CN109752953B

CN109752953B - Building energy supply system model prediction regulation and control method of integrated electric refrigerator

Info

Publication number: CN109752953B
Application number: CN201811168372.XA
Authority: CN
Inventors: 戚艳; 王旭东; 李国栋; 吴莉萍; 穆云飞; 吴磊; 霍现旭; 胡晓辉; 丁一; 马世乾; 袁中琛; 赵玉新; 康宁; 李雪
Original assignee: State Grid Corp of China SGCC; State Grid Tianjin Electric Power Co Ltd; Electric Power Research Institute of State Grid Tianjin Electric Power Co Ltd
Current assignee: State Grid Corp of China SGCC; State Grid Tianjin Electric Power Co Ltd; Electric Power Research Institute of State Grid Tianjin Electric Power Co Ltd
Priority date: 2018-10-08
Filing date: 2018-10-08
Publication date: 2022-01-18
Anticipated expiration: 2038-10-08
Also published as: CN109752953A

Abstract

The invention relates to a building energy supply system model prediction regulation and control method of an integrated electric refrigerator, which is technically characterized in that: the method comprises the following steps: step 1, building energy supply system model composed of building virtual energy storage system, electric refrigerator, diesel generating unit, fuel cell and accumulator is established; and 2, respectively providing a model before the day, a model in the day and a rolling part model after a scheduling model framework is established, further establishing a multi-time scale prediction scheduling model of the building energy supply system, and performing multi-time scale optimization scheduling according to the requirements of the building energy supply system and building users. The building energy utilization system integrates the building virtual energy storage model with the electric refrigerator, integrates the virtual energy storage model into the model prediction regulation and control method of the building energy supply system, and further excavates the flexibility of building energy utilization on the premise of ensuring the indoor temperature comfort of the building.

Description

Building energy supply system model prediction regulation and control method of integrated electric refrigerator

Technical Field

The invention belongs to the technical field of optimal control of an energy supply system containing electric energy instead of load, and relates to a multi-time scale model prediction regulation and control method of a building energy supply system, in particular to a building energy supply system model prediction regulation and control method of an integrated electric refrigerator.

Background

With the continuous highlighting of energy and environmental problems on a global scale, it has become a global consensus to actively develop renewable energy sources and improve energy utilization efficiency. The 2016 international energy prospect released by the U.S. energy information agency shows that the energy consumption of buildings accounts for about 20% of the total energy that can be delivered globally. Data of 'Chinese building energy conservation development report' in 2016 show that the share of the total energy consumption of the buildings in China exceeds 27%, and the proportion still continues to increase with the continuous improvement of the urbanization level in China and the adjustment of the industrial structure. Therefore, the terminal energy utilization system represented by the building has huge energy-saving and emission-reducing potential. The energy-saving potential of the demand side represented by building is fully developed, and the method has important significance for solving the increasingly prominent contradiction between energy demand increase and energy shortage and the contradiction between energy utilization and environmental protection in the development process of the human society.

In recent years, more and more electric energy is integrated on the building side instead of the load, forming a building-oriented energy supply system. The existing research shows that the optimization scheduling and energy management of a building energy supply system are carried out on the premise of not carrying out large-scale investment transformation, and the safe consumption of distributed new energy on a user side can be effectively promoted. The traditional single day-ahead regulation and control method cannot completely reflect the influence of errors of new energy power generation and load prediction on the optimized operation of the building energy supply system, and the time coupling characteristics of the building energy supply system energy supply equipment in actual operation under day-ahead scheduling cannot be considered in single time scale modeling, so that the optimization result may not be consistent with the actual operation condition of the building energy supply system. Meanwhile, the robust optimization result has certain conservatism, the calculated amount is large, and convergence is difficult; the random optimization depends on the probability distribution of random variables, and the selection and design of massive scenes also increase the calculated amount.

In order to solve the problems, a dispatching mode combining manual day-ahead dispatching plan and automatic power generation control adopted by active dispatching of a large power grid is used for reference, and more attention is paid to the optimization dispatching research of a building energy supply system in multiple time scales. The main idea is as follows: the method comprises the steps of making a unit combination and an operation plan reference value based on prediction data in a day-ahead stage, and carrying out power adjustment on a controllable Distributed Generator (DG) based on real-time data and the deviation left by a superior stage in a real-time operation stage. The multi-time scale method is further applied to a building energy supply system, the existing research provides a multi-agent method-based commercial building multi-time scale energy management method, and the operation cost of the building is reduced in the environment with uncertain predicted data. The existing research also provides a double-layer energy management method aiming at multiple time scales of office type buildings, and the energy management of the buildings is realized by respectively scheduling the controllable DG and the controllable electric energy alternative loads (a building temperature control system and an electric automobile) at the day-ahead scheduling stage and the day-in real-time adjusting stage. The research plays an important role in predicting energy management of the building energy supply system in an uncertain environment, but the open-loop optimization scheduling method adopted by the in-day real-time scheduling is to solve the in-day operation scheme of the building energy supply system based on the single-section optimal power flow and issue all optimization instructions at one time, so that the state change condition of the building energy supply system in a future period is difficult to perceive in advance, and the obtained optimization scheme is poor in robustness.

Disclosure of Invention

The invention aims to overcome the defects of the prior art and provides a building energy supply system model prediction regulation and control method of an integrated electric refrigerator, which has the advantages of reasonable design, simple and quick application, strong practical value, high flexibility and strong robustness of an obtained optimization scheme.

The invention solves the practical problem by adopting the following technical scheme:

a building energy supply system model prediction regulation and control method of an integrated electric refrigerator comprises the following steps:

step 1, building energy supply system model composed of building virtual energy storage system, electric refrigerator, diesel generating unit, fuel cell and accumulator is established;

and 2, respectively providing a model before the day, a model in the day and a rolling part model after a scheduling model framework is established, further establishing a multi-time scale prediction scheduling model of the building energy supply system, and performing multi-time scale optimization scheduling according to the requirements of the building energy supply system and building users.

Further, the specific steps of step 1 include:

(1) building a building virtual energy storage system model;

(2) building a building energy supply system energy supply unit model;

further, the specific steps of step 2 include:

(1) constructing a multi-time scale prediction scheduling framework of a building energy supply system;

(2) constructing a day-ahead economic dispatching model;

(3) performing rolling correction within the day;

(4) and performing multi-time scale optimization scheduling according to the requirements of a building energy supply system and building users.

The invention has the advantages and beneficial effects that:

1. the method can effectively solve the problem that the deviation between the optimized dispatching scheme of the building energy supply system and the actual operation scene is large due to prediction errors through model prediction regulation and control on the premise of ensuring the indoor temperature comfort of the building, and further reduces the energy consumption cost;

2. the building energy utilization system integrates the building virtual energy storage model with the electric refrigerator, integrates the virtual energy storage model into the model prediction regulation and control method of the building energy supply system, and further excavates the flexibility of building energy utilization on the premise of ensuring the indoor temperature comfort of the building.

