CN113240204A - Energy station capacity optimal configuration method and system considering renewable energy consumption area - Google Patents

Energy station capacity optimal configuration method and system considering renewable energy consumption area Download PDF

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CN113240204A
CN113240204A CN202110672982.9A CN202110672982A CN113240204A CN 113240204 A CN113240204 A CN 113240204A CN 202110672982 A CN202110672982 A CN 202110672982A CN 113240204 A CN113240204 A CN 113240204A
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房方
温港成
刘亚娟
张怡
吴秋伟
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North China Electric Power University
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Abstract

The method and the system for optimizing and configuring the capacity of the regional energy station in consideration of the consumption of the renewable energy sources comprise the following steps: forecasting to obtain each electric heating and cooling air load data of each demand point in the park, and then selecting the position of the optimal energy station and the demand to be met according to the position of each demand point and the demand of each energy; according to minimum moment of energyEstablishing the position of each demand point and the array, and taking the demand as a guide minimum energy distance and construction cost as an objective function to obtain the optimal energy station position, the optimal demand point distribution and the load demand to be met; planning and optimizing each energy supply device of the energy station according to annual electric heating and cooling air load data of demand points in the garden, and taking permeability, environmental pollution and operation cost of renewable energy sources in use as objective functions; number of energy stations M from MmaxTo MminAnd (4) sequentially iterating according to the steps, and finding the optimal energy station number M and the optimal capacity of equipment in each energy station by taking the minimum objective function as an optimal result.

Description

Energy station capacity optimal configuration method and system considering renewable energy consumption area
Technical Field
The invention relates to an optimal configuration method and an optimal configuration system, in particular to an optimal configuration method and an optimal configuration system for a park comprehensive energy station considering renewable energy consumption.
Background
In order to improve the energy utilization rate of the comprehensive energy station, the number, the positions, the equipment capacity configuration and the energy supply network layout of the energy stations are subjected to unified planning research from the perspective of station-network overall planning, and a park comprehensive energy station-network double-layer planning optimization model is provided based on the electricity, heat and cold multi-energy complementary characteristics.
Most of the existing capacity allocation considers the optimization of energy storage equipment, a capacity allocation scheme of each equipment in a complete energy system is not given, and the situation of multi-energy complementary energy supply is not considered, and the existing energy supply mode only adopts the near principle for energy supply, but in the actual situation, load points and load points directly have complex load characteristics, and the near principle cannot be simply adopted for energy supply.
Disclosure of Invention
Aiming at the problems, the invention provides a regional energy station capacity optimal configuration method and system considering renewable energy consumption.
The capacity configuration method comprises the following steps:
step 1: forecasting to obtain each electric heating and cooling air load data of each demand point in the park, and then selecting the position of the optimal energy station and the demand to be met according to the position of each demand point and the demand of each energy;
step 2: establishing a minimum energy distance matrix and the position of each demand point according to the minimum energy distance matrix and the position of each demand point, and taking the demand as a guide minimum energy distance and the construction cost as an objective function to obtain the optimal energy station position and the optimal demand point distribution and the load demand to be met;
and step 3: planning and optimizing each energy supply device of the energy station according to annual electric heating and cooling air load data of demand points in the garden, and taking permeability, environmental pollution and operation cost of renewable energy sources in use as objective functions;
and 4, step 4: number of energy stations M from MmaxTo MminAnd (4) sequentially iterating according to the step 2 and the step 3, and finding the optimal energy station number M and the capacity of equipment in each energy station by taking the minimum objective function as an optimal result.
In addition, the invention also discloses a regional energy station capacity optimization configuration system considering the consumption of the renewable energy.
Has the advantages that:
the system adopts the electric heat pump and the electric refrigeration equipment to further consume renewable energy, the energy starts to flow in multiple directions according to the load requirement, and the system operates in a multi-energy complementary mode, so that the consumption of fossil energy is reduced, and the environmental cost is greatly reduced.
