CN110460040B - Micro-grid operation scheduling method considering intelligent building heat balance characteristic - Google Patents

Micro-grid operation scheduling method considering intelligent building heat balance characteristic Download PDF

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CN110460040B
CN110460040B CN201910636654.6A CN201910636654A CN110460040B CN 110460040 B CN110460040 B CN 110460040B CN 201910636654 A CN201910636654 A CN 201910636654A CN 110460040 B CN110460040 B CN 110460040B
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刘洪�
李吉峰
葛少云
刘静仪
张群华
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Abstract

A micro-grid operation scheduling method considering heat balance characteristics of an intelligent building comprises the steps of establishing an indoor heat balance model of the intelligent building in the micro-grid, wherein a heat generating heat source comprises the following steps: the building is the production of heat of outside window illumination radiation, the production of heat of building indoor heat source and indoor air heat exchange power, the production of heat of heating equipment and indoor air heat exchange power in the building, and the heat dissipation heat source includes: convection heat exchange between the inner surface of the building wall and air, heat consumption by penetration of the building inner window body and heat consumption by invasion/ventilation of cold air in the building; establishing a micro-grid system operation scheduling optimization model, which comprises the following steps: and taking the cost optimization as an objective function, a power balance constraint, an energy storage device constraint and a flexible load constraint. The method and the system have the advantages that the optimal operation scheduling is performed on the micro-grid on the premise of meeting the requirement of building thermal comfort, and the economical efficiency of the micro-grid operation is improved. The method can provide guidance for operation scheduling of the urban micro-grid, and is beneficial to improving the operation and management level of the urban micro-grid.

Description

Micro-grid operation scheduling method considering intelligent building heat balance characteristic
Technical Field
The invention relates to a micro-grid operation scheduling method. In particular to a micro-grid operation scheduling method considering the heat balance characteristic of an intelligent building.
Background
The energy is the basis of human survival and development and is the source power of social and economic development. The energy utilization efficiency is improved, and the effective clean supply of energy is guaranteed, so that the method becomes a necessary choice for solving the conflict between energy supply and demand and the contradiction between social development and environmental protection. In recent years, with the continuous development of distributed energy technology, a distributed energy supply mode realized by a micro-grid system receives more and more attention due to flexibility and reliability of energy supply, and also becomes a new distributed energy supply mode. However, with the access of renewable distributed energy, the influence of the terminal user on the system operation state is increasing, for example, some distributed power sources select to access the power distribution network from the low-voltage side, so detailed analysis needs to be performed on the terminal load characteristics while performing detailed modeling on the power output characteristics, the matching between the power output and the terminal load is a key factor for measuring the power performance and the influence on the power grid operation, and in addition, the performance after the access of the distributed photothermal system is also influenced by the heat load curve. Therefore, how to realize effective operation scheduling of the microgrid system is an important problem to be solved urgently at present. Energy consumption behaviors and strategies of terminal users in the micro-grid system can also have certain influence on operation scheduling results of the micro-grid system, and some research results show that the self clean energy consumption can be improved by 2-15% through response of a demand side. At present, although some researches consider the influence of user demand response factors in the research on the micro-grid dispatching operation, the influence of a specific load analysis and regulation strategy on energy consumption and transaction is not combined, and the subjective activity of a terminal demand side cannot be fully exerted.
According to the statistical data in the relevant building energy reports of the world and China, the share of the building energy consumption in the total energy consumption reaches nearly 40%, wherein about half of the energy consumption is used for meeting the requirements of temperature control load and a heating and ventilation system.
Disclosure of Invention
The invention aims to solve the technical problem of providing a micro-grid operation scheduling method which is targeted for carrying out optimal operation scheduling on a micro-grid on the premise of meeting the requirement of thermal comfort of a building and takes the thermal balance characteristic of the intelligent building into consideration.
The technical scheme adopted by the invention is as follows: a micro-grid operation scheduling method considering heat balance characteristics of an intelligent building comprises the steps of establishing an indoor heat balance model of the intelligent building in the micro-grid, wherein a heat generating heat source comprises the following steps: the building is the production of heat of outside window illumination radiation, the production of heat of building indoor heat source and indoor air heat exchange power, the production of heat of heating equipment and indoor air heat exchange power in the building, and the heat dissipation heat source includes: the heat consumption of the inner surface of the building wall and air convection heat exchange, the penetration of the window body in the building and the cold air invasion/ventilation in the building; establishing a micro-grid system operation scheduling optimization model, which comprises the following steps: and taking the cost optimization as an objective function, a power balance constraint, an energy storage device constraint and a flexible load constraint.
The indoor heat balance model of the intelligent building in the microgrid is as follows:
Figure BDA0002130553340000011
in the formula, Q 1 ～Q 6 For different heat-generating/dissipating heat sources, Q 1 The inner surface of the wall body exchanges heat with air in a convection way;
Figure BDA0002130553340000021
consuming heat for window penetration>
Figure BDA0002130553340000022
Heat is generated for the irradiation of light outside the window; q 3 Heat consumption for cold air invasion/ventilation; q 4 Increase the sensible heat of the air in unit time; q 5 Heat is generated by heat exchange power of an indoor heat source and indoor air; q 6 The heat exchange power between the heating equipment and the indoor air is utilized to generate heat.
