hot water tank temperature set point, 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 are tested to evaluate how the power consumption of the buildings responds to changes in the indoor temperature set point and the hot water tank temperature set point after the PID control strategy is implemented. In the experiment, the power consumption data before and after the set point adjustment are compared to verify the ability of the PID control strategy to accurately track the aggregated power. The experimental results are used to evaluate the applicability and effectiveness of the PID control strategy in actual grid demand response. Through the above steps, the present invention can achieve effective demand response control of heterogeneous thermal control loads, improve the flexibility and efficiency of the power system, reduce energy costs, and improve energy efficiency.

[0046] The above description is only a preferred embodiment of the present invention and does not constitute any other form of limitation to the present invention. Any modification or equivalent change made based on the technical essence of the present invention still falls within the scope of protection required by the present invention.

Claims

1. A method for implementing heterogeneous temperature control load demand response based on PID control, characterized in that: The steps include: Step (1), constructing a temperature control load model including different types and parameters, and collecting actual weather data; Step (2), designing a regional simulator containing 100 buildings, each building is equipped with different types of temperature control loads, and simulating the response of different buildings under the same temperature set point command; 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 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 for determining the optimal set point deviation under the premise of satisfying comfort and energy efficiency; 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.

2. According to claim 1, a method for implementing heterogeneous temperature control load demand response based on PID control is characterized in that: 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.

3. According to claim 1, a method for implementing heterogeneous temperature control load demand response based on PID control is characterized in that: In step (2), the regional simulator includes 100 buildings, each of which is equipped with different types of temperature control loads, and the building types include lightweight buildings, near-zero energy buildings, and large passive buildings, simulating the responses of different buildings under the same temperature set point command; The following formula is used to calculate the normalized power consumption during simulation of actual heterogeneity: Among them, P normalized is the normalized power consumption, Q ER,i , Q GSHP,i and Q EH,i are the power of electric radiator, ground source heat pump and electric heater respectively, S ER,i , S GSHP,i and S EH , i is the corresponding switch state.

4. According to claim 1, a method for implementing heterogeneous temperature control load demand response based on PID control is characterized in that: In step (3), the PID control strategy includes proportional control, integral control and differential control, and each control parameter is dynamically adjusted according to real-time power consumption data; The PID control strategy is described using the following formula: Where, e(k) = P(k) - P d represents the deviation between the current power and the expected power, Δe(k)=e(k)-e(k-1) represents the rate of change of the deviation, and are the proportional and derivative control parameters, and are the offsets of the indoor temperature set point and the hot water tank temperature set point, respectively.

5. According to claim 1, a method for implementing heterogeneous temperature control load demand response based on PID control is characterized in that: The step (3) is specifically as follows: 100 building simulators are tested to evaluate how the building's power consumption responds to changes in indoor temperature set points and hot water tank temperature set points after the implementation of the PID control strategy. In the experiment, the PID control strategy is verified to accurately track the aggregated power by comparing the power consumption data before and after the set point adjustment. The experimental results are used to evaluate the applicability and effectiveness of the PID control strategy in actual power grid demand response.

Images