CN115860788A - Day-ahead random optimization method and system for comprehensive energy system containing flexible electric heating load - Google Patents
Day-ahead random optimization method and system for comprehensive energy system containing flexible electric heating load Download PDFInfo
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Abstract
The invention discloses a day-ahead random optimization method for a comprehensive energy system containing a flexible electric heating load, which comprises the steps of obtaining system data information of the comprehensive energy system containing the flexible electric heating load to be analyzed; fitting historical wind, solar and load data and generating an uncertainty scene in the comprehensive energy system; constructing a day-ahead optimization model and a flexible electric heating load demand response model of the garbage power plant; and constructing a day-ahead random optimization model of the comprehensive energy system and solving to complete a day-ahead random optimization process of the comprehensive energy system containing the flexible electric heating load. The invention also discloses a system for realizing the day-ahead random optimization method of the comprehensive energy system containing the flexible electric heating load. According to the invention, a garbage power generation meter and carbon emission are added into a traditional comprehensive energy system optimization model, and a load side flexible load scheduling means is considered; therefore, the invention can effectively improve the utilization rate of energy and has good accuracy, high reliability and higher efficiency.
Description
Technical Field
The invention belongs to the field of electrical automation, and particularly relates to a day-ahead random optimization method and system for a comprehensive energy system with a flexible electric heating load.
Background
With the development of economic technology and the improvement of living standard of people, electric energy becomes essential secondary energy in production and life of people, and brings endless convenience to production and life of people. Therefore, ensuring stable and reliable supply of electric energy is one of the most important tasks of the power system.
Currently, with the increasing environmental problems, integrated Energy Systems (IES) have begun to play an important role in power Systems. The IES can integrate and utilize different energy sources, realize multi-energy complementation, improve the utilization rate of the energy sources and simultaneously reduce carbon emission. Currently, there are many operational optimization schemes for IES; however, the existing optimized operation schemes have some defects: the existing optimization operation scheme does not well reflect the daily variability of source load uncertainty when the uncertainty of renewable energy is processed; in addition, new energy such as wind and light is considered in the comprehensive energy system, and biomass energy such as garbage power generation is not considered; with the continuous development of cities, garbage power plants are gradually rising, and the operation of the IES system is more and more influenced.
Therefore, the existing comprehensive energy system day-ahead random optimization scheme has poor accuracy and reliability and low scheme efficiency.
Disclosure of Invention
The invention aims to provide a method for randomly optimizing a flexible electric heating load-containing comprehensive energy system in the day ahead, which has the advantages of good accuracy, high reliability and higher efficiency.
The invention also aims to provide a system for realizing the method for randomly optimizing the comprehensive energy system containing the flexible electric heating load in the day ahead.
The invention provides a method for randomly optimizing a comprehensive energy system containing flexible electric heating loads in the future, which comprises the following steps:
s1, acquiring system data information of a comprehensive energy system containing a flexible electric heating load to be analyzed;
s2, fitting historical wind and light load data by adopting a Gaussian autoregressive model, and generating an uncertainty scene in the comprehensive energy system;
s3, constructing a garbage power plant day-ahead optimization model and a flexible electric heating load demand response model according to the data information obtained in the step S2;
s4, constructing a day-ahead random optimization model of the comprehensive energy system according to the model constructed in the step S3;
s5, solving the comprehensive energy system day-ahead random optimization model constructed in the step S4, and accordingly completing the day-ahead random optimization process of the comprehensive energy system with the flexible electric heating load.
The method for acquiring the system data information of the comprehensive energy system containing the flexible electric heating load to be analyzed comprises the following steps:
acquiring day-ahead output data of renewable energy sources and loads in the comprehensive energy system; obtaining model parameters of a carbon capture power plant and a garbage power plant; the obtained data is 24-point data or 96-point data.
The step S2 of fitting the historical wind-solar-load data by adopting the Gaussian autoregressive model specifically comprises the following steps:
fitting the wind power by adopting a Gaussian autoregressive model:
with each hour as a time step, the corresponding normalized level is expressed as:
wherein X (k) is a standardized wind energy; ε (k) is random noise that follows a normal distribution;
and &>
Is an autoregressive model parameter and is obtained by using a Yule-Walker equation, and/or is combined with a new autoregressive model parameter>
c i ＝E[X(k)E(k-1)]And
wherein c is 1 And c 2 For Yule-Walker equation coefficients, in>
σ is the standard deviation of random gaussian increments in the autoregressive time series; c. C 0 Is Yule-Walker equation coefficient;
fitting the wind energy historical time sequence data by using a univariate second-order autoregressive model:
X(k)′＝X(k)+μ(k)
wherein X (k)' is the normalized wind energy after the introduction of the daily change; μ (k) is an additional term representing random noise that follows a normal distribution;
the normalized wind power level is converted into non-gaussian wind energy output:
P w (k)＝W[X(k)′+μ(k mod N T )]
in the formula P w (k) To normalize the wind energy; w () is an S-morph function; mod is the remainder operator; n is a radical of T Is a scheduling period;
establishing a wind power plant output power prediction error estimation model, and estimating by adopting an autoregressive moving average model:
in the formula
To predict errorsThe ratio of the difference to the predicted power; alpha (alpha) ("alpha") i And beta j Are all model parameters; />
Obeying a mean of 0 and a variance of ξ 2 White gaussian distribution of (3); p and q are model autoregressive orders;
obtaining alpha in the formula by using a least square method through statistics of historical prediction errors i 、β j And xi 2 (ii) a Under each time interval, the prediction error percentage of the wind power can be obtained by recursion in the above formula, and the output power of the wind power is
The step S2 of generating the uncertainty scene in the integrated energy system specifically includes the following steps:
using quantile regression in scene reduction, and considering the influence of uncertainty on power system scheduling under different quantiles;
setting co-generation combination scenario m groups P = (P) 1 ,p 2 ,...,p m ) T Wherein p is i ＝(P wi ,P si ,P Di ) T ，P wi Wind power output, P, for the ith group of scenes si Photovoltaic output, P, for the ith group of scenes Di (ii) load requirements for the ith group of scenes; design matrix in regression model