(1) The inner surface of the wall body and air exchange heat by convection Q 1 The formula of (1) is as follows:
Figure BDA0002130553340000023
in the formula (I), the compound is shown in the specification,
Figure BDA0002130553340000024
the total number of walls between rooms 1 and 2;
Figure BDA0002130553340000025
The surface temperature of the wall between the rooms 1 and 2;
Figure BDA0002130553340000026
Is the thermal resistance of the wall between the rooms 1 and 2; t is 2 Is the temperature of the room 2;
(2) Heat dissipation by window infiltration
Figure BDA0002130553340000027
The formula of (1) is as follows:
Figure BDA0002130553340000028
in the formula, C p,air Is the specific heat capacity of air; rho w Is the air density; l is the outdoor air permeation quantity; t is ∞ Is ambient temperature;
(3) The outside window illumination radiation generates heat
Figure BDA0002130553340000029
The formula of (1) is as follows:
Figure BDA00021305533400000210
in the formula, pi 1,2 The coefficients are identified for the wall, wherein the number of windows is 1 and the number of windows is 0;
Figure BDA00021305533400000211
is the window permeability;
Figure BDA00021305533400000212
Is the window volume;
Figure BDA00021305533400000213
The window illumination intensity;
(4) Heat consumption Q of the cold air intrusion/ventilation 3 The formula of (1) is as follows:
Q 3 ＝0.278C p,air ρ w V(t)(T 2 -T ∞ ) (5)
in the formula, V (t) is the ventilation volume in the t period, and is approximately calculated by adopting a ventilation frequency method;
(5) The building air heat display value in unit time is increased by Q 4 The formula of (1) is as follows:
Figure BDA00021305533400000214
in the formula (I), the compound is shown in the specification,
Figure BDA00021305533400000215
is the heat capacity of room 2;
(6) The heat exchange power of the indoor heat source and the indoor air generates heat Q 5 The formula of (1) is as follows:
Q 5 ＝3.8W/m 2 ×S room (7)
in the formula, S room Is the area of the room;
(7) Heating equipment and indoor air heat exchange power heat production Q 6 The formula of (1) is as follows:
Q 6 ＝m HVAC ×C p,air ×(T HVAC -T 2 ) (8)
in the formula, T HVAC The air outlet temperature of the heating and ventilation system; m is HVAC Is the air flow of the heating and ventilating system.
The optimal cost is taken as an objective function to be expressed as follows:
Figure BDA0002130553340000031
wherein T is the total number of scheduling time periods; p is a radical of B,i,G (t) the price of electricity purchased by the energy service provider i from the external power grid during the period t; p is B,i,G (t) purchasing electric quantity from an external power grid by an energy service provider i in a period t;
Figure BDA0002130553340000032
the operation and maintenance cost of the energy service provider i in the period t; p is a radical of formula S,i,G (t) is the price for selling electricity to the external power grid by the energy service provider i in the period t; p S,i,G (t) selling the electric quantity of the energy service provider i to the external power grid in the period t; t is set (t) setting an optimal temperature for the building for a period of t; t is in (t) room temperature for a period of t; gamma is a penalty factor, is taken as the sensitivity degree of a building user to the temperature comfort degree, is defined as a user sensitivity coefficient, has the unit of element/DEG C, is selected according to different user sensitivities, and the larger the numerical value is, the larger the penalty brought by deviating from the optimal set temperature is; and the smaller the penalty.
The operation and maintenance cost of the energy service provider i
Figure BDA0002130553340000033
Means the life loss cost of the energy storage device in the microgrid>
Figure BDA0002130553340000034
The calculation formula is as follows:
Figure BDA0002130553340000035
in the formula, mu ES Is the regulating factor of the electricity storage system;
Figure BDA0002130553340000036
one-time purchase cost of the energy storage device; n (-) is a discharge depth function, and a fourth-order function is adopted to represent the relation between the cycle life and the discharge depth; ES (t) is the electric energy proportion of the energy storage device in the period t.
The power balance constraint is to ensure that the power supply and demand in the microgrid reach real-time balance under the condition of considering the energy storage effect, and the formula is as follows:
Figure BDA0002130553340000037
in the formula, P i PV (t) photovoltaic output of an energy service provider i at a time period t; p i WT (t) the fan output of the energy service provider i in a time period t;
Figure BDA0002130553340000038
storing energy and discharging power for an energy service provider i in a time period t; p is B,i,G (t) the electric quantity purchased by the energy service provider i from the external power grid in the period t; p i HVAC (t) the power consumption requirement of the heating and ventilation system of the energy service provider i in the period t; p O (t) power of electric loads of equipment except the heating and ventilation system in the time period t;
Figure BDA00021305533400000311
Storing energy and charging power for an energy service provider i in a time period t; p is a radical of S,i,G (t) selling the electric quantity of the energy service provider i to an external power grid in a period t; p is i HVAC,f (t) the power consumption requirement of a fresh air fan of the heating and ventilation system of the energy service provider i at the time period t; p i HVAC,h And (t) the power consumption requirement of the energy service provider i for the operation of the heating and ventilation system in the period t.