Drawings

FIG. 1 is a schematic diagram of a building energy supply system for an integrated electric refrigeration system of the present invention;

FIG. 2 is a multi-time scale optimized dispatching framework diagram of the building energy supply system of the present invention;

FIG. 3 is a flow chart of a multi-time scale optimal scheduling method of the building energy supply system of the present invention;

fig. 4(a) is a diagram of a controllable DG day-ahead scheduling scheme of the building energy supply system of the present invention-day-ahead unit plan without consideration of virtual energy storage scheduling;

fig. 4(b) is a diagram of a controllable DG day-ahead scheduling scheme of the building energy supply system of the present invention-day-ahead unit plan under consideration of virtual energy storage scheduling;

fig. 5(a) is a diagram of a 3-day-ahead cooling scheduling scheme for a building of the present invention-a cooling scheduling scheme without taking into account virtual energy storage;

fig. 5(b) is a diagram of a 3-day-ahead cooling scheduling scheme for a building of the present invention-a cooling scheduling scheme considering virtual energy storage;

FIG. 6 is a diagram of the day-ahead scheduling results of the building 3 virtual energy storage system of the present invention;

FIG. 7 is a graph comparing the power tracking effect of the building energy supply system tie line of the present invention;

FIG. 8(a) is a diagram of the results of the day-to-day optimized dispatch of a controllable DG and a battery in accordance with the present invention;

FIG. 8(b) is a comparison graph of the tracking effect of the SOC of the storage battery of the present invention;

FIG. 9 is a graph comparing the power tracking effect of the building energy supply system tie line of the present invention;

fig. 10(a) is a diagram of a virtual energy storage intra-day correction scheme for the building 3 of the present invention;

fig. 10(b) is a diagram of an intra-day cooling scheduling scheme for the building 3 of the present invention without consideration of virtual energy storage;

fig. 10(c) is a diagram of an in-day cooling scheduling scheme of the building 3 of the present invention in consideration of virtual energy storage.

Detailed Description

The embodiments of the invention will be described in further detail below with reference to the accompanying drawings:

the invention provides a building energy supply system model prediction regulation and control method of an integrated electric refrigerator, which is different from the traditional open-loop optimization method. A Model Predictive Control (MPC) method is used for providing a multi-time scale optimization scheduling method combining day-ahead economic scheduling and day-in rolling correction of a building energy supply system.

A Model Predictive Control (MPC) method adopts a rolling optimization strategy and a feedback correction idea, so that the problems that regulation and control of a building energy supply system under an open-loop optimization method are high in prediction dependency, greatly influenced by environmental factors and large in deviation between a day-ahead regulation and control scheme and actual operation requirements can be effectively solved, and meanwhile, the obtained optimization method has good anti-interference capability and robustness.

Therefore, on the basis of the research, the invention further researches a multi-time scale optimization scheduling method combining the day-ahead economic scheduling and the day-in rolling correction of the building energy supply system based on the MPC method. In the day-ahead real-time scheduling stage, the building energy supply system is based on renewable energy, outdoor environment and energy load short-term power prediction information on the basis of a day-ahead optimized economic scheduling model, and in day-ahead real-time scheduling, the rolling optimized scheduling based on MPC in a limited time window is used for replacing the traditional single-section optimized scheduling, so that the state change condition of the building energy supply system in a future period can be better sensed, and further the output and energy use plan of each controllable distributed power supply and controllable load can be optimized and adjusted in advance. In addition, the heat storage characteristic of the building envelope structure is considered, the heating/cooling load of the building envelope structure can be adjusted within the indoor temperature comfort range of the building, and the building can have certain flexibility. In order to effectively utilize the flexibility of the building energy utilization system, the building virtual energy storage model of the integrated electric refrigerator is constructed, and the virtual energy storage model is integrated into a multi-time scale optimization scheduling model of the building energy supply system, so that the flexibility of the building energy utilization is further developed on the premise of ensuring the indoor temperature comfort of the building.

A schematic diagram of a building energy supply system is shown in fig. 1, and includes a plurality of buildings, controllable Distributed Generators (DGs), an energy storage system, and communication links. Each building system includes an electrical chiller, conventional electrical equipment, and a rooftop photovoltaic system. The Building Energy supply System Energy Management System (MEMS) interacts with each Building Energy Management System (BEMS), predicts and monitors each Building Energy consumption behavior, and reduces the operation cost of each Building on the premise of meeting the comfort level of a user. The functions of each unit are introduced as follows:

BEMS: and the BEMS of each building acquires the prediction data of the illumination intensity and the outdoor temperature in the prediction time domain from the weather station through the communication link in each control time domain according to the set prediction time domain and the set control time domain. And simultaneously, the BEMS acquires the current indoor temperature of the building, the uncontrollable load and the operation data and the operation information of the roof photovoltaic system through a sensor arranged on the building. And uploading the calculation result to the MEMS through a communication link after simulation prediction and data preprocessing.

MEMS: and collecting the prediction results uploaded by each BEMS, carrying out optimization calculation according to a preset scheduling target and power price information, then issuing the power utilization curve subjected to optimization calculation to each BEMS, and executing corresponding instructions by each building.

The power transmission direction: bidirectional power flow exists between the building energy supply system and an external power grid, and if the load demand of the building energy supply system exceeds the total electric quantity provided by a power supply inside the system, power is absorbed from the power grid; and if the load demand of the building energy supply system does not reach the total electric quantity provided by the power supply in the system, sending the surplus electric quantity into the power grid.

Communication control mode: there is a need for two-way communication between the BEMS and MEMS of each building. Also, a unidirectional communication link is required between the weather station and the BEMS.

the specific steps of the step 1 comprise:

(1) building virtual energy storage system model

The step 1, the step (1), comprises the following specific steps:

firstly, building a building thermal dynamic model

The building can be modeled as a virtual energy storage unit exchanging heat with the outdoor in consideration of the heat storage characteristics of the building; based on a building thermal balance equation, a quantitative mathematical relation among the building indoor temperature, the refrigeration demand and the outdoor temperature can be obtained, as shown in formula (1):

in the above formula, ρ is the air density; c is the specific heat capacity of air; v is the indoor air capacity; t is_inIs the indoor temperature;

(i)

the heat (kW) transferred between the outer wall of the building and the outdoor is represented, and the sum of the heat transferred by all the outer walls is represented by a formula (2);

(ii)

the heat (kW) transferred between the building external window and the outside is represented and is the sum of the heat transferred by all the external windows, as shown in formula (3);

(iii)

the heat value (kW) of the indoor heat source can be obtained through prediction or measurement;

(iv)

for solar radiation the heat transferred through the outer wall (kW),according to ISO 13790, is the sum of the heat transferred by solar radiation through all external walls, as shown in formula (4);

(v)

the heat (kW) transmitted through the outer window for solar radiation can be calculated by the formula (5); the invention assumes that all the outsides of the building are uniformly distributed on four walls of the east, the west, the south and the north;

(vi)

the refrigeration power (kW) of the refrigeration equipment;

T_outis the outdoor temperature; u shape_wallThe heat transfer coefficient of the building outer wall; u shape_winThe heat transfer coefficient of the building external window; f_wall,jThe area of the building outer wall j; f_win,jThe area of the building external window j is shown; SC is a shading coefficient, and the value of the SC is related to whether a shading plate, a glass material and the like exist; r_se,jThermal resistance of heat convection and heat radiation between the outer wall and outdoor air; alpha is alpha_wThe heat transfer loss coefficient of the wall body; tau is_winThe heat transfer loss coefficient of the wall body; i is_T,jTotal solar radiation intensity (kW/m) received for the outer wall j per square meter of wall/window²) The expression is shown as a formula (6);

wherein I_b、I_dAnd I represents the intensity of direct radiation, scattered radiation and total radiation, respectively, in kW/m, on the same horizontal surface²；ρ_gThe reflectivity of the ground is 0.2; theta is an illumination incidence angle; r_bIs an illumination geometric factor, which is used for describing the ratio of the direct radiation intensity of illumination on an inclined plane to the direct radiation intensity of illumination on a horizontal plane, and the calculation expression of the geometric factor is shown as (7);

wherein, theta_zThe lighting zenith angle; (ii) a

Constructing a building virtual energy storage system model;

