A contribution-aware grid-interactive residential building collaborative operation optimization method

Abstract

The present invention discloses a contribution-aware grid-interactive residential building collaborative operation optimization method, comprising the following steps: (1) modeling the operation cost minimization problem of grid-interactive high-efficiency residential buildings in the normal operation phase and the collaborative operation double-layer optimization problem in the grid service phase; (2) proposing a physical consistency neural network-assisted hierarchical model predictive control method to solve the above-mentioned operation cost minimization problem; (3) proposing a distributed collaborative operation optimization algorithm guided by binary search based on physical consistency neural network-assisted hierarchical model predictive control to solve the above-mentioned collaborative operation double-layer optimization problem; (4) deploying the proposed collaborative operation optimization method in an actual system. Compared with existing methods, the method of the present invention can identify the contribution of each residential building in providing grid services and provide a basis for corresponding economic compensation, thereby reducing building energy costs and improving grid service capabilities while maintaining high user comfort.

Description

Technical Field

[0001] The present invention relates to a contribution-aware grid-interactive residential building collaborative operation optimization method, belonging to the intersection of residential building HVAC systems and artificial intelligence. Background Art

[0002] In recent years, the power grid has faced significant pressure from increasing peak power demand, limited transmission and distribution capacity, and the integration of large-scale renewable energy sources. To alleviate these pressures, the concept of grid-interactive, efficient buildings was proposed by the U.S. Department of Energy in 2019. A grid-interactive, efficient building is a highly energy-efficient building that leverages smart technologies and on-site distributed energy resources (such as rooftop photovoltaics, battery storage systems, and backup generators) to provide demand flexibility while simultaneously optimizing energy costs, grid services, and user comfort. Compared to existing automated demand response systems, grid-interactive, efficient buildings offer higher energy efficiency and load flexibility. Specifically, automated demand response primarily achieves load flexibility by shedding or shifting loads based on dispatch instructions from the grid or system operator, contributing less to system energy efficiency improvements. Grid-interactive buildings achieve higher energy efficiency and load flexibility through intelligent, comprehensive, and autonomous management of distributed energy resources and building flexible loads. According to a U.S. Department of Energy analysis, grid-interactive, efficient buildings are expected to provide cumulative benefits of $100 billion to $200 billion to the U.S. power system from 2021 to 2040 and reduce carbon emissions by 80 million tons annually. Therefore, its research has important economic and social value. At the same time, grid-interactive efficient buildings can provide various types of grid services, such as power generation energy services, power generation capacity services, emergency reserve services, frequency regulation services, and voltage support services. However, due to limited service capacity, a single residential building cannot provide grid services, so it is necessary to consider the coordination between multiple residential buildings. Specifically, through the coordination of the operation of multiple residential buildings by load aggregators (e.g., trusted third parties), multiple buildings can efficiently provide grid services while minimizing the negative impact on building operating costs and their user needs (e.g., user thermal comfort). Since the economic incentives provided by distribution system operators help reduce building operating costs, a win-win situation can be achieved between buildings participating in grid services and distribution system operators. In summary, it is crucial to design an efficient method for the coordinated operation of grid-interactive residential buildings. However, due to the existence of uncertain parameters (such as outdoor temperature, electricity price, photovoltaic and base load), time coupling constraints (energy storage system level), spatial coupling constraints (the total demand of all residential buildings meets the power upper limit), nonlinear objective functions (such as the objective function is a nonlinear non-convex and inseparable function), the difficulty in establishing a clear and accurate building thermal dynamic model, and the difficulty in obtaining a large amount of operating data, it is challenging to achieve the coordinated operation optimization of multiple grid-interactive buildings.

[0003] To address these challenges, existing grid-interactive, efficient building operation optimization methods are no longer suitable. Specifically, existing operation optimization methods are mainly divided into model-based and learning-based methods. Model-based methods mainly include robust optimization, the alternating vector multiplier method, and distributed model predictive control. These methods require a clear model of the building's thermal dynamics. However, because a building's indoor temperature depends on many factors (such as the building structure and materials, the external environment (such as external temperature, humidity, and solar radiation intensity), and internal heat gains from users and lighting systems), establishing an accurate and easily controllable building thermal dynamics model is difficult. Although existing learning-based operation optimization methods (such as soft actor-critic algorithms, multi-agent deep reinforcement learning, multi-agent actor-attention critic algorithms, and multi-agent hierarchical deep reinforcement learning) do not require knowledge of a clear building thermal dynamics model and support real-time decision-making, these methods require a large number of interactions with the real environment or the establishment of a high-fidelity simulation environment. This leads to high deployment costs and barriers to entry, making them unsuitable for practical implementation. In addition, existing methods ignore the identification of changes in electricity demand for each grid-interactive building due to its participation in providing grid services, and thus cannot calculate the economic compensation that each grid-interactive efficient building should receive, which is not conducive to the successful deployment of grid-interactive efficient buildings in practice.

[0004] In summary, the existing grid-interactive building operation optimization methods are insufficient in overcoming the above challenges. It is urgent to study a new contribution-aware method for the collaborative operation optimization of grid-interactive buildings, so as to optimize the operating costs of grid-interactive buildings and provide efficient grid services while maintaining high user comfort. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a contribution-aware grid-interactive residential building collaborative operation optimization method, which aims to make up for the shortcomings of the existing operation optimization methods that do not consider the contribution of grid-interactive buildings to grid services and make corresponding economic compensation, and solve the technical problems that the existing model-based methods require a clear indoor environment dynamic model and the existing learning-based methods require a large amount of operation data.