The t time period energy service provider i warms up power consumption demand P of new trend fan of system of leading to i HVAC,f (t) is:
Figure BDA0002130553340000039
in the formula,. DELTA.P eq,HVAC Is the equivalent total pressure drop; eta HVAC,fan 、η HVAC,motor The operating efficiencies of the fan and the engine are respectively, and the product of the operating efficiencies of the fan and the engine is 0.15;
the t time period energy service provider i heating and ventilation system runs with the electricity demand P i HVAC,h (t) is:
Figure BDA00021305533400000310
in the formula, C COP Energy efficiency ratio of thermoelectricity; t is in Is the room temperature.
The energy storage device constraint means that the energy storage device needs to satisfy the charging and discharging power and capacity constraint of the device in the operation process, and is represented as follows:
Figure BDA0002130553340000041
Figure BDA0002130553340000042
in the formula (I), the compound is shown in the specification,
Figure BDA0002130553340000044
the capacity of the energy storage device at the current t time period;
Figure BDA0002130553340000045
Maximum/minimum capacity of energy storage devices of the energy service provider i, respectively;
Figure BDA0002130553340000046
The charging/discharging power of the energy storage device in the current t period;
Figure BDA0002130553340000047
Maximum charge/discharge power of the energy storage device for energy facilitator i;
in order to meet the energy storage and release requirements of an energy service provider on energy storage equipment at the starting time of the next scheduling period, the energy storage of the energy storage equipment at the starting time and the ending time of the scheduling period needs to be kept consistent, namely:
Figure BDA0002130553340000043
in the formula (I), the compound is shown in the specification,
Figure BDA0002130553340000048
respectively charging and discharging power of the energy storage device in a time period t;
Figure BDA0002130553340000049
Respectively the charging and discharging efficiency of the energy storage device; Δ t is the scheduling time interval.
The flexible load constraint is the heating and ventilation system regulation constraint, and the air supply mass flow and air supply temperature constraint of the heating and ventilation system needs to be met in the process of scheduling the flexible load of the micro-grid system terminal:
0≤m HVAC ≤m HVAC,max (17)
T HVAC,min ≤T HVAC ≤T HVAC,max (18)
in the formula, m HVAC Air flow for a heating ventilation system; m is HVAC,max The maximum value of the air supply mass flow of the heating and ventilation system is obtained; t is HVAC The air outlet temperature of the heating and ventilation system; t is HVAC,max 、T HVAC,min The maximum value and the minimum value of the air supply temperature of the heating and ventilating system are respectively.
And calling a commercial solver CPLEX to solve an indoor thermal balance model and a microgrid system operation scheduling optimization model of the intelligent building in the microgrid based on a YALMIP platform in an MATLAB environment.
According to the micro-grid operation scheduling method considering the heat balance characteristic of the intelligent building, the micro-grid can be used as a research object, the intelligent building is used as a terminal load unit of the micro-grid, the operation characteristic of a heating and ventilation system in the intelligent building and the actual temperature requirement of personnel in the building are comprehensively considered, the micro-grid is subjected to operation scheduling analysis by combining actual equipment resources in the micro-grid, the micro-grid is subjected to optimal operation scheduling on the premise of meeting the building heat comfort requirement in a targeted manner, and the economy of micro-grid operation is improved. The method and the system fully utilize the comfortable sensitive interval of the human body to the external temperature and excavate the schedulable energy utilization potential in the microgrid system, thereby analyzing the influence generated by the terminal energy utilization and demand response on the operation scheduling of the microgrid system, providing guidance for the operation scheduling of the urban microgrid, being beneficial to improving the operation and management level of the urban microgrid and promoting the reasonable development of the operation scheduling technology of the urban microgrid.
Drawings
FIG. 1 is a schematic diagram of an indoor thermal equilibrium model of an intelligent building in a microgrid according to the present invention;
FIG. 2 is a system architecture diagram of an embodiment of the present invention;
fig. 3a is a diagram of a scheduling result of the microgrid system 1 in fig. 2;
fig. 3b is a diagram of a scheduling result of the microgrid system 2 in fig. 2;
fig. 3c is a diagram illustrating a scheduling result of the microgrid system 3 in fig. 2;
fig. 3d is a diagram illustrating a scheduling result of the microgrid system 4 in fig. 2.
Detailed Description
The following describes in detail a micro grid operation scheduling method considering the heat balance characteristics of an intelligent building according to the present invention with reference to embodiments and drawings.