Drawings
FIG. 1 is a flow chart of a method for optimal configuration of a campus integrated energy site with consideration of renewable energy consumption according to the present invention;
FIG. 2(a) (b) (c) (d) (e) is a graph of the result of the optimal configuration method of the park integrated energy station considering renewable energy consumption according to the present invention;
FIG. 3 is a system diagram of a campus energy complex optimal configuration system with consideration of renewable energy consumption according to the present invention;
FIG. 4 is a non-directional connection diagram of 50 load points in the optimal configuration method of the park integrated energy station considering renewable energy consumption according to the present invention;
FIG. 5 is a distribution scheme of load points and energy stations decided by the optimal configuration method of the park integrated energy station considering renewable energy consumption according to the present invention;
fig. 6 is an operation cost in a daily scene of the optimal configuration method of the park integrated energy station considering the consumption of the renewable energy according to the present invention.
Detailed Description
The invention provides a park comprehensive energy station optimal configuration method and system considering renewable energy consumption
Referring to fig. 1, the capacity optimization configuration method includes the following steps:
step 1: according to the obtained electric heating and cooling air load data of each demand point in the park, selecting the position of an optimal energy station and the demand to be met according to the position of each demand point and the demand of various energy sources;
due to the fact that energy supply pipelines between the energy station and the demand point are laid complicatedly, the shortest distance from the demand point i to the energy station j is obtained according to the Dijstar algorithm. Inputting information according to various pipeline energy distances into a lower model, converting the problem of solving the optimal distribution relation between the energy station and the demand point into a planning problem, and solving the planning problem through a cplex solver to obtain the optimal distribution relation between the energy station and the demand point.
The step 1 further comprises the following steps:
s11: load data of each demand point obtained through prediction is collected, and the energy distance is obtained through calculation according to the demand quantity and the position of the demand point: the concrete expression is as follows: establishing an undirected graph between an energy station and a demand point according to a laying scheme given by the existing power grid, heat supply network and air network;
s12, according to the transfer characteristics and the laying cost of the electricity, heat and gas, combining the load demand point and the energy supply line laying diagram of the energy supply point to give an undirected graph comprehensively, and adding different weight information on each side, wherein, as the heat supply network and the air supply network have great transfer delay and the pipeline maintenance cost is higher, penalty coefficients are additionally added to the weights on the sides of the undirected graph during calculation; the energy distances of the power grid, the heat supply network and the air network are weights of edges in the undirected graph. The energy distance calculation method of the pipe network is as follows.
Wherein the energy span of the power grid is:
Figure BDA0003119477830000031
Figure BDA0003119477830000032
Figure BDA0003119477830000033
Figure BDA0003119477830000034
in the formula:
Figure BDA0003119477830000035
respectively setting the construction cost, the maintenance cost and the power grid loss cost of the power grid pipeline in a planning period;
Figure BDA0003119477830000036
the construction cost for the pipeline unit;
Figure BDA0003119477830000037
the annual maintenance cost of the pipeline is calculated;
Figure BDA0003119477830000038
the unit grid loss cost;
Figure BDA0003119477830000039
for the point a of electrical load demand1To energy station b1The length of a line formed by passing road network nodes; delta Ploss(S, t) providing active power loss generated by the operation of the section of the line by a lower layer decision variable;
Figure BDA00031194778300000310
for the point a of electrical load demand1To energy station b1The power grid energy distance is formed by passing road network nodes;
the energy distance of a heat supply network and the energy distance of an air network are additionally added with a penalty coefficient Cp；
Figure BDA00031194778300000311
Where τ is the delay factor per unit line length.
At the moment, an undirected graph with weight can be obtained according to the existing pipe network laying lines and the energy distance obtained by calculation according to the formula. Fig. 4 is an undirected graph with weight information. Each point is a load demand point or a pipe network node, and the connecting line is a laying line of the pipe network.
