CN114757414A - Comprehensive energy system robust optimization planning method considering supply and demand uncertainty - Google Patents

Comprehensive energy system robust optimization planning method considering supply and demand uncertainty Download PDF

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CN114757414A
CN114757414A CN202210381143.6A CN202210381143A CN114757414A CN 114757414 A CN114757414 A CN 114757414A CN 202210381143 A CN202210381143 A CN 202210381143A CN 114757414 A CN114757414 A CN 114757414A
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沈昊天
张华良
徐玉杰
陈海生
朱轶林
张志来
李文凯
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Zhongke Nanjing Future Energy System Research Institute
Institute of Engineering Thermophysics of CAS
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Abstract

The invention discloses a comprehensive energy system robust optimization planning method considering supply and demand uncertainty, which comprises the following steps: analyzing the wind-solar output samples and the cold-heat-electricity load samples on the supply and demand sides by adopting a BP neural network prediction model to respectively obtain uncertain sets of prediction errors of the wind-solar output samples and the cold-heat-electricity load samples; establishing a double-layer optimization model of the comprehensive energy system of the intelligent park, wherein the lower-layer model takes economic evaluation indexes and environmental protection evaluation indexes as weighted system operation costs, the upper-layer model takes the cost of the year-over full life cycle as a target, and the equipment capacity of the comprehensive energy system as a decision variable; based on the uncertain set, carrying out robustness modification on the double-layer optimization model, and establishing a double-layer robustness optimization scheduling model of the comprehensive energy system; and solving the double-layer robust optimization scheduling model of the comprehensive energy system to obtain the optimal capacity configuration of the comprehensive energy system. The planning method can realize the economical and low-carbon operation of the system and ensure the reliability and robustness of various energy supplies of the system.

Description

Comprehensive energy system robust optimization planning method considering supply and demand uncertainty
Technical Field
The invention belongs to the field of comprehensive energy systems, and particularly relates to a comprehensive energy system planning method considering multi-target and considering supply and demand bilateral uncertainty and robust optimization.
Background
The comprehensive energy system can realize complementation, mutual assistance and coordination optimization of multiple energy sources, and the construction of the comprehensive energy system is beneficial to promoting the coordination development of energy sources in China and realizing the double-carbon target. How to effectively utilize the planning method to realize the optimal capacity allocation of the comprehensive energy system is a key point and a hot spot of future research. However, due to the uncertainty of the renewable resource generated output, it is necessary to predict the uncertainty of the supply-side renewable resource generated output. In the uncertainty prediction research of the demand side load, most uncertainty prediction of the electrical load is considered, uncertainty interval prediction of the thermal load and the cold load is rarely considered, and strong coupling relation between the cold-heat-electrical load of the user side is rarely considered, so that a prediction model of various load demand intervals such as electricity-heat-cold-gas is necessary to construct. In addition, at present, the research on the comprehensive energy system at home and abroad rarely considers the relationship between the overall efficiency and the capacity configuration of the comprehensive energy system, so that the energy utilization cost of the comprehensive energy system is higher, and the economical efficiency is poorer.
Disclosure of Invention
The invention provides a comprehensive energy system robust optimization planning method considering supply and demand uncertainty, aiming at the defects that the existing comprehensive energy system capacity optimization method does not consider the uncertainty of both supply and demand sides and the problem that the stronger coupling relation between cold-heat-electric loads at a user side is not considered.
In order to achieve the purpose, the invention adopts the technical scheme that:
a comprehensive energy system robust optimization planning method considering supply and demand uncertainty comprises the following steps:
analyzing wind-solar output samples and cold-heat-electricity load samples on both sides of supply and demand by adopting a BP neural network prediction model to respectively obtain uncertain sets of prediction errors of the wind-solar output samples and the cold-heat-electricity load samples;
establishing a double-layer optimization model of the comprehensive energy system of the intelligent park, wherein the lower-layer model takes economic evaluation indexes and environmental protection evaluation indexes as weighted system operation costs, the upper-layer model takes the annual full life cycle cost converted as a target, and the equipment capacity of the comprehensive energy system as a decision variable;
thirdly, based on the uncertain set, carrying out robustness transformation on the double-layer optimization model established in the second step, and establishing a double-layer robust optimization scheduling model of the comprehensive energy system;
and step four, solving the double-layer robust optimization scheduling model of the comprehensive energy system to obtain the optimal capacity configuration of the comprehensive energy system.
