A building integrated energy adaptive coordination control system

Abstract

The application discloses a kind of integrated energy adaptive coordination control systems suitable for building, it is related to wisdom energy technical field, including: field layer, for by data collector from each energy consumption sensor in building Energy consumption data acquisition;Network layer, for the energy consumption data is transmitted to management layer by Internet of Things;The management layer includes coordination module, energy-saving rate tracking module, control module and price reset module;The coordination module is stored with multiple for coordinating each energy supply, with the minimum energy supply cost as target Coordination model;The control module is used to select a coordination model from multiple coordination models and send the output strategy of the coordination model to the corresponding energy node, so that the energy node adjusts energy supply according to the output strategy.The present application can achieve the goal of global energy consumption minimum, and give scientific and reasonable price suggestion.

Description

Technical Field

[0001] This invention relates to the field of smart energy technology, and in particular to a comprehensive energy adaptive coordination control system suitable for buildings. Background Technology

[0002] With the rapid development of information technology and energy technology, the Energy Internet has emerged. It connects a large number of new energy nodes, such as power networks, oil networks, and natural gas networks, which are composed of distributed energy collection devices, distributed energy storage devices, and various types of loads, by comprehensively utilizing advanced power electronics technology, information technology, and intelligent management technology, so as to realize a bidirectional flow of energy and a peer-to-peer energy exchange and sharing network.

[0003] Energy conservation in buildings and other structures is a current research hotspot in the field of energy coordination. How to apply the energy internet to buildings is an urgent solution to be found. Moreover, existing adaptive coordinated control strategies generally optimize based on the known unit price of energy with the goal of minimizing costs to achieve the most economical effect, but sometimes they cannot reduce total energy consumption. Furthermore, the use of a single coordinated control strategy in existing technologies can only achieve good results in some situations, and it is difficult to achieve global optimization in practical application scenarios.

[0004] In view of this, the present invention is hereby proposed. Summary of the Invention

[0005] This invention provides a comprehensive energy adaptive coordination control system suitable for buildings to achieve the goal of minimizing overall energy consumption, and also provides scientific and reasonable price recommendations.

[0006] This invention provides a comprehensive energy adaptive coordination control system suitable for buildings, comprising:

[0007] The field layer is used to collect energy consumption data from various energy-consuming sensors within the building via data acquisition devices;

[0008] The network layer is used to transmit the energy consumption data to the management layer via the Internet of Things;

[0009] The management layer includes a coordination module, an energy efficiency tracking module, a control module, and a price reset module;

[0010] The coordination module stores multiple coordination models for coordinating various energy supplies with the goal of minimizing energy supply costs.

[0011] The control module is used to select one coordination model from multiple coordination models and send the output strategy of the coordination model to the corresponding energy node, so that the energy node can adjust the energy supply according to the output strategy;

[0012] The energy saving rate tracking module is used to calculate the current energy saving rate based on the energy supply before coordination and the energy supply after coordination; if the current energy saving rate is lower than a threshold, the control module is notified to change the coordination model until the current energy saving rate is lower than the threshold.

[0013] The price reset module is used to reverse the changes in energy consumption cost and energy saving rate over time, reset the unit price of each energy source, and send the reset unit price to the corresponding energy node.

[0014] Optionally, the energy consumption sensor includes at least two of the following: water meter, electricity meter, gas meter, and heat / cold meter.

[0015] Optionally, the coordination module stores at least a day-ahead scheduling optimization model and an intraday rolling optimization model;

[0016] The optimization objective of the day-ahead scheduling optimization model and the intraday rolling optimization model is to minimize the energy supply cost.

[0017] Optionally, the energy saving rate represents the degree of energy saving after coordination;

[0018] The current energy saving rate A is calculated using the following formula:

[0019]

[0020] Where N is the total number of energy types, P i Q represents the coordinated energy supply. i This refers to the energy supply before coordination.

[0021] Optionally, the threshold is calculated as follows:

[0022] Calculate the energy saving rate for each historical time period and construct the probability distribution curve of the energy saving rate;

[0023] The energy-saving rate at a set quantile on the probability distribution curve is taken as the threshold.

[0024] Optionally, the energy saving rate tracking module is used to detect the arrival of the next coordination cycle after the current energy saving rate is lower than the threshold, and recalculate the current energy saving rate; if the current energy saving rate is lower than the threshold, it notifies the control module to change the coordination model until the current energy saving rate is lower than the threshold.

[0025] Optionally, reversing the changes in energy consumption cost and energy saving rate over time, and resetting the unit price of each energy source, includes:

[0026] Plot the energy efficiency rate over time.