further, building a virtual energy storage system model based on the building heat storage characteristics; in order to ensure the comfort of a building user, the building virtual energy storage model needs to consider the range of indoor temperature set points, and the basic idea is that the refrigeration requirement of the building can be adjusted to a certain extent within the comfort range of the user; therefore, when the electricity price is lower, the refrigerating capacity can be increased (namely, the refrigerating machine can be started in advance or the electric power of the refrigerating machine can be increased), redundant cold energy is stored in the building, the electricity consumption is increased equivalently by the virtual energy storage system, and the building energy supply system charges the virtual energy storage system; similarly, when the electricity price is higher, the refrigerating capacity can be reduced (namely the refrigerating machine can be turned off in advance or the electric power of the refrigerating machine can be reduced), and by utilizing the cold energy stored in advance, the electricity consumption is reduced by the virtual energy storage system, and the building energy supply system discharges electricity to the virtual energy storage system; accordingly, the charge and discharge power of the virtual energy storage system can be obtained, as shown in the formula (8);

wherein,

the charging and discharging power of the building virtual energy storage system at the moment t is positive, and the charging is negative;

building refrigeration electric power for not adjusting indoor temperature;

building cooling electric power for regulating indoor temperature within a temperature comfort range is considered;

the formula (1) and the formula (8) jointly form a mathematical model of the building virtual energy storage system: when the room temperature of the building is adjusted within the temperature comfort range of a user, the refrigeration requirement (equal to the refrigeration power of refrigeration equipment) of the building and the charge and discharge power of the virtual energy storage system are obtained through the formula (1) and the formula (8) respectively, and then the charge and discharge power of the virtual energy storage system is effectively managed so as to participate in the optimized scheduling of the energy supply system of the building.

(2) Building a building energy supply system energy supply unit model;

the step 1, the step (2) comprises the following specific steps:

firstly, establishing a generator model

The fuel cost of the generator is determined by the unit characteristic parameters and the output power, and is shown as the formula (9):

in the formula: f. of_fuel(. h) is a fuel cost function of the generator; p_DE,tIs the power output of the generator; a. b and c are fuel cost coefficients of the diesel generator;

establishing fuel cell model

Fuel cell Fuel cost is determined by output Power and efficiency, parameter η_FCDetermining, as shown in equation (10):

f_fuel(P_FC,t)＝C_gas×P_gas,t＝C_gas×(P_FC,tΔt/η_FC) (10)

in the formula: p_FC,tIs the power output of the fuel cell; eta_FCIs the efficiency of the fuel cell; c_gasIs the natural gas price; p_gas,tGas power consumed for the fuel cell; Δ t is the corresponding power output period;

establishing electric refrigerator model

FIG. 1 illustrates a building energy supply system in which the cooling demand is satisfied by the consumption of electrical energy by an electrical chiller; the refrigeration equipment adopted by the building energy supply system is a compression type electric refrigerator (hereinafter referred to as electric refrigerator), and the refrigeration power of the refrigeration equipment is shown as the formula (11):

Q_EC,t＝P_EC,t×COP_EC (11)

in the formula: q_EC,tThe output is the refrigeration power output of the electric refrigerator; p_EC,tElectrical power consumed for the electrical refrigerator; COP_ECThe energy efficiency ratio of the electric refrigerator;

fourthly, establishing a storage battery model

The State of Charge (SOC) is the ratio of the residual capacity of the storage battery to the rated capacity and represents the State of Charge of the storage battery; SOC value SOC of storage battery at t moment_tAs shown in equation (12):

in the formula: p_bt,tRated charge-discharge power (discharge is positive, charge is negative) for the storage battery; CAP (common Place Capacity)_btThe rated capacity of the storage battery; eta_ch，η_disThe charge-discharge efficiency of the storage battery; delta is the self-discharge rate of the storage battery; Δ t is the corresponding charge-discharge period.

Step 2, respectively providing a model before the day, a model in the day and a rolling part model after a scheduling model framework is established, further establishing a multi-time scale prediction scheduling model of the building energy supply system, and performing multi-time scale optimization scheduling according to the requirements of the building energy supply system and building users;

the invention divides the optimized scheduling of the energy supply system of the integrated electric refrigerator building into two stages of day-ahead scheduling and day-in rolling correction.

The specific steps of the step 2 comprise:

(1) constructing a multi-time scale prediction scheduling framework of the building energy supply system as shown in FIG. 2;

the step 2, the step (1), comprises the following specific steps:

economic dispatch in the day

As shown in fig. 2, in the day-ahead economic dispatching stage, based on the predicted values of the electrical load power, the outdoor temperature, the illumination intensity and the new energy generation of each building in the hour level of the day, the information such as the indoor temperature comfort degree constraint of each building user, the controllable DG (Distributed Generator, DG) technical characteristics of the building energy supply system and the day-ahead market electricity price is comprehensively considered to obtain the day-ahead n based on the lowest operation cost of the building energy supply system, and the like₁Optimizing a scheduling scheme of the building energy supply system in each scheduling time period;

the scheduling scheme specifically includes: the method comprises the following steps that (1) an optimized scheduling scheme (a refrigerator scheduling scheme and an indoor temperature scheduling scheme) of each building, a controllable DG optimized scheduling scheme of a building energy supply system, an optimized scheduling scheme of an energy storage system and a tie line power exchange set point are adopted;

② rolling correction in day

In order to embody the significance of the day-ahead economic optimization scheduling, the day-ahead prediction scheduling plan is in accordance with the day-ahead plan; however, due to prediction errors of predicted values of power of the electrical load, outdoor temperature, illumination intensity and new energy power generation in the day-ahead hour level, the day-to-day actual operation plan of the building energy supply system is deviated from the day-ahead scheduling plan; in order to eliminate deviation, an intra-day rolling correction link is added, and the optimization scheduling result of the long time scale in the day is corrected based on the current operating state of a building energy supply system and the prediction data of the short time scale;

as shown in FIG. 2, the intra-day modification phase is implemented by performing rolling optimization in a period of 15min, and dividing the whole scheduling time axis into n₂A number of the scheduled time periods,wherein the prediction time domain is N_pA time period of control time domain of N_cA period of time, N_p≥N_c；

At the time of t, the current time section is utilized to the next prediction time domain N_pShort-term prediction information in the building energy supply system optimization method is obtained by optimizing and solving the N based on an MPC (Model Predictive Control, MPC) method by taking the minimum error between the building energy supply system tie line power and the day-ahead plan value as a target on the premise of not changing the start-stop plan and the energy storage charge-discharge state of the controllable DG in the day-ahead plan and meeting the power balance and various constraints_cCorrecting plan sequences of all controllable DGs, energy storage systems and building electrical loads of the building energy supply system in each time period;

issuing only a correction plan for the next time period in the t-time section, i.e. N_cA first control sequence in a rework planning sequence for the building energy supply system for a time period; when the next scheduling period comes, repeating the process;

and through rolling correction in the day, the power of the building energy supply system tie line tracks the planned value in the day ahead, so that the safe and economic operation of the building energy supply system is realized.

The optimization target considers the day-ahead plan value tracked by the power of the day-interior tie line, and also comprehensively considers the requirement of the day-interior energy storage SOC on tracking the day-ahead plan value, so that the energy storage system is ensured to play a role of 'peak clipping and valley filling' in day scheduling on the premise of meeting the constraint of day operation energy balance.