In the first step, the uncertain set is one or a combination of at least two of a box-type uncertain set, an ellipsoid uncertain set and a polyhedron uncertain set.
In the second step, the economic evaluation index of the lower layer model is represented by the annual full life cycle cost converted by the comprehensive energy system of the intelligent park, and the economic evaluation index comprises initial investment cost, operation and maintenance cost, replacement cost and energy cost; the environmental evaluation index of the lower model is characterized by the carbon emission directly generated by natural gas combustion power generation in the comprehensive energy system and the indirect carbon emission generated by electric power purchased from a power grid.
In the third step, the constraint conditions in the optimized scheduling model include an electric power balance constraint, a thermal power balance constraint, a cold power balance constraint, an energy device power constraint, a storage battery balance constraint and a heat storage tank balance constraint of the comprehensive energy system.
In the third step, when robustness improvement is performed, the robust optimization algorithm adopted includes, but is not limited to, a box-type robust linear programming method, a Benders dual method, and a column and constraint generation algorithm.
In the third step, the robust optimization scheduling model includes, but is not limited to, a single-stage and a two-stage robust optimization model.
In the fourth step, the lower layer model is solved by adopting a mixed linear integer programming solver, and an optimized result is fed back to the upper layer model to be used as an optimization basis; and solving the upper layer model by adopting a genetic algorithm, and finally solving the global optimum value through iteration of an upper layer and a lower layer.
Compared with the prior art, the comprehensive energy system robust optimization capacity configuration method considering uncertainty is provided, economic low-carbon operation of the system can be realized, energy supply reliability and robustness of the system can be guaranteed, and multi-objective optimization scheduling of the comprehensive energy system of the intelligent park is realized.
The technical scheme of the invention has the following advantages:
1. according to the comprehensive energy system planning method considering uncertainty and robust optimization, wind power, photovoltaic output and cold, heat and power loads are predicted by adopting a BP neural network tool according to natural factors and actual load demand changes of a park, and robust modification is performed on the basis of obtaining an uncertain set, so that capacity configuration optimization work is performed, and the reliability and robustness of energy supply of the comprehensive energy system are improved.
2. The comprehensive energy system robust optimization capacity configuration method considering uncertainty adopts a double-layer planning solution method. Establishing a double-layer optimization model of the comprehensive energy system, wherein the upper layer model takes the minimum life cycle cost as a target function; the lower layer takes economy and environment as targets, optimizes various energy loads and equipment output, and further feeds back the energy loads and the equipment output to the upper layer model and corrects the energy loads and the equipment output, so that the cooperative optimization of electricity-heat-cold-network in the comprehensive energy system is completed.
3. The comprehensive energy system robust optimization capacity configuration method considering the uncertainty optimizes the solution design model by using a method combining the genetic algorithm and the mixed integer linear programming, so that the optimization method is simpler, the convergence rate is high, the solution efficiency is high, and the global search capability is strong.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a planning method of the present invention;
FIG. 2 is a system architecture diagram of a customer-level integrated energy system architecture of the present invention;
FIG. 3 is a wind power forecast data graph;
FIG. 4 is a graph of photovoltaic prediction data;
FIG. 5 is a graph of predicted data for electrical loads;
FIG. 6 is a graph of predicted data for cooling load;
FIG. 7 is a graph of predicted data for thermal load;
FIG. 8 is an electrical power balance diagram;
FIG. 9 is a cold power balance diagram;
FIG. 10 is a thermal power balance diagram.
Detailed Description
The invention will be described in more detail below with reference to the accompanying drawings and specific embodiments.
The invention provides a comprehensive energy system robust optimization planning method considering supply and demand uncertainty, the flow of which is shown in figure 1, and the applied comprehensive energy system is shown in figure 1.