[0027] Reset the cost-over-time curve according to the reverse curve of the energy saving rate over time;

[0028] Based on the proportion of energy consumption types and the cost of resetting, the unit price of each energy source is obtained by fitting the data.

[0029] Optionally, resetting the cost-over-time curve according to the reverse curve of the energy-saving rate over time includes:

[0030] The energy cost is calculated based on the unit price of each energy source and the energy consumption data in each time period.

[0031] Calculate the average cost based on the cost over all time periods, and calculate the average energy saving rate based on the energy saving rate over all time periods.

[0032] At the time period where the average energy saving rate is located, the average cost is used as the initial value, and the cost change curve over time is reset according to the reverse change curve of the energy saving rate over time.

[0033] Optionally, the management layer also includes a payment module that receives payment amounts from building users' terminals and delivers the amounts to the corresponding energy suppliers through interaction with the banking system.

[0034] Optionally, multiple energy nodes may also be included to regulate the building’s energy supply according to the output strategy.

[0035] The system provided by this invention includes a field layer, a network layer, and a management layer. It applies the concept of the Internet of Things (IoT) to building energy management, saving communication costs. The coordination module stores multiple coordination models aimed at minimizing energy supply costs. By selecting a coordination model with an energy saving rate below a threshold, the optimal model is chosen from multiple models and continuously updated over time to maintain the energy saving rate below the threshold, achieving global optimization. The price reset module ensures that the cost of energy consumption and the energy saving rate change inversely over time; that is, a higher energy saving rate corresponds to lower costs. It provides corresponding price suggestions, thereby guiding users' energy consumption habits and achieving a virtuous cycle of energy saving. Attached Figure Description

[0036] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0037] Figure 1 This is a structural diagram of a comprehensive energy adaptive coordination control system for buildings provided in an embodiment of the present invention;

[0038] Figure 2These are the energy saving rate and cost variation curves over time provided in the embodiments of the present invention;

[0039] Figure 3 These are the curves showing the change in energy saving rate and replacement cost over time, as provided in the embodiments of the present invention.

[0040] Figure 4 This is a structural diagram of another integrated energy adaptive coordination control system for buildings provided by the present invention. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0042] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0043] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0044] This invention provides a comprehensive energy adaptive coordination control system suitable for buildings. See [link to relevant documentation]. Figure 1 It includes the following structures:

[0045] The field layer is used to collect energy consumption data from various energy consumption sensors within the building via a data acquisition unit. Optionally, the energy consumption sensors include at least two of the following: water meters, electricity meters, gas meters, and heat / cold energy meters. Correspondingly, the energy consumption data includes water consumption, electricity consumption, gas consumption, and heat / cold energy consumption. Furthermore, since different types of energy consumption data have different dimensions, the data acquisition unit converts various energy consumption data into standard fuel oil energy consumption data.

[0046] The network layer is used to transmit the energy consumption data to the management layer via the Internet of Things (IoT). The network layer acts as a bridge for communication between the management layer and the field layer.

[0047] The management layer is used for unified monitoring, control, and management of all energy-consuming sensors, and interacts with various energy nodes and the banking system. Specifically, the management layer includes a coordination module, an energy-saving rate tracking module, a control module, and a price reset module; all models are integrated into the management layer in software form.

[0048] The coordination module stores multiple coordination models aimed at minimizing energy supply costs, used to coordinate various energy supplies. The day-ahead scheduling optimization model and the intraday rolling optimization model are described in detail below.

[0049] The day-ahead scheduling optimization model optimizes the start-up and shutdown status and output plan of each energy unit for the next day, with the goal of minimizing energy supply costs, while also satisfying various operational constraints such as power balance constraints, ramp-up constraints, and line safety constraints. For example, the objective function is to minimize power supply costs.

[0050] The intraday rolling optimization model is based on a model predictive control strategy. It continuously adjusts equipment output and energy purchase agreements with the upstream energy network to revise the reference plan (e.g., day-ahead dispatch). Its objective function is to minimize the total operating cost within a rolling cycle, which can also be referred to as the energy supply cost.

[0051] It should be noted that the day-ahead scheduling optimization model and the intraday rolling optimization model are only examples. Other coordination models that coordinate the supply of various energy sources with the goal of minimizing energy supply costs can also be stored. These will not be elaborated on in this embodiment.

[0052] The control module is used to select one coordination model from multiple coordination models and send the output strategy of the coordination model to the corresponding energy node, so that the energy node can adjust the energy supply according to the output strategy. The selection strategy is to randomly select one and output the output power of each energy source. The energy node is the supplier node of each energy source, such as the power grid, water supply company, and energy storage company.