Wherein X p ＝(x 1 ,x 2 ,…,x p ) T ∈R p Intercept y = (y) for p-dimensional interpretation vector 0 ,y 0 ,...,y 0 ) T Regression parameter β = (β) 1 ,β 2 ,...,β p ) T ∈R p Then the vector form regression equation is P = y + X T Beta, the i-th set of sample regression equations is p i ＝y i +X i T β；
Obtaining differences according to a fitting model by adopting historical data samplesWind power output power P under different time periods in scene wn,t (k) Photovoltaic output power P sn,t (k) Load power P Dn,t (k) (ii) a And setting different fractional digit points at the same time;
if the number of the set quantiles is greater than or equal to 4, distributing probability weight for the corresponding quantiles by adopting the following formula:
in the formula alpha 1 Is the first quantile weight, r 1 Is the first quantile, r 2 Is the second quantile, r 3 Is the third decimal place, α 2 Is a second fractional weight, α e Is the weight of the e-th quantile, r e+1 Is the e +1 quantile, e =3, \ 8230;, R-2, alpha R Is the Rth quantile weight; the probability distribution function of the continuous random variable H is F (H) = P (H ≦ H), and the corresponding quantile is represented as H r ＝F- 1 (r)＝inf{hF(h)≥r}；
According to different set quantiles, solving the following optimization problem in quantile regression by using a least square method:
wherein beta is a regression parameter, p i Wind, photovoltaic and load output samples, y, in the ith set of scenes i Is intercept, X i Designing the matrix, p, for group i r () Is a check function and
u is a calculation formula
r is quantile;
changing different r values, and respectively solving the models to construct a reduced scene model, so that output values under different quantiles can be obtained;
the generated wind power output power under different scenes is reduced by using a quantile regression theory to obtain the wind power output power at different quantiles in different time periods
Photovoltaic output power>
And the load power->
And
s3, constructing a day-ahead optimization model of the garbage power plant, which specifically comprises the following steps:
in a flue gas treatment system of an urban garbage power plant, loss exists when flue gas is treated; this part loss P α,t Is denoted as P α,t ＝ω α (α 1,t +α 3,t ) Wherein ω is α Is the unit processing energy consumption coefficient, alpha, of the flue gas processing system 1,t The part alpha provided by the smoke generated by WEPP operation in the smoke volume for processing the smoke at the time t 3,t The amount of the flue gas provided by the gas storage device when the flue gas treatment is carried out at the moment t;
in the operation of municipal waste plants, the following operating constraints must be met:
0≤W WI ≤W WI,max
P WI,min ≤P WI,t ≤P WI,max
R WI,down ≤P WI,t -P WI,t-1 ≤R WI,up
5％·V WI,max ≤V WI,t ≤95％·V WI,max
P wα,t +P pvα,t +P cgα,t +P WIα,t ＝P α,t
in the formula W WI The sum of the output power of the urban garbage power plant in one day; p WI,t The output value of the urban garbage power plant at the moment t in one day is obtained; w is a group of WI,max The output limit of the urban garbage power plant in one day is set; p is WI,min The minimum value of the output of the urban garbage power plant at different times in one day; p WI,max The maximum output value of the urban garbage power plant at different times in a day; r WI,down Is a down-hill climbing speed constraint; r is WI,up An uphill speed constraint; v WI,t The gas storage amount of the flue gas storage tank at the moment t; v WI,max The maximum capacity of the gas storage device; p is wα,t The energy consumption for flue gas treatment provided by the wind power plant at the time t; p is pvα,t The energy consumption for treating the flue gas is provided for the photovoltaic electric field at the t time period; p cgα,t The energy consumption for treating the flue gas provided by the carbon capture power plant at the t period; p WIα,t The energy consumption is handled to the flue gas that provides for the municipal refuse power plant of t period.
S3, constructing a flexible electric heating load demand response model, which specifically comprises the following steps:
the following equation is adopted as a flexible electric load model:
in the formula F EDR Is a flexible electrical load; c EDR Compensating a cost coefficient for a flexible load unit participating in scheduling; delta P DR,t The flexible electrical load participating in scheduling in the t period changes, and the fact that the electrical load is increased is indicated by the fact that the value is positive, and the fact that the electrical load is decreased is indicated by the fact that the value is negative; Δ t is the change time;
the following equation was used as the flexible thermal load model:
in the formula H L,t Flexible thermal load; s is the heat supply area; w is the external temperature difference heat dissipation coefficient of the building;
is the indoor temperature at time t; />
Is the outdoor temperature at time t; c is hot melting of unit heat supply area;
the following equation is used as the comfort constraint:
in the formula T 0 Initially setting a temperature for a user; sigma is a temperature adjustable quantity;
compensating for the user if user comfort is affected, so the following equation is used as the flexible thermal load scheduling cost F HDR ：
Wherein gamma is a subsidy cost coefficient for adjusting the indoor temperature of unit area;
thus, the load-side scheduling cost F DR Is F DR ＝F EDR +F HDR ；
The following equation is adopted as the flexible electric heating load response threshold value constraint:
in the formula K p Responding to a threshold for a flexible electrothermal load; Δ H DR,t The amount of thermal load adjustment for the t period.
S4, constructing a day-ahead random optimization model of the comprehensive energy system specifically comprises the following steps:
the following formula is adopted as an objective function of a day-ahead random optimization model of the comprehensive energy system:
wherein s is the s-th scene; s is the total scene number reduced by using quantile regression; rho s Probability of the s-th scene; f cg,s Fuel cost of carbon capture plant for the s-th scenario, and
a cg,s 、b cg,s and c cg,s Carbon capture plant Fuel cost coefficient, P, for the s-th scenario cg,s,t The power output value of the carbon capture power plant at the moment t of the s-th scene is obtained; f cc,s Cost of carbon emission for the s-th scenario, and F cc,s ＝λ cc,s,t (C w -C T )，λ cc,s,t Trading prices for carbon emissions, C w Based on the carbon emission and->
δ h Carbon emission allocation coefficient, P, for unit electricity quantity of carbon capture power plant cg,t Carbon capture plant output value at time t, C T Is the actual carbon footprint and->
B cg The actual emission allocation amount coefficient of the unit electric quantity of the carbon capture power plant; f WI,s Operating cost of refuse power plant for s-th scene, and F WI,s ＝P WI,s,t λ cc,s,t (e α -δ h )，P WI,s,t For the refuse power plant electric power at time t of the s-th scenario, lambda cc,s,t For the carbon transaction price at time t, e, in the s-th scenario α The unit output smoke discharge intensity of the garbage power plant, delta h Carbon emission benchmark per unit of electricityDegree; f gas,s Natural gas fuel cost for the s-th scenario, and F gas,s ＝δ gas (V CHP,t +V GB,t -V P2G,t )，δ gas Is the natural gas market unit natural gas price, V CHP,t Natural gas consumed by CHP units, V GB,t Natural gas consumed by gas-fired boilers, V P2G,t Natural gas produced for P2G; f grid,s The power purchase cost for the upper-level power grid for the IES of the s-th scene is increased