The method comprises the following steps:
step S1, acquiring basic operation parameters of the comprehensive energy system, wherein the basic parameters comprise renewable energy sources such as photovoltaic energy, wind power and the like, predicted output of cold, heat and electricity loads and an uncertain error set, and capacity and power constraints, power grid interaction power and the like of micro-combustion engines, electric boilers, gas boilers, absorption refrigerators, electric refrigerators, storage batteries, heat storage tanks and other equipment in the system; in this embodiment, a BP neural network prediction model may be used to analyze wind-solar output samples and cooling, heating and power load samples on both sides of a supply and demand system, so as to obtain uncertainty sets of prediction errors of the wind-solar output samples and the cooling, heating and power load samples, where the uncertainty sets are one or a combination of a box-type uncertainty set, an ellipsoid uncertainty set, and a polyhedron uncertainty set, and specifically include the following contents:
step S11, analyzing the wind power output sample through a BP neural network prediction model to obtain a prediction output and an error uncertain set of the wind power output sample;
step S12, analyzing the photovoltaic output sample through a BP neural network prediction model to obtain a prediction output and error uncertain set of the photovoltaic output sample;
step S13, analyzing the electric load sample through a BP neural network prediction model to obtain a prediction load and an error uncertain set thereof;
step S14, analyzing the cold load sample through a BP neural network prediction model to obtain a prediction load and an error uncertain set thereof;
step S15, analyzing the heat load sample through a BP neural network prediction model to obtain a prediction load and an error uncertain set thereof;
s2, establishing a double-layer optimization model of the comprehensive energy system of the intelligent park, wherein the lower layer model takes economic evaluation indexes and environmental protection evaluation indexes as weighted system operation costs, the upper layer model takes the cost of the whole life cycle converted to the year as a target, and the equipment capacity of the comprehensive energy system is taken as a decision variable;
s21, wherein the lower layer model optimization operation objective function is expressed as:
Obj F＝min(ATC,CEV)
in the formula, ATC is the annual total cost (namely economic evaluation index) of the comprehensive energy system of the intelligent park; CEV is annual CO of comprehensive energy system of smart park2Discharge amount (namely environmental protection evaluation index).
The economic evaluation index is represented by the full life cycle cost converted to the year by the comprehensive energy system of the intelligent park, and comprises initial investment cost, operation and maintenance cost, replacement cost and energy cost;
further, the economic index is the annual total cost of the comprehensive energy system defined as:
ATC＝Caic+Co&m+Cre+Ce
in the formula, ATC is the annual total cost of the comprehensive energy system of the intelligent park; caicInitial investment cost for a smart park integrated energy system; co&mAnnual operation maintenance cost of the comprehensive energy system of the smart park; creReplacement cost of equipment in the comprehensive energy system of the smart park; ceThe cost of energy for system outsourcing.
The operation and maintenance cost mainly refers to the maintenance cost of the equipment in operation:
Figure BDA0003591910580000041
in the formula, Co&m,lIs the unit operation and maintenance cost of the equipment l, and n is the total number of all the equipment related to the operation and maintenance cost.
The replacement cost is determined by the life cycle of the equipment and the initial investment of the equipment, and the replacement cost of the equipment is reduced to each year:
Figure BDA0003591910580000042
Figure BDA0003591910580000043
in the formula, CrkIs the initial investment cost of equipment k, FrkIs the capital recovery factor, a _ k is the useful life of equipment k, h is the total number of all equipment associated with the replacement cost, i is the discount rate; and y is the total life cycle of the integrated energy system.
The outsourcing energy cost is the purchase cost of gas consumed by the gas turbine and the gas boiler and the purchase cost of electricity from the power grid when the internal electric quantity of the system is insufficient:
Figure BDA0003591910580000051
in the formula, CfuelIs the gas price of natural gas, Fd,tIs the natural gas consumption at time t; cgrid_sElectricity prices for electricity supply to the grid, Ed,t,sPurchasing the electric quantity in the system at the time t; d is the total number of all equipment that needs to consume the natural gas.
The environmental evaluation index is characterized by carbon emission directly generated by natural gas combustion power generation in the comprehensive energy system and indirect carbon emission generated by electric power purchased from a power grid:
Figure BDA0003591910580000052
in the formula, muc,gIs a carbon emission factor for natural gas combustion; vpguIs the amount of natural gas consumed by the gas turbine; vgbIs consumption of gas-fired boilerThe amount of natural gas of (a); mu.sc,eIs an indirect carbon emission factor generated by power grid electricity purchasing; egrid，buyPurchasing electric quantity from the power grid for the system at the time t; t is the natural gas consumption plant runtime.
