CN110912120A - Comprehensive energy system optimal scheduling method considering renewable energy power generation uncertainty and user thermal comfort - Google Patents

Comprehensive energy system optimal scheduling method considering renewable energy power generation uncertainty and user thermal comfort Download PDF

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CN110912120A
CN110912120A CN201911169117.1A CN201911169117A CN110912120A CN 110912120 A CN110912120 A CN 110912120A CN 201911169117 A CN201911169117 A CN 201911169117A CN 110912120 A CN110912120 A CN 110912120A
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heat
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CN110912120B (en
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李扬
王春玲
张萌
张儒峰
陈继开
陈厚合
李国庆
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Northeast Electric Power University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A30/00—Adapting or protecting infrastructure or their operation
    • Y02A30/60—Planning or developing urban green infrastructure
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
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Abstract

An optimized scheduling method of a comprehensive energy system considering the power generation uncertainty of renewable energy and the thermal comfort of users is characterized by comprising the steps of constructing a physical model of the comprehensive energy system; establishing an optimized scheduling model of the comprehensive energy system based on opportunity constraint planning; discretizing a probability density function of wind-solar output based on a sequence operation theory to generate a corresponding probabilistic sequence; obtaining an expected value of wind-solar combined output at each time period through a probabilistic sequence; converting the chance constraint form of the spinning standby into a deterministic constraint form; obtaining an indoor thermal comfort temperature range according to the membership function; building a thermal transient balance equation of the building, and solving the heat load demand; inputting initial parameters; solving the comprehensive energy system scheduling model; it is checked whether a solution exists. If so, terminating the flow; otherwise, updating the confidence coefficient; and outputting a final comprehensive energy system optimization scheduling scheme. The system has the advantages of low power generation cost, flexible and reliable operation and the like.

Description

Comprehensive energy system optimal scheduling method considering renewable energy power generation uncertainty and user thermal comfort
Technical Field
The invention relates to an optimized dispatching method of a comprehensive energy system considering the power generation uncertainty of renewable energy sources and the thermal comfort of users, and belongs to the technical field of economic operation of the comprehensive energy system.
Background
With the increasing energy crisis and environmental pollution, active exploitation of renewable energy has been considered as one of the important options for ensuring system safety and sustainable energy supply. However, the rapid development of renewable energy also presents a great challenge to the operation of power systems, and the traditional "fixed-heat" constraint and the problem of renewable energy consumption greatly limit the flexibility and economy of system operation. Under the background, an Integrated Energy System (IES) can realize coordinated planning and optimized operation among multiple heterogeneous Energy subsystems in a region, so that the advantage of effectively improving the Energy utilization efficiency while meeting diversified Energy utilization requirements in the System is widely paid attention by researchers at home and abroad. The improvement of the system operation flexibility by utilizing the IES is an important technical means for improving the high-proportion renewable energy consumption capability in the power system, and has great practical significance for promoting energy transformation and guaranteeing energy safety.
Compared with the traditional power supply, the renewable energy power generation has volatility and randomness, and the consumption of the renewable energy power generation requires higher flexibility of the system. However, the traditional coal-fired thermal power generating unit has limited regulating capacity, and particularly in the winter heating period, the heating demand is large, the regulating capacity is further reduced by the operation mode of 'fixing power by heat', and the grid-connected space of renewable energy sources is compressed. Therefore, how to improve the flexibility of the system to promote the consumption level of renewable energy is an urgent problem to be solved.
At present, scholars at home and abroad have conducted some beneficial explorations aiming at the problem of promoting the consumption of renewable energy sources based on IES. However, in the existing method, uncertainty of various Distributed Generation (DG) is rarely considered at the same time, and it is a heat load model that satisfies thermal comfort of users from the perspective of user experience. So far, no literature report and practical application of an IES (intelligent electronic equipment) optimization scheduling method considering various distributed power generation including heat storage, electric boilers, electric energy storage, wind and light and the like are found.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a scientific and reasonable comprehensive energy system optimal scheduling method considering the power generation uncertainty of renewable energy sources and the thermal comfort of users, which can reduce the power generation cost of the system, effectively improve the operation flexibility of the system, promote the absorption capacity of renewable energy sources such as wind and light and the like, and has strong applicability and good effect, and the problems of wind and light abandonment are better and effectively solved.
The purpose of the invention is realized by the following technical scheme: an Integrated Energy System (IES) physical model aiming at realizing the lowest power generation cost is constructed; then, converting an original stochastic Programming scheduling model into a deterministic mixed integer linear Programming model by adopting a solution method based on a Chance-Constrained Programming (CCP) optimized scheduling model; and finally, solving the model by using a CPLEX solver to obtain a global optimal solution, wherein the method specifically comprises the following steps:
1) building an Integrated Energy System (IES) physical model;
2) establishing an Integrated Energy System (IES) optimization scheduling model based on opportunity-Constrained Programming (CCP);
3) discretizing a probability density function of wind-solar output based on a sequence operation theory to generate a corresponding probabilistic sequence;
4) obtaining an expected value of wind-solar combined output at each time period through a probabilistic sequence;
5) converting the chance constraint form of the spinning standby into a deterministic constraint form;
6) obtaining an indoor thermal comfort temperature range according to the membership function;
7) building a thermal transient balance equation of the building, and solving the heat load demand;
8) inputting initial parameters;
9) solving an Integrated Energy System (IES) optimization scheduling model by using a CPLEX solver;
10) checking whether a solution exists, and if so, terminating the flow; otherwise, updating the confidence coefficient, and turning to the step 8) to solve again;
11) and finally outputting an Integrated Energy System (IES) optimization scheduling scheme including the numerical value corresponding to the variable to be optimized and the optimization objective function value.