The invention relates to a micro-grid operation scheduling method considering heat balance characteristics of an intelligent building, which comprises the steps of establishing an indoor heat balance model of the intelligent building in the micro-grid, wherein a heat generating heat source comprises the following steps: the building is the production of heat of outside window illumination radiation, the production of heat of building indoor heat source and indoor air heat exchange power, the production of heat of heating equipment and indoor air heat exchange power in the building, and the heat dissipation heat source includes: convection heat exchange between the inner surface of the building wall and air, heat consumption by penetration of the building inner window body and heat consumption by invasion/ventilation of cold air in the building; establishing a micro-grid system operation scheduling optimization model, which comprises the following steps: and taking the cost optimization as an objective function, a power balance constraint, an energy storage device constraint and a flexible load constraint. And then, based on a YALMIP platform in an MATLAB environment, calling a commercial solver CPLEX to solve an indoor thermal balance model and a microgrid system operation scheduling optimization model of the intelligent building in the microgrid.
In an actual cooling or heating scene, the interior of an intelligent building is usually simulated into a single isothermal air conditioning area, an indoor heat balance model of the intelligent building in a microgrid is usually adopted to model the single heating/cooling area in the building, and the indoor heat balance model of the intelligent building in the microgrid of the building is composed of heat resistance and heat capacity of the building, and the heat resistance and the heat capacity have the capacities of heat transmission and heat storage respectively. The building comprises wall body nodes and indoor air nodes, wherein the nodes are mutually connected through thermal resistance and are grounded through thermal capacitance. Based on the assumption of single isothermal temperature inside the building, the whole building model is formed by polymerization of a plurality of similar single areas, and on the basis, the air supply temperature and the air supply mass flow of equipment in the heating and ventilation system are adjusted through the building heating and ventilation system, so that the aim of carrying out centralized control on the flexible load is fulfilled. The indoor heat balance model of the intelligent building in the microgrid constructed by the invention is shown as an attached figure 1.
The indoor heat balance model of the intelligent building in the microgrid is as follows:
Figure BDA0002130553340000051
in the formula, Q 1 ～Q 6 For different heat-generating/dissipating heat sources, Q 1 The inner surface of the wall body exchanges heat with air in a convection way;
Figure BDA0002130553340000053
for heat consumption by window penetration>
Figure BDA0002130553340000054
Heat is generated for outside-window illumination radiation; q 3 Heat consumption for cold air invasion/ventilation; q 4 Increase the sensible heat of the building air in unit time; q 5 Heat is generated by heat exchange power of an indoor heat source and indoor air; q 6 The heat exchange power between the heating equipment and the indoor air is generated to produce heat. Wherein:
(1) The inner surface of the wall body and air exchange heat by convection Q 1 The formula of (1) is as follows:
Figure BDA0002130553340000052
in the formula (I), the compound is shown in the specification,
Figure BDA0002130553340000055
the total number of walls between rooms 1 and 2;
Figure BDA0002130553340000056
The surface temperature of the wall between the rooms 1 and 2;
Figure BDA0002130553340000057
Is the thermal resistance of the wall between the rooms 1 and 2; t is 2 Is the temperature of the room 2;
(2) Heat dissipation by window infiltration
Figure BDA0002130553340000058
The formula of (1) is as follows:
Figure BDA0002130553340000059
in the formula, C p,air Is the specific heat capacity of air; rho w Is the air density; l is the outdoor air permeation quantity; t is ∞ Is ambient temperature;
(3) The outside window illumination radiation generates heat
Figure BDA0002130553340000064
The formula (c) is as follows:
Figure BDA0002130553340000061
in the formula, pi 1,2 The coefficients are identified for the wall, wherein the number of windows is 1 and the number of windows is 0;
Figure BDA0002130553340000065
is the window permeability;
Figure BDA0002130553340000066
Is the window volume;
Figure BDA0002130553340000067
The window illumination intensity;
(4) Heat consumption Q of the cold air intrusion/ventilation 3 The formula of (1) is as follows:
Q 3 ＝0.278C p,air ρ w V(t)(T 2 -T ∞ ) (5)
in the formula, V (t) is the ventilation volume in the t period, and is approximately calculated by adopting a ventilation frequency method;
(5) The building air heat display value in unit time is increased by Q 4 The formula of (1) is as follows:
Figure BDA0002130553340000062
in the formula (I), the compound is shown in the specification,
Figure BDA0002130553340000068
is the heat capacity of room 2;
(6) The heat exchange power of the indoor heat source and the indoor air generates heat Q 5 The formula of (1) is as follows:
Q 5 ＝3.8W/m 2 ×S room (7)
in the formula, S room Is the area of the room;
(7) Heating equipment and indoor air heat exchange power heat production Q 6 The formula of (1) is as follows:
Q 6 ＝m HVAC ×C p,air ×(T HVAC -T 2 ) (8)
in the formula, T HVAC The air outlet temperature of the heating and ventilation system; m is a unit of HVAC Is the air flow of the heating and ventilating system.
Operation scheduling optimization model of (II) micro-grid system
The micro-grid system is uniformly scheduled and managed by an energy service provider. The energy service provider optimizes internal operation by optimizing an energy purchase strategy from an external power grid, managing the running state of owned equipment and the supply condition of flexible loads.
For internal dispatching of the micro-grid system, an energy service provider takes cost optimization as an operation optimization target, and the forming factors comprise energy purchasing cost from an external power grid, operation cost of equipment units, penalty function items influencing temperature comfort of users and income of selling electric energy to the external power grid. The penalty function term of the invention is set as the indoor actual temperature T at the moment T in Deviation from the set optimum temperature T set Is measured as a function of (c).