(2) Constructing a day-ahead economic dispatching model;

the step 2, the step (2), comprises the following specific steps:

firstly, the aim of the day-ahead scheduling stage of the building energy supply system is to minimize the operation cost on the basis of ensuring the temperature comfort of a user;

therefore, the day-ahead scheduling objective function of the building energy supply system of the integrated electric refrigerator is set to be the minimum operation cost, as shown in formula (13):

in the formula: t is the whole scheduling period, and can be taken 24 hours a day; the first item is the cost of purchasing electricity from the power distribution network by the building energy supply system; p_grid,tThe power supply system supplies power to the building, and the power supply system exchanges electric power with the power distribution network, wherein the power purchase is positive and the power sale is negative; c_ph,tAnd C_se,tRespectively purchasing electricity from the power distribution network and selling electricity to the power distribution network for the building energy supply system; the second term is the fuel cost, maintenance cost and start-up cost of the controllable DG in the building energy supply system, as shown by equations (9), (10), P_DG,i,tRepresents DG_iPower output over a time period t; rho_DG,iRepresents DG_iThe cost of use and maintenance; rho_su,iRepresents DG_iThe start-up cost of (a); u'_DG,i,tIndicating the starting state of the i distributed power supplies in a t time period (1 indicates starting, and 0 indicates not starting); the third item is the use and maintenance cost of an energy storage system, a photovoltaic system and a refrigeration system in a building energy supply system; p_PV,tAnd P_EC,tRespectively photovoltaic output and refrigerator electric power at the time t; rho_PV、ρ_btAnd ρ_ECRespectively representing the use and maintenance costs of unit power of unit time periods of the photovoltaic, the storage battery and the electric refrigerator;

the constraint condition consists of two parts of building energy supply system operation constraint and building operation constraint;

1) the constraint conditions of the day-ahead economic dispatching model are as follows:

building energy supply system purchase electricity restraint:

wherein,

and

respectively purchasing upper and lower limits of the power of the building energy supply system;

electric power balance constraint:

P_PV,tand P_EC,tRespectively photovoltaic output and refrigerator electric power at the time t; p_bt,tThe charging and discharging power of the energy storage battery at the time t; p_el,tBuilding electrical load at time t;

and (3) operation constraint of the controllable distributed power generation unit:

wherein, U_DG,i,tAnd U ″)_DG,i,tRespectively representing the running state (1 represents an on state, 0 represents an off state) and the off state (1 represents off, 0 represents not off) of the i distributed power supplies in the t time period;

and

is the upper and lower limits of the distributed power supply i;

at the same time, DG is also subject to minimum boot time

And minimum shutdown time

The constraint of (2):

battery operating constraints

Wherein,

and

respectively is the upper limit and the lower limit of the charging and discharging power of the energy storage system; e_bt,tThe electric quantity of the energy storage system at the moment t;

2) and (4) operation constraint of each building:

cold load balancing constraints

Wherein,

electric power is used for refrigerating the electric refrigerator;

building thermal balance constraint:

and (3) restricting the upper and lower limits of the indoor temperature of the building:

(3) performing intraday roll corrections

The step 2, the step (3) comprises the following specific steps:

as shown in fig. 2, the intra-day correction adopts an MPC method to correct the deviation between the intra-day actual operation plan and the day-ahead scheduling plan; the rolling correction method based on the MPC mainly comprises the following steps: at the current time T ', the MEMS and each BEMS are respectively based on the current state x (T') of the building energy supply system and the current indoor temperature T of each building_in(t'), using the prediction time domain N_pPredicting the future states of a building energy supply system and the indoor temperature of the building by using short-term prediction information in each time period; next, the MEMS solves the optimization problem under the premise of satisfying the current and future constraint conditions to obtain the future control time domain N_cA control instruction sequence of a building energy supply system in each time period; then, the first value of the control command sequence is applied to each BEMS and each DG controller; at the same time, at time t' +1And updating the state of the building energy supply system to be x (T' +1) and the indoor temperature T of the building_in(t' +1), and the above steps are repeatedly performed;

the mathematical model is presented next:

firstly, building a building energy supply system prediction model

Exchanging power (P) with the building energy supply system and the distribution network tie line at the present moment t_grid(t')), controllable DG output (P)_DG(t')), energy storage charging and discharging power (P)_bt(t ')), energy storage system SOC (SOC (t')), and building chiller power consumption (P)_EC(t ')) the vector x (t') is a state variable of the building energy supply system, see equation (30); with controllable DG output increment (Δ P)_DG(t')), energy storage capacity increment (Δ P)_bt(t')) and the incremental power consumption (Δ P) of the building chiller_EC(t ')) the vector u (t') is a control variable of the building energy supply system, see equation (31); short-term prediction of power increment (delta P) by photovoltaic and fan system output_PV(t′)、ΔP_WT(t')), building power short-term predicted power increase (Δ P)_el(t')) is a disturbance input to the building energy supply system, see equation (32); and (4) exchanging power by using the building energy supply system connecting line as an output variable of the building energy supply system, and obtaining an expression (33). A state space prediction model for the building energy supply system may be established as shown in equation (34).

x(t′)＝[P_grid(t′),P_DG(t′),P_bt(t′),SOC(t′),P_EC(t′)]^T (30)

u(t′)＝[ΔP_DG(t′),ΔP_bt(t′),ΔP_EC(t′)]^T (31)

r(t′)＝[ΔP_PV(t′),ΔP_WT(t′),ΔP_el(t′)]^T (32)

y(t′)＝P_grid(t′) (33)

In the formula: the state space matrices A, B, C and D are expressed as shown in equations (35) - (39).

Wherein E is_nAnd E_mThe energy supply system is an identity matrix, and n and m are the number of controllable DGs and the number of buildings in the building energy supply system respectively;

the SOC recurrence coefficient of the storage battery is expressed as follows:

D＝[1 0 0 0 0] (39)

from the equation (34), the load is N based on the photovoltaic system_pThe short-term prediction information in each time period can be predicted to obtain N by repeatedly iterating the state space prediction model_pThe building energy supply system exchanges power with the distribution grid tie-line and each building chiller consumes electrical power during a time period.

Establishing indoor temperature predicting model for building

The recursive equation of the building indoor temperature variation is shown as the following formula (40):

in the formula:

and

respectively considering the heat transferred by the building outer wall and the outdoor, the heat transferred by the building outer window and the outdoor, the indoor heat, the heat transferred by the solar heat radiation through the outer wall and the heat transferred by the solar heat radiation through the outer window of the short-term prediction increment information lambda (t') of the building input variable;

for accounting for incremental electric power consumption (delta P) of building refrigerating machine_EC(t')) a refrigeration power output; building input variable short-term prediction incremental information lambda (t') is shown as a formula (41);

as can be seen from equations (40) and (41), the building short-term prediction information λ (t') and N are used as the basis_pThe predicted information of the electric power consumption of the building refrigerating machine in each time period (obtained by rolling and solving the building energy supply system state space prediction model of the formula (34)) can be N_pIn each time period, carrying out rolling solution and prediction on the indoor temperature;

establishing rolling optimization model

In order to eliminate the deviation between the daily actual operation plan and the day-ahead scheduling plan of the building energy supply system, the daily rolling optimization aims at tracking the daily planned value for the power value of the building energy supply system tie line in each control time domain; meanwhile, in order to ensure that the energy storage system plays a role of 'peak clipping and valley filling' in scheduling in a day, reduce frequent charging and discharging times and prolong the service life of the storage battery, a penalty item of charging and discharging power increment of the storage battery is added in a rolling optimization objective function in the day, so that the SOC of the storage battery in the day tracks a planned value in the day ahead;

building energy supply system prediction model and prediction time domain N_pThe short-term prediction information in the building energy supply system can be obtained in the current control time domain N_cInternal outputThe variable Y, namely the power value of the building energy supply system tie line in the current control time domain, is expressed as shown in a formula (42); based on a day-ahead scheduling model, the current control time domain N of the building energy supply system can be obtained_cInner day-ahead tracking target vector F_dayNamely the day-ahead planned value of the building energy supply system tie line power, the expression of which is shown as the formula (43);

the objective function for which the roll optimization can be derived is as follows:

in the formula:

the penalty value of the charge and discharge power increment of the storage battery corresponding to the time t' is expressed as follows:

in the formula: theta_btA penalty factor corresponding to the penalty item;

constraint conditions of rolling optimization in the day are the same as those of equations (14) to (29), and are not described again; based on a rolling optimization model, the EMES can solve to obtain a correction control sequence u (t ') of the building energy supply system in a control time domain corresponding to each scheduling time t ', then a first value of an optimization result is added to the building energy supply system, and the whole optimization process is repeated based on new short-term prediction information at the time t ' + 1; the invention solves the multi-time scale optimization scheduling problem by using CPLEX under MATLAB.

