CN112381397A - Real-time energy control method for building by comprehensive energy - Google Patents

Real-time energy control method for building by comprehensive energy Download PDF

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CN112381397A
CN112381397A CN202011270642.5A CN202011270642A CN112381397A CN 112381397 A CN112381397 A CN 112381397A CN 202011270642 A CN202011270642 A CN 202011270642A CN 112381397 A CN112381397 A CN 112381397A
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程维杰
程韧俐
史军
刘金生
陈择栖
余涛
李捷
何晓峰
周招鹤
黄双
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Abstract

The invention discloses a real-time energy control method for a comprehensive energy building, which comprises the following steps: s1, establishing an economic dispatching model of the comprehensive energy building, and constructing a target function with minimum total cost expectation considering uncertainty factors; step S2, converting the multi-period decision problem into a recursion problem according to the Bellman optimality principle, and constructing an approximate form of a value function; step S3, training the constructed value function based on the successive projection approximation method to obtain a convergent approximation function; and step S4, putting the converged approximate function into online operation, and solving the real-time energy control problem of the comprehensive energy building time by time. On one hand, the real-time coordination and complementation of various energy sources in the comprehensive energy building are realized, and the scheduling benefit is improved; on the other hand, the method can effectively cope with the influence of various uncertain factors on system scheduling, and realizes random cooperative scheduling.

Description

Real-time energy control method for building by comprehensive energy
Technical Field
The invention relates to the technical field of power grid operation and control, in particular to a real-time energy control method for a comprehensive energy building.
Background
The building with comprehensive energy is an important application form of a comprehensive energy system, and a building energy management system is built by taking Combined Cooling, Heating and Power (CCHP) as a key technology and combining advanced control, communication, management and other means. The load of large commercial buildings exceeds 30% of the total load of cities, and the exploitation of the energy management potential of the power load represented by the comprehensive energy buildings has important significance for improving the power utilization mode and realizing scientific power utilization.
Disclosure of Invention
The invention aims to solve the technical problem of providing a real-time energy control method for a comprehensive energy building so as to realize real-time coordination and complementation of various energy sources in the comprehensive energy building and cope with the influence of various uncertain factors on system scheduling.
In order to solve the technical problem, the invention provides a real-time energy control method for a comprehensive energy building, which comprises the following steps:
s1, establishing an economic dispatching model of the comprehensive energy building, and constructing a target function with minimum total cost expectation considering uncertainty factors;
step S2, converting the multi-period decision problem into a recursion problem according to the Bellman optimality principle, and constructing an approximate form of a value function;
step S3, training the constructed value function based on the successive projection approximation method to obtain a convergent approximation function;
and step S4, putting the converged approximate function into online operation, and solving the real-time energy control problem of the comprehensive energy building time by time.
Further, the power balance constraint of the economic dispatch model of the integrated energy building established in the step S1 is as follows:
Figure BDA0002777592880000011
wherein,
Figure BDA0002777592880000021
for the active power transmitted by the distribution network to the building at time t,
Figure BDA0002777592880000022
the output electric power is supplied for the combined supply of cold and heat power at the moment t,
Figure BDA0002777592880000023
the active power of the energy storage system at the moment t, the discharge is positive, the charge is negative,
Figure BDA0002777592880000024
load of building at time t, NESSThe number of energy storage systems;
the energy storage system is constrained as follows:
Figure BDA0002777592880000025
Pi,t,min≤Pi,t≤Pi,t,max
Ei,t,min≤Ei,t≤Ei,t,max
wherein E isi,tEnergy of the ith energy storage system at time t, Pi,tIs time tThe power of the ith energy storage system, which is greater than 0 represents discharging, and less than 0 represents charging; pi,t,max、Pi,t,minUpper and lower limits of power, Ei,t,max、Ei,t,minRespectively an upper limit constraint and a lower limit constraint for energy.
Further, the economic dispatch model of the integrated energy building established in step S1 includes a hot water load model, a room temperature regulation load model, and a combined cooling, heating and power model.