(1) The optimal cost is taken as an objective function to be expressed as follows:
Figure BDA0002130553340000063
wherein T is the total number of scheduling time periods; p is a radical of formula B,i,G (t) the price of electricity purchased by the energy service provider i from the external power grid during the period t; p is B,i,G (t) purchasing electric quantity from an external power grid by an energy service provider i in a period t;
Figure BDA0002130553340000069
the operation and maintenance cost of the energy service provider i in the period t; p is a radical of formula S,i,G (t) the price of electricity sold by the energy service provider i to the external power grid in the period of t; p is S,i,G (t) selling the electric quantity of the energy service provider i to the external power grid in the period t; t is set (t) setting an optimal temperature for the building for a period of t; t is in (t) room temperature for a period of t; gamma is a penalty factor, is regarded as the sensitivity degree of a building user to the temperature comfort degree, is defined as a user sensitivity coefficient, has the unit of element/DEG C, is selected according to different user sensitivities, and the larger the numerical value is, the greater the penalty brought by deviating from the optimal set temperature is; and the smaller the penalty.
The operation and maintenance cost of the energy service provider i
Figure BDA0002130553340000074
Means the life loss cost of the energy storage device in the microgrid>
Figure BDA0002130553340000075
The calculation formula is as follows:
Figure BDA0002130553340000071
in the formula, mu ES Is the regulating factor of the electricity storage system;
Figure BDA0002130553340000076
one-time purchase cost of the energy storage equipment; n (-) is a discharge depth function, and a fourth-order function is adopted to represent the relation between the cycle life and the discharge depth; ES (t) is the electric energy proportion of the energy storage device in the period t. />
(2) The power balance constraint is to ensure that the power supply and demand in the microgrid reach real-time balance under the condition of considering the energy storage effect, and the formula is as follows:
Figure BDA0002130553340000077
in the formula, P i PV (t) photovoltaic output of an energy service provider i at a time period t; p i WT (t) the fan output of the energy service provider i in a time period t;
Figure BDA0002130553340000078
storing energy and discharging power for the energy service provider i in the t time period; p B,i,G (t) the electric quantity purchased by the energy service provider i from the external power grid in the period t; p i HVAC (t) the power consumption requirement of the heating and ventilation system of the energy service provider i in the period t; p O (t) power of electric loads of equipment except the heating and ventilation system in the time period t;
Figure BDA0002130553340000079
Storing energy and charging power for an energy service provider i in a time period t; p is a radical of formula S,i,G (t) selling the electric quantity of the energy service provider i to the external power grid in the period t; p i HVAC,f (t) the power consumption requirement of a fresh air fan of the heating and ventilation system of the energy service provider i at a time period t; p i HVAC,h And (t) the power consumption requirement of the energy service provider i for the operation of the heating and ventilation system in the period t. Wherein:
the t time period energy service provider i warms up power consumption demand P of new trend fan of system of leading to i HVAC,f (t) is:
Figure BDA0002130553340000072
in the formula,. DELTA.P eq,HVAC Is the equivalent total pressure drop; eta HVAC,fan 、η HVAC,motor The operating efficiencies of the fan and the engine are respectively, and the product of the operating efficiencies of the fan and the engine is 0.15;
and the t-period energy service provider i is required to supply power P for the heating and ventilation system to operate i HVAC,h (t) is:
Figure BDA0002130553340000073
in the formula, C COP The energy efficiency ratio of thermoelectricity; t is a unit of in Is the room temperature.
(3) The energy storage device constraint means that the energy storage device needs to satisfy the charging and discharging power and capacity constraint of the device in the operation process, and is represented as follows:
Figure BDA00021305533400000710
Figure BDA00021305533400000711
in the formula (I), the compound is shown in the specification,
Figure BDA00021305533400000712
the capacity of the energy storage device at the current time t;
Figure BDA00021305533400000713
Maximum/minimum capacity of energy storage devices of the energy service provider i, respectively;
Figure BDA0002130553340000082
The charging/discharging power of the energy storage device in the current t period;
Figure BDA0002130553340000083
Maximum charge/discharge power of the energy storage device for energy facilitator i;
in order to meet the energy storage and release requirements of an energy service provider on energy storage equipment at the starting time of the next scheduling period, the energy storage of the energy storage equipment at the starting time and the ending time of the scheduling period needs to be kept consistent, namely:
Figure BDA0002130553340000084
in the formula (I), the compound is shown in the specification,
Figure BDA0002130553340000085
respectively charging and discharging power of the energy storage equipment in a time period t;
Figure BDA0002130553340000086
Respectively the charging and discharging efficiency of the energy storage device; Δ t is the scheduling time interval.