S13: solving the shortest path from each energy candidate station to each demand point and the optimal laying scheme by adopting a Dijstar algorithm according to the established weighted undirected graph;
the steps of the Dijstar algorithm are as follows:
a. initially, only the source point is included, i.e., S ═ v, and v is a distance of 0. U contains vertices other than v, namely: if v has an edge with a vertex U in U, the (U, v) is a normal weight, and if U is not an edge adjacent point of v, the (U, v) weight is infinite;
b, selecting a vertex k with the minimum distance v from U, and adding k into S (the selected distance is the shortest path length from v to k);
c. modifying the distance of each vertex in the U by taking k as a newly considered middle point; if the distance from the source point v to the vertex u (passing through the vertex k) is shorter than the original distance (not passing through the vertex k), the distance value of the vertex u is modified, and the weight of the distance of the vertex k of the modified distance value is added to the upper side.
d. Repeating steps b and c until all vertices are contained in S.
The shortest pipe network laying line between the load point and the demand point can be obtained through a Dijstar algorithm.
Step 2: establishing a target function which takes the demand as a guide minimum energy distance and the construction cost as a target function according to the minimum energy distance matrix and the positions of all demand points, converting the target function into a mathematical programming problem, and solving through a cplex solver to obtain the optimal energy station position, the optimal distribution of the demand points and the load demand to be met;
the method further comprises the following steps:
s21: the method comprises the steps of conducting preliminary optimization by taking demand as a guide minimum energy distance and lowest construction cost as a target function, selecting M optimal energy stations from candidate energy stations, and dividing N demand points into M parts by the shortest energy distance, demand and operating cost to supply energy for the M energy stations;
Figure BDA0003119477830000041
in the formula: a. theiThe demand point set represents an electric load aggregator when i is 1, represents a thermal load aggregator when i is 2, and represents a gas load aggregator when i is 3; b isiThe candidate energy station set is characterized in that the candidate energy station set represents a power grid candidate energy station when i is 1, represents a hot candidate energy station when i is 2, and represents a gas grid energy candidate station when i is 3;
Figure BDA0003119477830000042
as energy source station biThe construction and operation costs of (2);
Figure BDA0003119477830000043
to energy source a for demand pointiThe required amount of (c);
Figure BDA0003119477830000044
is a load demand point aiTo energy station biEnergy distance of (d); when energy station biWhen selected
Figure BDA0003119477830000045
Otherwise
Figure BDA0003119477830000046
When the load is aggregatediFrom energy station biWhen providing energy service
Figure BDA0003119477830000047
Otherwise
Figure BDA0003119477830000048
The constraints are:
Figure BDA0003119477830000049
Figure BDA00031194778300000410
Figure BDA00031194778300000411
Figure BDA0003119477830000051
s22: solving the mathematical programming problem through a solver to obtain a supply and demand relationship between each demand point and the energy station, inputting a lower layer model according to the step 3 to solve the load demand of the demand point to be supplied by each energy station to obtain the optimal working condition of each device after operation optimization;
fig. 5 is a distribution scheme of energy stations and load demand points obtained by the first iteration, where the number of energy stations is also P is 5, the central point is an energy station, and the others are load demand points. The undirected graph obtained by step 1 is input to the allocation scheme between the energy stations and the demand points obtained by the cplex solver in step 2. The figure shows the allocation scheme when P is 5, in the case analysis of the present invention, P is the number of energy stations, and P is iterated from 5 to 10, and the calculation results in the optimal result when P is 5. So all results are P ═ 5 when shown.