Further, the hot water load model is as follows:
Figure BDA0002777592880000026
wherein V is the volume of the water tank, CwThe specific heat capacity of the water is shown as,
Figure BDA0002777592880000027
is the temperature of the water tank at time t,
Figure BDA0002777592880000028
the volume and temperature, respectively, of the cold water injected at time t, deltat representing the time interval,
Figure BDA0002777592880000029
is the thermal power at the time of t,
Figure BDA00027775928800000210
respectively the upper and lower limits of the hot water temperature acceptable by the user;
further, the room temperature adjustment load model is represented by the following formula when the building is refrigerated:
Figure BDA00027775928800000211
the following formula is adopted during heating:
Figure BDA00027775928800000212
wherein, CairIs the specific heat capacity of the air,
Figure BDA0002777592880000031
is the temperature in the room at time t,
Figure BDA0002777592880000032
is the outdoor temperature at time t, R is the house thermal resistance, Δ t represents the time interval,
Figure BDA0002777592880000033
respectively is refrigeration power and heating power;
further, the operation constraints of the combined cooling heating and power model are as follows:
Figure BDA0002777592880000034
wherein,
Figure BDA0002777592880000035
thermal power, electric power and natural gas consumption eta respectively output by combined cooling, heating and power supply at the moment te、ηhRespectively for the efficiency of electricity production and heat production of the combined cooling, heating and power system, QgasIs the heat value of natural gas.
Further, the objective function is that the expected total cost in the scheduling period is the minimum, as shown in the following formula:
Figure BDA0002777592880000036
Figure BDA0002777592880000037
wherein, CtFor the total cost at time t, StFor the state of the energy storage system, including load power
Figure BDA0002777592880000038
Electricity price information pDN；xtAs decision variables, including charging and discharging power of energy storage system
Figure BDA0002777592880000039
Thermal power output by combined supply of cold, heat and electricity
Figure BDA00027775928800000310
Electric power purchase
Figure BDA00027775928800000311
wtAs random information, including variation information of outdoor temperature
Figure BDA00027775928800000312
Change information of electricity price
Figure BDA00027775928800000313
T is the total period of the schedule,
Figure BDA00027775928800000314
is the cost of the natural gas at time t,
Figure BDA00027775928800000315
is the price of the online shopping at the time t,
Figure BDA00027775928800000316
is the uncomfortable cost of the temperature control load at time t,
Figure BDA00027775928800000317
is the cost of the i energy storage systems at time t.
Further, the step S2 specifically includes:
converting the objective function to the following formula:
Vt(St)＝min(Ct(St,xt,wt)+ξE(Vt+1(St+1|St)))
wherein, Vt(St) For energy storage systems in StValue function of the state, Vt+1(St+1|St) For energy storage systems in StOn the premise of the state, the value function at the moment of t +1, xi is a conversion factor;
the approximation function is constructed using piecewise linear methods as follows:
Figure BDA0002777592880000041
at the same time, the following formula must be satisfied:
Figure BDA0002777592880000042
wherein n represents the number of iterations, β represents the total number of segments, r represents the r-th segment, ρ is the length of each segment, ytrIs the amount of resources per segment.
Further, the step S3 adopts the SPAR method to solve the approximation function, including:
step S31, initialization
Figure BDA0002777592880000043
Generating N training samples by using a Monte Carlo method, wherein each training sample contains the change situation of various random quantities in a day in a comprehensive energy building, and the iteration number N is 1, and t is 1;
step S32, updating the system state according to the latest random variable and using the slope of each segment after the last iteration
Figure BDA0002777592880000044
Solving the approximate function constructed in the step S2 to obtain each decision variable
Figure BDA0002777592880000045
Post-decision system state
Figure BDA0002777592880000046
Post-decision tunable capacity
Figure BDA0002777592880000047
Step S33, calculating a temporary value of the slope from the updated samples:
Figure BDA0002777592880000048
wherein g is a temporary vector, a is a step size,
Figure BDA0002777592880000049
in the interest of marginal benefit,
Figure BDA00027775928800000410
is an approximate slope;
step S34, performing projection operation on the temporary vector to obtain an approximate slope component of the nth iteration:
Figure BDA0002777592880000051
step S35, when T is T +1, the process returns to step S32, and when T > T, the process goes to step S36;
in step S36, let N be N +1 and t be 1, return to step S32, and terminate the loop when N > N.