(4) Performing multi-time scale optimization scheduling according to the requirements of a building energy supply system and building users;

the flow chart of the multi-time scale model predictive scheduling method provided by the invention is shown in fig. 3. The method comprises two stages of day-ahead economic optimization scheduling and day-in rolling correction, and the specific process is as follows:

the step 2, the step (4) comprises the following specific steps:

building a multi-time scale prediction scheduling model of a building energy supply system;

firstly, initializing a system: the optimization target and the scheduling time scale of the building energy supply system at each stage are set according to the requirements of the building energy supply system and building users, the optimization target in the day-ahead scheduling is set to be the minimum running cost of the building energy supply system (see formula (13)), and the optimization target in the day-interior correction stage is set to be the power of the building energy supply system tie line tracking the day-ahead scheduling (see formula (44)).

Second, economic dispatch before day: on the basis of the predicted value of the power of each building electrical load, the predicted value of outdoor temperature, the predicted value of illumination intensity and the predicted value of new energy power generation at the level of the day hour, the lowest running cost of a building energy supply system is taken as a target, and information such as indoor temperature comfort degree constraint of each building user, the technical characteristics of the controllable distributed power generation units of the building energy supply system, the day-ahead market electricity price and the like is comprehensively considered to obtain a day-ahead scheduling plan in n1 time periods in the day;

and updating the system state: updating the system state according to short-term prediction information in the day (building load, renewable energy output, outdoor environment and indoor heat source heat gain);

rolling correction in day: based on short-term prediction information in the day, the deviation between the actual operation plan in the day and the day-ahead scheduling plan is corrected by performing rolling optimization scheduling on the refrigeration loads of all buildings and the controllable DGs of the energy supply systems of the buildings.

In upper layer scheduling, the main tasks are:

the MEMS obtains the refrigeration load of each building and the day-by-day correction control sequence of each controllable DG in the current control time domain Nc by taking an equation (44) as a rolling optimization target based on a building energy supply system prediction model, short-term prediction information in a prediction time domain Np and a day-by-day tie line power set value in the current control time domain Nc. And the first value of the correction control sequence is transmitted to the control centers of each building BEMS and each DG, so that the day correction plan is executed.

In the underlying management, the main tasks are:

on one hand, each BEMS receives a building refrigeration load in-day correction control instruction issued by the MEMS and performs correction regulation and control on the building refrigerator; and on the other hand, the comfort level requirements of building users and the operation constraints of the refrigerating machines are collected, and relevant information is uploaded to the MEMS for calculation and use by an upper-layer dispatching system.

On one hand, each controllable DG controller receives a controllable DG output correction control instruction issued by the MEMS to correct and regulate the DG; and on the other hand, the operating parameters and the related constraints of all DGs are uploaded to the MEMS for calculation and use by an upper-layer scheduling system.

In the embodiment, a multi-time scale optimization scheduling analysis is performed on the building energy supply system with multiple buildings in fig. 1. The day-ahead scheduling prediction data is hour-level prediction data, and the day-interior scheduling short-term prediction data is 15 min-level data. The invention assumes that the short-term prediction power is simulated by respectively superposing random prediction errors on the day-ahead prediction power, and the specific expression is shown as the formula (46):

in the formula:

and

respectively representing short-term prediction uncertainty threshold values of the input variables; r (t) is a random number obeying a uniform distribution of U (-1, 1).

The building energy supply system comprises four different types of buildings, wherein the building 1 is a residential building, and the refrigerating time is 0: 00-9: 00 and 18: 00-23: 00; the building 2 is an office building, and the refrigerating time is 8: 00-20: 00; the building 3 is an apartment building, and the refrigerating time is all day; the building 4 is a commercial building, and the refrigerating time is 10: 00-22: 00. During the cooling time, the indoor temperature set point is 22.5 ℃, and the comfort range is set to be 20-25 ℃. The air density rho and the air specific heat capacity C are respectively 1.2kg/m3 and 1000J/(kg. DEG.C.). The coal consumption coefficient of the diesel generator is set as follows: a is 44($/h/MW2), b is 65.34($/h/MW), and c is 1.1825 ($/h). The purchase price of natural gas is set to 42.5 $/MWh.

a) Day-ahead economic dispatch

The day-ahead optimization scheduling results of the controllable DGs and the storage batteries of the building energy supply system are shown in FIG. 4. It can be seen that when the electricity price is high (11: 00-12: 00 and 14: 00-18: 00), the electricity purchasing cost of the building energy supply system is high, the storage battery is discharged at the maximum power, and at the moment, the diesel generator and the fuel battery both generate electricity at the rated output power. When the electricity price is low (1: 00-10: 00 and 19: 00-23: 00), the electricity purchasing cost of the building energy supply system is low, and the DG is controlled to stop; in addition, the storage battery is charged when the fan, the photovoltaic and the power grid purchase power meet the load requirements, so that the energy storage is reasonably utilized to carry out 'arbitrage', and the operation economy of a building energy supply system is improved.

However, at 08: 00-09: 00, due to the limitation of the power purchasing power of the power grid (equation (15)), the building energy supply system needs to schedule a controllable DG to supplement the power shortage under the scene without considering the virtual energy storage scheduling. Since fuel cells run on lower cost fuel than diesel engines, building energy supply systems in this scenario schedule fuel cells to supplement power shortages, as shown in fig. 4 (a). In consideration of the virtual energy storage scheduling scene, the building refrigeration load is adjusted within the indoor temperature comfort range, so that the power consumption of the electric refrigerator is reduced, no power shortage exists in the period of 08: 00-09: 00, and the building energy supply system does not need to schedule a controllable DG (as shown in fig. 4 (b)), so that the operation cost of the building energy supply system is reduced.

The results of the cooling schedule of each building in the building energy supply system before the day are shown in fig. 5(a) and fig. 5 (b). It can be seen that the electric refrigerator operates only during the cooling time. The indoor temperature is maintained at the temperature set point of 22.5 c for the cooling time without consideration of the virtual energy storage schedule. And in the refrigeration time considering the energy storage scheduling, the indoor temperature is adjusted within the range of 20-25 ℃, and the building refrigeration load is correspondingly adjusted, so that the building virtual energy storage is optimally scheduled. The daily operation cost of the building energy supply system without the virtual energy storage scheduling is $327.93, the operation cost of the building virtual energy storage scheduling is $298.88, and the operation cost is reduced by 8.86% compared with the operation cost of the scheduling without introducing the building virtual energy storage. The day-ahead scheduling result of the building virtual energy storage system is shown in fig. 6.

b) Intraday roll correction

And in the intra-day rolling correction stage, a time section is taken every 15min, namely, the rolling optimization is started every 15 min. When the prediction time domain is selected, on one hand, the situation that the indoor temperature changes slowly relative to the electrical characteristic quantity and a longer prediction time domain is needed to increase the information quantity of the future change trend is considered, so that the set target is more in line with the actual situation; on the other hand, the prediction error of the prediction data also increases as the prediction horizon increases, resulting in an increase in cost. By combining the two aspects, the time interval of 15min and the whole optimized regulation and control time scale of 24h, the invention selects the prediction time domain and the control time domain to be 4h, namely N_p＝N_cThe simulation was performed 16 times, i.e. the intra-day correction phase was optimized 96 times with scrolling.