[0053] For each coordination model, it outputs a strategy according to its own modeling logic, and it also needs to be evaluated whether it can reach the energy saving rate threshold. Specifically, the energy saving rate tracking module is used to calculate the current energy saving rate based on the energy supply before and after coordination, as shown in the following formula:

[0054]

[0055] Where N is the total number of energy types, P iQ represents the coordinated energy supply (power). i Energy supply (power) before coordination.

[0056] If the current energy saving rate is lower than the threshold, the control module is notified to change the coordination model, and the output strategy of the changed coordination model is sent to the corresponding energy node. The current energy saving rate is then calculated by the energy saving rate tracking module and compared with the threshold. If the current energy saving rate is lower than the threshold, the control module needs to be notified again to update the coordination model. This process is repeated until the current energy saving rate is lower than the threshold, thus achieving global optimization. In some cases, if none of the coordination models can achieve an energy saving rate lower than the threshold, the coordination model with the lowest energy saving rate is selected for control.

[0057] Optionally, in this embodiment, the energy saving rate is recalculated and a suitable coordination model is selected according to a coordination cycle (e.g., one day). Specifically, the energy saving rate tracking module is used to detect the arrival of the next coordination cycle after the current energy saving rate is lower than the threshold, and recalculate the current energy saving rate; if the current energy saving rate is lower than the threshold, the control module is notified to change the coordination model until the current energy saving rate is lower than the threshold.

[0058] In the above embodiments, the threshold can be set empirically or according to the following method: calculate the energy-saving rate for each historical time period and construct a probability distribution curve of the energy-saving rate; take the energy-saving rate at a set quantile on the probability distribution curve as the threshold. The time period can be customized, such as 2 hours. Count the number of occurrences of different energy-saving rates and construct a probability distribution curve accordingly. When the statistical sample is large enough, the probability distribution approximates a normal distribution. The quantile can be customized, for example, 80%.

[0059] The threshold is obtained by using probability density, thus avoiding the irrationality of manual setting.

[0060] Figure 2 This is a curve showing the change in energy saving rate and cost over time, provided by an embodiment of the present invention. The horizontal axis represents time, and the vertical axis represents energy saving rate and cost. It can be seen that the changes in energy saving rate and cost over time are inconsistent; there are periods with high energy saving rates but high costs, and periods with low energy saving rates but low costs. Ideally, users should be guided to increase energy saving rates while reducing costs, necessitating a reset of the cost change curve; and a reset of the unit price of each energy source while maintaining the user's energy consumption habits. This includes the following steps:

[0061] Step 1: Plot the curve of energy saving rate over time.

[0062] Step 2: Reset the cost-over-time curve according to the reverse curve of the energy saving rate over time.

[0063] First, based on the unit price B of each energy source in each time period. ij And the energy consumption data (i.e., the collected energy consumption data) C ij Calculate the cost of energy consumption D j i represents the energy type, j represents the time period, and so on.

[0064] Calculate the average cost based on the cost of all time periods, and calculate the average energy saving rate based on the energy saving rate of all time periods; at the time period where the average energy saving rate is located, use the average cost as the initial value, and reset the cost-time curve according to the reverse curve of the energy saving rate over time.

[0065] refer to Figure 2 Assuming an average energy saving rate of 83% and a time period of 5, and an average cost of 8W, then starting from time period 5, with 8W as the initial value, redraw the cost-over-time curves to the left and right according to the negative slope of the energy saving rate. See [reference needed]. Figure 3 The cost curve after resetting is represented by a dashed line.

[0066] Step 3: Based on the proportion of energy consumption types and the cost of resetting, obtain the unit price of each energy source.

[0067] The collected energy consumption data is analyzed, and the total energy consumption W of each energy type is calculated for each time period. The proportion of each energy type is then compared to obtain the percentage of energy consumption. The percentage of energy consumption types reflects the energy usage habits of building users.

[0068] Select any few points on the curve showing the cost of resetting over time, denoted as M. i Using the proportion of energy consumption type at the selected location as the independent variable x and the cost as the dependent variable D, a linear fit is performed. Assuming we select points j = 1, 5, and 8, there are three energy types, as shown in the following formula:

[0069] W1×(n1x 11 +n2x 21 +n3x 31 )=D1

[0070] W5×(n1x 15 +n2x 25 +n3x 35 )=D5

[0071] W8×(n1x 18 +n2x 28 +n3x 38 )=D8

[0072] In this context, the first subscript of x represents different energy consumption types, and the second subscript represents the time period. n1, n2, and n3 represent the unit price of the energy to be solved.

[0073] The price reset module sends the unit price of each energy source to the corresponding energy node so that the energy node can make adjustments based on this information.