S22, the wisdom comprehensive energy garden adopts gas turbine (PGU), wind turbine generator system (WT), photovoltaic power generation (PV) and electric wire netting to satisfy regional power load' S demand. The heating system consists of a heat source, a heat supply network and a heat load. Thermal energy is provided by the PGU, Gas Boiler (GB) and Electric Boiler (EB). The cooling system is similar in structure, and cooling power is converted from an absorption refrigerator (AC) and an electric refrigerator (EC).
The relationship between the active output of the fan and the wind speed can be expressed as follows:
Figure BDA0003591910580000053
in the formula, VciIs cut into the wind speed, VrIs rated wind speed, VcoIs cut-out wind speed, f (v) is a fitted polynomial, PwIs the rated output power of the wind turbine.
Wherein, the output force of the single photovoltaic module can be expressed as:
Figure BDA0003591910580000054
in the formula IθPer 1000 denotes the peak hours, PPVSThe rated power of the photovoltaic array is defined, and eta is the system efficiency which is generally between 80 and 85 percent; gamma is the power temperature coefficient, T, of the photovoltaic modulerefTo be referenced to ambient temperature, TcIs the measured temperature.
The mathematical model of the gas turbine based on combined cooling, heating and power can be expressed as follows:
Figure BDA0003591910580000055
Qwat(t)＝CV(t)Δt
in the formula, VGT(t) the amount of natural gas consumed by the gas turbine at time t; l isNGIs the low calorific value of natural gas; Δ t is the time step; p ise(t) the electric power generated by the gas turbine at time t; etaeThe power generation efficiency of the gas turbine; qwat(t) represents the amount of heat of the hot water module generating hot water; c represents the specific heat capacity of the smoke; v (t) represents the volume of flue gas entering the hot water module;
wherein, the mathematical model of the gas-fired hot water boiler can be expressed as:
Pgb(t)＝Pgas,gb(t)ηgb
Figure BDA0003591910580000061
Hgb(t)＝Pgb(t)Δt
in the formula, Pgb(t) is the heat production power of the gas-fired boiler at the moment t, and delta t is the time step; pgas,gb(t) is the natural gas consumption power of the gas boiler at time t, ηgbIs the conversion efficiency, Q, of a gas boilergb(t) the amount of natural gas consumed by the gas boiler at time t, L the lower heating value coefficient of natural gas, Hgb(t) is the actual heat production of the gas boiler during the period t.
The mathematical model of the lithium bromide absorption refrigerator can be expressed as follows:
Pac＝copacQac
in the formula, PacIs the refrigeration power of a lithium bromide absorption refrigerator, copacIs the refrigeration coefficient, Q, of the lithium bromide absorption chilleracIs the heat input power of the lithium bromide absorption refrigerator.
Wherein, the mathematical model of the electric refrigerating unit can be expressed as:
Pec＝copecQec
in the formula, PecIs the refrigeration power of the electric refrigerating unit, copecIs the refrigeration efficiency of an electric refrigeration unit, QecIs the input electrical power to the electrical refrigeration unit.
Wherein, the charging mathematical model of the power storage device can be expressed as:
Figure BDA0003591910580000062
in the formula, Pc(t) the charging power of the energy storage battery at time t, PDG(t) output power, P, of all power supplies in the integrated energy system at time tload(t) electric load of the integrated energy system at time t, ηinvThe efficiency of the energy storage system inverter; pcmaxThe maximum charging power of the energy storage battery is obtained; SOCmaxThe upper limit value of the state of charge of the energy storage battery; SOC (t) is the state of charge of the energy storage battery at the current time t; ebessIs the capacity of the energy storage battery; etacCharging efficiency of the energy storage battery; Δ t is unit time and is taken as l h.
Figure BDA0003591910580000071
In the formula, SOC (t +1) is the state of charge of the energy storage battery at the time of t + 1; delta is the self-discharge rate of the energy storage battery, and the unit percent per hour; pcAnd (t) is the charging power of the energy storage battery.
Wherein, the discharge mathematical model of the power storage device can be expressed as:
Figure BDA0003591910580000072
in the formula, Pd(t) is the discharge power of the energy storage cell at time t, PdmaxFor maximum discharge power, SOC, of energy storage cellsminIs the lower limit of the state of charge, η, of the energy storage batterydThe discharge efficiency of the energy storage battery.