In the Integrated Energy System (IES) physical model of step 1), the thermal Power unit, the Combined Heat and Power unit (CHP), the fan, the photovoltaic and the electrical Energy storage are Combined to provide Power demand for the user, the electric boiler absorbs part of the electric Energy provided by the Power supply side and converts the electric Energy into Heat Energy, and the Combined Heat and Power unit (CHP) equipped with the Heat storage device is Combined to provide corresponding Heat demand for the user, wherein the Heat load demand is the heating capacity of the building with the heating System.
The Integrated Energy System (IES) optimization scheduling model construction process in the step 2) is as follows:
(a) selecting an optimization target, wherein the minimum power generation cost of an Integrated Energy System (IES) containing renewable energy power generation is selected as the optimization target, and because the output of a fan and photovoltaic power generation in the system is not controllable, the cost is added with a rotating standby cost, so that the expression of an optimization objective function is as follows:
Figure BDA0002288236160000031
in formula (1): c1Representing the sum of the fuel cost and the reserve cost, C, of the thermal power generating unit2Representing the sum of the fuel cost and the standby cost of the cogeneration unit; c3Representing the sum of the charge-discharge cost and the standby cost of the electric energy storage device; pitGenerating power of the thermal power generating unit i in a time period t; a isi、biAnd ciRespectively representing the fuel cost coefficients of the thermal power generating units i; pe,itGenerating power of the cogeneration unit i in a time period t; pt CHAnd Pt DCRespectively representing electric energy storage charging and discharging power;
Figure BDA0002288236160000032
the total heating power of the heat-storage cogeneration unit i in the time period t is obtained;
Figure BDA0002288236160000033
storing and discharging heat power of the heat storage device i in a period t; a isir、birAnd cirRespectively representing the fuel cost coefficients of the cogeneration unit i; cVThe heat-electricity ratio of the cogeneration unit; gamma rayi、δiMu respectively represents the standby cost coefficients of a thermal power generating unit, a cogeneration unit and electricity energy storage, Rit、Re,it、
Figure BDA0002288236160000034
Representing the standby capacity of a thermal power generating unit, a cogeneration unit and electricity energy storage; g1And g2Respectively representing the charge and discharge cost coefficients of the electric energy storage device;
(b) determining constraint conditions, wherein the constraint conditions of the scheduling model comprise an energy balance constraint, a thermal Power unit operation constraint, a Combined Heat and Power (CHP) operation constraint, a Heat storage constraint, an electric energy storage constraint, an electric boiler constraint and a rotation standby constraint, and specifically comprise the following steps:
energy balance constraint: including an electrical balance constraint and a thermal balance constraint,
Figure BDA0002288236160000035
in the formula (2), PcThe consumption of renewable energy sources is increased; pltIs the electrical load of the system for a period t,
Figure BDA0002288236160000036
is the thermal load of the system for a period t; pEB,tThe electric power of the electric boiler in a period t;
Figure BDA0002288236160000037
the heating power of the electric boiler in a period t;
and (3) operation constraint of the thermal power generating unit: comprises unit output constraint and climbing constraint,
Figure BDA0002288236160000038
in the formula (3), PimaxAnd PiminThe maximum power generation power and the minimum power generation power of the thermal power generating unit i are respectively; r isdiThe maximum downward gradient rate r of the thermal power generating unituiThe maximum upward climbing rate of the thermal power generating unit is obtained;
combined Heat and Power (CHP) operation constraints: comprises the electric output restraint, the thermal output restraint and the climbing restraint of the unit,
Figure BDA0002288236160000041
in the formula (4), Pe,imaxAnd Pe,iminThe maximum power generation power and the minimum power generation power of the cogeneration unit i are respectively;
Figure BDA0002288236160000042
the upper limit value of the thermal output of the cogeneration unit;
Figure BDA0002288236160000043
is the maximum downward climbing rate of the cogeneration unit,
Figure BDA0002288236160000044
the maximum upward climbing rate of the cogeneration unit;
heat storage restraint: comprises heat storage and discharge power constraint and heat storage capacity constraint,
Figure BDA0002288236160000045
in the formula (5), the reaction mixture is,
Figure BDA0002288236160000046
and
Figure BDA0002288236160000047
the maximum heat storage power and the minimum heat storage power of the heat storage device are respectively; cmaxAnd CminThe maximum and minimum heat storage capacities of the heat storage device are respectively set; c (0) and C (T)end) Respectively representing the initial and final values of the heat storage quantity in 1 scheduling period of the heat storage device;
electric energy storage restraint: including electrical energy storage output constraints and capacity constraints,
Figure BDA0002288236160000048
in the formula (6), the reaction mixture is,
Figure BDA0002288236160000049
the maximum power of charging and discharging of the zinc bromine battery; smaxAnd SminMaximum and minimum allowable capacities of the zinc-bromine battery respectively; s (0) and S (T)end) Respectively obtaining the initial and final values of the content of the electric energy storage device in 1 scheduling period;
electric boiler restraint: the output of the electric boiler is restricted,
0≤PEB,t≤PEB,max(7)
in the formula (7), PEB,maxThe maximum power consumption of the electric boiler is obtained;
restraint of renewable energy sources: the consumption of the renewable energy is constrained by the amount,
0≤Pc≤Et(8)
in the formula (8), EtThe expected value of the renewable energy output is obtained;
rotating standby constraint: comprises standby constraint of a thermal power generating unit, standby constraint of a cogeneration unit, standby constraint of electric energy storage and opportunity constraint expression of rotary standby,
Figure BDA0002288236160000051
in the formula (9), α is a given confidence levelDCDischarge efficiency for electrical energy storage; pt DGThe actual value of the wind-light combined output is obtained;
to sum up, the Integrated Energy System (IES) optimal scheduling is modeled as follows:
Figure BDA0002288236160000052
in the formula (10), J (x, ξ) is an objective function, ξ is a random parameter vector, and Gk(x, ξ) is a constraint condition, Pr{. is the probability of event establishment, β is a pre-specified confidence level, H is the traditional deterministic constraint;
Figure BDA0002288236160000053
is the minimum value taken by the objective function J (x, ξ) at a probability level not lower than β.