Further, the step S4 specifically includes:
step S41, let t equal to 1;
step S42, updating the random information of the current time interval, including the error of the electricity price and the error of the outdoor temperature;
step S43, calculating the optimal decision of the t time period according to the approximate function constructed in the step S2 by using the trained approximate function;
in step S44, let T equal to T +1, if T ≦ T, the process returns to step S42, and if T > T, the loop ends.
The embodiment of the invention has the beneficial effects that: on one hand, real-time coordination and complementation of various energy sources in the comprehensive energy building are realized, and scheduling benefits are improved; on the other hand, the method can effectively cope with the influence of various uncertain factors on system scheduling, and realizes random cooperative scheduling.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for controlling real-time energy of an integrated energy building according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments refers to the accompanying drawings, which are included to illustrate specific embodiments in which the invention may be practiced.
Referring to fig. 1, an embodiment of the invention provides a method for controlling real-time energy of a comprehensive energy building, including:
s1, establishing an economic dispatching model of the comprehensive energy building, and constructing a target function with minimum total cost expectation considering uncertainty factors;
step S2, converting the multi-period decision problem into a recursion problem according to the Bellman optimality principle, and constructing an approximate form of a value function;
step S3, training the constructed value function based on the successive projection approximation method to obtain a convergent approximation function;
and step S4, putting the converged approximate function into online operation, and solving the real-time energy control problem of the comprehensive energy building time by time.
Further, the step S1 includes the following steps:
establishing an economic dispatching model of the comprehensive energy building, wherein the power balance constraint is as follows:
Figure BDA0002777592880000061
wherein,
Figure BDA0002777592880000062
for the active power transmitted by the distribution network to the building at time t,
Figure BDA0002777592880000063
the output electric power is supplied for the combined supply of cold and heat power at the moment t,
Figure BDA0002777592880000064
the active power of the energy storage system at the moment t, the discharge is positive, the charge is negative,
Figure BDA0002777592880000065
load of building at time t, NESSThe number of energy storage systems.
The energy storage system is constrained as follows:
Figure BDA0002777592880000066
Pi,t,min≤Pi,t≤Pi,t,max (3)
Ei,t,min≤Ei,t≤Ei,t,max (4)
wherein E isi,tEnergy of the ith energy storage system at time t, Pi,tThe power of the ith energy storage system at the moment t is greater than 0 to indicate discharging, and less than 0 to indicate charging; equations (3) and (4) are the upper and lower energy and power constraints, respectively.
Hot water load model: assuming that the tank is always full, ignoring the dynamic course of water flow, the mathematical model of the tank can be expressed by equation (5):
Figure BDA0002777592880000067
wherein V is the volume of the water tank, CwThe specific heat capacity of the water is shown as,
Figure BDA0002777592880000071
is the temperature of the water tank at time t,
Figure BDA0002777592880000072
the volume and temperature, respectively, of the cold water injected at time t, deltat representing the time interval,
Figure BDA0002777592880000073
is the thermal power at the time of t,
Figure BDA0002777592880000074
respectively the upper and lower limits of the hot water temperature acceptable by the user.
Load model regulation at room temperature: the building can be represented by the discretization mathematical model of the formula (6) when cooling, can be represented by the mathematical model of the formula (7) when heating,
Figure BDA0002777592880000075
Figure BDA0002777592880000076
wherein, CairIs the specific heat capacity of the air,
Figure BDA0002777592880000077
is the temperature in the room at time t,
Figure BDA0002777592880000078
is the outdoor temperature at time t, R is the house thermal resistance, Δ t represents the time interval,
Figure BDA0002777592880000079
respectively, the refrigeration power and the heating power.