[0006] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0007] The present invention provides a contribution-aware grid-interactive residential building collaborative operation optimization method, comprising:

[0008] (1) Model the two-level optimization problem of minimizing the operating cost of grid-interactive high-efficiency residential buildings in the normal operation phase and the collaborative operation in the grid service phase respectively;

[0009] (2) A physical consistency neural network-assisted hierarchical model predictive control method is proposed to solve the above-mentioned operation cost minimization problem;

[0010] (3) A distributed collaborative operation optimization algorithm based on physically consistent neural network-assisted hierarchical model predictive control guided by binary search is proposed to solve the above-mentioned collaborative operation two-level optimization problem;

[0011] (4) Deploy the proposed collaborative operation optimization method in a practical system.

[0012] Optionally, the modeled problem of minimizing operating costs of grid-interactive high-efficiency residential buildings during normal operation mainly includes decision variables, constraints, and an objective function.

[0013] The decision variable χ includes the energy storage system input power c of residential building j managed by load aggregator i in time slot t. i,j,t , energy storage system output power d i,j,t , HVAC system input power e i,j,t and the electricity demand of residential buildings P i,j,t .

[0014] The constraints include:

[0015]

[0016] c i,j,t ·d i,j,t =0, (5)

[0017] P i,j,t +r i,j,t +d i,j,t =b i,j,t +e i,j,t +c i,j,t , (6)

[0018]

[0019] Among them: B i,j,t represents the storage capacity of the energy storage system of residential building j in load aggregator i in time slot t; σ c,i,j and σ d,i,j Respectively represent the input efficiency and output efficiency of the energy storage system; and Respectively represent the minimum storage capacity and maximum storage capacity of the energy storage system; and Respectively represent the maximum input power and maximum output power of the energy storage system; Δt represents the time slot length; r i,j,t represents the photovoltaic power output in time slot t, b i,j,t represents the fixed load of time slot t; T i,j,trepresents the indoor temperature of residential building j in load aggregator i at time slot t, T t out represents the outdoor temperature at time slot t, ξ i,j,t represents the indoor thermal disturbance of residential building j in load aggregator i at time slot t, represents the indoor thermal dynamics of residential building j in load aggregator i; represents the maximum HVAC system input power of residential building j in load aggregator i; It represents the upper limit of electricity demand of residential building j in load aggregator i during the grid service stage.

[0020] The objective function is:

[0021]

[0022] in: represents the expectation operator, acting on the uncertain system parameters (such as the fixed load b i,j,t , photovoltaic output r i,j,t , outdoor temperature T t out and electricity prices, etc.); C i,j,1,t and C i,j,2,t They represent the operating cost of residential building j in load aggregator i in time slot t and the loss cost of the energy storage system, respectively. The calculation formula is as follows:

[0023]

[0024] C i,j,2,t =ψ(c i,j,t +d i,j,t ),

[0025] Where: s t and w t They represent the electricity purchase price and electricity selling price respectively; ψ represents the depreciation coefficient of the energy storage system.

[0026] Optionally, the established two-level optimization problem for the coordinated operation of grid-interactive high-efficiency residential buildings in the grid service stage mainly includes decision variables, constraints and objective functions.

[0027] The decision variable is the peak power demand limit of each building in the grid service period t time slot ρ t .

[0028] The constraints include:

[0029] 0≤ρ t ≤ρ max , (12)

[0030]

[0031] c i,j,t ·d i,j,t =0, (20)

[0032] P i,j,t +r i,j,t +d i,j,t =b i,j,t +e i,j,t +c i,j,t , (twenty one)

[0033]

[0034] Where: max Indicates the maximum peak power demand limit; represents the optimal power demand of load aggregator i in time slot t; P limit,t represents the sum of the power caps of all residential buildings in time slot t; represents the optimal power demand of each building in time slot t; X1 represents the initial decision quantity before the proposed binary search; α represents a weight coefficient.

[0035] The objective function is:

[0036]

[0037] Where: Γ(·) represents the economic compensation related to the change in electricity demand of all grid-interactive residential buildings. In the present invention, is the economic compensation coefficient.

[0038] Optionally, the operation optimization method based on the physical consistency neural network assisted hierarchical model predictive control is designed as follows:

[0039] The proposed grid-interactive high-efficiency residential building operation cost minimization problem in normal operation phase is transformed into the upper-layer indoor comfort temperature deviation minimization sub-problem and the lower-layer operation cost minimization sub-problem.

[0040] Deploy the upper-level model predictive control method assisted by the indoor thermal dynamic model based on the physical consistency neural network to solve the problem of minimizing the indoor comfort temperature deviation and obtain the upper-level optimal decision in the time slot t.

[0041] The optimal decision of the upper-level model predictive control method is input into the lower-level model predictive control, and the lower-level model predictive control method is deployed to solve the operation cost minimization subproblem to obtain the optimal decision of the lower level in time slot t

[0042] Optionally, the proposed upper floor indoor comfort temperature deviation minimization sub-problem is as follows:

[0043]

[0044]

[0045] Where: X represents the predicted length, t′ represents the corresponding time slot within the predicted length, C i,j,3,t′ Indicates the indoor comfort temperature deviation value; It is a dynamic model of indoor thermal comfort based on physical consistency neural network, which mainly consists of black box module and physical module. The black box module is composed of a deep neural network, whose input is outdoor light intensity and output is indoor temperature disturbance T caused by light intensity. b,i,j,t′+1 ; The physical module is composed of the indoor temperature T of the current time slot i,j,t′ , the outdoor temperature at the current time slot HVAC system input power e in the current time slot i,j,t′ It can be represented by a clear physical linear model, namely Where: γ and ν represent the learnable parameters that ensure the physical consistency of the neural network.