In the step 3), the probability distribution of the photovoltaic power and the fan output power is discretized by using a sequence operation theory to obtain corresponding probabilistic sequences a (i) respectivelyat) And b (i)bt)。
In the step 4), the expected value E of intermittent wind-solar joint output predicted in the t periodtThe calculation formula is as follows:
Figure BDA0002288236160000054
in the formula (11), NatThe photovoltaic output probability sequence length is obtained; n is a radical ofbtIs windThe length of the machine output probability sequence; q is a discretization step length; m isatq is the m th photovoltaic period taThe output value of the seed state; m isbtq is the m th time period of the fan tbOutput value of the seed state.
In the step 5), a probabilistic sequence c (i) corresponding to the t-period wind-solar joint outputct) The probabilistic sequence a (i) can be utilizedat) And b (i)bt) According to the definition of the volume sum, the volume sum is as follows:
Figure BDA0002288236160000061
to facilitate handling of spinning standby constraints, a new class of 0-1 variables is defined
Figure BDA0002288236160000062
It satisfies the following relationship:
Figure BDA0002288236160000063
equation (13) shows that in the time period t, when the system rotation reserve capacity is larger than the wind-light output expected value and the wind-light m < th > powerctSeed out force mctThe difference of q is 1, otherwise 0,
the chance constraint form of spinning reserve can therefore be simplified to:
Figure BDA0002288236160000064
the variable 0-1 is used in formula (14)
Figure BDA0002288236160000065
While
Figure BDA0002288236160000066
The expression (c) is not compatible with the solution form of Mixed-integer linear Programming (MILP), and equation (15) must be used instead of equation (13),
Figure BDA0002288236160000067
in the formula, L is a large number, and L is large
Figure BDA0002288236160000068
When formula (15) is equivalent to
Figure BDA0002288236160000069
λ is a very small positive number, since
Figure BDA00022882361600000610
Is a variable from 0 to 1, therefore
Figure BDA00022882361600000611
Can only equal 1, otherwise it is 0.
And 6), describing the indoor thermal comfort temperature by adopting a membership function based on fuzzy mathematics as a theoretical basis, and further participating in the optimization of a heating system, wherein a temperature range with the membership of 1 is taken as an upper limit value and a lower limit value of the indoor thermal comfort temperature.
In the step 7), a transient heat balance equation of the building is constructed to describe the influence of the change of the heat supplied by the heating system on the temperature of the building, so that a relationship is established between the heat and the temperature, and a finally obtained heat load model is as follows:
Figure BDA00022882361600000612
in formula (16), Tin(t) room temperature for a period of t; t isout(t) outdoor temperature for a period of t; k is the comprehensive heat transfer coefficient of the building; f is the building surface area; v is the building volume; cairIs the specific heat capacity of the indoor air; rhoairIs the density of the indoor air;
Figure BDA0002288236160000071
the heating power of the t period.
The initial parameters input in the step 8) comprise: the system comprises thermal power generating unit parameters, cogeneration unit parameters, building parameters, fan parameters, photovoltaic module parameters, electric energy storage parameters, heat storage parameters, electric boiler parameters, the number of dispatching time segments, electric load predicted values and upper and lower limit values of optimized variables.
The invention relates to an IES (Integrated energy System) optimal scheduling method considering renewable energy power generation uncertainty and user thermal comfort, which comprises the steps of firstly constructing an IES (Integrated energy System) physical model aiming at realizing the lowest power generation cost; then, converting an original stochastic programming scheduling model into a deterministic mixed integer linear programming model by adopting a solution method based on a Chance-constrained programming (CCP) optimized scheduling model; and finally, a CPLEX solver is adopted to solve the model to obtain a global optimal solution, so that the problems of wind abandonment and light abandonment in the prior art are effectively solved, the power generation cost of the system is reduced, the operation flexibility of the system is improved, the consumption of renewable energy sources such as wind and light is promoted, and the method has the waiting advantages of being scientific and reasonable, strong in applicability and good in effect.
Drawings
FIG. 1 is a flow chart of an integrated energy system optimization scheduling method of the present invention that considers renewable energy generation uncertainty and user thermal comfort;
FIG. 2 is a schematic diagram of an integrated energy system architecture;
FIG. 3 is a schematic representation of a membership function for thermal comfort temperature;
FIG. 4 is a single line schematic diagram of a modified IEEE-30 node system;
FIG. 5 is a schematic diagram showing the variation of the heat storage and release power of the heat storage device;
fig. 6 is a schematic diagram of the electric output situation of each unit.
Detailed Description
The preferred embodiments will be described in detail below with reference to the accompanying drawings. It should be emphasized that the following description is merely exemplary in nature and is not intended to limit the scope of the invention or its application.