δ grid,t Cost coefficient, P, for power purchase of IES to upper-level power grid grid,t Purchasing electric quantity for the upper-level power grid at the time t; f DR,s Scheduling cost for the load side of the s-th scene;
the following formula is adopted as a constraint condition:
and power balance constraint:
P cg,t +P WI,t +P CHP,t +P w,t +P PV,t +P dis,t +P grid,t ＝P l,t +P cha,t
in the formula P cg,t The output of the carbon capture power plant is obtained at the moment t; p is WI,t The output of the refuse power plant is at the moment t; p CHP,t The electric power of the CHP unit at the t moment is the t moment; p is w,t Wind power output at the moment t; p PV,t Photovoltaic output at time t; p dis,t Is the discharge power of the energy storage system; p grid,t Purchasing power for a superior power grid; p cha,t Charging power for the energy storage system; p is l,t Is a load value at time t, and P l,t ＝P in,t +ΔP DR,t ，P in,t As value of stiffness load, Δ P DR,t Electrical load for flexible change at time t; (ii) a
And thermal power balance constraint:
H CHP,t +H GB,t +H TSD,t ＝H l,t +H TSC,t
in the formula H CHP,t The heat power of CHP at the t moment; h GB,t Thermal power at time GB; h TSD,t For heat release of the heat storage device at time tPower; h TSC,t The charging power of the heat storage device at the moment t; h l,t Is the thermal load demand at time t, and H L,t ＝H in,t +ΔH DR,t ，H in,t For a rigid thermal load at time t,. DELTA.H DR,t Thermal load for compliance change at time t;
output restraint of the CHP unit and the gas boiler:
in the formula P CHP,min The minimum value of the electric power of the CHP unit; p CHP,t Is CHP unit electric power; p CHP,max The maximum value of the electric power of the CHP unit; h CHP,min The minimum value of the heat power of the CHP unit; h CHP,t The heat power of the CHP unit; h CHP,max The maximum value of the heat power of the CHP unit; RP CHP,down The power is the electric power for climbing the slope under the CHP unit; RP CHP,up The electric power for climbing the CHP unit; RH (relative humidity) CHP,down The heat power of the downward climbing of the CHP unit; RH (relative humidity) CHP,up The heat power for climbing the slope of the CHP unit; h GB,min Is the GB minimum output value; h GB,t Is GB output value; h GB,max The maximum value of GB output; RH (relative humidity) GB,down The heat power of climbing under GB; RH (relative humidity) GB,up The GB uphill thermal power;
CCPP operating constraints:
P cg,min ≤P cg,t ≤P cg,max
R cg,down ≤P cg,t -P cg,t-1 ≤R cg,up
P A ≤P GC,t +P windC,t +P PVC,t +P WIC,t ≤P C,max
P C,max ＝ω C e g P cg,t
in the formula P cg,min The minimum value of CCPP output; p cg,t Is CCPP output value; p cg,max The maximum value of CCPP output; r cg,down Is CCPP down-hill climbing rate; r cg,up The rate of ascent for CCPP; p A Energy consumption for IES-P2G system; p GC,t Carbon capture energy consumption and flue gas treatment energy consumption provided for CCPP; p windC,t Carbon capture energy consumption is provided for wind power in a time period t; p PVC,t Carbon capture energy consumption provided for photovoltaic at a time t; p WIC,t Carbon capture energy consumption provided for WEPP at time t; p is C,max The upper limit of the energy consumption for the operation of the carbon capture system in the period t; omega C The running energy consumption of CCPP processing unit CO2 is obtained; e.g. of the type g CO2 generated for CCPP unit output; lambda cc,t The flue gas split ratio of the carbon capture system in the t period; q CC,t The amount of CO2 trapped by the carbon trapping power plant at the moment t;
and (3) operation constraint of the energy storage system and the heat storage system:
U cha,t P cha,min ≤P cha,t ≤U cha,t P cha,max
U dis,t P dis,min ≤P dis,t ≤U dis,t P dis,max
U cha,t +U dis,t ≤1
SOC min ≤SOC t ≤SOC max
U hcha,t H TSC,min ≤H TSC,t ≤U hcha,t H TSC,max
U hdis,t H TSD,min ≤H TSD,t ≤U hdis,t H TSD,max
U hcha,t +U hdis,t ≤1
in the formula of U cha,t Charging state variables for energy storage; p cha,min Charging the minimum value for the stored energy; p cha,max Maximum value of energy storage charging; u shape dis,t Is an energy storage discharge state variable; p dis,min Is the minimum value of energy storage and discharge; p dis,max Is the maximum value of energy storage and discharge; p is cha,t Charging value for stored energy; p is dis,t Is the energy storage discharge value; SOC (system on chip) t The SOC state of energy storage at the time t; eta ch Charging efficiency for energy storage; eta dis The energy storage discharge efficiency is obtained; Δ T is a scheduling interval; SOC (system on chip) min Is the SOC minimum value of the stored energy; SOC max The maximum value of the SOC of the stored energy is; u shape hcha,t Charging state variables of the heat storage system; h TSC,min Charging the heat storage system to the minimum value; h TSC,t Charging the heat storage system with heat; h TSC,max The maximum value of heat charging of the heat storage system is obtained; u shape hdis,t Is a heat release state variable of the heat storage system; h TSD,min A minimum heat release value for the heat storage system; h TSD,max Is the maximum heat release of the heat storage system; h TSD,t The heat release value of the heat storage system.
The invention also discloses a system for realizing the day-ahead random optimization method of the comprehensive energy system containing the flexible electric heating load, which comprises a data acquisition module, a fitting and scene generation module, a model construction module, an optimization model construction module and an optimization solution module; the data acquisition module, the fitting and scene generation module, the model construction module, the optimization model construction module and the optimization solution module are sequentially connected in series; the data acquisition module is used for acquiring system data information of the comprehensive energy system containing the flexible electric heating load to be analyzed and uploading the data to the fitting and scene generation module; the fitting and scene generating module is used for fitting historical wind and solar load data by adopting a Gaussian autoregressive model according to the acquired data, generating an uncertainty scene in the comprehensive energy system and uploading the data to the model building module; the model construction module is used for constructing a garbage power plant day-ahead optimization model and a flexible electric heating load demand response model according to the acquired data, and uploading the data to the optimization model construction module; the optimization model building module is used for building a day-ahead random optimization model of the comprehensive energy system according to the acquired data and uploading the data to the optimization solving module; and the optimization solving module is used for solving the constructed day-ahead random optimization model of the comprehensive energy system according to the acquired data, so that the day-ahead random optimization process of the comprehensive energy system containing the flexible electric heating load is completed.
According to the day-ahead random optimization method and system for the comprehensive energy system containing the flexible electric heating load, the garbage power generation meter and the carbon emission are added into the traditional comprehensive energy system optimization model, and a load side flexible load scheduling means is considered; therefore, the invention can effectively improve the utilization rate of energy and has good accuracy, high reliability and higher efficiency.
Drawings
FIG. 1 is a schematic process flow diagram of the process of the present invention.
FIG. 2 is a functional block diagram of the system of the present invention.