[0046] Optionally, the proposed lower-level running cost minimization sub-problem is as follows:

[0047]

[0048] c i,j,t′ ·d i,j,t′ =0, (32)

[0049] P i,j,t′ +r i,j,t′ +d i,j,t′ =b i,j,t′ +e i,j,t′ +c i,j,t′ , (33)

[0050]

[0051] Where: C i,j,1,t′ and C i,j,2,t′ They represent the operating cost of each building and the loss cost of the energy storage system in the time slot t′ within the prediction length.

[0052] Optionally, the deployed upper model predictive control method mainly uses the gradient descent method to solve the optimal HVAC system decision for the current time slot and the next X-1 time slots, that is, The specific solution process is as follows: First, set the current iteration step to k = 0 and the maximum iteration step to k. max :

[0053] (1.1) Predictive control output decision based on upper-level model The proposed physical embedded neural network prediction

[0054] (1.2) Calculate the indoor comfort deviation between the current time slot and the next X-1 time slots

[0055] (1.3) Based on and The gradient is calculated according to the following formula:

[0056]

[0057] (1.4) Based on the obtained gradient, the gradient optimizer optimizes the upper-level decision with gradient minimization as the update direction. The specific calculation formula is as follows:

[0058]

[0059] in: and are the exponential moving average of gradient and the exponential moving average of squared gradient respectively; β and For update and Hyperparameters of and They are and bias-corrected estimates of ; express The function is used to update the decision of the current iteration step; ζ and ∈ are the learning rate and hyperparameters respectively.

[0060] (1.5) If the current And k<k max , k=k+1 and jump to reiterate; otherwise, the iteration ends and the optimal HVAC system decision is output

[0061] The deployed lower-level model predictive control method mainly introduces four auxiliary variables θ i,j,t′ ,κ i,j,t′ ,I c,t′ ,I d,t′ Convert (P4) into the following mixed integer linear programming problem (P5) to solve the optimal energy storage charging and discharging decision for the current time slot and the next X-1 time slots, that is:

[0062]

[0063] st(28)-(29),(43)

[0064]

[0065] I c,t′ +Id,t′ ≤1, (46)

[0066] I c,t′ ,I d,t′ ∈{0,1},(47)

[0067]

[0068] θ i,j,t ≥0, (50)

[0069] κ i,j,t ≥0, (51)

[0070] in: make and P i,j,t′ =θ i,j,t′ -κ i,j,t′ and |P i,j,t′ |=θ i,j,t′ +κ i,j,t′ ; The decision variable of (P5) is c i,j,t′ ,d i,j,t′ ,θ i,j,t′ ,κ i,j,t′ ,I c,t′ ,I d,t′ .

[0071] Optionally, the distributed collaborative operation optimization algorithm based on hierarchical model predictive control assisted binary search proposed in the power grid service stage is designed as follows:

[0072] If the current operation time slot t is in the grid service stage, firstly obtain the initial decision X1 of the current time slot according to the operation optimization method based on hierarchical model predictive control; then set the upper limit of the current power demand peak limit to ρ u =ρ max , the lower limit of the current peak power demand limit is ρ l =ρ min , is a threshold. And the current iteration step k=0 is less than the maximum number of iterations k max , iterate according to the following steps to solve the optimal power demand peak limit and optimal decision

[0073] (2.1) Let the current peak power demand limit be ρ t =(ρ u +ρ l ) / 2;

[0074] (2.2) The distributed system operator willt Transmitted to all load aggregators i;

[0075] (2.3) The load aggregator will t Transmitted to all grid-interactive, efficient residential buildingsj;

[0076] (2.4) According to the rule-based decision adjustment method, all buildings adjust X1 to obtain the optimal decision

[0077] (2.5) All buildings will be optimally decided Transmit to the corresponding load aggregator;

[0078] (2.6) Distributed system operators obtain optimal power demand

[0079] (2.7) If the current Then ρ l =ρ t On the contrary, ρ u =ρ t ;

[0080] (2.8) If ρ u -ρ l >ω and k<k max , return to (8.1) and continue iteration, k = k + 1; otherwise, end the iteration and output the current optimal ρ t and optimal decision

[0081] Optionally, the proposed rule-based decision adjustment method is designed as follows: (3.1) If and but

[0082] (3.2) If and but

[0083] (3.3) If and but (3.4) If and and but

[0084] (3.5) If and and but

[0085] Optionally, the actual operation system for deploying the proposed collaborative operation optimization method includes a grid-interactive high-efficiency residential building operation module and a real-time decision-making module based on hierarchical model predictive control assisted binary search.

[0086] The grid-interactive high-efficiency residential building operation module is responsible for receiving the optimal decision output by the real-time decision module and executing it in each grid-interactive high-efficiency residential building.

[0087] The real-time decision-making module based on hierarchical model predictive control assisted binary search includes an information acquisition submodule, an upper-level model predictive control real-time decision-making submodule, a lower-level model predictive control real-time decision-making submodule, a binary search collaborative operation optimization submodule, and an optimal control strategy output submodule.

[0088] The information collection submodule collects the latest status of each component of each grid-interactive high-efficiency residential building after decision-making, such as indoor building temperature and the real-time storage capacity of the energy storage system. This information is then sent to the upper and lower model predictive control real-time decision submodules.

[0089] The upper-level model predictive control real-time decision-making submodule reads the indoor temperature status information from the information acquisition submodule. It then uses the operational optimization method to determine the optimal HVAC system decision. Finally, this optimal HVAC system decision is sent to the lower-level model predictive control real-time decision-making submodule.