In order to verify the effect of the multi-time scale optimization model prediction scheduling method provided by the invention on stabilizing the power fluctuation of the building energy supply system tie line, the following three comparison scenes are set respectively based on a DA-P strategy (Day-P programming strategy), a traditional single-section open-loop optimization strategy and an MPC-based intraday rolling optimization strategy:

scenario one (DA-P strategy): in the actual operation stage in the day, the power difference of the building energy supply system tie lines caused by the predicted data errors is completely stabilized by an external power grid, and the controllable DG, the storage battery and the virtual energy storage system of the building energy supply system are not optimally scheduled.

Scene two (traditional single-section open-loop optimization strategy): according to a day-ahead scheduling plan, an intra-day correction scheme of the controllable DG, the storage battery and the virtual energy storage system of the building energy supply system is solved based on a traditional single-section open-loop optimization strategy, and all optimization instructions are issued at one time to perform intra-day correction on the day-ahead plan of the building energy supply system.

Scenario three (MPC roll optimization strategy): according to a day-ahead scheduling plan, solving a day-in correction scheme of the controllable DGs of the building energy supply system, the storage batteries and the virtual energy storage system based on an MPC (MPC) optimization strategy, and replacing one-time off-line full-time optimization with repeated rolling optimization of a limited time period to realize day-in rolling correction of the day-ahead plan of the building energy supply system.

Building energy supply system tie line power pairs for the three scenarios are shown in fig. 7. It can be seen that, in the first scenario, a DA-P strategy is adopted, the power of the building energy supply system tie line fluctuates sharply near a daily planned value, and the controllable and friendly scheduling of the building energy supply system accessing to the power distribution network is difficult to realize. And in the second scene and the third scene, the output of the controllable DG, the storage battery and the virtual energy storage system is optimized and adjusted in the day operation stage, so that the power fluctuation of the tie line of the building energy supply system can be obviously reduced, and the power tracking effect of the tie line is better. However, comparing scene two with scene three found that:

1) in the stable power period (such as 06: 00-09: 00) of the tie line of the building energy supply system, the tracking effect of the tie line of the scene two and the tracking effect of the tie line of the scene three are not very different.

2) In a time period (such as a time period 10: 00-12: 00 circled in a figure 7) when the power fluctuation of a connecting line of a building energy supply system is large, due to the fact that the MPC method is adopted in the third scene, the scheduling requirement of a future period of time can be better looked ahead, and therefore the output of the controllable DG, the storage battery and the virtual energy storage system can be adjusted in advance; and a single-section open-loop optimization method is adopted in the second scene, so that the control variable is difficult to adjust in advance, the problem that the output adjustment is not timely caused by DG climbing limitation, storage battery capacity limitation and virtual energy storage system comfort limitation is caused, and the day-ahead planned value can be tracked in a long time.

The in-day optimized scheduling result of the controllable DG and the storage battery obtained after the third scenario adopts the rolling optimization adjustment is shown in fig. 8 (a). Therefore, the controllable DG of the building energy supply system and the storage battery are ensuredOn the basis of no change of the start-stop plan and the charge-discharge plan, respective output is corrected, and therefore the dispatching requirement of tracking the day-ahead planned value of a building energy supply system tie line is responded. Charge and discharge punishment factor theta in different storage battery days_btThe following SOC tracking effect is shown in fig. 8 (b). It can be seen that the SOC tracking effect corresponding to the penalty factor with a larger value is better, and vice versa. Therefore, the effect of the day SOC tracking of the storage battery is greatly influenced by the value of the punishment factor, and the day charging and discharging punishment factor of the storage battery can be flexibly selected by the building energy supply system according to the actual running condition.

In order to further verify the effect of the virtual energy storage system optimization scheduling on stabilizing the power fluctuation of the building energy supply system tie line, a comparison scene four is added: according to the day-ahead scheduling plan, an intra-day correction scheme of the controllable DGs and the storage batteries of the building energy supply system is solved based on an MPC optimization strategy, and scheduling adjustment is not performed on the virtual energy storage system. The newly added power pairs of the building energy supply system tie lines of the scene four, the scene three and the scene one are shown in fig. 9, and the comparison result shows that:

1) and the scene four does not schedule the virtual energy storage system in the rolling correction stage in the day, so the power tracking effect of the building energy supply system tie line is not good as that of the scene three. However, in the starting-up period (such as 10: 00-19: 00) of the controllable DG of the building energy supply system, the power fluctuation of the tie line of the building energy supply system can be reduced to a certain extent due to the fact that the output of the controllable DG of the building energy supply system and the output of the storage battery are subjected to intra-day rolling correction in the scene four, and therefore the power tracking effect of the tie line is better than that of the scene one.

2) In the controllable DG shutdown period of the building energy supply system (for example, 09: 00-10: 00 in the red frame period of fig. 9), as the number of DG units schedulable by the building energy supply system decreases and the virtual energy storage system is not scheduled in the scene four, the schedulable capability of the building energy supply system decreases, so that the tie line tracking effect is poor and is almost different from that of the DA-P strategy in the scene one.

The intra-day optimized scheduling results of each virtual energy storage system obtained after the third scenario adopts the rolling optimization adjustment are shown in fig. 10(a) and an annex a 6. As can be seen, in the day-in rolling correction stage, in the bottom management, each BEMS calculates, based on each building indoor temperature scheduling instruction generated in the day-ahead scheduling plan and short-term daily prediction data, a refrigeration demand (see orange solid line in fig. 10 (a)) of the virtual energy storage system, which does not participate in the day-in correction, in the day-in operation stage according to the mathematical model of the virtual energy storage system introduced in section 1.1, and transmits the refrigeration demand to the upper MEMS. In upper-layer scheduling, the MEMS obtains the building refrigeration demand of which virtual energy storage participates in the intra-day correction by solving the intra-day correction optimal scheduling model (see a blue solid line in fig. 10 (a)); and then, based on the refrigeration demand uploaded by each BEMS at the bottom layer and not participating in the intra-day correction of the virtual energy storage system, obtaining the intra-day correction instruction of the charge and discharge power of the virtual energy storage system according to the formula (8), as shown in a black bar chart in fig. 10 (a). It can be seen from the figure that after the virtual energy storage is introduced and corrected within a day, the refrigeration demand load of the building fluctuates up and down on the basis of the refrigeration demand load curve when no virtual energy storage exists. The part higher than the reference is cold accumulation, namely 'charging'; the part below the reference is cooled down, i.e. "discharged". And the difference value of the refrigeration demand load of the building under the two conditions is the charge and discharge power of the virtual energy storage system based on the building. Through the charge-discharge scheduling to virtual energy storage system, can stabilize building energy supply system tie line power fluctuation to a certain extent.

The modified cooling scheme for each building day in a building energy supply system is shown in fig. 10(b), 10(c) and appendix a 7. Comparing 10(b) with 10(c), the indoor temperature of the building and the power consumption of the refrigerating machine are obviously changed compared with the planned value before the day after the virtual energy storage is introduced to the day correction, and the indoor temperature corresponding to the scheme without introducing the virtual energy storage to the day correction is consistent with the planned value of the indoor temperature before the day. Therefore, the BEMS can adjust the charging and discharging power of the virtual energy storage system through indoor temperature adjustment, so that the aim of stabilizing the power fluctuation of the building energy supply system tie line is fulfilled in the day correction stage.