(4) The indoor thermal comfort adopted by the invention adopts the estimated average thermal sensation index and the estimated percentage of unsatisfied persons to evaluate the thermal comfort grade according to GB/T18049-determination of average thermal sensation index of medium thermal environment and the percentage index of estimated unsatisfied persons and the stipulation of thermal comfort conditions issued by China. In the heating season, energy conservation is considered as much as possible under the condition of meeting the comfortable requirement from the principle of energy conservation, so that the environment which is colder is selected, namely the environment with the PMV of more than or equal to-1 and less than or equal to 0, the upper temperature limit corresponding to the PMV =0 is 24 ℃, and the heating design temperature range in winter is 18-24 ℃. In the cooling season: for the I-class building, when the indoor relative humidity is between 40% and 70% and the PMV value is between 0 and 0.5, the comfortable temperature range is 24 to 26 ℃ through calculation of the thermal comfort zone, and similarly for the II-class building, the comfortable temperature range is 27 to 28 ℃ through calculation of the thermal comfort zone.
The flexible load constraint is the heating and ventilation system regulation constraint, and the air supply mass flow and air supply temperature constraint of the heating and ventilation system needs to be met in the process of scheduling the flexible load of the micro-grid system terminal:
0≤m HVAC ≤m HVAC,max (17)
T HVAC,min ≤T HVAC ≤T HVAC,max (18)
in the formula, m HVAC Air flow for a heating ventilation system; m is HVAC,max The maximum value of the air supply mass flow of the heating and ventilation system is obtained; t is a unit of HVAC The air outlet temperature of the heating and ventilation system; t is HVAC,max 、T HVAC,min The maximum value and the minimum value of the air supply temperature of the heating and ventilating system are respectively.
Specific examples are given below:
(1) Basic overview of the implementation
The improved standard 33-node power distribution system is used as a main system structure, wherein a part of nodes are connected with a micro-grid system comprising an intelligent building, a distributed power supply and energy storage equipment, and the specific architecture is shown in figure 2. The types of buildings, building information, and configurations of devices in different microgrid systems are shown in table 1.
Table 1 examples alternative energy production/conversion equipment types and parameters
Figure BDA0002130553340000081
Figure BDA0002130553340000091
The buildings in the micro-grid system are all independent buildings, and for different buildings in the same micro-grid system, the energy service businessman can adopt the same heating and ventilation control strategy according to the heat supply grade of the buildings. The relevant parameters of the building and heating and ventilation system are shown in table 2 and table 3, respectively. Relevant parameters of the devices in the microgrid system are shown in table 4, wherein the initial capacity of the energy storage device is selected to be 50% of the total capacity, and the maximum charge and discharge power is 20% of the capacity of the devices.
TABLE 2 construction parameters
Figure BDA0002130553340000092
TABLE 3 heating and ventilating System parameters
Parameter name Parameter value
ρ w (kg/m 3 ) 1.29
C p,air (J/(kg·℃) 1005
C COP 3
P static (Pa) 135
m HVAC,max (kg/s) 0.5
T HVAC,max /T HVAC,min (℃) 20/30
TABLE 4 Equipment parameters
Figure BDA0002130553340000093
Figure BDA0002130553340000101
(2) Run schedule result analysis
Based on the example setting, the method analyzes the day-ahead scheduling operation result of the multi-microgrid system, wherein the scheduling time step delta t =1h. The scheduling results inside different microgrid systems are shown in fig. 3a to 3 d. According to the dispatching result, the fan and the photovoltaic belong to clean energy without cost, so that the energy service provider can use the clean energy as much as possible to meet the power demand of users, and the outsourcing power cost of the micro-grid system is reduced.
The invention specifically analyzes the micro-grid systems with the energy storage devices of the micro-grid 1 and the micro-grid 3, and compared with the micro-grid system without the energy storage devices, the energy storage devices can be matched with the virtual energy storage characteristics of the building to further improve the operation flexibility of the micro-grid system. For the microgrid 1, in terms of energy dispatching, the photovoltaic output is greater from 11 to 14, in this period, the microgrid system can be self-sufficient and has surplus power available for trading, and at other times, the microgrid system has to meet the own power demand by purchasing power from the outside due to insufficient photovoltaic output. The energy storage equipment can selectively discharge at the time with higher electric price such as 16; charging can be selected at the moment when the distributed power supply has high output or the external electricity price is low so as to guarantee subsequent effective scheduling. In the aspect of operation of the heating and ventilation equipment, because the buildings in the microgrid 1 are all II-level buildings, the heating and ventilation system takes electricity saving as a main operation target, and in the time period of 1; in the time period of 6; in the time period of 8; in the time period of 10; in the time period from 13 to 16, the indoor temperature is gradually reduced along with the reduction of the outdoor temperature, however, due to the virtual energy storage characteristic of the building, the indoor temperature can still meet the constraint condition, and the heating and ventilation system can still save the electric energy by reducing the output power; during the time period from 17 to 00.
Because the micro-grid systems all use the optimal operation economy as the scheduling target, the micro-grid 3 is similar to the operation scheduling strategy of the micro-grid 1, however, because the buildings in the micro-grid systems are all I-class buildings, the requirement on the indoor temperature is relatively high, and therefore, the heating and ventilation system always needs to maintain the indoor temperature at a high level, and compared with the micro-grid 1, the heating and ventilation system consumes more electric energy.