Figure BDA0003591910580000073
In the formula, the SOC (t +1) is the energy storage battery at the moment of t +1The state of charge of; etadThe discharge efficiency of the energy storage battery is unit%/h; pdAnd (t) is the discharge power of the energy storage battery.
Wherein the mathematical model of the thermal storage device may be expressed as:
Hhss_in(t)＝(1-χh)·Hhss_in(t-1)+Qin(t)·Δt·ηin
Hhss_out(t)＝(1-χh)·Hhss_out(t-1)+Qout(t)·Δt·ηout
in the formula, subscript in represents stored heat, and subscript out represents released heat; hhss_in(t) is the stored heat quantity of the heat storage tank at the t-th time, Hhss_out(t) is the heat released from the heat storage tank at the t-th time, chihIs the heat loss rate of the heat storage tank, Hhss_in(t-1) is the stored heat of the heat storage tank at time t-1, Hhss_out(t-1) is the heat released from the heat storage tank at the time of t-1, Qin(t) is the input thermal power of the thermal storage tank at time t, ηinIs the input conversion efficiency, Q, of the heat storage tankout(t) is the output thermal power of the thermal storage tank at time t, ηoutIs the output conversion efficiency of the heat storage tank.
S23, then, the optimal operation objective function of the upper layer model of the integrated energy system is as follows:
Obj F＝min(ATC)
s24, establishing a comprehensive energy system lower layer optimized scheduling model for the lower layer model, wherein the constraint conditions in the optimized scheduling model comprise electric power balance constraint, thermal power balance constraint, cold power balance constraint, energy equipment power constraint, storage battery balance constraint and heat storage tank balance constraint of the comprehensive energy system, and the method specifically comprises the following steps:
establishing the electric power balance constraint conditions of the comprehensive energy system as follows:
Pwt(t)+Ppv(t)+Ppgu(t)-Pbat(t)+Pbuy(t)＝Pload(t)+Peb(t)+Pec(t)
wherein, Pwt(t) wind power for time period t; p ispv(t) photovoltaic power for time period t；Pbat(t) battery charging power for time period t; pbuy(t) purchasing electric power from the large power grid in a period t; p ispgu(t) a combustion engine electrical power for a time period t; peb(t) electric boiler power for time period t; pec(t) an electric refrigerator power for a time period t; ploadAnd (t) the electric load power of the comprehensive energy system in the time period t.
Establishing a thermal power balance constraint condition of the comprehensive energy system as follows:
Hpgu(t)+Heb(t)-Hhss(t)+Hgb(t)＝Hload(t)
wherein Hpgu(t) thermal power generated by the gas turbine for a time period t; heb(t) the thermal power generated by the electric boiler is in a time period t; hgb(t) thermal power generated by the gas boiler for a time period t; hhss(t) the heat absorption power of the heat storage tank in the time period t; hload(t) the integrated energy system thermal load power for time period t.
The cold power balance constraint conditions of the comprehensive energy system are established as follows:
Cac(t)+Cec(t)＝Cload(t)
wherein, Cac(t) is the refrigeration power of the absorption refrigerator in time period t; cec(t) the refrigeration power of the absorption chiller for time period t; cloadAnd (t) the comprehensive energy system cold load power of the time period t.
The production and energy conversion equipment of the integrated energy system mainly comprises: the power constraint conditions of the energy equipment of the comprehensive energy system are as follows:
0≤Ppv≤Ppv_max
0≤Pwt≤Pr
Ppgu_min≤Ppgu≤Ppgu_max
Pec_min≤Pec≤Pec_max
Pac_min≤Pac≤Pac_max
in the formula (I), the compound is shown in the specification,Ppv_maxis the upper limit of the output of the photovoltaic, PrIs the upper limit of the output of the fan, Ppgu_maxAnd Ppgu_minUpper and lower limits of output, P, of the combustion enginegb_maxAnd Pgb_minIs the upper and lower output limits, P, of the gas boilerec_maxAnd Pec_minUpper and lower output limits, P, of the electric refrigerator ECac_maxAnd Pac_minAre the upper and lower limits of the output of the absorption chiller AC.