And step 3: planning and optimizing each energy supply device of the energy station according to annual electric heating and cooling air load data of demand points in the garden, and taking permeability, environmental pollution and operation cost of renewable energy sources in use as objective functions;
the method further comprises the following steps:
s31: in consideration of consuming renewable energy, the use of the renewable energy is promoted by adding an electric heat pump, an air conditioner and other equipment in energy optimization scheduling;
photovoltaic unit output model:
Figure BDA0003119477830000052
wherein alpha ispvIs the power derating coefficient, P, of the photovoltaic unitPVZInstalled capacity for photovoltaics, AtIs the actual irradiance of the photovoltaic unit at the time t, ASIs irradiance under standard conditions, alphaTIs the power temperature coefficient, TstpIs the temperature under standard conditions, and T is the real-time temperature;
gas turbine process model:
Figure BDA0003119477830000053
Figure BDA0003119477830000054
in the formula (f)GTIs the load factor of the gas turbine;
Figure BDA0003119477830000055
and
Figure BDA0003119477830000056
rated power generation efficiency and rated heating efficiency of the gas turbine are respectively set; a is1、c1、b1And d1Is the power generation efficiency coefficient of the gas turbine; a is2、b2、c2Is the heating efficiency coefficient of the gas turbine.
Electric heat pump output model:
Figure BDA0003119477830000061
wherein, chi is the heating efficiency of the electric heat pump, QUIs the heat energy converted by the electric heat pump, W is the electric energy consumed by the electric heat pump, QpuIs electrically heatedHeating capacity of pump, PpuThe input power of the electric heat pump;
wind turbine generator output model:
Figure BDA0003119477830000062
in the formula: v is the wind farm actual wind speed; v. ofci vcr vcoRespectively the cut-in wind speed, the rated wind speed and the cut-out wind speed; pR(v) The rated active power of the wind power plant.
An energy storage output model:
Figure BDA0003119477830000063
wherein, Ce,t+1Residual capacity of the battery at time t +1, Ce,tIs the residual capacity of the battery at time t, alpha is the self-discharge efficiency of the battery, betacAnd betadRespectively, the charge-discharge efficiency, P, of the batterye,tFor the charging and discharging power of the storage battery, Δ t is the charging and discharging time period.
S32: wherein the objective function is operation and maintenance cost, environmental pollution and preferential consumption of renewable energy sources;
the operation and maintenance cost is as follows:
1. investment cost
Figure BDA0003119477830000064
In the formula: psisAnd psirThe annual value coefficients of the energy supply pipelines of the s type and the r type are respectively set; hj,sAnd
Figure BDA0003119477830000065
the construction cost for the installation capacity and unit capacity of the equipment s in the energy station j; phi is ar,j,kThe installation capacity of the r-th energy supply pipeline between the energy stations j and k is divided into a power grid and a heat supply network; y isr,j,kSupply energy pipeline for the r kind between energy station j and kThe installation factor corresponding to the installation capacity (divided into a power grid heat supply network) of the network; beta is arThe cost coefficient of constructing a line with a unit length for the r-th energy supply pipeline;
2. running cost
Figure BDA0003119477830000066
In the formula: p is a radical oft,j,sAnd
Figure BDA0003119477830000067
respectively the output power and the corresponding operating cost of the equipment s in the energy station j at the moment t;
3. cost of fuel
Figure BDA0003119477830000071
In the formula:
Figure BDA0003119477830000072
the fuel gas consumption of equipment s in the energy station j at the moment t; v. ofLHVThe combustion heat value of natural gas; piFIs the natural gas price.
4. Cost of electricity purchase and sale
Figure BDA0003119477830000073
In the formula:
Figure BDA0003119477830000074
and
Figure BDA0003119477830000075
respectively the power purchasing power and the power selling power of the energy j and the superior power grid at the time t,
Figure BDA0003119477830000076
and
Figure BDA0003119477830000077
respectively obtaining the electricity purchasing price and the electricity selling price at the time t;
environmental pollution:
Figure BDA0003119477830000078
Figure BDA0003119477830000079
Figure BDA00031194778300000710
in the formula: eENVIs the total emission of the system;
Figure BDA00031194778300000711
and
Figure BDA00031194778300000712
are each CO2And NOxThe discharge amount of (c); k is a radical ofaAnd kbCO as natural gas and electricity, respectively2A discharge coefficient; k is a radical ofCHPAnd kGBNO of CHP and GB, respectivelyxA discharge coefficient; pCHP(S, t) and PGB(S, t) are the output of the CHP and the GB at the moment t respectively;
an objective function:
minClow＝Cinv+CFuel+Cm+Cgrid+EENV
in the formula: clowThe lower layer annual total cost; cFuelIs the cost of the fuel; cmThe annual operating cost; cgridCost for electricity purchase and sale; eENVIs an environmental cost.