Referring to fig. 1, a method for optimizing and scheduling an integrated energy System considering renewable energy power generation uncertainty and user thermal comfort includes first constructing an Integrated Energy System (IES) physical model aiming at achieving a minimum power generation cost; then, converting an original stochastic programming scheduling model into a deterministic mixed integer linear programming model by adopting a solution method based on a Chance-constrained programming (CCP) optimized scheduling model; and finally, solving the model by using a CPLEX solver to obtain a global optimal solution, wherein the method specifically comprises the following steps:
1) building an Integrated Energy System (IES) physical model; referring to fig. 2, a thermal Power generating unit, a Combined Heat and Power (CHP), a fan, a photovoltaic and an electric energy storage unit are Combined to provide Power demand for a user, a part of electric energy provided by a absorption Power side of an electric boiler is converted into Heat energy, and the Combined Heat and Power (CHP) provided with a Heat storage device is Combined to provide corresponding Heat demand for the user, wherein the Heat load demand is the heating capacity of a building with a heating system.
2) Establishing an Integrated Energy System (IES) optimization scheduling model based on opportunity-Constrained Programming (CCP);
(a) selecting an optimization target, wherein the minimum power generation cost of an Integrated Energy System (IES) containing renewable energy power generation is selected as the optimization target, and because the output of a fan and photovoltaic power generation in the system is not controllable, the cost is added with a rotating standby cost, so that the expression of an optimization objective function is as follows:
Figure BDA0002288236160000081
in formula (1): c1Representing the sum of the fuel cost and the reserve cost, C, of the thermal power generating unit2Representing the sum of the fuel cost and the standby cost of the cogeneration unit; c3Representing the sum of the charge-discharge cost and the standby cost of the electric energy storage device; pitGenerating power of the thermal power generating unit i in a time period t; a isi、biAnd ciRespectively representing the fuel cost coefficients of the thermal power generating units i; pe,itFor cogeneration unit i in time period tThe generated power of (c); pt CHAnd Pt DCRespectively representing electric energy storage charging and discharging power;
Figure BDA0002288236160000082
the total heating power of the heat-storage cogeneration unit i in the time period t is obtained;
Figure BDA0002288236160000083
storing and discharging heat power of the heat storage device i in a period t; a isir、birAnd cirRespectively representing the fuel cost coefficients of the cogeneration unit i; cVThe heat-electricity ratio of the cogeneration unit; gamma rayi、δiMu respectively represents the standby cost coefficients of a thermal power generating unit, a cogeneration unit and electricity energy storage, Rit、Re,it、
Figure BDA0002288236160000084
Representing the standby capacity of a thermal power generating unit, a cogeneration unit and electricity energy storage; g1And g2Respectively representing the charge and discharge cost coefficients of the electric energy storage device;
(b) determining constraint conditions, wherein the constraint conditions of the scheduling model comprise an energy balance constraint, a thermal Power unit operation constraint, a Combined Heat and Power (CHP) operation constraint, a Heat storage constraint, an electric energy storage constraint, an electric boiler constraint and a rotation standby constraint, and specifically comprise the following steps:
energy balance constraint: including an electrical balance constraint and a thermal balance constraint,
Figure BDA0002288236160000091
in the formula (2), PcThe consumption of renewable energy sources is increased; pltIs the electrical load of the system for a period t,
Figure BDA0002288236160000092
is the thermal load of the system for a period t; pEB,tThe electric power of the electric boiler in a period t;
Figure BDA0002288236160000093
the heating power of the electric boiler in a period t;
and (3) operation constraint of the thermal power generating unit: comprises unit output constraint and climbing constraint,
Figure BDA0002288236160000094
in the formula (3), PimaxAnd PiminThe maximum power generation power and the minimum power generation power of the thermal power generating unit i are respectively; r isdiThe maximum downward gradient rate r of the thermal power generating unituiThe maximum upward climbing rate of the thermal power generating unit is obtained;
combined Heat and Power (CHP) operation constraints: comprises the electric output restraint, the thermal output restraint and the climbing restraint of the unit,
Figure BDA0002288236160000095
in the formula (4), Pe,imaxAnd Pe,iminThe maximum power generation power and the minimum power generation power of the cogeneration unit i are respectively;
Figure BDA0002288236160000096
the upper limit value of the thermal output of the cogeneration unit;
Figure BDA0002288236160000097
is the maximum downward climbing rate of the cogeneration unit,
Figure BDA0002288236160000098
the maximum upward climbing rate of the cogeneration unit;
heat storage restraint: comprises heat storage and discharge power constraint and heat storage capacity constraint,
Figure BDA0002288236160000099
in the formula (5), the reaction mixture is,
Figure BDA00022882361600000910
and
Figure BDA00022882361600000911
the maximum heat storage power and the minimum heat storage power of the heat storage device are respectively; cmaxAnd CminThe maximum and minimum heat storage capacities of the heat storage device are respectively set; c (0) and C (T)end) Respectively representing the initial and final values of the heat storage quantity in 1 scheduling period of the heat storage device;
electric energy storage restraint: including electrical energy storage output constraints and capacity constraints,
Figure BDA0002288236160000101
in the formula (6), the reaction mixture is,
Figure BDA0002288236160000102
the maximum power of charging and discharging of the zinc bromine battery; smaxAnd SminMaximum and minimum allowable capacities of the zinc-bromine battery respectively; s (0) and S (T)end) Respectively obtaining the initial and final values of the content of the electric energy storage device in 1 scheduling period;
electric boiler restraint: the output of the electric boiler is restricted,
0≤PEB,t≤PEB,max(7)
in the formula (7), PEB,maxThe maximum power consumption of the electric boiler is obtained;
restraint of renewable energy sources: the consumption of the renewable energy is constrained by the amount,
0≤Pc≤Et(8)
in the formula (8), EtThe expected value of the renewable energy output is obtained;
rotating standby constraint: comprises standby constraint of a thermal power generating unit, standby constraint of a cogeneration unit, standby constraint of electric energy storage and opportunity constraint expression of rotary standby,
Figure BDA0002288236160000103
in the formula (9), α is a given confidence levelDCDischarge efficiency for electrical energy storage; pt DGThe actual value of the wind-light combined output is obtained;
to sum up, the Integrated Energy System (IES) optimal scheduling is modeled as follows:
Figure BDA0002288236160000104
in the formula (10), J (x, ξ) is an objective function, ξ is a random parameter vector, and Gk(x, ξ) is a constraint condition, Pr{. is the probability of event establishment, β is a pre-specified confidence level, H is the traditional deterministic constraint;
Figure BDA0002288236160000105
is the minimum value taken by the objective function J (x, ξ) at a probability level not lower than β.