The method comprises the following steps of establishing a balance constraint condition of a storage battery of the comprehensive energy system:
SOCmin≤SOC(t)≤SOCmax
Figure BDA0003591910580000081
wherein SOC (t) is the state of charge of the energy storage battery at time t; SOCmin、SOCmaxIs the maximum and minimum value of the state of charge, P, of the energy storage cellch,min、Pdis,minIs the minimum value of charge and discharge power, Pch,max、Pdis,maxIs the maximum value of charging and discharging power
Establishing balance constraint conditions of a heat storage tank of the comprehensive energy system as follows:
Hsocmin≤Hsoc(t)≤Hsocmax
Figure BDA0003591910580000091
wherein Hsoc (t) is the percentage of the residual heat of the heat storage tank at the time t; hsocmin、HsocmaxIs the maximum and minimum constraint of the residual capacity of the heat storage tank, Hch,min、Hdis,minIs the minimum value of the heat storage and release power, Hch,max、Hdis,maxIs the maximum value of the heat storage and release power.
S25, establishing a robust optimization scheduling model of the lower-layer comprehensive energy system according to the uncertain set of prediction errors of the fan, the photovoltaic and the cooling, heating and power loads:
step A1, establishing wind power output prediction error robust optimization constraints, taking a box type robust optimization method as an example:
Pwt_r(t)＞＝Pwt(t)+w1(t)
wtzone(t)*Pwt(t)＜＝w1(t)＜＝wtzone(t)*Pwt(t),uncertain(w1(t))
wherein, Pwt(t) wind power, P, over a period of time twt_rThe method is characterized in that the actual output of the fan considering the uncertainty of the wind power output is taken into account, w1(t) is an uncertain variable of the output of the fan, and wtzone is an uncertain interval of the wind power output; uncertain (w1(t)) is used to indicate the uncertainty of w1 (t).
Step A2, establishing photovoltaic output prediction error robust optimization constraints, taking a box type robust optimization method as an example:
Ppv_r(t)＞＝Ppv(t)+w2(t)
pvzone(t)*Ppv(t)＜＝w1(t)＜＝pvzone(t)*Ppv(t),uncertain(w2(t))
wherein, Ppv(t) photovoltaic power for a time period t, Ppv_rThe actual output of the fan considering the uncertainty of the photovoltaic output, w2(t) is the uncertain variable of the photovoltaic output, pvzone is the uncertain interval of the photovoltaic output, and uncertain (w2(t)) is used for indicating the uncertainty of w2 (t).
Step A3, establishing electric load prediction error robust optimization constraint, taking a box type robust optimization method as an example:
Pload_r(t)＞＝Pload(t)+w3(t)
Pzone(t)*Pload(t)＜＝w3(t)＜＝Pzone(t)*Pload(t),uncertain(w3(t))
wherein, Pload(t) electric load power for a time period t, Pload_rIs the actual electrical load considering the uncertainty of the electrical load, w3(t) is the uncertainty variable of the electrical load, Pzone is the uncertainty interval of the electrical load, and uncertain (w3(t)) is used to indicate the uncertainty of w3 (t).
Step A4, establishing robust optimization constraint of cold load prediction error, taking a box type robust optimization method as an example:
Cload_r(t)＞＝Cload(t)+w4(t)
Czone(t)*Cload(t)＜＝w4(t)＜＝Czone(t)*Cload(t),uncertain(w4(t))
wherein, Cload(t) Cold load Power for time period t, Cload_rIs the actual cooling load considering the uncertainty of the cooling load, w4(t) is the uncertainty variable of the cooling load, Czone is the uncertainty interval of the cooling load, and uncertain (w4(t)) is used to indicate the uncertainty of w4 (t).
Step A5, establishing robust optimization constraint of thermal load prediction error, taking a box type robust optimization method as an example:
Hload_r(t)＞＝Hload(t)+w5(t)
Hzone(t)*Hload(t)＜＝w5(t)＜＝Hzone(t)*Hload(t),uncertain(w5(t))
wherein Hload(t) Heat load Power for time period t, Hload_rIs the actual heat load considering the uncertainty of the heat load, w5(t) is the uncertainty variable of the heat load, Hzone is the uncertainty interval of the heat load, and uncertain (w5(t)) is used to indicate the uncertainty of w5 (t).