And S33, solving the model mathematical programming problem through a cplex solver to obtain the optimal operation condition of each device in each energy station.
Referring to fig. 2, five diagrams are respectively shown, when a district-level comprehensive energy system is simulated, since district load points are scattered, scribing is adopted for energy supply, distribution relations between energy supply points and load points are determined by scribing according to the method in the second step, and the operation conditions of each device of each energy supply point (energy station) are obtained in a typical daily scene obtained by performing operation optimization in the third step, and are input to the fourth step to obtain a final capacity planning result.
And 4, step 4: number of energy stations M from MmaxTo MminIteration is performed according to the step 2 and the step 3 in sequence, and the optimal energy station number M and the capacity of equipment in each energy station are found by taking the minimum objective function as an optimal result;
the method further comprises the following steps:
s41: the number of the initialized energy stations is M, when M is from Mmin，MmaxAnd carrying out the configuration result of the energy station with the optimal iteration demand. The optimal configuration result is the operation state of each device corresponding to the iteration number which minimizes the objective function, namely M corresponding to the ith iteration is Mmin，MmaxThe target function is minimized, and the output M and the working condition of equipment in each energy station are output.
S42: wherein the capacity of each device is the maximum output in the operation scheduling optimization result;
the capacity of the general operating equipment is selected to meet the maximum value of the operating optimization output,
PBESS＝max(|ΔP1|,|ΔP2|,L,|ΔPN|)
in the formula: pBESSSelecting a value for the power of the energy storage system; | Δ PiAnd l (i ═ 1,2,3.. and N) is the output requirement value of the energy storage system calculated at each moment. Based on the above power determination values, the capacity of the device is selected as follows;
EBESS＝max(N1,N2)
N1＝(|ΔP1ΔT|,|ΔP1ΔT+ΔP2ΔT|,L,∣ΔP1ΔT+ΔP2ΔT+L+ΔPNΔT∣)
Figure BDA0003119477830000081
in formula (4) -formula (6): eBESSSelecting a value for the capacity of the energy storage system; Δ T is the data sampling time interval; 1 to m1,m2～m3,L mj～mnA time period for which the device is operating;
the energy storage required power follows normal distribution, and as can be known from the principle of normal distribution 3 δ, about 99.7% of the cases belong to the interval μ -3 δ and μ +3 δ, that is, the output power of the energy storage system required for stabilizing the new energy power fluctuation under the condition of 99.7% of the cases. The selected values of the stored energy power are as follows:
PBESS＝max(|μ-3δ|,|μ+3δ|)
Figure BDA0003119477830000082
Figure BDA0003119477830000083
in the formula: pBESSSelecting a value for the energy storage power;
Figure BDA0003119477830000091
the average value of the energy storage power is obtained; k is the number of samples; μ and δ are mean and standard deviation of the sample data, respectively. In view of the fact that the calculation of the energy storage capacity allocation based on historical output data is essentially an estimation method, the energy storage power P is utilizedBESSMultiplying by the number delta T of the energy storage continuous output hours to estimate the selected value of the energy storage capacity, namely EBESS＝PBESSΔT。
The decision results are as follows:
unit (KW) Energy station 1 Energy station 2 Energy station 3 Energy station 4 Energy station 5
Gas turbine 6012 7084 5442 8772 5993
Boiler 5468 6193 6372 6422 4731
Electric refrigeration 1957 1957 2303 2303 1721
Electric heat pump 2745 3363 1736 3977 3217
Absorption refrigerator 3489 3966 3031 4860 3528
The table above shows the maximum power of each device in each energy station obtained by the decision.