3) Discretizing a probability density function of wind-solar output based on a sequence operation theory to generate a corresponding probabilistic sequence; in the step 3), the probability distribution of the photovoltaic power and the fan output power is discretized by using a sequence operation theory to obtain corresponding probabilistic sequences a (i) respectivelyat) And b (i)bt)。
4) Obtaining an expected value of wind-solar combined output at each time period through a probabilistic sequence; in the step 4), the expected value E of intermittent wind-solar joint output predicted in the t periodtThe calculation formula is as follows:
Figure BDA0002288236160000111
in the formula (11), NatThe photovoltaic output probability sequence length is obtained; n is a radical ofbtThe output probability sequence length of the fan is taken as the output probability sequence length of the fan; q is a discretization step length; m isatq is the m th photovoltaic period taThe output value of the seed state; m isbtq is the m th time period of the fan tbOutput value of the seed state.
5) Converting the chance constraint form of the spinning standby into a deterministic constraint form; in the step 5), a probabilistic sequence c (i) corresponding to the t-period wind-solar joint outputct) The probabilistic sequence a (i) can be utilizedat) And b (i)bt) According to the definition of the volume sum, the volume sum is as follows:
Figure BDA0002288236160000112
to facilitate handling of spinning standby constraints, a new class of 0-1 variables is defined
Figure BDA0002288236160000113
It satisfies the following relationship:
Figure BDA0002288236160000114
equation (13) shows that in the time period t, when the system rotation reserve capacity is larger than the wind-light output expected value and the wind-light m < th > powerctSeed out force mctThe difference of q is 1, otherwise 0,
the chance constraint form of spinning reserve can therefore be simplified to:
Figure BDA0002288236160000115
the variable 0-1 is used in formula (14)
Figure BDA0002288236160000116
While
Figure BDA0002288236160000117
The expression (2) is not compatible with the solution form of Mixed-Integer Linear Programming (MILP), and equation (15) must be used instead of equation (13),
Figure BDA0002288236160000118
in the formula, L is a large number, and L is large
Figure BDA0002288236160000121
When formula (15) is equivalent to
Figure BDA0002288236160000122
λ is a very small positive number, since
Figure BDA0002288236160000123
Is a variable from 0 to 1, therefore
Figure BDA0002288236160000124
Can only equal 1, otherwise it is 0.
6) Obtaining an indoor thermal comfort temperature range according to the membership function; in the step 6), fuzzy mathematics is taken as a theoretical basis, and a membership function is adopted to describe indoor thermal comfort temperature so as to participate in optimization of a heating system. As shown in fig. 3, the temperature range with the membership degree of 1 is taken as the upper and lower limit values of the indoor thermal comfort temperature.
7) Building a thermal transient balance equation of the building, and solving the heat load demand; in the step 7), a transient heat balance equation of the building is constructed to describe the influence of the change of the heat supplied by the heating system on the temperature of the building, so that a relationship is established between the heat and the temperature, and a finally obtained heat load model is as follows:
Figure BDA0002288236160000125
in formula (16), Tin(t) room temperature for a period of t; t isout(t) outdoor temperature for a period of t; k is the comprehensive heat transfer coefficient of the building; f is the building surface area; v is the building volume; cairIs the specific heat capacity of the indoor air; rhoairIs the density of the indoor air;
Figure BDA0002288236160000126
the heating power of the t period.
8) Inputting initial parameters; the initial parameters input in the step 8) comprise: the system comprises thermal power generating unit parameters, cogeneration unit parameters, building parameters, fan parameters, photovoltaic module parameters, electric energy storage parameters, heat storage parameters, electric boiler parameters, the number of dispatching time segments, electric load predicted values and upper and lower limit values of optimized variables.
9) Solving an Integrated Energy System (IES) optimization scheduling model by using a CPLEX solver;
10) checking whether a solution exists, and if so, terminating the flow; otherwise, updating the confidence coefficient, and turning to the step 8) to solve again;
11) and finally outputting an Integrated Energy System (IES) optimization scheduling scheme including the numerical value corresponding to the variable to be optimized and the optimization objective function value.