Step A6, establishing robust optimization electric power balance constraint of wind, light and load prediction errors, taking a box type robust optimization method as an example:
Pwt_r(t)+Ppv_r(t)+Ppgu(t)-Pbat(t)+Pbuy(t)＝Pload_r(t)+Peb(t)+Pec(t)
wherein, Pwt_r(t) wind power actual output power considering uncertainty; ppv_r(t) photovoltaic actual output power taking uncertainty into account; pbat(t) battery charging power for time period t; pbuy(t) power purchased from the large power grid for a period of t; p ispgu(t) the combustion engine electric power for a time period t; peb(t) electric boiler power for time period t; p isec(t) an electric refrigerator power for a time period t; pload_r(t) actual power of the electrical load taking into account uncertainty.
Step A7, establishing wind, light and load prediction error robust optimization cold power balance constraint, taking a box type robust optimization method as an example:
Cac(t)+Cec(t)＝Cload_r(t)
wherein, Cac(t) is the refrigeration power of the absorption refrigerator in time period t; cec(t) the refrigeration power of the absorption chiller for time period t; cload_r(t) is the actual power of the cooling load taking into account the uncertainty.
Step A8, establishing robust optimization thermal power balance constraint of wind, light and load prediction errors, taking a box type robust optimization method as an example:
Hpgu(t)+Heb(t)-Hhss(t)+Hgb(t)＝Hload_r(t)
wherein Hpgu(t) thermal power generated by the gas turbine for a time period t; heb(t) the thermal power generated by the electric boiler is in a time period t; hgb(t) thermal power generated by the electric boiler for a time period t; hhss(t) the heat absorption power of the heat storage tank in the time period t; hload_r(t) is the actual power of the thermal load taking into account the uncertainty.
It should be noted that the comprehensive energy system robust optimization scheduling model established in step S2 is not limited to the robust optimization models of single stage, two stages, and the like; the method adopts a box type robust linear programming method, but the robust optimization algorithm in practical application can also adopt but not limited to intelligent algorithms such as Benders dual method, and generation of column and constraint;
step S3, solving the optimized scheduling model established in the step S24 by adopting a mixed linear integer programming solver;
by constructing an objective function with the minimum running cost in a dispatching cycle, on the basis of meeting power balance and safety constraints, the upper-layer optimization plans out the optimal capacity of various energy equipment, the objective function is the life cycle cost of the intelligent park comprehensive energy system, the lower-layer optimization establishes evaluation indexes and constraint conditions from the aspects of economy and environmental protection, the starting and stopping and output states of energy storage, flexible load and combined heat and power supply controllable units in the system are optimized, and the multi-objective optimization dispatching of the intelligent park comprehensive energy system is realized. The method of the invention can realize the economic and environment-friendly operation of the system, ensure the reliability of various energy supplies of the system and ensure the safe, stable, efficient and economic operation of the comprehensive energy system. In addition, uncertainty is considered, so that the capacity equipment of the energy equipment optimized by the method can meet the condition that the comprehensive energy system can still stably run under the worst condition, and the stability and the reliability are enhanced.
The embodiment of the invention utilizes a double-layer optimization model to solve. Lower tier energy scheduling to integrate energy system cost and CO2And taking the discharge amount as a target, taking energy balance, equipment operation and power grid exchange power limitation as constraints, establishing an IES (interactive electronic systems) optimization design model based on a Yalmip platform in an MATLAB (matrix laboratory) environment, calling a Cplex solver through a Yalmip toolbox to solve the mixed integer linear programming problem, and feeding an optimization result back to an upper layer to serve as an optimization basis. The upper layer aims at the lowest cost of the life cycle of the comprehensive energy system, and the solution is carried out through a genetic algorithm. And finally, solving a global optimum value through iteration of an upper layer and a lower layer.
In the present invention, the lower layer model solver is not limited to Cplex or Gurobi.
The structure diagram of the user-level integrated energy system of the embodiment is shown in fig. 1, and the equipment elements in the system include: photovoltaic, wind power, gas turbines, electric boilers, gas boilers, absorption refrigerators, electric energy storage, thermal energy storage, and the like, and performs power interaction with a superior distribution network. The basic data of photovoltaic, wind power and load comprise energy load prediction data obtained through a BP neural network, wherein the data are predicted in a wind power mode in FIG. 2, in a photovoltaic mode in FIG. 3, in an electric load mode in FIG. 4, in a cold load mode in FIG. 5 and in a heat load mode in FIG. 6; the price of the power purchased from the power grid is shown in table 1. The energy equipment capacity allocation for the integrated energy system campus is shown in table 2.