Step 2, calculating the shortest energy distance, the corresponding path matrix and the distribution relation, and solving the optimal operation condition of each device of the energy station and the comprehensive energy system adopted by the method through a cplex solver according to the distribution relation of the energy station and the load demand point, and step 3, namely performing operation optimization according to the load distribution relation in the flow chart. The number P of the energy stations is uncertain and needs to be finished through iteration, the optimal number is found through iteration in a certain range according to the number P, then the working condition of each device is output, and the device capacity is obtained through decision in step 4. The location of the energy station and the capacity of the respective device can be obtained in summary. The planning result obtained by the method not only considers the operation condition but also considers the distribution relation between the energy station and the demand point.
The method mainly plans the energy station according to the position of the load point and the energy demand characteristic curve thereof. The main innovation point is that the position information of the load point and the consumption of renewable energy sources are considered. And finding the optimal distribution relation between the energy station and the demand point in the step two, thereby greatly reducing the energy consumption in the transfer process. In the third step, the use efficiency and the operation cost of the renewable energy are effectively improved through the comprehensive energy system designed in the method.
The invention also discloses a park comprehensive energy station optimal configuration system considering renewable energy consumption, which comprises the regional energy station capacity optimal configuration method considering renewable energy consumption, and is characterized in that: the system comprises an electric heating pump, electric refrigeration, an absorption refrigeration unit, a gas turbine unit and a heat supply boiler, wherein the electric load is met by the gas turbine unit and purchasing electricity to a power grid, the heat load is met by the heat supply boiler and the electric heating pump unit, the cold load is mainly met by the absorption refrigeration unit and the electric refrigeration, and the energy system adopts a multi-energy complementary mode to improve the use efficiency of energy.
Referring to fig. 6, a comparison of the operating costs of the present and other integrated energy systems is made herein for operating the integrated energy system and other integrated energy systems at typical day scenes. It can be seen that the comprehensive energy system adopted by the method effectively reduces the operation cost.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are merely illustrative of the principles of the invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (6)

1. The regional energy station capacity optimal configuration method considering the consumption of renewable energy sources is characterized by comprising the following steps: the method comprises the following steps:
step 1: forecasting to obtain each electric heating and cooling air load data of each demand point in the park, and then selecting the position of the optimal energy station and the demand to be met according to the position of each demand point and the demand of each energy;
step 2: establishing a minimum energy distance matrix and the position of each demand point according to the minimum energy distance matrix and the position of each demand point, and taking the demand as a guide minimum energy distance and the construction cost as an objective function to obtain the optimal energy station position and the optimal demand point distribution and the load demand to be met;
and step 3: planning and optimizing each energy supply device of the energy station according to annual electric heating and cooling air load data of demand points in the garden, and taking permeability, environmental pollution and operation cost of renewable energy sources in use as objective functions;
and 4, step 4: number of energy stations M from MmaxTo MminAnd (4) sequentially iterating according to the step 2 and the step 3, and finding the optimal energy station number M and the capacity of equipment in each energy station by taking the minimum objective function as an optimal result.