[0090] The lower-level model predictive control real-time decision-making submodule reads the energy storage system status from the information acquisition submodule and the optimal HVAC system decision from the upper-level model predictive control real-time decision-making submodule. It then uses the aforementioned operational optimization method to determine the optimal energy storage system decision. Finally, the optimal HVAC system decision and energy storage system decision are transmitted to the binary search collaborative operational optimization module.

[0091] The binary search collaborative operation optimization submodule first determines whether each grid-interactive high-efficiency residential building needs to participate in grid service in the current time slot. If so, the collaborative operation optimization method is used to further solve the optimal decision, and the latest decision is sent to the optimal control strategy output submodule. If not, the optimal decision of the upper and lower model predictive control real-time decision submodules is sent to the optimal control strategy output submodule.

[0092] The optimal control strategy output submodule reads the decision issued by the binary search collaborative operation optimization submodule and sends it to the grid-interactive efficient residential building operation module.

[0093] Overall, the technical solution of this invention can identify the contribution of each residential building to providing grid services and provide a basis for corresponding economic compensation. This can reduce building energy costs and improve grid service capabilities while maintaining high user comfort. Furthermore, compared with existing technologies, it can achieve the following beneficial effects:

[0094] (1) Compared with the existing model-based methods, the proposed method does not require the knowledge of the explicit building thermodynamic model. By constructing a building thermodynamic model based on a physical consistency neural network, it accurately captures the indoor thermal dynamics and deploys it in the proposed operation optimization algorithm to assist the upper-level model predictive control in obtaining the optimal decision and maintaining a comfortable indoor environment.

[0095] (2) Compared with existing learning-based methods, the method proposed in this invention does not require a large number of interactions with the actual environment and has a low deployment threshold. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] Figure 1 This is a flow chart of the contribution-aware grid-interactive residential building collaborative operation optimization method proposed in the present invention;

[0097] Figure 2 This is a comparison chart of the total operating costs of the method of the present invention and other methods;

[0098] Figure 3 This is a comparison chart of average temperature offsets between the method of the present invention and other methods;

[0099] Figure 4 This is a comparison chart of the power limit offset of the method of the present invention and other methods. DETAILED DESCRIPTION

[0100] The present invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0101] Example 1:

[0102] like Figure 1 As shown in FIG, the design flow chart of the contribution-aware grid-interactive residential building collaborative operation optimization method provided by the present invention includes the following steps:

[0103] 1. Model the two-level optimization problem of minimizing the operating cost of grid-interactive high-efficiency residential buildings in the normal operation phase and the collaborative operation in the grid service phase respectively;

[0104] 1.1. In this embodiment, the problem of minimizing the operating cost of a grid-interactive high-efficiency residential building during normal operation includes decision variables, constraints, and an objective function.

[0105] The decision variables χ include the energy storage system input power c of residential building j managed by load aggregator i in time slot t i,j,t , energy storage system output power d i,j,t , HVAC system input power e i,j,t and the electricity demand of residential buildings P i,j,t .

[0106] Constraints include:

[0107]

[0108] c i,j,t ·d i,j,t =0, (5)

[0109] P i,j,t +r i,j,t +d i,j,t =b i,j,t +e i,j,t +c i,j,t , (6)

[0110]

[0111] Among them: B i,j,t represents the storage capacity of the energy storage system of residential building j in load aggregator i in time slot t; σ c,i,j and σ d,i,j Respectively represent the input efficiency and output efficiency of the energy storage system; and Respectively represent the minimum storage capacity and maximum storage capacity of the energy storage system; and Respectively represent the maximum input power and maximum output power of the energy storage system; Δt represents the time slot length; r i,j,t represents the photovoltaic power output in time slot t, b i,j,t represents the fixed load of time slot t; T i,j,t represents the indoor temperature of residential building j in load aggregator i at time slot t, T t out represents the outdoor temperature at time slot t, ξ i,j,t shows the indoor thermal disturbance of residential building j in load aggregator i in time slot t, represents the indoor thermal dynamics of residential building j in load aggregator i; represents the maximum HVAC system input power of residential building j in load aggregator i; It represents the upper limit of electricity demand of residential building j in load aggregator i during the grid service stage.

[0112] The objective function is:

[0113]

[0114] in: represents the expectation operator, acting on the uncertain system parameters (such as the fixed load b i,j,t , photovoltaic output r i,j,t , outdoor temperature T t out and electricity prices, etc.); C i,j,1,t and C i,j,2,t They represent the operating cost of residential building j in load aggregator i in time slot t and the loss cost of the energy storage system, respectively. The calculation formula is as follows:

[0115]

[0116] C i,j,2,t =ψ(c i,j,t +d i,j,t ),

[0117] Where: s t and w t They represent the electricity purchase price and electricity selling price respectively; ψ represents the depreciation coefficient of the energy storage system.

[0118] 1.2. In this embodiment, the two-level optimization problem for the coordinated operation of grid-interactive residential buildings during the grid service phase includes decision variables, constraints, and an objective function.

[0119] The decision variable is the peak power demand limit ρ in time slot t during the grid service phase t .

[0120] Constraints include:

[0121] 0≤ρ t ≤ρ max , (12)

[0122]

[0123]

[0124] c i,j,t ·d i,j,t =0, (20)

[0125] P i,j,t +r i,j,t +d i,j,t =b i,j,t +e i,j,t +c i,j,t , (twenty one)

[0126]

[0127] Where: maxIndicates the maximum peak power demand limit; represents the optimal power demand of load aggregator i in time slot t; P limit,t represents the sum of the power caps of all residential buildings in time slot t; represents the optimal power demand of each building in time slot t; X1 represents the initial decision quantity before the proposed binary search; α represents a weight coefficient;

[0128] The objective function is:

[0129]

[0130] Where: Γ(·) represents the economic compensation related to the change in electricity demand of all grid-interactive residential buildings. In the embodiment, is the economic compensation coefficient.