Detailed Description
FIG. 1 is a schematic flow chart of the method of the present invention: the invention provides a method for randomly optimizing a comprehensive energy system containing flexible electric heating loads in the future, which comprises the following steps:
s1, acquiring system data information of a comprehensive energy system containing a flexible electric heating load to be analyzed; the method specifically comprises the following steps:
acquiring day-ahead output data of renewable energy sources and loads in the comprehensive energy system; obtaining model parameters of a carbon capture power plant and a garbage power plant; the obtained data is 24 points of data or 96 points of data;
s2, fitting historical wind and light load data by adopting a Gaussian autoregressive model, and generating an uncertainty scene in the comprehensive energy system;
in specific implementation, a Gaussian autoregressive model is adopted to fit historical wind and light load data, and the method specifically comprises the following steps:
gaussian linear time series models are commonly used to fit historical time series data, while having easily understandable progressive and transitional properties; fitting the wind power by adopting a Gaussian autoregressive model:
with each hour as a time step, the corresponding normalized level is expressed as:
wherein X (k) is a standardized wind energy; ε (k) is random noise that follows a normal distribution;
and &>
Is an autoregressive model parameter and is obtained by a Yule-Walker equation, and is/are>
c i ＝E[X(k)E(k-1)]And
wherein c is 1 And c 2 Is a Yule-Walker equation coefficient, device for selecting or keeping>
σ is the standard deviation of random gaussian increments in the autoregressive time series; c. C 0 Is Yule-Walker equation coefficient;
in order to better fit historical data, fitting wind energy historical time series data by using a univariate second-order autoregressive model, wherein the model can reproduce asymptotic distribution of power output, and introduces daily variation into an AR (2) model to embody daily variation and volatility of the power output; fitting the wind energy historical time sequence data by using a univariate second-order autoregressive model:
X(k)′＝X(k)+μ(k)
wherein X (k)' is the normalized wind energy after the introduction of the daily change; μ (k) is an additional term representing random noise that follows a normal distribution; from the perspective of asymptotic distribution and short-term fluctuation, the AR (2) model is suitable for outputting wind energy sum (including photovoltaic, load and the like) at a certain scale;
the normalized wind power level is converted into non-gaussian wind energy output:
in the formula P w (k) To normalize the wind energy; w () is an S deformation function; mod is the remainder operator; n is a radical of T Is a scheduling period;
establishing a wind power plant output power prediction error estimation model, and estimating by adopting an autoregressive moving average model:
in the formula
Is the ratio of the prediction error to the prediction power; alpha is alpha i And beta j Are all model parameters; />
Obeying a mean of 0 and a variance of ξ 2 White gaussian distribution of (3); p and q are model autoregressive orders;
obtaining alpha in the formula by using a least square method through statistics of historical prediction errors i 、β j And xi 2 (ii) a Under each time interval, the prediction error percentage of the wind power can be obtained by recursion in the above formula, and the output power of the wind power is
At the same time, a photovoltaic output P sn (k) Load P Dn (k) The sample also has daily variation and volatility; fitting the model (the modeling and fitting are carried out by the same steps as the modeling of the wind power model) to obtain the output power of the model;
generating an uncertainty scene in the integrated energy system, specifically comprising the steps of:
using quantile regression in scene reduction, and considering the influence of uncertainty on power system scheduling under different quantiles;
setting co-generation combination scenario m groups P = (P) 1 ,p 2 ,...,p m ) T Wherein p is i ＝(P wi ,P si ,P Di ) T ，P wi Wind power output, P, for the ith group of scenes si Photovoltaic output, P, for the ith group of scenes Di (ii) load requirements for the ith group of scenes; design matrix in regression model
Wherein X p ＝(x 1 ,x 2 ,...,x p ) T ∈R p Intercept y = (y) for p-dimensional interpretation vector 0 ,y 0 ,...,y 0 ) T Regression parameter β = (β) 1 ,β 2 ,...,β p ) T ∈R p Then the vector form regression equation is P = y + X T β, ith group of sample regression equation being->
Obtaining wind power output power P in different time periods under different scenes according to the fitting model by adopting historical data samples wn,t (k) Photovoltaic output power P sn,t (k) Load power P Dn,t (k) (ii) a And setting different fractional digit points at the same time;
if the number of the set quantiles is greater than or equal to 4, distributing probability weight for the corresponding quantiles by adopting the following formula:
in the formula of alpha 1 Is the first quantile weight, r 1 Is the first quantile, r 2 Is the second quantile, r 3 Is the third decimal place, α 2 Is a second fractional weight, α e Is the weight of the e-th quantile, r e+1 Is the e +1 quantile, e =3, \ 8230;, R-2, alpha R Is as followsR quantile weight; the probability distribution function of the continuous random variable H is F (H) = P (H ≦ H), and the corresponding quantile is represented as H r ＝F -1 (r)＝inf{h|F(h)≥r}；
According to different set quantiles, the following optimization problems in quantile regression are solved by using a least square method:
where beta is the regression parameter, p i Wind, photovoltaic and load output samples, y, in the ith set of scenes i Is intercept, X i Designing the matrix, p, for the i-th group r () Is a check function and
u is a calculation formula
r is the quantile;
changing different r values, respectively solving the models, thereby constructing a reduced scene model and obtaining the output value P under different quantiles r ＝{(P wn r1 ,P sn r1 ,P Dn r1 ),...,(P wn rr ,P sn rr ,P Dn rr )}；
The generated wind power output power under different scenes is reduced by using a quantile regression theory to obtain the wind power output power at different quantiles in different time periods
Photovoltaic output power>
And the load power->
Is composed of
/>
And
s3, constructing a garbage power plant day-ahead optimization model and a flexible electric heating load demand response model according to the data information obtained in the step S2;
the method comprises the following steps of constructing a day-ahead optimization model of the garbage power plant, and specifically comprising the following steps:
in an urban garbage power plant, after urban garbage is recycled, the garbage is classified and then is incinerated in a garbage classification mode to generate electricity, and flue gas generated after the garbage is used for generating electricity is recycled to generate electricity. In an urban garbage power plant, a flue gas treatment system is a central core part; in a flue gas treatment system of an urban garbage power plant, loss exists when flue gas is treated; this part loss P α,t Is denoted as P α,t ＝ω α (α 1,t +α 3,t ) Wherein ω is α Is the unit processing energy consumption coefficient, alpha, of the flue gas processing system 1,t The part of the flue gas volume subjected to flue gas treatment at time t and provided by the flue gas generated by WEPP operation, alpha 3,t The amount of the flue gas provided by the gas storage device when the flue gas treatment is carried out at the moment t;
in the operation of municipal refuse power plants, the following operating constraints must be met:
5％·V WI,max ≤V WI,t ≤95％·V WI,max
P wα,t +P pvα,t +P cgα,t +P WIα,t ＝P α,t