A combined cooling heating and power model: the combined cooling heating and power system comprises a power generation device, a waste heat recovery device and a refrigeration system, and the operation of the combined cooling and heating and power system is restricted as follows:
Figure BDA00027775928800000710
wherein,
Figure BDA00027775928800000711
thermal power, electric power and natural gas consumption eta respectively output by combined cooling, heating and power supply at the moment te、ηhRespectively for the efficiency of electricity production and heat production of the combined cooling, heating and power system, QgasIs the heat value of natural gas.
The total cost of energy management is targeted by the integrated energy building operator to be the lowest, including fuel costs, i.e., gas and electricity purchase costs, uncomfortable costs for temperature control loads, and operating costs for energy storage systems, as shown in the following equation:
Figure BDA00027775928800000712
Figure BDA0002777592880000081
wherein T is a total scheduling period;
Figure BDA0002777592880000082
is the natural gas cost at time t; p is a radical ofgasIs the natural gas price;
Figure BDA0002777592880000083
the price of the online shopping electricity at the time t;
Figure BDA0002777592880000084
active power, p, transmitted from distribution network to buildingDNIs the electricity price at time t;
Figure BDA0002777592880000085
is the uncomfortable cost of the temperature control load at time t; p is a radical ofindoorAnd pwtThe sensitivity coefficients of the indoor temperature and the hot water temperature are respectively; t isindoorsetAnd TwtsetValues of indoor temperature and hot water temperature set for a user, respectively;
Figure BDA0002777592880000086
is the cost of the ith energy storage system at time t; p is a radical ofESSIs the energy storage system operating cost coefficient.
Considering the uncertainty of electricity price, outdoor temperature, etc. in real-time energy management, the objective function should be the minimum expected total cost in the scheduling period (one day in the embodiment of the present invention), as shown in the following formula:
Figure BDA0002777592880000087
Figure BDA0002777592880000088
wherein, CtFor the total cost at time t, StFor the state of the energy storage system, including load power
Figure BDA0002777592880000089
Electricity price information pDN；xtAs decision variables, including charging and discharging power of energy storage system
Figure BDA00027775928800000810
Thermal power output by combined supply of cold, heat and electricity
Figure BDA00027775928800000811
Electric power purchase
Figure BDA00027775928800000812
wtAs random information, including variation information of outdoor temperature
Figure BDA00027775928800000813
Change information of electricity price
Figure BDA00027775928800000814
Further, the step S2 includes the following steps:
converting the multi-period decision problem into a recursion problem according to the Bellman optimality principle, and constructing an approximate form of a value function, namely converting formula (11) into formula (13):
Vt(St)＝min(Ct(St,xt,wt)+ξE(Vt+1(St+1|St))) (13)
wherein, Vt(St) Is a system at StValue function of the state, Vt+1(St+1|St) Is a system at StOn the premise of the state, the value function at the t +1 moment has the meaning of the influence of the current decision on the cost in the subsequent time period, and the operation of taking the expected value is a random variable WtAnd xi is a conversion factor and is generally within 0-1.
The approximation function is constructed by a piecewise linear method, namely:
Figure BDA0002777592880000091
at the same time, the following requirements must be met:
Figure BDA0002777592880000092
wherein n represents the number of iterations, β represents the total number of segments, r represents the r-th segment, ρ is the length of each segment, ytrIs the amount of resources per segment.
Further, the step S3 includes the following steps:
the steps of solving the approximation function by adopting the SPAR method are as follows:
step S31, initialization
Figure BDA0002777592880000093
N training samples are generated by using a Monte Carlo method, and each training sample contains various random numbers in a day of a building with comprehensive energy resourcesThe variation of the machine amount. Making the iteration number n equal to 1 and t equal to 1;
step S32, updating the system state according to the latest random variable and using the slope of each segment after the last iteration
Figure BDA0002777592880000094
Solving the equation (14) to obtain each decision variable
Figure BDA0002777592880000095
Post-decision system state
Figure BDA0002777592880000096
Post-decision tunable capacity
Figure BDA0002777592880000097
And the like.
Step S33, calculating a temporary value of the slope from the updated samples:
Figure BDA0002777592880000101
wherein g is a temporary vector, a is a step size,
Figure BDA0002777592880000102
in the interest of marginal benefit,
Figure BDA0002777592880000103
is an approximate slope.