[0131] 2. A physically consistent neural network-assisted hierarchical model predictive control method is proposed to solve the above-mentioned operating cost minimization problem;

[0132] In this embodiment, the specific design is as follows:

[0133] 2.1. The proposed grid-interactive high-efficiency residential building operation cost minimization problem in the normal operation phase is transformed into an upper-layer indoor comfort temperature deviation minimization sub-problem and a lower-layer operation cost minimization sub-problem;

[0134] 2.1.1. The sub-problem of minimizing the deviation of the upper indoor comfortable temperature is as follows:

[0135]

[0136] Where: X represents the predicted length, t′ represents the corresponding time slot within the predicted length, C i,j,3,t′ Indicates the indoor comfort temperature deviation value; It is a dynamic model of indoor thermal comfort based on physical consistency neural network, which mainly consists of black box module and physical module. The black box module is composed of a deep neural network, whose input is outdoor light intensity and output is indoor temperature disturbance T caused by light intensity. b,i,j,t′+1 ; The physical module is composed of the indoor temperature T of the current time slot i,j,t′ , the outdoor temperature at the current time slot HVAC system input power e in the current time slot i,j,t′ It can be represented by a clear physical linear model, namely Where: γ and ν represent the learnable parameters that ensure the physical consistency of the neural network.

[0137] 2.1.2. The sub-problem of minimizing the lower-level operating cost is as follows:

[0138]

[0139] c i,j,t′ ·d i,j,t′ =0, (32)

[0140] P i,j,t′ +r i,j,t′ +d i,j,t′ =b i,j,t′ +e i,j,t′ +c i,j,t′ , (33)

[0141]

[0142] Where: C i,j,1,t′ and C i,j,2,t′ They represent the operating cost of each building and the loss cost of the energy storage system in the time slot t′ within the prediction length.

[0143] 2.2. Deploy the upper-level model predictive control method assisted by the indoor thermal dynamic model based on the physical consistency neural network to solve the problem of minimizing the indoor comfort temperature deviation, and use the gradient descent method to solve the optimal HVAC system decision for the current time slot and the next X-1 time slots, that is, The specific solution process is as follows: First, set the current iteration step to k = 0 and the maximum iteration step to k. max :

[0144] 2.2.1. Predictive control output decision based on upper-level model The proposed physical embedded neural network prediction

[0145] 2.2.2. Calculate the indoor comfort deviation between the current time slot and the next X-1 time slots

[0146] 2.2.3 Based on and The gradient is calculated according to the following formula:

[0147]

[0148] 2.2.4. Based on the obtained gradient, the gradient optimizer optimizes the upper-level decision with gradient minimization as the update direction. The specific calculation formula is as follows:

[0149]

[0150] in: and are the exponential moving average of gradient and the exponential moving average of squared gradient respectively; β and For update and Hyperparameters of and They are and bias-corrected estimates of ; express The function is used to update the decision of the current iteration step; ζ and ∈ are the learning rate and hyperparameters respectively.

[0151] 2.2.4, If the current And k<k max , k=k+1 and jump to reiterate; otherwise, the iteration ends and the optimal HVAC system decision is output

[0152] 2.3. Input the optimal decision of the upper-level model predictive control method into the lower-level model predictive control, and deploy the lower-level model predictive control method to solve the sub-problem of minimizing the operating cost. By introducing four auxiliary variables θ i,j,t′ ,κ i,j,t′ ,I c,t′ ,I d,t′ Convert (P4) into the following mixed integer linear programming problem (P5) to solve the optimal energy storage charging and discharging decision for the current time slot and the next X-1 time slots, that is:

[0153]

[0154] st(28)-(29),(43)

[0155]

[0156] I c,t′ +I d,t′ ≤1, (46)

[0157] I c,t′ ,I d,t′ ∈{0,1},(47)

[0158]

[0159] θ i,j,t ≥0, (50)

[0160] κ i,j,t ≥0, (51)

[0161] in: make and P i,j,t′ =θ i,j,t′ -κ i,j,t′ and |P i,j,t′ |=θ i,j,t′ +κ i,j,t′; The decision variable of (P5) is c i,j,t′ ,d i,j,t′ ,θ i,j,t′ ,κ i,j,t′ ,I c,t′ ,I d,t′ .

[0162] 3. A distributed collaborative operation optimization algorithm based on physically consistent neural network-assisted hierarchical model predictive control guided by binary search is proposed to solve the above-mentioned collaborative operation two-level optimization problem;

[0163] In this embodiment, if the current operation time slot t is in the grid service stage, the initial decision X1 of the current time slot is first obtained according to the operation optimization method based on physical consistency neural network assisted hierarchical model predictive control; then the upper limit of the current power demand peak is set to ρ u =ρ max , the lower limit of the current peak power demand limit is ρ l =ρ min , is a threshold. And the current iteration step k=0 is less than the maximum number of iterations k max , iterate according to the following steps to solve the optimal power demand peak limit and optimal decision

[0164] 3.1. Let the current peak power demand limit be ρ t =(ρ u +ρ l ) / 2;

[0165] 3.2、Distributed system operators will t Transmitted to all load aggregators i;