in the formula W WI The sum of the output power of the urban garbage power plant in one day; p WI,t The output value of the urban garbage power plant at the time t in one day is obtained; w WI,max The output limit of the urban garbage power plant in one day is set; p WI,min The minimum value of the output of the urban garbage power plant at different times in one day; p WI,max The maximum output value of the urban garbage power plant at different times in a day; r WI,down Is a down-hill climbing speed constraint; r WI,up Is the uphill speed constraint; v WI,t The gas storage amount of the flue gas storage tank at the moment t; v WI,max The maximum capacity of the gas storage device; p wα,t The energy consumption for flue gas treatment provided by the wind power plant at the time t; p is pvα,t The energy consumption for flue gas treatment provided by the photovoltaic electric field in the period t; p is cgα,t The energy consumption for treating the flue gas provided by the carbon capture power plant at the t period; p WIα,t The energy consumption for flue gas treatment provided by the municipal waste power plant at the time t is reduced;
the method comprises the following steps of constructing a flexible electric heating load demand response model:
the load of the power grid is divided into a rigid load and a flexible load, wherein the rigid load refers to an uninterruptible load, and the flexible load refers to a response load which can be regulated and controlled by an excitation means; the controllability of the load side on the space is realized by adjusting the flexible load, so that the peak regulation effect of the load side is achieved; establishing a mathematical model of the flexible electric load and the flexible heat load according to the characteristics of the flexible load;
the following equation is adopted as a flexible electric load model:
in the formula F EDR Is a flexible electrical load; c EDR Compensating a cost coefficient for a flexible load unit participating in scheduling; delta P DR,t The flexible electrical load change quantity participating in scheduling in the t time period is represented as positive, and the electrical load is reduced; Δ t is the change time;
the model utilizes the time delay characteristic of heat supply of a heat supply network and the comfortable fuzziness of a heat user to the temperature to increase the flexible regulation capacity of the heat load; the following equation was used as the flexible thermal load model:
in the formula H L,t Is a flexible thermal load; s is the heat supply area; w is the heat dissipation coefficient of the temperature difference outside the building, 1.03 multiplied by 10 5 J/m 2 .℃；
Is the indoor temperature at time t; />
Is the outdoor temperature at time t; c is hot melt of unit heat supply area and takes the value of 1.03 multiplied by 10 5 J/m 2 .℃；
When the heat source transmits heat energy through the heat supply network to cause the indoor temperature change of a user, the comfort degree of the user to the temperature is in a certain range, and the following formula is adopted as the comfort degree constraint:
in the formula T 0 Initially setting a temperature for a user; sigma is a temperature adjustable quantity;
compensating for the user if user comfort is affected, so the following equation is used as the flexible thermal load scheduling cost F HDR ：
Wherein gamma is a subsidy cost coefficient for adjusting the indoor temperature of unit area;
thus, the load-side scheduling cost F DR Is F DR ＝F EDR +F HDR ；
In order not to influence the satisfaction degree of a user, the loads which can be flexibly adjusted in each time interval are limited, so that flexible electric heating load response threshold value constraint is introduced; the following equation is adopted as the flexible electric heating load response threshold value constraint:
in the formula K p Responding to a threshold for a flexible electrothermal load; Δ H DR,t Adjusting the thermal load for a period t;
s4, constructing a day-ahead random optimization model of the comprehensive energy system according to the model constructed in the step S3; the method specifically comprises the following steps:
establishing a comprehensive energy system day-ahead interval optimization model aiming at minimizing the operation cost, the fuel cost and the load side scheduling cost of different devices; the model not only considers the influence of adding a garbage power plant in the IES, but also considers the influence of the uncertainty of renewable energy sources and flexible electric heating load; the following formula is adopted as an objective function of a day-ahead random optimization model of the comprehensive energy system:
wherein s is the s-th scene; s is the total scene number reduced by quantile regression; rho s Probability of the s-th scene; f cg,s Fuel cost of carbon capture plant for the s-th scenario, and
a cg,s 、b cg,s and c cg,s Carbon capture plant Fuel cost coefficient, P, for the s-th scenario cg,s,t The power output value of the carbon capture power plant at the moment t of the s-th scene is obtained; f cc,s Cost of carbon emission for the s-th scenario, and F cc,s ＝λ cc,s,t (C w -C T )，λ cc,s,t Trading prices for carbon emissions, C w Based on the carbon emission and->
δ h Carbon emission allocation coefficient, P, for unit electricity quantity of carbon capture power plant cg,t A carbon capture plant output value at time t, C T Is the actual carbon footprint and->
B cg Actual discharge allocation rate coefficient of unit electric quantity of the carbon capture power plant; f WI,s Operating cost of refuse power plant for s-th scene, and F WI,s ＝P WI,s,t λ cc,s,t (e α -δ h )，P WI,s,t For the refuse power plant electric power at time t of the s-th scenario, lambda cc,s,t For the carbon transaction price at time t in the s-th scenario, e α The unit output smoke discharge intensity of the garbage power plant, delta h A carbon emission baseline per unit of electricity; f gas,s Natural gas fuel cost for the s-th scenario, and F gas,s ＝δ gas (V CHP,t +V GB,t -V P2G,t )，δ gas Is the natural gas market unit natural gas price, V CHP,t Natural gas consumed by CHP units, V GB,t Natural gas consumed by gas-fired boilers, V P2G,t Natural gas produced for P2G; f grid,s The power purchase cost for the upper-level power grid for the IES of the s-th scene is increased
δ grid,t Power purchase cost coefficient, P, for IES to upper-level power grid grid,t Purchasing electric quantity for the upper-level power grid at the time t; f DR,s Scheduling cost for the load side of the s-th scene;
the following formula is adopted as a constraint condition:
and power balance constraint:
P cg,t +P WI,t +P CHP,t +P w,t +P PV,t +P dis,t +P grid,t ＝P l,t +P cha,t
in the formula P cg,t The output of the carbon capture power plant is obtained at the moment t; p WI,t The output of the refuse power plant is at the moment t; p CHP,t The electric power of the CHP unit at the t moment is the t moment; p w,t Wind power output at the moment t; p PV,t Photovoltaic output at time t; p dis,t Is the discharge power of the energy storage system; p grid,t Purchasing power for a superior power grid; p cha,t Charging power for the energy storage system; p l,t Is a load value at time t, and P l,t ＝P in,t +ΔP DR,t ，P in,t As value of stiffness load, Δ P DR,t An electrical load that is a flexible change at time t; (ii) a
Thermal power balance constraint:
H CHP,t +H GB,t +H TSD,t ＝H l,t +H TSC,t
in the formula H CHP,t The heat power of CHP at the t moment; h GB,t Thermal power at time t GB; h TSD,t The heat release power of the heat storage device at the moment t; h TSC,t The charging power of the heat storage device at the moment t; h l,t Is the thermal load demand at time t, and H L,t ＝H in,t +ΔH DR,t ，H in,t For a rigid thermal load at time t,. DELTA.H DR,t Thermal load for compliance change at time t;
output restraint of the CHP unit and the gas boiler:
in the formula P CHP,min The minimum value of the electric power of the CHP unit; p CHP,t The CHP unit electric power; p CHP,max The maximum value of the electric power of the CHP unit; h CHP,min The minimum value of the heat power of the CHP unit; h CHP,t The heat power of the CHP unit; h CHP,max The maximum value of the heat power of the CHP unit; RP CHP,down The power is the electric power for climbing the slope under the CHP unit; RP CHP,up The electric power for climbing the CHP unit; RH (relative humidity) CHP,down The heat power of the downward climbing of the CHP unit; RH (relative humidity) CHP,up The heat power for climbing the slope of the CHP unit; h GB,min Is the GB minimum output value; h GB,t Is GB output value; h GB,max The maximum value of GB output; RH (relative humidity) GB,down The heat power of climbing under GB; RH (relative humidity) GB,up The GB uphill thermal power;
CCPP operating constraints:
P cg,min ≤P cg,t ≤P cg,max
R cg,down ≤P cg,t -P cg,t-1 ≤R cg,up
P A ≤P GC，t +P windC，t +P PVC，t +P WIC，t ≤P C，max
P C，max ＝ω C e g P cg，t
in the formula P cg,min The minimum value of CCPP force; p is cg,t Is CCPP output value; p cg,max The maximum value of CCPP output; r is cg,down Is CCPP down-hill climbing rate; r cg,up The rate of ascent for CCPP; p is A Energy consumption for IES-P2G system; p is GC,t Carbon capture energy consumption and flue gas treatment energy consumption provided for CCPP; p is windC,t Carbon capture energy consumption is provided for wind power at t time period; p is PVC,t Carbon capture energy consumption provided for photovoltaic at a time t; p is WIC,t Carbon capture energy consumption provided for WEPP at time t; p is C,max The upper limit of the energy consumption for the operation of the carbon capture system in the period t; omega C The running energy consumption of CCPP processing unit CO2 is obtained; e.g. of the type g CO2 generated for CCPP unit output; lambda [ alpha ] cc,t The flue gas split ratio of the carbon capture system in the t period; q CC,t The amount of CO2 trapped by the carbon trapping power plant at the moment t;
and (3) operation constraint of the energy storage system and the heat storage system:
U cha,t P cha,min ≤P cha,t ≤U cha,t P cha,max
U dis,t P dis,min ≤P dis,t ≤U dis,t P dis,max
U cha，t +U dis，t ≤1
SOC min ≤SOC t ≤SOC max
U hcha,t H TSC,min ≤H TSC,t ≤U hcha,t H TSC,max
U hdis,t H TSD,min ≤H TSD,t ≤U hdis,t H TSD,max
U hcha,t +U hdis,t ≤1
in the formula of U cha,t An energy storage charging state variable; p cha,min Charging the minimum value for the stored energy; p is cha,max Maximum value for energy storage charging; u shape dis,t Is an energy storage discharge state variable; p is dis,min Is the minimum value of energy storage and discharge; p dis,max Is the maximum value of energy storage and discharge; p cha,t Charging value for stored energy; p is dis,t Is the energy storage discharge value; SOC (system on chip) t The SOC state for energy storage at the time t; eta ch Charging efficiency for energy storage; eta dis The energy storage discharge efficiency; Δ T is a scheduling interval; SOC (system on chip) min Is the SOC minimum value of the stored energy; SOC max The maximum value of the SOC of the stored energy is; u shape hcha,t Charging state variables for the heat storage system; h TSC,min The minimum value of heat charge of the heat storage system is obtained; h TSC,t Charging value for the heat storage system; h TSC,max The maximum value of heat charging of the heat storage system is obtained; u shape hdis,t Is a heat release state variable of the heat storage system; h TSD,min A minimum heat release value for the heat storage system; h TSD,max Is the maximum heat release of the heat storage system; h TSD,t Discharging heat value for the heat storage system;
and S5, solving the day-ahead random optimization model of the comprehensive energy system constructed in the step S4, so as to complete the day-ahead random optimization process of the comprehensive energy system with the flexible electric heating load.
FIG. 2 is a schematic diagram of system functional modules of the system of the present invention: the system for realizing the day-ahead random optimization method of the comprehensive energy system containing the flexible electric heating load comprises a data acquisition module, a fitting and scene generation module, a model construction module, an optimization model construction module and an optimization solution module; the data acquisition module, the fitting and scene generation module, the model construction module, the optimization model construction module and the optimization solution module are sequentially connected in series; the data acquisition module is used for acquiring system data information of the comprehensive energy system containing the flexible electric heating load to be analyzed and uploading the data to the fitting and scene generation module; the fitting and scene generating module is used for fitting historical wind and solar load data by adopting a Gaussian autoregressive model according to the acquired data, generating an uncertainty scene in the comprehensive energy system and uploading the data to the model building module; the model construction module is used for constructing a garbage power plant day-ahead optimization model and a flexible electric heating load demand response model according to the acquired data, and uploading the data to the optimization model construction module; the optimization model construction module is used for constructing a day-ahead random optimization model of the comprehensive energy system according to the acquired data and uploading the data to the optimization solution module; and the optimization solving module is used for solving the constructed day-ahead random optimization model of the comprehensive energy system according to the acquired data, so that the day-ahead random optimization process of the comprehensive energy system containing the flexible electric heating load is completed.
Claims (8)
1. A day-ahead random optimization method for a comprehensive energy system containing flexible electric heating loads comprises the following steps:
s1, acquiring system data information of a comprehensive energy system containing a flexible electric heating load to be analyzed;
s2, fitting historical wind and light load data by adopting a Gaussian autoregressive model, and generating an uncertainty scene in the comprehensive energy system;
s3, constructing a day-ahead optimization model and a flexible electric heating load demand response model of the garbage power plant according to the data information obtained in the step S2;
s4, constructing a day-ahead random optimization model of the comprehensive energy system according to the model constructed in the step S3;
and S5, solving the day-ahead random optimization model of the comprehensive energy system constructed in the step S4, so as to complete the day-ahead random optimization process of the comprehensive energy system with the flexible electric heating load.
2. The method for randomly optimizing the comprehensive energy system with the flexible electric heating load in the day ahead according to claim 1, wherein the step S1 of obtaining the system data information of the comprehensive energy system with the flexible electric heating load to be analyzed specifically comprises the following steps:
acquiring day-ahead output data of renewable energy sources and loads in the comprehensive energy system; obtaining model parameters of a carbon capture power plant and a garbage power plant; the obtained data is 24-point data or 96-point data.
3. The method for randomly optimizing the integrated energy system with the flexible electric heating load in the day ahead according to claim 2, wherein the step S2 of fitting the historical wind and solar load data by adopting the Gaussian autoregressive model specifically comprises the following steps:
fitting the wind power by adopting a Gaussian autoregressive model:
with each hour as a time step, the corresponding normalized level is expressed as:
wherein X (k) is a standardized wind energy; ε (k) is random noise that follows a normal distribution;
and &>
Is an autoregressive model parameter and is obtained by a Yule-Walker equation, and is/are>
c i ＝E[X(k)E(k-1)]And
wherein c is 1 And c 2 Is a Yule-Walker equation coefficient, device for combining or screening>
Sigma is the standard deviation of random Gaussian increment in the autoregressive time sequence; c. C 0 Is Yule-Walker equation coefficient;