Claims (9)

1. The utility model provides a little electric wire netting operation scheduling method of intelligent building heat balance characteristic of considering, which is characterized in that, including the indoor heat balance model of the intelligent building of establishing in little electric wire netting, wherein, the heat production heat source includes: the building is the production of heat of outside window illumination radiation, the indoor heat source of building and the production of heat of indoor air heat exchange power, the heating equipment in the building and the production of heat of indoor air heat exchange power, and the heat dissipation heat source includes: the heat consumption of the inner surface of the building wall and air convection heat exchange, the penetration of the window body in the building and the cold air invasion/ventilation in the building; establishing a micro-grid system operation scheduling optimization model, which comprises the following steps: taking the cost optimum as a target function, a power balance constraint, an energy storage device constraint and a flexible load constraint;
the energy storage device constraint means that the energy storage device needs to satisfy the charging and discharging power and capacity constraint of the device in the operation process, and is represented as follows:
Figure FDA0004054681430000011
Figure FDA0004054681430000012
in the formula (I), the compound is shown in the specification,
Figure FDA0004054681430000013
the capacity of the energy storage device at the current t time period;
Figure FDA0004054681430000014
Maximum/minimum capacity of energy storage devices of the energy service provider i, respectively;
Figure FDA0004054681430000015
The charging/discharging power of the energy storage device in the current t period;
Figure FDA0004054681430000016
The maximum charging/discharging power of the energy storage equipment of the energy service provider i;
in order to meet the energy storage and discharge requirements of an energy service provider on the energy storage equipment at the starting time of the next scheduling period, the energy storage of the energy storage equipment at the starting time and the ending time of the scheduling period needs to be kept consistent, namely:
Figure FDA0004054681430000017
in the formula (I), the compound is shown in the specification,
Figure FDA0004054681430000018
respectively charging and discharging power of the energy storage device in a time period t;
Figure FDA0004054681430000019
Respectively the charging and discharging efficiency of the energy storage device; Δ t is a scheduling time interval; t is the total number of scheduling periods.
2. The microgrid operation scheduling method considering heat balance characteristics of an intelligent building as claimed in claim 1, wherein an indoor heat balance model of the intelligent building in the microgrid is as follows:
Figure FDA00040546814300000110
in the formula, Q 1 ～Q 6 For different heat generating/dissipating sources, Q 1 The inner surface of the wall body exchanges heat with air in a convection way;
Figure FDA00040546814300000111
for heat consumption by window penetration>
Figure FDA00040546814300000112
Heat is generated for outside-window illumination radiation; q 3 Heat consumption for cold air invasion/ventilation; q 4 Increase the sensible heat of the building air in unit time; q 5 Heat is generated for the heat exchange power of an indoor heat source and indoor air; q 6 The heat exchange power between the heating equipment and the indoor air is utilized to generate heat.
3. The microgrid operation scheduling method considering heat balance characteristics of an intelligent building according to claim 2, characterized in that:
(1) The inner surface of the wall body and air exchange heat Q by convection 1 The formula (c) is as follows:
Figure FDA00040546814300000113
in the formula (I), the compound is shown in the specification,
Figure FDA00040546814300000114
the total number of walls between the rooms 1 and 2;
Figure FDA00040546814300000115
The surface temperature of the wall between the rooms 1 and 2;
Figure FDA00040546814300000116
Is the thermal resistance of the wall between the rooms 1 and 2; t is a unit of 2 Is the temperature of room 2;
(2) Heat dissipation by infiltration of said window
Figure FDA0004054681430000021
The formula (c) is as follows:
Figure FDA0004054681430000022
in the formula, C p,air Is the specific heat capacity of air; rho w Is the air density; l is the outdoor air permeation quantity; t is ∞ Is ambient temperature;
(3) The outside window illumination radiation generates heat
Figure FDA0004054681430000023
The formula of (1) is as follows:
Figure FDA0004054681430000024
in the formula, pi 1,2 The coefficients are identified for the wall, wherein the number of windows is 1 and the number of windows is 0;
Figure FDA0004054681430000025
is the window permeability;
Figure FDA0004054681430000026
Is the window volume;
Figure FDA0004054681430000027
The window illumination intensity;
(4) Heat consumption Q of the cold air intrusion/ventilation 3 The formula of (1) is as follows:
Q 3 ＝0.278C p,air ρ w V(t)(T 2 -T ∞ ) (5)
in the formula, V (t) is the ventilation volume in the t period, and is approximately calculated by adopting a ventilation frequency method;
(5) The building air heat display value in unit time is increased by Q 4 The formula of (1) is as follows:
Figure FDA0004054681430000028
in the formula (I), the compound is shown in the specification,
Figure FDA0004054681430000029
is the heat capacity of room 2;
(6) The heat exchange power of the indoor heat source and the indoor air generates heat Q 5 The formula of (1) is as follows:
Q 5 ＝3.8W/m 2 ×S room (7)
in the formula, S room Is the area of the room;
(7) Heating equipment and indoor air heat exchange power heat production Q 6 The formula of (1) is as follows:
Q 6 ＝m HVAC ×C p,air ×(T HVAC -T 2 ) (8)
in the formula, T HVAC The air outlet temperature of the heating and ventilation system; m is HVAC Is the air flow of the heating and ventilating system.