Step S34, performing projection operation on the temporary vector to obtain an approximate slope component of the nth iteration:
Figure BDA0002777592880000104
step S35, when T is T +1, the process returns to step S32, and when T > T, the process goes to step S36;
in step S36, let N be N +1 and t be 1, return to step S32, and terminate the loop when N > N.
Further, the step S4 includes the following steps:
step S41, let t equal to 1;
step S42, updating random information of the current time interval, including the error of electricity price, the error of outdoor temperature and the like;
step S43, calculating the optimal decision of the t time period according to the formula (14) by using the trained approximate function;
in step S44, let T equal to T +1, if T ≦ T, the process returns to step S42, and if T > T, the loop ends.
As can be seen from the above description, the embodiments of the present invention have the following beneficial effects: on one hand, real-time coordination and complementation of various energy sources in the comprehensive energy building are realized, and scheduling benefits are improved; on the other hand, the method can effectively cope with the influence of various uncertain factors on system scheduling, and realizes random cooperative scheduling.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.

Claims (10)

1. A real-time energy control method for a comprehensive energy building is characterized by comprising the following steps:
s1, establishing an economic dispatching model of the comprehensive energy building, and constructing a target function with minimum total cost expectation considering uncertainty factors;
step S2, converting the multi-period decision problem into a recursion problem according to the Bellman optimality principle, and constructing an approximate form of a value function;
step S3, training the constructed value function based on the successive projection approximation method to obtain a convergent approximation function;
and step S4, putting the converged approximate function into online operation, and solving the real-time energy control problem of the comprehensive energy building time by time.
2. The method for controlling the real-time energy of the integrated energy buildings according to the claim 1, wherein the power balance constraint of the economic dispatch model of the integrated energy buildings established in the step S1 is as follows:
Figure FDA0002777592870000011
wherein,
Figure FDA0002777592870000012
for the active power transmitted by the distribution network to the building at time t,
Figure FDA0002777592870000013
the output electric power is supplied for the combined supply of cold and heat power at the moment t,
Figure FDA0002777592870000014
the active power of the energy storage system at the moment t, the discharge is positive, the charge is negative,
Figure FDA0002777592870000015
load of building at time t, NESSThe number of energy storage systems;
the energy storage system is constrained as follows:
Figure FDA0002777592870000016
Pi,t,min≤Pi,t≤Pi,t,max
Ei,t,min≤Ei,t≤Ei,t,max
wherein E isi,tEnergy of the ith energy storage system at time t, Pi,tThe power of the ith energy storage system at the moment t is greater than 0, namely discharging, and less than 0, namely charging; pi,t,max、Pi,t,minUpper and lower limits of power, Ei,t,max、Ei,t,minRespectively an upper limit constraint and a lower limit constraint for energy.
3. The real-time energy control method for the integrated energy buildings according to the claim 2, wherein the economic dispatch models of the integrated energy buildings established in the step S1 include a hot water load model, a room temperature regulation load model and a combined cooling, heating and power model.
4. The integrated energy building real-time energy control method according to claim 3, wherein the hot water load model is as follows:
Figure FDA0002777592870000021
wherein V is the volume of the water tank, CwThe specific heat capacity of the water is shown as,
Figure FDA0002777592870000022
is the temperature of the water tank at time t,
Figure FDA0002777592870000023
the volume and temperature, respectively, of the cold water injected at time t, deltat representing the time interval,
Figure FDA0002777592870000024
is the thermal power at the time of t,
Figure FDA0002777592870000025
respectively the upper and lower limits of the hot water temperature acceptable by the user.
5. The method as claimed in claim 4, wherein the room temperature regulated load model is expressed as follows during cooling of the building:
Figure FDA0002777592870000026
the following formula is adopted during heating:
Figure FDA0002777592870000027
wherein, CairIs the specific heat capacity of the air,
Figure FDA0002777592870000028
is the temperature in the room at time t,
Figure FDA0002777592870000029
is the outdoor temperature at time t, R is the house thermal resistance, Δ t represents the time interval,
Figure FDA00027775928700000210
respectively, the refrigeration power and the heating power.