Fig. 4 is a system single line diagram of a modified IEEE-30 node, and the embodiment is a specific application of the integrated energy system optimal scheduling method considering renewable energy power generation uncertainty and user thermal comfort in the system. The comprehensive energy system comprises 4 thermal power generating units, an electric energy storage device, an electric boiler and two heat storage devices, wherein 2 cogeneration units replace the thermal power generating units 1 and 2 respectively, a grid-connected node of a wind power plant is 16, and a grid-connected node of a photovoltaic electric field is 17. Based on the proposed IES optimal scheduling method, the resulting thermal storage device and the output of each unit are shown in fig. 5 and 6, respectively.
As can be seen from fig. 5, the heat storage device has different heat storage and release conditions in different periods. The reason is that when the electric load is large and the heat load demand is small, the cogeneration unit increases the output power, and the redundant heat is stored in the heat storage device except for meeting the heat load; when wind power output is large, power load is small and wind abandon occurs, the cogeneration unit utilizes heat in the heat storage device to supply heat, and the output of the unit (including heat output and electric output) is reduced, so that the space for absorbing wind power is increased.
As can be seen from fig. 6, since the fuel cost of the cogeneration unit is significantly lower than that of the thermal power unit, the cogeneration unit provides a large electric power output during the peak period of the electric load; in the thermal power generating unit, since the fuel cost of the thermal power generating units 1 and 4 is lower than that of the thermal power generating units 2 and 3, the thermal power generating units 1 and 4 give priority to output.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (9)

1. A comprehensive energy system optimization scheduling method considering renewable energy power generation uncertainty and user thermal comfort is characterized by comprising the following steps:
1) building an Integrated Energy System (IES) physical model;
2) establishing an Integrated Energy System (IES) optimization scheduling model based on opportunity-Constrained Programming (CCP);
3) discretizing a probability density function of wind-solar output based on a sequence operation theory to generate a corresponding probabilistic sequence;
4) obtaining an expected value of wind-solar combined output at each time period through a probabilistic sequence;
5) converting the chance constraint form of the spinning standby into a deterministic constraint form;
6) obtaining an indoor thermal comfort temperature range according to the membership function;
7) building a thermal transient balance equation of the building, and solving the heat load demand;
8) inputting initial parameters;
9) solving an Integrated Energy System (IES) optimization scheduling model by using a CPLEX solver;
10) checking whether a solution exists, and if so, terminating the flow; otherwise, updating the confidence coefficient, and turning to the step 8) to solve again;
11) and finally outputting an Integrated Energy System (IES) optimization scheduling scheme including the numerical value corresponding to the variable to be optimized and the optimization objective function value.
2. The method of claim 1, wherein the method comprises the steps of: in the Integrated Energy System (IES) physical model of step 1), the thermal Power generating unit, the Combined Heat and Power generating unit (CHP), the fan, the photovoltaic and the electrical energy storage are Combined to provide Power demand for the user, the electric boiler absorbs part of the electric energy provided by the Power supply side and converts the electric energy into Heat energy, and the Combined Heat and Power generating unit (CHP) equipped with the Heat storage device is Combined to provide corresponding Heat demand for the user, wherein the Heat load demand is the heating capacity of the building with the heating system.
3. The method of claim 1, wherein the method comprises the steps of: the Integrated Energy System (IES) optimization scheduling model construction process in the step 2) is as follows:
(a) selecting an optimization target, wherein the minimum power generation cost of an Integrated Energy System (IES) containing renewable energy power generation is selected as the optimization target, and because the output of a fan and photovoltaic power generation in the system is not controllable, the cost is added with a rotating standby cost, so that the expression of an optimization objective function is as follows:
Figure FDA0002288236150000021
in formula (1): c1Representing the sum of the fuel cost and the reserve cost, C, of the thermal power generating unit2Representing the sum of the fuel cost and the standby cost of the cogeneration unit; c3Representing the sum of the charge-discharge cost and the standby cost of the electric energy storage device; pitGenerating power of the thermal power generating unit i in a time period t; a isi、biAnd ciRespectively representing the fuel cost coefficients of the thermal power generating units i; pe,itGenerating power of the cogeneration unit i in a time period t; pt CHAnd Pt DCRespectively representing electric energy storage charging and discharging power;
Figure FDA0002288236150000022
for cogeneration units with heat storage itTotal heating power for a time period;
Figure FDA0002288236150000023
storing and discharging heat power of the heat storage device i in a period t; a isir、birAnd cirRespectively representing the fuel cost coefficients of the cogeneration unit i; cVThe heat-electricity ratio of the cogeneration unit; gamma rayi、δiMu respectively represents the standby cost coefficients of a thermal power generating unit, a cogeneration unit and electricity energy storage, Rit、Re,it、
Figure FDA0002288236150000024
Representing the standby capacity of a thermal power generating unit, a cogeneration unit and electricity energy storage; g1And g2Respectively representing the charge and discharge cost coefficients of the electric energy storage device;
(b) determining constraint conditions, wherein the constraint conditions of the scheduling model comprise an energy balance constraint, a thermal Power unit operation constraint, a Combined Heat and Power (CHP) operation constraint, a Heat storage constraint, an electric energy storage constraint, an electric boiler constraint and a rotation standby constraint, and specifically comprise the following steps:
energy balance constraint: including an electrical balance constraint and a thermal balance constraint,
Figure FDA0002288236150000025