fitting the wind energy historical time sequence data by using a univariate second-order autoregressive model:
X(k)′＝X(k)+μ(k)
wherein X (k)' is the normalized wind energy after the introduction of the daily change; μ (k) is an additional term representing random noise that follows a normal distribution;
the normalized wind power level is converted into non-gaussian wind energy output:
P w (k)＝W[X(k)′+μ(kmodN T )]
in the formula P w (k) To normalize the wind energy; w () is an S-morph function; mod is the remainder operator; n is a radical of hydrogen T Is a scheduling period;
establishing a wind power plant output power prediction error estimation model, and estimating by adopting an autoregressive moving average model:
in the formula
Is the ratio of the prediction error to the prediction power; alpha is alpha i And beta j Are all model parameters; />
Obeying a mean of 0 and a variance of ξ 2 White gaussian distribution of (3); p and q are model autoregressive orders;
obtaining alpha in the formula by using a least square method through statistics of historical prediction errors i 、β j And xi 2 (ii) a Under each time interval, the prediction error percentage of the wind power can be obtained by recursion in the above formula, and the output power of the wind power is
4. The method for randomly optimizing the integrated energy system with the flexible electric heating load in the day ahead according to claim 3, wherein the step S2 of generating the uncertainty scene in the integrated energy system specifically comprises the following steps:
using quantile regression in scene reduction, and considering the influence of uncertainty on power system scheduling under different quantiles;
setting co-generation combination scenario m groups P = (P) 1 ,p 2 ,...,p m ) T Wherein p is i ＝(P wi ,P si ,P Di ) T ，P wi Wind power output, P, for the ith group of scenes si Photovoltaic output, P, for the ith group of scenes Di (ii) load requirements for the ith group of scenes; design matrix in regression model
Wherein X p ＝(x 1 ,x 2 ,...,x p ) T ∈R p Intercept y = (y) for p-dimensional interpretation vector 0 ,y 0 ,...,y 0 ) T Regression parameter β = (β) 1 ,β 2 ,...,β p ) T ∈R p Then the vector form regression equation is P = y + X T Beta, the i-th set of sample regression equations is p i ＝y i +X i T β；
Obtaining wind power output power P in different time periods under different scenes according to the fitting model by adopting historical data samples wn,t (k) Photovoltaic output power P sn,t (k) Load power P Dn,t (k) (ii) a And setting different fractional digit points at the same time;
if the number of the set quantiles is greater than or equal to 4, distributing probability weight for the corresponding quantiles by adopting the following formula:
in the formula of alpha 1 Is the first quantile weight, r 1 Is the first quantile, r 2 Is the second quantile, r 3 Is the third decimal place, α 2 Is a second fractional weight, α e Is the weight of the e-th quantile, r e+1 Is the e +1 quantile, e =3, \ 8230;, R-2, alpha R Is the Rth quantile weight; the probability distribution function of the continuous random variable H is F (H) = P (H ≦ H), and the corresponding quantile is represented as H r ＝F- 1 (r)＝inf{h|F(h)≥r}；
According to different set quantiles, solving the following optimization problem in quantile regression by using a least square method:
wherein beta is a regression parameter, p i For wind power, light in the ith group of scenesSamples of the volt and load output, y i Is intercept, X i Designing the matrix, p, for the i-th group r () Is a check function and
u is a calculation formula>
r is the quantile;
changing different r values, and respectively solving the models to construct a reduced scene model, so that output values under different quantiles can be obtained;
the generated wind power output power under different scenes is reduced by using a quantile regression theory to obtain the wind power output power at different quantiles in different time periods
Photovoltaic output power->
And the load power->
Is composed of
And
5. the method for randomly optimizing the day-ahead comprehensive energy system with the flexible electric heating load according to claim 4, wherein the step S3 of constructing the day-ahead optimization model of the garbage power plant specifically comprises the following steps:
in a cityIn a flue gas treatment system of a municipal waste power plant, loss exists when flue gas is treated; this part loss P α,t Is denoted as P α,t ＝ω α (α 1,t +α 3,t ) Wherein ω is α Is the unit processing energy consumption coefficient, alpha, of the flue gas processing system 1,t The part of the flue gas volume subjected to flue gas treatment at time t and provided by the flue gas generated by WEPP operation, alpha 3,t The amount of the flue gas provided by the gas storage device when the flue gas treatment is carried out at the moment t;
in the operation of municipal refuse power plants, the following operating constraints must be met:
0≤W WI ≤W WI,max
P WI，min ≤P WI，t ≤P WI，max
R WI,down ≤P WI,t -P WI,t-1 ≤R WI,up
5％·V WI,max ≤V WI,t ≤95％·V WI,max
P wα,t +P pvα,t +P cgα,t +P WIα,t ＝P α,t
in the formula W WI The sum of the output of the urban garbage power plant in one day; p is WI,t The output value of the urban garbage power plant at the time t in one day is obtained; w WI,max The output limit of the urban garbage power plant in one day is set; p WI,min The minimum value of the output of the urban garbage power plant at different times in one day; p WI,max The maximum output value of the urban garbage power plant at different times in a day; r WI,down Is a down-hill climbing speed constraint; r is WI,up Is the uphill speed constraint; v WI,t The gas storage amount of the flue gas storage tank at the moment t; v WI,max The maximum capacity of the gas storage device; p is wα,t The energy consumption for flue gas treatment provided by the wind power plant at the time t; p is pvα,t The energy consumption for flue gas treatment provided by the photovoltaic electric field in the period t; p cgα,t Is carbon in t periodCollecting the flue gas treatment energy consumption provided by a power plant; p WIα,t The energy consumption is handled to the flue gas that provides for the municipal refuse power plant of t period.
6. The method for randomly optimizing the integrated energy system with the flexible electric heating load in the day ahead according to claim 5, wherein the step S3 of constructing the flexible electric heating load demand response model specifically comprises the following steps:
the following formula is adopted as a flexible electric load model:
in the formula F EDR Is a flexible electrical load; c EDR Compensating a cost coefficient for a flexible load unit participating in scheduling; delta P DR,t The flexible electrical load change quantity participating in scheduling in the t time period is represented as positive, and the electrical load is reduced; Δ t is the change time;
the following equation was used as the flexible thermal load model:
in the formula H L,t Is a flexible thermal load; s is the heat supply area; w is the external temperature difference heat dissipation coefficient of the building; t is t in Is the indoor temperature at time t; t is t out The outdoor temperature at time t; c is hot melting of unit heat supply area;
the following equation is used as the comfort constraint:
|T t in -T 0 |≤σ
in the formula T 0 Initially setting a temperature for a user; sigma is a temperature adjustable quantity;
if the comfort of the user is influenced, the user is compensated, so that the following formula is adopted as the flexible heat load scheduling cost F HDR ：
Wherein gamma is a subsidy cost coefficient for adjusting the indoor temperature of unit area;
thus, the load-side scheduling cost F DR Is F DR ＝F EDR +F HDR ；
The following equation is adopted as the flexible electric heating load response threshold value constraint:
in the formula K p Responding to a threshold for a flexible electrothermal load; Δ H DR,t The amount of thermal load adjustment for the t period.