6. The method for controlling the real-time energy of the integrated energy buildings according to claim 5, wherein the operation constraints of the combined cooling, heating and power supply model are as follows:
Figure FDA00027775928700000211
wherein,
Figure FDA00027775928700000212
thermal power, electric power and natural gas consumption eta respectively output by combined cooling, heating and power supply at the moment te、ηhRespectively for the efficiency of electricity production and heat production of the combined cooling, heating and power system, QgasIs the heat value of natural gas.
7. The method of claim 1, wherein the objective function is a minimum desired value of a total cost in a scheduling period, as shown in the following equation:
Figure FDA0002777592870000031
Figure FDA0002777592870000032
wherein, CtFor the total cost at time t, StFor the state of the energy storage system, including load power
Figure FDA0002777592870000033
Electricity price information pDN；xtAs decision variables, including charging and discharging power of energy storage system
Figure FDA0002777592870000034
Thermal power output by combined supply of cold, heat and electricity
Figure FDA0002777592870000035
Electric power purchase
Figure FDA0002777592870000036
wtAs random information, including variation information of outdoor temperature
Figure FDA0002777592870000037
Change information of electricity price
Figure FDA0002777592870000038
T is the total period of the schedule,
Figure FDA0002777592870000039
is the cost of the natural gas at time t,
Figure FDA00027775928700000310
is the price of the online shopping at the time t,
Figure FDA00027775928700000311
is temperature control at time tThe cost of the load is not comfortable,
Figure FDA00027775928700000312
is the cost of the i energy storage systems at time t.
8. The integrated energy building real-time energy control method according to claim 7, wherein the step S2 specifically comprises:
converting the objective function to the following formula:
Vt(St)＝min(Ct(St,xt,wt)+ξE(Vt+1(St+1|St)))
wherein, Vt(St) For energy storage systems in StValue function of the state, Vt+1(St+1|St) For energy storage systems in StOn the premise of the state, the value function at the moment of t +1, xi is a conversion factor;
the approximation function is constructed using piecewise linear methods as follows:
Figure FDA00027775928700000313
at the same time, the following formula must be satisfied:
Figure FDA0002777592870000041
wherein n represents the number of iterations, β represents the total number of segments, r represents the r-th segment, ρ is the length of each segment, ytrIs the amount of resources per segment.
9. The method for controlling the real-time energy of the integrated energy buildings according to claim 8, wherein the step S3 is implemented by using a SPAR method to obtain the approximate function, and the method comprises the following steps:
step S31, initialization
Figure FDA0002777592870000042
Generating N training samples by using a Monte Carlo method, wherein each training sample contains the change situation of various random quantities in a day in a comprehensive energy building, and the iteration number N is 1, and t is 1;
step S32, updating the system state according to the latest random variable and using the slope of each segment after the last iteration
Figure FDA0002777592870000043
Solving the approximate function constructed in the step S2 to obtain each decision variable
Figure FDA0002777592870000044
Post-decision system state
Figure FDA0002777592870000045
Post-decision tunable capacity
Figure FDA0002777592870000046
Step S33, calculating a temporary value of the slope from the updated samples:
Figure FDA0002777592870000047
wherein g is a temporary vector, a is a step size,
Figure FDA0002777592870000048
in the interest of marginal benefit,
Figure FDA0002777592870000049
is an approximate slope;
step S34, performing projection operation on the temporary vector to obtain an approximate slope component of the nth iteration:
Figure FDA00027775928700000410
step S35, when T is T +1, the process returns to step S32, and when T > T, the process goes to step S36;
in step S36, let N be N +1 and t be 1, return to step S32, and terminate the loop when N > N.
10. The integrated energy building real-time energy control method according to claim 9, wherein the step S4 specifically comprises:
step S41, let t equal to 1;
step S42, updating the random information of the current time interval, including the error of the electricity price and the error of the outdoor temperature;
step S43, calculating the optimal decision of the t time period according to the approximate function constructed in the step S2 by using the trained approximate function;
in step S44, let T equal to T +1, if T ≦ T, the process returns to step S42, and if T > T, the loop ends.
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