According to the invention, a building virtual energy storage system model is constructed by utilizing the heat storage characteristics of the building. Then, applying the virtual energy storage system to a building microgrid multi-time scale model prediction optimization scheduling model, wherein the conclusion is as follows:

1) in the day-ahead scheduling stage, the virtual energy storage system model is integrated into the day-ahead optimization scheduling model of the building microgrid, so that the flexibility of energy utilization of the building can be fully utilized, and the operation cost of the building energy supply system is reduced to a certain extent.

2) And in the day rolling correction stage, the virtual energy storage system model is integrated into a building microgrid day rolling optimization scheduling model, and the power fluctuation of the tie line caused by the prediction error in the day can be effectively stabilized by adjusting the room temperature of the building within the temperature comfort range.

3) The MPC method is adopted for intraday rolling optimization, the output of the controllable DG, the storage battery and the virtual energy storage system can be adjusted in advance, and the problem that the output of the traditional single-section open-loop optimization method is not adjusted timely due to DG climbing limitation, storage battery capacity limitation and virtual energy storage system comfort limitation is solved. Meanwhile, the MPC rolling optimization method replaces the traditional one-time off-line full-time open-loop optimization with the repeated rolling optimization in a limited time period, and the formed scheduling method is better in robustness and more suitable for the optimal scheduling of the building energy supply system in the uncertain prediction environment.

It should be emphasized that the examples described herein are illustrative and not restrictive, and thus the present invention includes, but is not limited to, those examples described in this detailed description, as well as other embodiments that can be derived from the teachings of the present invention by those skilled in the art and that are within the scope of the present invention.

Claims

1. A building energy supply system model prediction regulation and control method of an integrated electric refrigerator is characterized by comprising the following steps: the method comprises the following steps:

the specific steps of the step 1 comprise:

(1) building a building virtual energy storage system model;

(2) building a building energy supply system energy supply unit model;

the step 1, the step (1), comprises the following specific steps:

firstly, building a building thermal dynamic model

(i)

the heat energy kW transferred between the outer wall of the building and the outdoor is the sum of the heat energy transferred by all the outer walls, and the formula (2) is shown;

(ii)

the heat kW transferred between the building external window and the outdoor is the sum of the heat transferred by all the external windows, and is shown in a formula (3);

(iii)

the heat value kW of the indoor heat source can be obtained through prediction or measurement;

(iv)

the heat transmitted through the outer walls for solar thermal radiation kW, according to ISO 13790, the heat transmitted through all outer walls for solar thermal radiationThe sum is shown as a formula (4);

(v)

the heat kW transmitted through the outer window for solar radiation can be calculated by the formula (5); the invention assumes that all the outsides of the building are uniformly distributed on four walls of the east, the west, the south and the north;

(vi)

the refrigerating power kW of the refrigerating equipment;

T_outis the outdoor temperature; u shape_wallThe heat transfer coefficient of the building outer wall; u shape_winThe heat transfer coefficient of the building external window; f_wall,jThe area of the building outer wall j; f_win,jThe area of the building external window j is shown; SC is a shading coefficient, and the value of the SC is related to whether the shading plate and the glass material exist or not; r_se,jThermal resistance of heat convection and heat radiation between the outer wall and outdoor air; alpha is alpha_wThe heat transfer loss coefficient of the wall body; tau is_winThe heat transfer loss coefficient of the wall body; i is_T,jThe total solar radiation intensity kW/m received by the outer wall j per square meter of wall/window²The expression is shown as a formula (6);

wherein I_b、I_dAnd I represents the intensity of direct radiation, scattered radiation and total radiation, respectively, in kW/m, on the same horizontal surface²；ρ_gThe reflectivity of the ground is 0.2; theta is the illumination incidence angle; r_bIs an illumination geometric factor, is used for describing the ratio of the direct radiation intensity of illumination on an inclined plane to the direct radiation intensity of illumination on a horizontal plane, and the calculation expression is shown as (7);

wherein, theta_zThe lighting zenith angle;

constructing a building virtual energy storage system model;

obtaining the charge and discharge power of the virtual energy storage system, as shown in formula (8):

wherein,

building refrigeration electric power for not adjusting indoor temperature;

the formula (1) and the formula (8) jointly form a mathematical model of the building virtual energy storage system.

2. The building energy supply system model prediction regulation and control method of the integrated electric refrigerator as claimed in claim 1, characterized in that: the step 1, the step (2) comprises the following specific steps:

firstly, establishing a generator model

establishing fuel cell model

f_fuel(P_FC,t)＝C_gas×P_gas,t＝C_gas×(P_FC,tΔt/η_FC) (10)

establishing electric refrigerator model

The refrigeration equipment adopted by the building energy supply system is a compression type electric refrigerator, and the refrigeration power is as shown in formula (11):

Q_EC,t＝P_EC,t×COP_EC (11)

fourthly, establishing a storage battery model

The SOC is the ratio of the residual electric quantity of the storage battery to the rated capacity and represents the SOC of the storage battery; SOC value SOC of storage battery at t moment_tAs shown in equation (12):

in the formula: p_bt,tThe rated charge-discharge power of the storage battery is that the discharge is positive and the charge is negative; CAP (common Place Capacity)_btThe rated capacity of the storage battery; eta_ch，η_disThe charge-discharge efficiency of the storage battery; delta is the self-discharge rate of the storage battery; Δ t is the corresponding charge-discharge period.

3. The building energy supply system model prediction regulation and control method of the integrated electric refrigerator as claimed in claim 1, characterized in that: the specific steps of the step 2 comprise:

(2) constructing a day-ahead economic dispatching model;

(3) performing rolling correction within the day;

4. The building energy supply system model predictive control method of the integrated electric refrigerator as claimed in claim 3, characterized in that: the step 2, the step (1), comprises the following specific steps:

economic dispatch in the day

In the day-ahead economic dispatching stage, based on the predicted values of the power of each building electrical load, the outdoor temperature, the illumination intensity and the new energy power generation in the hour level in the day ahead, the lowest running cost of the building energy supply system is taken as a target, and the indoor temperature comfort degree constraint of each building user, the technical characteristics of the controllable distributed power supply of the building energy supply system and the electricity of the market in the day ahead are comprehensively consideredInformation of price, get n day ahead₁Optimizing a scheduling scheme of the building energy supply system in each scheduling time period;

② rolling correction in day

In the intra-day correction stage, rolling optimization is carried out by taking 15min as a period, and the whole scheduling time axis is divided into n₂A scheduling period in which the predicted time domain is N_pA time period of control time domain of N_cA period of time, N_p≥N_c；

At the time of t, the current time section is utilized to the next prediction time domain N_pThe short-term prediction information in the building energy supply system optimization method is used for optimizing and solving N based on a model prediction regulation and control method by taking the minimum error between the building energy supply system tie line power and the day-ahead plan value as the target on the premise of not changing the start-stop plan and the energy storage charge-discharge state of the controllable DG in the day-ahead plan and meeting the power balance and various constraints_cCorrecting plan sequences of all controllable DGs, energy storage systems and building electrical loads of the building energy supply system in each time period;

issuing only a correction plan for the next time period in the t-time section, i.e. N_cA first control sequence in a rework planning sequence for the building energy supply system for a time period; when the next scheduling period comes, the process is repeated.