TABLE 1
Figure BDA0003591910580000111
TABLE 2
Figure BDA0003591910580000121
Electric heat-cold power balance scheduled day before after the steps according to the present invention are operated, including the electric power balance diagram of fig. 7, the cold power balance diagram of fig. 8, and the thermal power balance diagram of fig. 9; on the basis of consuming renewable energy to the maximum extent, the energy utilization requirements of various energy forms including cold, heat and electricity of a user-level comprehensive energy system can be met through coordinately controlling the starting, the stopping and the output of each controllable unit, and the economical efficiency of the system operation can be ensured under the guidance of the electricity price and the operation cost of each device. Meanwhile, the flexibility of system operation can be improved by utilizing the time shifting characteristics of energy storage systems such as storage batteries and heat storage tanks.
Therefore, the method can realize the economical and low-carbon operation of the system, ensure the reliability and the robustness of various energy supplies of the system and realize the multi-objective optimized dispatching of the comprehensive energy system of the intelligent park.
The foregoing shows and describes the general principles, principal features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited by the foregoing examples, which are provided to illustrate the principles of the invention, and that various changes and modifications may be made without departing from the spirit and scope of the invention, which is also intended to be covered by the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (7)

1. A comprehensive energy system robust optimization planning method considering supply and demand uncertainty is characterized by comprising the following steps: the method comprises the following steps:
analyzing wind-solar output samples and cold-heat-electricity load samples on both sides of supply and demand by adopting a BP neural network prediction model to respectively obtain uncertain sets of prediction errors of the wind-solar output samples and the cold-heat-electricity load samples;
establishing a double-layer optimization model of the comprehensive energy system of the intelligent park, wherein the lower-layer model takes economic evaluation indexes and environmental protection evaluation indexes as weighted system operation costs, the upper-layer model takes the full life cycle cost converted to the year as a target, and the equipment capacity of the comprehensive energy system as a decision variable;
thirdly, based on the uncertain set, carrying out robustness modification on the double-layer optimization model established in the second step, and establishing a double-layer robust optimization scheduling model of the comprehensive energy system;
and step four, solving the double-layer robust optimization scheduling model of the comprehensive energy system to obtain the optimal capacity configuration of the comprehensive energy system.
2. The method of claim 1, wherein: in the first step, the uncertain set is one or a combination of at least two of a box-type uncertain set, an ellipsoid uncertain set and a polyhedron uncertain set.
3. The method of claim 1, wherein: in the second step, the economic evaluation index of the lower layer model is represented by the annual full life cycle cost converted by the comprehensive energy system of the intelligent park, and the economic evaluation index comprises initial investment cost, operation and maintenance cost, replacement cost and energy cost; the environmental evaluation index of the lower model is characterized by the carbon emission directly generated by natural gas combustion power generation in the comprehensive energy system and the indirect carbon emission generated by electric power purchased from a power grid.
4. The method of claim 1, wherein: in the third step, the constraint conditions in the optimized scheduling model comprise an electric power balance constraint, a thermal power balance constraint, a cold power balance constraint, an energy device power constraint, a storage battery balance constraint and a heat storage tank balance constraint of the comprehensive energy system.
5. The method of claim 1, wherein: in the third step, when robustness improvement is performed, the adopted robust optimization algorithm includes, but is not limited to, a box-type robust linear programming method, a Benders dual method, and a column and constraint generation algorithm.
6. The method of claim 1, wherein: in the third step, the robust optimization scheduling model includes, but is not limited to, a single-stage and a two-stage robust optimization model.
7. The method of claim 1, wherein: in the fourth step, the lower layer model is solved by adopting a mixed linear integer programming solver, and an optimized result is fed back to the upper layer model to be used as an optimization basis; and solving the upper layer model by adopting a genetic algorithm, and finally solving the global optimum value through iteration of an upper layer and a lower layer.
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Cited By (1)

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Publication number Priority date Publication date Assignee Title
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116027757A (en) * 2023-03-27 2023-04-28 中国科学院电工研究所 Comprehensive energy system operation regulation and control method for preparing fuel by photocatalysis-thermochemical coupling

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