2. The regional energy station capacity optimal configuration method considering renewable energy consumption according to claim 1, characterized by: the step 1 further comprises the following steps:
s11: load data of each demand point obtained through prediction is collected, and the energy distance is obtained through calculation according to the demand quantity and the position of the demand point: the concrete expression is as follows: establishing an undirected graph between an energy station and a demand point according to a laying scheme given by the existing power grid, heat supply network and air network;
s12, according to the transfer characteristics and the laying cost of the electricity, heat and gas, combining the load demand point and the energy supply line laying diagram of the energy supply point to give an undirected graph comprehensively, and adding different weight information on each side, wherein, as the heat supply network and the air supply network have great transfer delay and the pipeline maintenance cost is higher, penalty coefficients are additionally added to the weights on the sides of the undirected graph during calculation;
wherein the energy span of the power grid is:
Figure FDA0003119477820000011
Figure FDA0003119477820000012
Figure FDA0003119477820000013
Figure FDA0003119477820000021
in the formula:
Figure FDA0003119477820000022
respectively setting the construction cost, the maintenance cost and the power grid loss cost of the power grid pipeline in a planning period;
Figure FDA0003119477820000023
the construction cost for the pipeline unit;
Figure FDA0003119477820000024
the annual maintenance cost of the pipeline is calculated;
Figure FDA0003119477820000025
the unit grid loss cost;
Figure FDA0003119477820000026
for the point a of electrical load demand1To energy station b1The length of a line formed by passing road network nodes; delta Ploss(S, t) providing active power loss generated by the operation of the section of the line by a lower layer decision variable;
the energy distance of a heat supply network and the energy distance of an air network are additionally added with a penalty coefficient Cp；
S13: and solving the shortest path from each energy candidate station to each demand point and the optimal laying scheme by adopting a dijstar algorithm according to the established weighted undirected graph.
3. The regional energy station capacity optimal configuration method considering renewable energy consumption according to claim 1, characterized by: the step 2 further comprises the following steps:
the method further comprises the following steps:
s21: the method comprises the steps of conducting preliminary optimization by taking demand as a guide minimum energy distance and lowest construction cost as a target function, selecting M optimal energy stations from candidate energy stations, and dividing N demand points into M parts by the shortest energy distance, demand and operating cost to supply energy for the M energy stations;
Figure FDA0003119477820000027
in the formula: a. theiThe demand point set represents an electric load aggregator when i is 1, represents a thermal load aggregator when i is 2, and represents a gas load aggregator when i is 3; b isiThe candidate energy station set is characterized in that the candidate energy station set represents a power grid candidate energy station when i is 1, represents a hot candidate energy station when i is 2, and represents a gas grid energy candidate station when i is 3;
Figure FDA0003119477820000028
as energy source station biThe construction and operation costs of (2);
Figure FDA0003119477820000029
to energy source a for demand pointiThe required amount of (c);
Figure FDA00031194778200000210
is a load demand point aiTo energy station biEnergy distance of (d); when energy station biWhen selected
Figure FDA00031194778200000211
Otherwise
Figure FDA00031194778200000212
When the load is aggregatediFrom energy station biWhen providing energy service
Figure FDA00031194778200000213
Otherwise
Figure FDA00031194778200000214
The constraints are:
Figure FDA00031194778200000215
Figure FDA00031194778200000216
Figure FDA0003119477820000031
Figure FDA0003119477820000032
s22: and calculating to obtain the supply and demand relationship between each demand point and the energy station according to the model, and inputting the load demand of the demand point to be supplied by each energy station into a lower-layer model to perform operation optimization analysis to obtain the optimal working condition of each device.