5. The building energy supply system model predictive control method of the integrated electric refrigerator as claimed in claim 3, characterized in that: the step 2, the step (2), comprises the following specific steps:

setting a day-ahead scheduling objective function of a building energy supply system of an integrated electric refrigerator as the minimum running cost, as shown in formula (13):

in the formula: t is the whole scheduling period, and can be taken 24 hours a day; the first item is the cost of purchasing electricity from the power distribution network by the building energy supply system; p_grid,tThe electrical power that is exchanged with the electrical distribution network for the building energy supply system,the electricity purchasing is positive, and the electricity selling is negative; c_ph,tAnd C_se,tRespectively purchasing electricity from the power distribution network and selling electricity to the power distribution network for the building energy supply system; the second term is the fuel cost, maintenance cost and start-up cost of the controllable DG in the building energy supply system, as shown by equations (9), (10), P_DG,i,tRepresents DG_iPower output over a time period t; rho_DG,iRepresents DG_iThe cost of use and maintenance; rho_su,iRepresents DG_iThe start-up cost of (a); u'_DG,i,tRepresenting the starting state of the i distributed power supplies in a t time period; the third item is the use and maintenance cost of an energy storage system, a photovoltaic system and a refrigeration system in a building energy supply system; p_PV,tAnd P_EC,tThe photovoltaic output and the electric power consumed by the electric refrigerator at the time t are respectively; rho_PV、ρ_btAnd ρ_ECRespectively representing the use and maintenance costs of unit power of unit time periods of the photovoltaic, the storage battery and the electric refrigerator;

building energy supply system purchase electricity restraint:

wherein, _gridPand

electric power balance constraint:

P_PV,tand P_EC,tRespectively photovoltaic output and refrigerator electric power at the time t; p_bt,tFor storing energy at time tThe charging and discharging power of (1); p_el,tBuilding electrical load at time t; p_EC,n,tIs the power output of the refrigerator n at time t;

wherein, U_DG,i,tAnd U ″)_DG,i,tRespectively representing the operation states of the i distributed power supplies in a t time period: "1" indicates an on state and "0" indicates an off state; and an off state: "1" means off and "0" means not off;P _DG,i,tand

is the upper and lower limits of the distributed power supply i;

at the same time, DG is also subject to minimum boot time

And minimum shutdown time

The constraint of (2):

battery operating constraints

Wherein,P _btand

2) and (4) operation constraint of each building:

cold load balancing constraints

Wherein,

electric power is used for refrigerating the electric refrigerator;

building thermal balance constraint:

。

6. the building energy supply system model predictive control method of the integrated electric refrigerator as claimed in claim 3, characterized in that: the step 2, the step (3) comprises the following specific steps:

at the current time T ', the MEMS and each BEMS are respectively based on the current state x (T') of the building energy supply system and the current indoor temperature T of each building_in(t'), using the prediction time domain N_pPredicting the future states of a building energy supply system and the indoor temperature of the building by using short-term prediction information in each time period; next, the MEMS solves the optimization problem under the premise of satisfying the current and future constraint conditions to obtain the future control time domain N_cA control instruction sequence of a building energy supply system in each time period; then, the first value of the control command sequence is applied to each BEMS and each DG controller; meanwhile, at the moment T '+ 1, the building energy supply system state is updated to be x (T' +1) and the building indoor temperature T_in(t' +1), and the above steps are repeatedly performed;

firstly, building a building energy supply system prediction model

Building energy supply at current moment tSystem and distribution network tie line exchange power P_grid(t'), controllable DG output P_DG(t'), energy storage charging and discharging power P_bt(t '), energy storage system state of charge SOC (t'), and building chiller power consumption P_EC(t ') the vector x (t ') formed by the vector x (t ') is a state variable of the building energy supply system, see the formula (30); with controllable DG output increment Δ P_DG(t'), increment of stored energy output Δ P_bt(t') and increase of electric power consumption Δ P of building refrigerator_EC(t ') the vector u (t') is a control variable of the building energy supply system, see equation (31); short-term prediction power increment delta P by photovoltaic and fan system output_PV(t′)、ΔP_WT(t'), building power consumption short-term predicted power increment Δ P_el(t') is a disturbance input of the building energy supply system, see equation (32); the power exchanged by the building energy supply system connecting line is taken as an output variable of the building energy supply system, see formula (33); then a state space prediction model of the building energy supply system can be established as shown in a formula (34);

x(t′)＝[P_grid(t′),P_DG(t′),P_bt(t′),SOC(t′),P_EC(t′)]^T (30)

u(t′)＝[ΔP_DG(t′),ΔP_bt(t′),ΔP_EC(t′)]^T (31)

r(t′)＝[ΔP_PV(t′),ΔP_WT(t′),ΔP_el(t′)]^T (32)

y(t′)＝P_grid(t′) (33)

in the formula: the state space matrices A, B, C and D are expressed as shown in equations (35) - (39);

the SOC recurrence coefficient of the storage battery is expressed as follows:

D＝[1 0 0 0 0] (39)

from the equation (34), the load is N based on the photovoltaic system_pThe short-term prediction information in each time period can be predicted to obtain N by repeatedly iterating the state space prediction model_pIn each time period, the building energy supply system exchanges power with a power distribution network tie line and each building refrigerator consumes electric power;

establishing indoor temperature predicting model for building

in the formula:

and

increment of electric power consumption delta P for calculating building refrigerator_EC(t') a refrigeration power output; building input variable short-term prediction increment information lambda (t') is shown as a formula (41);

as can be seen from equations (40) and (41), the building short-term prediction information λ (t') and N are used as the basis_pThe prediction information of the electric power consumption of the building refrigerating machine in each time period is obtained by the rolling solution of the building energy supply system state space prediction model of the formula (34), and the prediction information can be obtained in N_pIn each time period, carrying out rolling solution and prediction on the indoor temperature;

establishing rolling optimization model

Building energy supply system prediction model and prediction time domain N_pThe short-term prediction information in the building energy supply system can be obtained in the current control time domain N_cThe output variable Y in the energy supply system is also the power value of the building energy supply system tie line in the current control time domain, and the expression is shown as a formula (42); based on a day-ahead scheduling model, the current control time domain N of the building energy supply system can be obtained_cInner day-ahead tracking target vector F_dayNamely the day-ahead planned value of the building energy supply system tie line power, the expression of which is shown as the formula (43);

in the formula:

in the formula: theta_btA penalty factor corresponding to the penalty item;

the constraint conditions for the intra-day rolling optimization are the same as those of equations (14) to (29); based on a rolling optimization model, the EMES can solve to obtain a correction control sequence u (t ') of the building energy supply system in a control time domain corresponding to each scheduling time t ', then a first value of an optimization result is added to the building energy supply system, and the whole optimization process is repeated based on new short-term prediction information at the time t ' + 1; the invention solves the multi-time scale optimization scheduling problem by using CPLEX under MATLAB.

7. The building energy supply system model predictive control method of the integrated electric refrigerator as claimed in claim 3, characterized in that: the step 2, the step (4) comprises the following specific steps:

firstly, initializing a system: setting an optimization target and a scheduling time scale of the building energy supply system at each stage according to the requirements of the building energy supply system and building users, setting the optimization target in the day-ahead scheduling to be the minimum running cost of the building energy supply system, and setting the optimization target in the correction stage in the day to track the tie line power of the building energy supply system scheduled in the day-ahead;

second, economic dispatch before day: based on the predicted values of the power of the electrical load of each building, the outdoor temperature, the illumination intensity and the new energy power generation in the day-ahead hour level, the information of indoor temperature comfort degree constraint of each building user, the technical characteristics of the controllable distributed power generation units of the building energy supply system, the day-ahead market electricity price and the like is comprehensively considered to obtain n days ahead by taking the lowest operation cost of the building energy supply system as a target₁A day-ahead dispatch plan for each time period;

and updating the system state: updating the system state according to short-term daily prediction information, building load, renewable energy output, outdoor environment and indoor heat source heat gain;