4. The microgrid operation scheduling method considering heat balance characteristics of an intelligent building according to claim 1, characterized in that the cost optimization as an objective function is expressed as:
Figure FDA00040546814300000210
wherein T is the total number of scheduling time periods; p is a radical of B,i,G (t) the price of electricity purchased by the energy service provider i from the external power grid during the period t; p B,i,G (t) the electric quantity purchased by the energy service provider i from the external power grid in the period t;
Figure FDA00040546814300000211
the operation and maintenance cost of the energy service provider i in the period t; p is a radical of S,i,G (t) the price of electricity sold by the energy service provider i to the external power grid in the period of t; p S,i,G (t) selling the electric quantity of the energy service provider i to the external power grid in the period t; t is set (t) setting an optimal temperature for the building for a period of t; t is in (t) room temperature for a period of t; gamma is a penalty factor, is taken as the sensitivity degree of a building user to the temperature comfort degree, is defined as a user sensitivity coefficient, has the unit of element/DEG C, is selected according to different user sensitivities, and the larger the numerical value is, the larger the penalty brought by deviating from the optimal set temperature is; and the smaller the penalty.
5. The microgrid operation scheduling method considering intelligent building heat balance characteristics of claim 4, characterized in that the operation and maintenance cost is an energy service provider i
Figure FDA0004054681430000031
Means the cost of life loss of an energy storage device in a microgrid->
Figure FDA0004054681430000032
The calculation formula is as follows:
Figure FDA0004054681430000033
in the formula, mu ES The regulating coefficient of the power storage system;
Figure FDA0004054681430000034
one-time purchase cost of the energy storage device; n (-) is a discharge depth function, and a fourth-order function is adopted to represent the relation between the cycle life and the discharge depth; ES (t) is the electric energy proportion of the energy storage device in the period t.
6. The microgrid operation scheduling method considering intelligent building heat balance characteristics according to claim 1, characterized in that the power balance constraint is to ensure that power supply and demand in a microgrid reach real-time balance under the condition of considering energy storage effect, and the formula is as follows:
Figure FDA0004054681430000035
in the formula, P i PV (t) photovoltaic output of an energy service provider i at a time period t; p i WT (t) the output of the fan of the energy service provider i is t time period;
Figure FDA0004054681430000036
storing energy and discharging power for an energy service provider i in a time period t; p B,i,G (t) purchasing electric quantity from an external power grid by an energy service provider i in a period t; p i HVAC (t) the power consumption requirement of the heating and ventilation system of the energy service provider i in the period t;
Figure FDA0004054681430000037
Storing energy and charging power for an energy service provider i in a time period t; p is a radical of formula S,i,G (t) selling the electric quantity of the energy service provider i to an external power grid in a period t; p i HVAC,f (t) the power consumption requirement of a fresh air fan of the heating and ventilation system of the energy service provider i at the time period t; p i HVAC,h (t) energy source for time period tThe service provider i is the power demand of the operation of the heating and ventilation system; p i O And (t) the power of the electric load of the equipment except the heating and ventilation system in the time period t.
7. The microgrid operation scheduling method considering intelligent building heat balance characteristics is characterized in that:
t time period energy service business i heating and ventilation system fresh air fan's power consumption demand P i HVAC,f (t) is:
Figure FDA0004054681430000038
in the formula,. DELTA.P eq,HVAC Is the equivalent total pressure drop; eta HVAC,fan 、η HVAC,motor The running efficiency of the fan and the engine is respectively, and the product of the two is 0.15; m is a unit of HVAC Air flow for a heating ventilation system;
and the t-period energy service provider i is required to supply power P for the heating and ventilation system to operate i HVAC,h (t) is:
Figure FDA0004054681430000039
in the formula, C COP Energy efficiency ratio of thermoelectricity; t is a unit of in Is the indoor temperature; c p,air Is the specific heat capacity of air; t is HVAC The air outlet temperature of the heating and ventilation system.
8. The microgrid operation scheduling method considering intelligent building heat balance characteristics as claimed in claim 1, wherein the flexible load constraint is a heating and ventilation system regulation constraint, and in the process of scheduling a microgrid system terminal flexible load, the constraints of air supply mass flow and air supply temperature of a heating and ventilation system need to be satisfied:
0≤m HVAC ≤m HVAC,max (17)
T HVAC,min ≤T HVAC ≤T HVAC,max (18)
in the formula, m HVAC Air flow for a heating ventilation system; m is HVAC,max The maximum value of the air supply mass flow of the heating and ventilation system is obtained; t is a unit of HVAC The air outlet temperature of the heating and ventilation system; t is HVAC,max 、T HVAC,min The maximum value and the minimum value of the air supply temperature of the heating and ventilating system are respectively.
9. The microgrid operation scheduling method considering intelligent building heat balance characteristics as claimed in claim 1, characterized in that a commercial solver CPLEX is invoked to solve indoor heat balance models and microgrid system operation scheduling optimization models of an intelligent building in a microgrid based on a YALMIP platform in an MATLAB environment.
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