in the formula (2), PcThe consumption of renewable energy sources is increased; pltIs the electrical load of the system for a period t,
Figure FDA0002288236150000026
is the thermal load of the system for a period t; pEB,tThe electric power of the electric boiler in a period t;
Figure FDA0002288236150000027
the heating power of the electric boiler in a period t;
and (3) operation constraint of the thermal power generating unit: comprises unit output constraint and climbing constraint,
Figure FDA0002288236150000028
in the formula (3), PimaxAnd PiminThe maximum power generation power and the minimum power generation power of the thermal power generating unit i are respectively; r isdiThe maximum downward gradient rate r of the thermal power generating unituiThe maximum upward climbing rate of the thermal power generating unit is obtained;
combined Heat and Power (CHP) operation constraints: comprises the electric output restraint, the thermal output restraint and the climbing restraint of the unit,
Figure FDA0002288236150000031
in the formula (4), Pe,imaxAnd Pe,iminThe maximum power generation power and the minimum power generation power of the cogeneration unit i are respectively;
Figure FDA0002288236150000032
the upper limit value of the thermal output of the cogeneration unit;
Figure FDA0002288236150000033
is the maximum downward climbing rate of the cogeneration unit,
Figure FDA0002288236150000034
the maximum upward climbing rate of the cogeneration unit;
heat storage restraint: comprises heat storage and discharge power constraint and heat storage capacity constraint,
Figure FDA0002288236150000035
in the formula (5), the reaction mixture is,
Figure FDA0002288236150000036
and
Figure FDA0002288236150000037
the maximum heat storage power and the minimum heat storage power of the heat storage device are respectively; cmaxAnd CminThe maximum and minimum heat storage capacities of the heat storage device are respectively set; c (0) and C (T)end) Respectively representing the initial and final values of the heat storage quantity in 1 scheduling period of the heat storage device;
electric energy storage restraint: including electrical energy storage output constraints and capacity constraints,
Figure FDA0002288236150000038
in the formula (6), the reaction mixture is,
Figure FDA0002288236150000039
the maximum power of charging and discharging of the zinc bromine battery; smaxAnd SminMaximum and minimum allowable capacities of the zinc-bromine battery respectively; s (0) and S (T)end) Respectively obtaining the initial and final values of the content of the electric energy storage device in 1 scheduling period;
electric boiler restraint: the output of the electric boiler is restricted,
0≤PEB,t≤PEB,max(7)
in the formula (7), PEB,maxThe maximum power consumption of the electric boiler is obtained;
restraint of renewable energy sources: the consumption of the renewable energy is constrained by the amount,
0≤Pc≤Et(8)
in the formula (8), EtThe expected value of the renewable energy output is obtained;
rotating standby constraint: comprises standby constraint of a thermal power generating unit, standby constraint of a cogeneration unit, standby constraint of electric energy storage and opportunity constraint expression of rotary standby,
Figure FDA0002288236150000041
in the formula (9)α is given a confidence level, γDCDischarge efficiency for electrical energy storage; pt DGThe actual value of the wind-light combined output is obtained;
to sum up, the Integrated Energy System (IES) optimal scheduling is modeled as follows:
Figure FDA0002288236150000042
in the formula (10), J (x, ξ) is an objective function, ξ is a random parameter vector, and Gk(x, ξ) is a constraint condition, Pr{. is the probability of event establishment, β is a pre-specified confidence level, H is the traditional deterministic constraint;
Figure FDA0002288236150000043
is the minimum value taken by the objective function J (x, ξ) at a probability level not lower than β.
4. The method of claim 1, wherein the method comprises the steps of: in the step 3), the probability distribution of the photovoltaic power and the fan output power is discretized by using a sequence operation theory to obtain corresponding probabilistic sequences a (i) respectivelyat) And b (i)bt)。
5. The method of claim 1, wherein the method comprises the steps of: in the step 4), the expected value E of intermittent wind-solar joint output predicted in the t periodtThe calculation formula is as follows:
Figure FDA0002288236150000044
in the formula (11), NatThe photovoltaic output probability sequence length is obtained; n is a radical ofbtThe output probability sequence length of the fan is taken as the output probability sequence length of the fan; q is a discretization step length; m isatq isPhotovoltaic t period mthaThe output value of the seed state; m isbtq is the m th time period of the fan tbOutput value of the seed state.
6. The method of claim 1, wherein the method comprises the steps of: in the step 5), a probabilistic sequence c (i) corresponding to the t-period wind-solar joint outputct) The probabilistic sequence a (i) can be utilizedat) And b (i)bt) According to the definition of the volume sum, the volume sum is as follows:
Figure FDA0002288236150000051
to facilitate handling of spinning standby constraints, a new class of 0-1 variables is defined
Figure FDA0002288236150000052
It satisfies the following relationship:
Figure FDA0002288236150000053
equation (13) shows that in the time period t, when the system rotation reserve capacity is larger than the wind-light output expected value and the wind-light m < th > powerctSeed out force mctThe difference of q is 1, otherwise 0,
the chance constraint form of spinning reserve can therefore be simplified to:
Figure FDA0002288236150000054
the variable 0-1 is used in formula (14)
Figure FDA0002288236150000055
While
Figure FDA0002288236150000056
Is incompatible with Mixed integer programmingIntegerLinear Programming, MILP) must be solved by replacing equation (13) with equation (15),
Figure FDA0002288236150000057
in the formula, L is a large number, and L is large
Figure FDA0002288236150000058
When formula (15) is equivalent to
Figure FDA0002288236150000059
λ is a very small positive number, since
Figure FDA00022882361500000510
Is a variable from 0 to 1, therefore
Figure FDA00022882361500000511
Can only equal 1, otherwise it is 0.
7. The method of claim 1, wherein the method comprises the steps of: and 6), describing the indoor thermal comfort temperature by adopting a membership function based on fuzzy mathematics as a theoretical basis, and further participating in the optimization of a heating system, wherein a temperature range with the membership of 1 is taken as an upper limit value and a lower limit value of the indoor thermal comfort temperature.