7. The method for randomly optimizing the integrated energy system with the flexible electric heating load in the day ahead according to claim 6, wherein the step S4 of constructing the integrated energy system randomly optimizing the day ahead specifically comprises the following steps:
the following formula is adopted as an objective function of a day-ahead random optimization model of the comprehensive energy system:
wherein s is the s-th scene; s is the total scene number reduced by quantile regression; ρ is a unit of a gradient s Probability of the s-th scene; f cg,s Fuel cost of carbon capture plant for the s-th scenario, and
a cg,s 、b cg,s and c cg,s Carbon capture plant Fuel cost coefficient, P, for the s-th scenario cg,s,t The power output value of the carbon capture power plant at the moment t of the s-th scene is obtained; f cc,s Cost of carbon emission for the s-th scenario, and F cc,s ＝λ cc,s,t (C w -C T )，λ cc,s,t Trading prices for carbon emissions, C w Based on the carbon emission and->
δ h Carbon emission allocation factor, P, for a unit of electricity of a carbon capture power plant cg,t A carbon capture plant output value at time t, C T Is the actual carbon footprint and->
B cg The actual emission allocation amount coefficient of the unit electric quantity of the carbon capture power plant; f WI,s Operating cost of refuse power plant for s-th scene, and F WI,s ＝P WI,s,t λ cc,s,t (e α -δ h )，P WI,s,t For the refuse power plant electric power at time t of the s-th scenario, lambda cc,s,t For the carbon transaction price at time t in the s-th scenario, e α The unit output smoke discharge intensity of the garbage power plant, delta h A carbon emission baseline per unit of electricity; f gas,s Natural gas fuel cost for the s-th scenario, and F gas,s ＝δ gas (V CHP,t +V GB,t -V P2G,t )，δ gas Is the natural gas market unit natural gas price, V CHP,t Natural gas consumed by CHP units, V GB,t Natural gas consumed by gas-fired boilers, V P2G,t Natural gas produced for P2G; f grid,s The power purchase cost for the upper-level power grid for the IES of the s-th scene is increased
δ grid,t Cost coefficient, P, for power purchase of IES to upper-level power grid grid,t Purchasing electric quantity for the upper-level power grid at the time t; f DR,s Scheduling cost for the load side of the s-th scene;
the following equation is used as a constraint condition:
and (3) power balance constraint:
P cg,t +P WI,t +P CHP,t +P w,t +P PV,t +P dis,t +P grid,t ＝P l,t +P cha,t
in the formula P cg,t The output of the carbon capture power plant is obtained at the moment t; p WI,t The output of the refuse power plant is at the moment t; p CHP,t The electric power of the CHP unit at the t moment is the t moment; p w,t Wind power output at the moment t; p PV,t Photovoltaic output at time t; p dis,t Is the discharge power of the energy storage system; p grid,t Purchasing power for a superior power grid; p cha,t Charging power for the energy storage system; p l,t Is a load value at time t, and P l,t ＝P in,t +ΔP DR,t ，P in,t As value of stiffness load, Δ P DR,t An electrical load that is a flexible change at time t; (ii) a
And thermal power balance constraint:
H CHP,t +H GB,t +H TSD,t ＝H l,t +H TSC,t
in the formula H CHP,t The heat power of CHP at the t moment; h GB,t Thermal power at time t GB; h TSD,t The heat release power of the heat storage device at the moment t; h TSC,t The charging power of the heat storage device at the moment t; h l,t Is the thermal load demand at time t, and H L,t ＝H in,t +ΔH DR,t ，H in,t For a rigid thermal load at time t,. DELTA.H DR,t Thermal load for compliance change at time t;
output restraint of the CHP unit and the gas boiler:
in the formula P CHP,min The minimum value of the electric power of the CHP unit; p CHP,t Is CHP unit electric power; p CHP,max The maximum value of the electric power of the CHP unit;H CHP,min the minimum value of the heat power of the CHP unit; h CHP,t The heat power of the CHP unit; h CHP,max The maximum value of the heat power of the CHP unit; RP CHP,down The power is the electric power for climbing the slope under the CHP unit; RP CHP,up The electric power for climbing the CHP unit; RH (relative humidity) CHP,down The heat power of the downward climbing of the CHP unit; RH (relative humidity) CHP,up The heat power for climbing the slope of the CHP unit; h GB,min Is the GB minimum output value; h GB,t Is GB output value; h GB,max The GB maximum value of the output; RH (relative humidity) GB,down The heat power of climbing under GB; RH (relative humidity) GB,up The GB uphill thermal power;
CCPP operating constraints:
P cg,min ≤P cg,t ≤P cg,max
R cg,down ≤P cg,t -P cg,t-1 ≤R cg,up
P A ≤P GCt +P windCt +P PVCt +P WICt ≤P Cmax
P C,max ＝ω C e g P cg,t
in the formula P cg,min The minimum value of CCPP output; p cg,t Is CCPP output value; p cg,max The maximum value of CCPP output; r cg,down Is CCPP down-hill climbing rate; r cg,up Is the CCPP uphill rate; p A Energy consumption for IES-P2G system; p GC,t Carbon capture energy consumption and flue gas treatment energy consumption provided for CCPP; p windC,t Carbon capture energy consumption is provided for wind power at t time period; p is PVC,t Carbon capture energy consumption provided for photovoltaic at a time t; p WIC,t Carbon capture energy consumption provided for WEPP at time t; p C,max The upper limit of the energy consumption for operating the carbon capture system in the period t; omega C The running energy consumption of CCPP processing unit CO2 is obtained; e.g. of a cylinder g CO2 generated by CCPP unit output; lambda [ alpha ] cc,t The flue gas split ratio of the carbon capture system in the t period; q CC,t The amount of CO2 trapped by the carbon trapping power plant at the moment t;
and (3) operation constraint of the energy storage system and the heat storage system:
U cha,t P cha,min ≤P cha,t ≤U cha,t P cha,max
U dis,t P dis,min ≤P dis,t ≤U dis,t P dis,max
U cha,t +U dis,t ≤1
SOC min ≤SOC t ≤SOC max
U hcha,t H TSC,min ≤H TSC,t ≤U hcha,t H TSC,max
U hdis,t H TSD,min ≤H TSD,t ≤U hdis,t H TSD,max
U hcha,t +U hdis,t ≤1
in the formula of U cha,t An energy storage charging state variable; p cha,min Charging the minimum value for the stored energy; p cha,max Maximum value for energy storage charging; u shape dis，t Is an energy storage discharge state variable; p dis，min Is the minimum value of energy storage and discharge; p dis，max Is the maximum value of energy storage and discharge; p cha,t A charging value for energy storage; p dis,t Is the energy storage discharge value; SOC t The SOC state of energy storage at the time t; eta ch Charging efficiency for energy storage; eta dis The energy storage discharge efficiency is obtained; Δ T is a scheduling interval; SOC min Is the SOC minimum value of the stored energy; SOC max The maximum value of the SOC of the stored energy is; u shape hcha,t Charging state variables for the heat storage system; h TSC,min Charging the heat storage system to the minimum value; h TSC,t Charging value for the heat storage system; h TSC,max The maximum value of heat charging of the heat storage system is obtained; u shape hdis,t A heat release state variable for the heat storage system; h TSD,min A minimum heat release value for the heat storage system; h TSD,max Is the maximum heat release of the heat storage system; h TSD,t The heat release value of the heat storage system.
8. A system for realizing the day-ahead random optimization method of the comprehensive energy system containing the flexible electric heating load according to any one of claims 1 to 7, which is characterized by comprising a data acquisition module, a fitting and scene generation module, a model construction module, an optimization model construction module and an optimization solution module; the data acquisition module, the fitting and scene generation module, the model construction module, the optimization model construction module and the optimization solution module are sequentially connected in series; the data acquisition module is used for acquiring system data information of the comprehensive energy system containing the flexible electric heating load to be analyzed and uploading the data to the fitting and scene generation module; the fitting and scene generating module is used for fitting historical wind and solar load data by adopting a Gaussian autoregressive model according to the acquired data, generating an uncertainty scene in the comprehensive energy system and uploading the data to the model building module; the model construction module is used for constructing a garbage power plant day-ahead optimization model and a flexible electric heating load demand response model according to the acquired data, and uploading the data to the optimization model construction module; the optimization model construction module is used for constructing a day-ahead random optimization model of the comprehensive energy system according to the acquired data and uploading the data to the optimization solution module; and the optimization solving module is used for solving the constructed day-ahead random optimization model of the comprehensive energy system according to the acquired data, so that the day-ahead random optimization process of the comprehensive energy system containing the flexible electric heating load is completed.
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