[0166] 3.3、Load aggregator will ρ t Transmitted to all grid-interactive, efficient residential buildingsj;

[0167] 3.4. According to the following rule-based decision adjustment method, all buildings are adjusted X1 to obtain the optimal decision

[0168] make

[0169] 3.4.1 If and but

[0170] 3.4.2 If and but

[0171] 3.4.3 If and but

[0172] 3.4.4 If and and but

[0173] 3.4.5 If and and but

[0174] 3.5. All buildings will be optimally decided Transmit to the corresponding load aggregator;

[0175] 3.6 Distributed system operators obtain optimal power demand

[0176] 3.7. If the current Then ρ l =ρ t On the contrary, ρ u =ρ t ;

[0177] 3.8 If And k<k max , return to (8.1) and continue iteration, k = k + 1; otherwise, end the iteration and output the current optimal ρ t and optimal decision

[0178] 4. Deploy the proposed collaborative operation optimization method in the actual system.

[0179] In this embodiment, the actual operation system includes a grid-interactive high-efficiency residential building operation module and a real-time decision-making module based on hierarchical model predictive control assisted binary search.

[0180] The grid-interactive high-efficiency residential building operation module is responsible for receiving the optimal decision output by the real-time decision module and executing it in each grid-interactive high-efficiency residential building.

[0181] The real-time decision-making module based on hierarchical model predictive control assisted binary search includes an information acquisition submodule, an upper-level model predictive control real-time decision-making submodule, a lower-level model predictive control real-time decision-making submodule, a binary search collaborative operation optimization submodule, and an optimal control strategy output submodule.

[0182] The information collection submodule collects the latest status of each component of each grid-interactive high-efficiency residential building after decision-making, such as indoor building temperature and the real-time storage capacity of the energy storage system. This information is then sent to the upper and lower model predictive control real-time decision submodules.

[0183] The upper-level model predictive control real-time decision-making submodule reads the indoor temperature status information from the information acquisition submodule. It then uses the operational optimization method to determine the optimal HVAC system decision. Finally, this optimal HVAC system decision is sent to the lower-level model predictive control real-time decision-making submodule.

[0184] The lower-level model predictive control real-time decision-making submodule reads the energy storage system status from the information acquisition submodule and the optimal HVAC system decision from the upper-level model predictive control real-time decision-making submodule. It then uses the aforementioned operational optimization method to determine the optimal energy storage system decision. Finally, the optimal HVAC system decision and energy storage system decision are transmitted to the binary search collaborative operational optimization module.

[0185] The binary search collaborative operation optimization submodule first determines whether each grid-interactive high-efficiency residential building needs to participate in grid service in the current time slot. If so, the collaborative operation optimization method is used to further solve the optimal decision, and the latest decision is sent to the optimal control strategy output submodule. If not, the optimal decision of the upper and lower model predictive control real-time decision submodules is sent to the optimal control strategy output submodule.

[0186] The optimal control strategy output submodule reads the decision issued by the binary search collaborative operation optimization submodule and sends it to the grid-interactive efficient residential building operation module.

[0187] In order to demonstrate the effectiveness of the method of the present invention, four comparative schemes were introduced.

[0188] Solution 1 uses a rule-based HVAC system operation strategy. In cooling mode, when the indoor temperature is above the upper limit of the comfort range, the HVAC system input power is set to maximum. When the indoor temperature is below the lower limit of the comfort range, the HVAC system is shut down, with the input power set to zero. When the indoor temperature is within the comfort range, the HVAC system input power remains unchanged.

[0189] Option 2 uses a deep reinforcement learning algorithm based on proximal policy optimization to train each residential building agent to optimize the input power decisions of the HVAC system and the charge and discharge power decisions of the energy storage system. Specifically, the state of each residential building agent in Option 2 includes outdoor temperature, indoor temperature, light intensity, and fixed load; the action is designed to be the input power of the HVAC system and the charge and discharge power of the energy storage system; and the reward function consists of three terms: operating cost, depreciation cost of the energy storage system, and deviation from the indoor comfortable temperature.

[0190] Solution 3 uses a deep reinforcement learning algorithm based on double-delayed deep deterministic policy gradients. The state, action, and reward function design of each agent in this solution are the same as those in Solution 2. It is worth noting that none of the above three solutions consider collaborative operation during the grid service phase.

[0191] Scheme 4 adopts the physical consistency neural network assisted hierarchical model predictive control method proposed by the method of the present invention to optimize the operation of the grid-interactive residential building system during the normal operation stage, but does not use the collaborative algorithm based on the binary search assisted by the above-mentioned operation optimization method proposed by the method of the present invention to optimize the operation during the grid service stage.

[0192] Figure 2-Figure 4 1 is a performance comparison chart of the method of the present invention and the comparative scheme, and Table 1 is a summary of the performance indicators of all schemes.

[0193] Table 1 Summary of performance indicators of the method of the present invention and other methods

[0194] Comparison plan Total operating cost ($) Average temperature deviation (℃) Average power limit exceeded (%) Economic compensation (kW) Option 1 1380.084 0.451 34.474 0 Option 2 1269.773 0 8.429 0 Option 3 1106.086 0.002 7.981 0 Option 4 843.466 0 41.595 0 Method of the present invention 803.690 0.042 6.724 453.463

[0195] like Figure 2 As shown, compared with all other solutions, the method of the present invention can reduce the total operating cost by 4.716%-41.765% (assuming that the economic compensation for each household is US$0.1 per kilowatt), which has a high operating cost saving potential and economic benefits. Figure 3 As shown, the average temperature deviation of the method of the present invention is better than that of the comparative scheme 1, and can reduce the average temperature deviation by 90.687%. Although the average temperature deviation of schemes 2, 3 and 4 is slightly better than that of the method of the present invention, the method of the present invention and the above three schemes can control the average temperature deviation to below 0.1°C, which also meets the user's demand for indoor thermal comfort. Moreover, the operation method adopted in scheme 4 is the method proposed in the method of the present invention in the normal operation stage. Without considering the grid service, the average temperature deviation can be controlled to 0, which also illustrates the effectiveness of the method of the present invention in meeting the comfort needs of indoor users. As shown in FIG. Figure 4 As shown, compared with all the comparison schemes, the method of the present invention can reduce the power limit over-limit by 15.750%-83.835%, and better meet the service demand of the power grid.