8. The method of claim 1, wherein the method comprises the steps of: in the step 7), a transient heat balance equation of the building is constructed to describe the influence of the change of the heat supplied by the heating system on the temperature of the building, so that a relationship is established between the heat and the temperature, and a finally obtained heat load model is as follows:
Figure FDA0002288236150000061
in formula (16), Tin(t) room temperature for a period of t; t isout(t) outdoor temperature for a period of t; k is the comprehensive heat transfer coefficient of the building; f is the building surface area; v is the building volume; cairIs the specific heat capacity of the indoor air; rhoairIs the density of the indoor air;
Figure FDA0002288236150000062
the heating power of the t period.
9. The method of claim 1, wherein the method comprises the steps of: the initial parameters input in the step 8) comprise: the system comprises thermal power generating unit parameters, cogeneration unit parameters, building parameters, fan parameters, photovoltaic module parameters, electric energy storage parameters, heat storage parameters, electric boiler parameters, the number of dispatching time segments, electric load predicted values and upper and lower limit values of optimized variables.
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Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111737873A (en) * 2020-06-24 2020-10-02 广东电网有限责任公司电力调度控制中心 Unit combination method for providing auxiliary service based on electrolytic aluminum load and related device
CN111967728A (en) * 2020-07-27 2020-11-20 国网河北省电力有限公司营销服务中心 Market building peak regulation capacity assessment method considering energy utilization comfort time-varying
CN112051762A (en) * 2020-08-05 2020-12-08 上海电气工程设计有限公司 Closed-loop management method and system for micro-grid and comprehensive energy
CN112365034A (en) * 2020-10-27 2021-02-12 燕山大学 Electric heating comprehensive energy system scheduling method and system
CN112615386A (en) * 2020-11-23 2021-04-06 国网浙江省电力有限公司台州供电公司 Wind power consumption-oriented optimal constant volume method for electric heating hybrid energy storage system
CN112668791A (en) * 2020-12-30 2021-04-16 华北电力大学（保定） Optimization method of combined heat and power system
CN113517690A (en) * 2021-07-02 2021-10-19 东北电力大学 Double-layer dispatching method for community comprehensive energy system with electric vehicle charging station
CN113610316A (en) * 2021-08-19 2021-11-05 东北电力大学 Optimal scheduling method for park comprehensive energy system considering comprehensive demand response in uncertain environment
CN113708404A (en) * 2021-08-19 2021-11-26 东北电力大学 Isolated microgrid optimization scheduling method based on automatic reinforcement learning multi-period prediction
CN113780663A (en) * 2021-09-14 2021-12-10 国网山东省电力公司经济技术研究院 Comprehensive energy system low-carbon scheduling method and system based on carbon transaction model
CN114548643A (en) * 2021-12-01 2022-05-27 东南大学 Optimization control method for comprehensive energy system
CN114938009A (en) * 2022-06-24 2022-08-23 国网安徽省电力有限公司淮北供电公司 Heat accumulation system based on power grid peak shaving
CN117422249A (en) * 2023-10-20 2024-01-19 国网甘肃省电力公司超高压公司 Day-ahead optimal scheduling method for comprehensive energy system
CN118100175A (en) * 2024-04-24 2024-05-28 中科军源(南京)智能技术有限公司 Energy management method of energy system based on distributed optimization

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160218505A1 (en) * 2013-08-28 2016-07-28 Robert Bosch Gmbh System and Method for Energy Asset Sizing and Optimal Dispatch
CN108832665A (en) * 2018-07-04 2018-11-16 四川大学 A kind of probabilistic electric heating integrated system Robust distributed coordination optimization scheduling model of consideration wind-powered electricity generation
CN109241655A (en) * 2018-09-27 2019-01-18 河海大学 A kind of electric-thermal interconnection integrated energy system chance constraint coordination optimizing method
CN109713713A (en) * 2018-10-19 2019-05-03 云南电网有限责任公司 A kind of Unit Commitment randomized optimization process based on the convex relaxation of chance constraint
CN110334479A (en) * 2019-07-24 2019-10-15 东南大学 A kind of probabilistic electric-thermal association system economic load dispatching method of consideration wind-powered electricity generation

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160218505A1 (en) * 2013-08-28 2016-07-28 Robert Bosch Gmbh System and Method for Energy Asset Sizing and Optimal Dispatch
CN108832665A (en) * 2018-07-04 2018-11-16 四川大学 A kind of probabilistic electric heating integrated system Robust distributed coordination optimization scheduling model of consideration wind-powered electricity generation
CN109241655A (en) * 2018-09-27 2019-01-18 河海大学 A kind of electric-thermal interconnection integrated energy system chance constraint coordination optimizing method
CN109713713A (en) * 2018-10-19 2019-05-03 云南电网有限责任公司 A kind of Unit Commitment randomized optimization process based on the convex relaxation of chance constraint
CN110334479A (en) * 2019-07-24 2019-10-15 东南大学 A kind of probabilistic electric-thermal association system economic load dispatching method of consideration wind-powered electricity generation

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
刘方 等: "基于序列运算的微网经济优化调度", 《电工技术学报》 *
李志伟 等: "基于机会约束目标规划的风-光-水-气-火-储联合优化调度", 《电力自动化设备》 *
金鹏 等: "基于序列运算理论的孤立微电网经济运行模型", 《中国电机工程学报》 *

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