[0074] The system provided by this invention includes a field layer, a network layer, and a management layer. It applies the concept of the Internet of Things (IoT) to building energy management, saving communication costs. The coordination module stores multiple coordination models aimed at minimizing energy supply costs. By selecting a coordination model with an energy saving rate below a threshold, the optimal model is chosen from multiple models and continuously updated over time to maintain the energy saving rate below the threshold, achieving global optimization. The price reset module ensures that the cost of energy consumption and the energy saving rate change inversely over time; that is, a higher energy saving rate corresponds to lower costs. It provides corresponding price suggestions, thereby guiding users' energy consumption habits and achieving a virtuous cycle of energy saving.

[0075] Figure 4 This is a structural diagram of another integrated energy adaptive and coordinated control system for buildings provided by the present invention. Figure 1 In addition to this, the management system also includes a payment module that receives payment amounts from building users' terminals and delivers the amounts to the corresponding energy suppliers through interaction with the banking system.

[0076] The user's terminal can be a mobile phone capable of communicating with the system, with an application installed. The user enters the payment amount through the application. After the payment module authenticates with the bank system, it provides the payment amount to the bank system. The bank system then transfers the amount to the corresponding energy supplier's account.

[0077] Optional, Figure 4 The system also includes multiple energy nodes used to regulate the building's energy supply according to output strategies.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A comprehensive energy adaptive coordination control system suitable for buildings, characterized in that, include: The field layer is used to collect energy consumption data from various energy-consuming sensors within the building via data acquisition devices; The network layer is used to transmit the energy consumption data to the management layer via the Internet of Things; The management layer includes a coordination module, an energy efficiency tracking module, a control module, and a price reset module; The coordination module stores multiple coordination models for coordinating various energy supplies with the goal of minimizing energy supply costs. The control module is used to select one coordination model from multiple coordination models and send the output strategy of the coordination model to the corresponding energy node, so that the energy node can adjust the energy supply according to the output strategy; The energy saving rate tracking module is used to calculate the current energy saving rate based on the energy supply before coordination and the energy supply after coordination; if the current energy saving rate is lower than a threshold, the control module is notified to change the coordination model until the current energy saving rate is lower than the threshold. The price reset module is used to reverse the changes in energy consumption cost and energy saving rate over time, reset the unit price of each energy source, and send the reset unit price to the corresponding energy node. The method of reversing the changes in energy consumption cost and energy saving rate over time, and resetting the unit price of each energy source, includes: Plot the energy efficiency rate over time. Reset the cost-over-time curve according to the reverse curve of the energy saving rate over time; Based on the proportion of energy consumption types and the cost of resetting, the unit price of each energy source is obtained by fitting the data. The step of resetting the cost-over-time curve according to the reverse curve of the energy-saving rate includes: The energy cost is calculated based on the unit price of each energy source and the energy consumption data in each time period. Calculate the average cost based on the cost over all time periods, and calculate the average energy saving rate based on the energy saving rate over all time periods. At the time period where the average energy saving rate is located, the average cost is used as the initial value, and the cost change curve over time is reset according to the reverse change curve of the energy saving rate over time.

2. The system according to claim 1, characterized in that, The energy consumption sensor includes at least two of the following: water meter, electricity meter, gas meter, and heat / cold meter.

3. The system according to claim 1, characterized in that, The coordination module stores at least a day-ahead scheduling optimization model and an intraday rolling optimization model; The optimization objective of the day-ahead scheduling optimization model and the intraday rolling optimization model is to minimize the energy supply cost.

4. The system according to claim 1, characterized in that, The energy saving rate indicates the degree of energy saving after coordination. The current energy saving rate A is calculated using the following formula: ； Where N is the total number of energy types, P i Q represents the coordinated energy supply. i This refers to the energy supply before coordination.

5. The system according to claim 1, characterized in that, The threshold is calculated as follows: Calculate the energy saving rate for each historical time period and construct the probability distribution curve of the energy saving rate; The energy-saving rate at a set quantile on the probability distribution curve is taken as the threshold.

6. The system according to claim 1, characterized in that, The energy saving rate tracking module is used to detect the arrival of the next coordination cycle after the current energy saving rate is lower than the threshold, and recalculate the current energy saving rate; if the current energy saving rate is lower than the threshold, it notifies the control module to change the coordination model until the current energy saving rate is lower than the threshold.

7. The system according to claim 1, characterized in that, The management layer also includes a payment module that receives payment amounts from building users' terminals and delivers the amounts to the corresponding energy suppliers through interaction with the banking system.

8. The system according to any one of claims 1-7, characterized in that, It also includes multiple energy nodes used to regulate the building's energy supply according to output strategies.

Images