Claims

1. A contribution-aware grid-interactive residential building collaborative operation optimization method, characterized in that: The collaborative operation optimization method comprises the following steps: Step 1: Considering the economic compensation for grid-interactive high-efficiency residential buildings when providing grid services, a two-layer optimization problem is modeled: the operation cost minimization problem of grid-interactive high-efficiency residential buildings in the normal operation phase and the collaborative operation problem of grid-interactive high-efficiency residential buildings in the grid service phase. The objective function in the running cost minimization problem is: in: represents the decision variable, represents the expectation operator, acting on the uncertain system parameters, including the fixed load b i,j,t , photovoltaic output r i,j,t , outdoor temperature T t out and buying and selling electricity prices; C i,j,1,t and C i,j,2,t denote the operating cost of residential building j and the loss cost of energy storage system in load aggregator i in time slot t respectively; The objective function in the collaborative two-level optimization problem is: Where: Γ(·) represents the economic compensation related to the change in electricity demand of all grid-interactive residential buildings, ρ t represents the peak power demand limit, ρ max Indicates the maximum peak power demand limit; represents the optimal power demand of load aggregator i in time slot t; Step 2: Propose an operation optimization method based on physically consistent neural network-assisted hierarchical model predictive control to solve the problem of minimizing the operating costs of grid-interactive high-efficiency residential buildings during normal operation; The operation optimization method based on physical consistency neural network assisted hierarchical model predictive control is designed as follows: (4.1) The proposed grid-interactive high-efficiency residential building operation cost minimization problem during normal operation is transformed into an upper-layer indoor comfort temperature deviation minimization sub-problem and a lower-layer operation cost minimization sub-problem; (4.2) Deploy the upper-level model predictive control method based on the physical consistency neural network indoor thermal dynamic model to solve the indoor comfort temperature deviation minimization problem and obtain the upper-level optimal decision for time slot t (4.3) Input the optimal decision of the upper-level model predictive control method into the lower-level model predictive control, and deploy the lower-level model predictive control method to solve the operating cost minimization subproblem to obtain the optimal decision of the lower level in time slot t The upper floor indoor comfort temperature deviation minimization subproblem proposed in step 4.1 is as follows: Where: X represents the predicted length, t′ represents the corresponding time slot within the predicted length, C i,j,3,t′ Indicates the indoor comfort temperature deviation value; It is a dynamic model of indoor thermal comfort based on physical consistency neural network, which consists of a black box module and a physical module. The black box module is composed of a deep neural network, whose input is outdoor light intensity and output is the indoor temperature disturbance ξ caused by light intensity. i,j,t′ ; The physical module is composed of the indoor temperature T of the current time slot i,j,t′ , the outdoor temperature T of the current time slot t′ out , the HVAC system input power e in the current time slot i,j,t′ composition; Step 3: A distributed collaborative operation optimization algorithm based on physically consistent neural network-assisted hierarchical model predictive control guided by binary search is proposed to solve the grid-interactive efficient residential building collaborative operation two-level optimization problem in the grid service phase; The distributed collaborative operation optimization algorithm based on hierarchical model predictive control assisted binary search proposed in step 3 in the power grid service stage is designed as follows: If the current operation time slot t is in the grid service phase, the initial decision of the current time slot is obtained according to the operation optimization method based on hierarchical model predictive control. Then set the upper limit of the current power demand peak as ρ u =ρ max , the lower limit of the current peak power demand limit is ρ l =ρ min , is a threshold; when And the current iteration step k=0 is less than the maximum number of iterations k max , iterate according to the following steps to solve the optimal power demand peak limit and optimal decision (8.1) Let the current peak power demand limit be ρ t =(ρ u +ρ l ) / 2; (8.2) The distributed system operator will t Transmitted to all load aggregators i; (8.3) The load aggregator will t Transmitted to all grid-interactive, efficient residential buildingsj; (8.4) According to the rule-based decision adjustment method, all building adjustments Get the best decision (8.5) All buildings will be optimally decided Transmit to the corresponding load aggregator; (8.6) Distributed system operators obtain optimal power demand (8.7) If the current Then ρ l =ρ t On the contrary, ρ u =ρ t ; (8.8) If And k <k max , return to (8.1) and continue iteration, k = k + 1; otherwise, end the iteration and output the current optimal ρ t and optimal decision Step 4: Deploy the proposed collaborative operation method in the actual system of grid-interactive high-efficiency residential buildings.