4. The regional energy station capacity optimal configuration method considering renewable energy consumption according to claim 1, characterized by: the step 3 further comprises the following steps:
s31: in consideration of consuming renewable energy, the use of the renewable energy is promoted by adding an electric heat pump, an air conditioner and other equipment in energy optimization scheduling;
photovoltaic unit output model:
Figure FDA0003119477820000033
wherein alpha ispvIs the power derating coefficient, P, of the photovoltaic unitPVZInstalled capacity for photovoltaics, AtFor actual radiation of photovoltaic unit at time tIllumination intensity, ASIs irradiance under standard conditions, alphaTIs the power temperature coefficient, TstpIs the temperature under standard conditions, and T is the real-time temperature;
gas turbine process model:
Figure FDA0003119477820000034
Figure FDA0003119477820000035
in the formula (f)GTIs the load factor of the gas turbine;
Figure FDA0003119477820000036
and
Figure FDA0003119477820000037
rated power generation efficiency and rated heating efficiency of the gas turbine are respectively set; a is1、c1、b1And d1Is the power generation efficiency coefficient of the gas turbine; a is2、b2、c2Is the heating efficiency coefficient of the gas turbine;
electric heat pump output model:
Figure FDA0003119477820000038
wherein, chi is the heating efficiency of the electric heat pump, QUIs the heat energy converted by the electric heat pump, W is the electric energy consumed by the electric heat pump, QpuIs the heating capacity of an electric heat pump, PpuThe input power of the electric heat pump;
wind turbine generator output model:
Figure FDA0003119477820000041
in the formula: v is the wind farm actual wind speed; v. ofcivcrvcoRespectively the cut-in wind speed, the rated wind speed and the cut-out wind speed; pR(v) Rated active power of the wind power plant;
an energy storage output model:
Figure FDA0003119477820000042
wherein, Ce,t+1Residual capacity of the battery at time t +1, Ce,tIs the residual capacity of the battery at time t, alpha is the self-discharge efficiency of the battery, betacAnd betadRespectively, the charge-discharge efficiency, P, of the batterye,tThe charging and discharging power of the storage battery is shown, and delta t is the charging and discharging time length;
s32: wherein the objective function is operation and maintenance cost, environmental pollution and preferential consumption of renewable energy sources.
5. The regional energy station capacity optimal configuration method considering renewable energy consumption according to claim 1, characterized by: the step 4 further comprises the following steps:
s41: the number of the initialized energy stations is M, when M is from Mmin，MmaxAnd carrying out the configuration result of the energy station with the optimal iteration demand. The optimal configuration result is the operation state of each device corresponding to the iteration number which minimizes the objective function, namely M corresponding to the ith iteration is Mmin，MmaxThe target function is minimized, and M and the working conditions of equipment in each energy station are output;
s42: wherein the capacity of each device is the maximum output in the operation scheduling optimization result;
the capacity of the general operating equipment is selected to meet the maximum value of the operating optimization output,
PBESS＝max(|ΔP1|,|ΔP2|,L,|ΔPN|)
in the formula: pBESSSelecting a value for the power of the energy storage system; | Δ PiEach of | (i ═ 1,2,3.., N) isAnd calculating the output required value of the energy storage system at any moment. Based on the above power determination values, the capacity of the device is selected as follows;
EBESS＝max(N1,N2)
N1＝(|ΔP1ΔT|,|ΔP1ΔT+ΔP2ΔT|,L,∣ΔP1ΔT+ΔP2ΔT+L+ΔPNΔT∣)
Figure FDA0003119477820000043
in formula (4) -formula (6): eBESSSelecting a value for the capacity of the energy storage system; Δ T is the data sampling time interval; 1 to m1,m2～m3,Lmj～mnA time period for which the device is operating;
the energy storage required power follows normal distribution, and as can be known from the principle of normal distribution 3 δ, about 99.7% of the cases belong to the interval μ -3 δ and μ +3 δ, that is, the output power of the energy storage system required for stabilizing the new energy power fluctuation under the condition of 99.7% of the cases. The selected values of the stored energy power are as follows:
PBESS＝max(|μ-3δ|,|μ+3δ|)
Figure FDA0003119477820000051
Figure FDA0003119477820000052
in the formula: pBESSSelecting a value for the energy storage power;
Figure FDA0003119477820000053
the average value of the energy storage power is obtained; k is the number of samples; μ and δ are mean and standard deviation of the sample data, respectively.
6. A campus integrated energy station optimal configuration system considering renewable energy consumption, comprising adopting the regional energy station capacity optimal configuration method considering renewable energy consumption of any one of claims 1 to 5, characterized in that: the system comprises an electric heat pump unit, electric refrigeration, an absorption type refrigerating unit, a gas turbine unit and a heat supply boiler, wherein the electric load is met by the gas turbine unit and power purchasing to a power grid, the heat load is met by the heat supply boiler and the electric heat pump unit, the cold load is met by the absorption type refrigerating unit and the electric refrigeration, and the energy system adopts a multi-energy complementary mode to improve the use efficiency of energy.
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