2. A contribution-aware grid-interactive residential building collaborative operation optimization method according to claim 1, characterized in that: The operation cost minimization problem of the grid-interactive high-efficiency residential building in the normal operation phase modeled in step 1 includes decision variables, constraints and objective functions; Decision variables in the operating cost minimization problem The energy storage system input power c of residential building j managed by load aggregator i in time slot t i,j,t , energy storage system output power d i,j,t , HVAC system input power e i,j,t and the electricity demand of residential buildings P i,j,t ; The constraints in the running cost minimization problem include: c i,j,t ·d i,j,t =0, (5) P i,j,t +r i,j,t +d i,j,t =b i,j,t +e i,j,t +c i,j,t , (6) Among them: B i,j,t represents the storage capacity of the energy storage system of residential building j in load aggregator i in time slot t; σ c,i,j and σ d,i,j Respectively represent the input efficiency and output efficiency of the energy storage system; and Respectively represent the minimum storage capacity and maximum storage capacity of the energy storage system; and Respectively represent the maximum input power and maximum output power of the energy storage system; △t represents the time slot length; r i,j,t represents the photovoltaic power output in time slot t, b i,j,t represents the fixed load of time slot t; T i,j,t represents the indoor temperature of residential building j in load aggregator i at time slot t, T t out represents the outdoor temperature at time slot t, ξ i,j,t represents the indoor thermal disturbance of residential building j in load aggregator i at time slot t, represents the indoor thermal dynamics of residential building j in load aggregator i; represents the maximum HVAC system input power of residential building j in load aggregator i; It represents the upper limit of electricity demand of residential building j in load aggregator i during the grid service stage.

3. The contribution-aware grid-interactive residential building collaborative operation optimization method according to claim 2, characterized in that: The two-level optimization problem for the coordinated operation of grid-interactive high-efficiency residential buildings in the grid service phase established in step 1 includes decision variables, constraints, and objective functions; The decision variables in the collaborative operation bi-level optimization problem are the peak power demand limit ρ of each building in the grid service period t time slot t ; The constraints in the collaboratively running bi-level optimization problem include: 0≤ρ t ≤ρ max , (12) c i,j,t ·d i,j,t =0, (20) P i,j,t +r i,j,t +d i,j,t =b i,j,t +e i,j,t +c i,j,t , (21) Where: max Indicates the maximum peak power demand limit; represents the optimal power demand of load aggregator i in time slot t; P limit,t represents the sum of the power caps of all residential buildings in time slot t; represents the optimal power demand of each building in time slot t; represents the initial decision amount before the proposed binary search; α represents a weight coefficient.

4. The contribution-aware grid-interactive residential building collaborative operation optimization method according to claim 3, characterized in that: The sub-problem of minimizing the lower-level running cost proposed in step 4.1 is as follows: c i,j,t′ ·d i,j,t′ =0, (32) P i,j,t′ +r i,j,t′ +d i,j,t′ =b i,j,t′ +e i,j,t′ +c i,j,t′ , (33) Where: C i,j,1,t′ and C i,j,2,t′ They represent the operating cost of each building and the loss cost of the energy storage system in the time slot t′ within the prediction length.

5. The contribution-aware grid-interactive residential building collaborative operation optimization method according to claim 4, characterized in that: The upper model predictive control method deployed in step 4.2 uses the gradient descent method to solve the optimal HVAC system decision for the current time slot and the next X-1 time slots, that is, The lower-level model predictive control method deployed in step 4.3 solves the optimal energy storage charging and discharging decision for the current time slot and the next X-1 time slots by converting the proposed (P4) into a mixed integer linear programming problem, that is, 6. A contribution-aware grid-interactive residential building collaborative operation optimization method according to claim 5, characterized in that: The rule-based decision adjustment method proposed in step 8.4 is designed as follows: (9.1) If and but (9.2) If and but (9.3) If and but (9.4) If and and but (9.5) If and and but 7. The contribution-aware grid-interactive residential building collaborative operation optimization method according to claim 1, characterized in that: The actual operation system for deploying the proposed collaborative operation optimization method in step 4 includes a grid-interactive high-efficiency residential building operation module and a real-time decision-making module based on hierarchical model predictive control assisted binary search; The grid-interactive high-efficiency residential building operation module is responsible for receiving the optimal decision output by the real-time decision module and executing it in each grid-interactive high-efficiency residential building; The real-time decision-making module based on hierarchical model predictive control assisted binary search includes an information acquisition submodule, an upper-layer model predictive control real-time decision-making submodule, a lower-layer model predictive control real-time decision-making submodule, a binary search collaborative operation optimization submodule, and an optimal control strategy output submodule; The information collection submodule is responsible for collecting the latest status of each component of each grid-interactive high-efficiency residential building after the decision is executed, including the building's indoor temperature and the real-time storage capacity of the energy storage system. It is then sent to the upper and lower model predictive control real-time decision submodules respectively; The upper-layer model predictive control real-time decision submodule reads the indoor temperature status sent by the information acquisition submodule; then, the operation optimization method is used to solve the optimal HVAC system decision; finally, the optimal HVAC system decision is sent to the lower-layer model predictive control real-time decision submodule; The lower-level model predictive control real-time decision submodule reads the energy storage system status issued by the information acquisition submodule and the optimal HVAC system decision issued by the upper-level model predictive control real-time decision submodule; then, the optimal energy storage system decision is solved using the operation optimization method; finally, the optimal HVAC system decision and the energy storage system decision are transmitted to the binary search collaborative operation optimization module; The binary search collaborative operation optimization submodule first determines whether the current time slot requires each grid-interactive high-efficiency residential building to participate in grid service; if so, the collaborative operation optimization method is used to further solve the optimal decision, and the latest decision is sent to the optimal control strategy output submodule; if the current time slot does not require participation in grid service, the optimal decision of the upper and lower model predictive control real-time decision submodules is sent to the optimal control strategy output submodule; The optimal control strategy output submodule reads the decision issued by the binary search collaborative operation optimization submodule and sends it to the grid-interactive efficient residential building operation module.

Images