CN114519449A - Operation optimization method for park energy system - Google Patents

Operation optimization method for park energy system Download PDF

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CN114519449A
CN114519449A CN202111456079.5A CN202111456079A CN114519449A CN 114519449 A CN114519449 A CN 114519449A CN 202111456079 A CN202111456079 A CN 202111456079A CN 114519449 A CN114519449 A CN 114519449A
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energy system
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亢猛
钟祎勍
王德学
王�琦
温港成
石鑫
房方
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Hebei Xiong'an Branch Of China Huaneng Group Co ltd
North China Electric Power University
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Abstract

The invention relates to the technical field of new energy optimization, and provides a method for optimizing operation of a park energy system, which comprises the following steps: dividing a park energy system into a supply side and a user side; considering the output uncertainty of the photovoltaic unit and the wind turbine unit at the supply side, and establishing a hub energy flow model and a carbon emission flow model at the supply side; considering the randomness of electric vehicle access at a user side, and establishing an energy hub matrix model at the user side; calculating a total cost of the supply side, the total cost including an operating cost, a carbon emission cost and a wind and light abandonment cost, the objective function being that the total cost is minimal; and solving the objective function to obtain a plurality of scene comprehensive analysis solutions. According to the invention, the uncertainty of user energy behaviors and the random access of the electric automobile are considered at a user side, and the uncertainty of the output of the fan and the photovoltaic unit is considered at a supply side; the total cost is the lowest as an optimization target, and the obtained solution ensures the full utilization of renewable energy sources and reduces carbon emission; the operation optimization model has robustness.

Description

Operation optimization method for park energy system
Technical Field
The invention relates to the technical field of new energy optimization, in particular to a method for optimizing operation of a park energy system.
Background
With the popularization of electric vehicles and the great increase of the installed capacity of renewable energy sources, the grid connection of the electric vehicles and the renewable energy sources brings huge challenges to energy systems.
Electric vehicles have various types, and existing research is limited to uncertain load of electric vehicles as consumer terminals, and does not consider the complex characteristics of actual electric vehicles.
The existing operation optimization method does not consider the access of the electric automobile, is insufficient in deep research on the utilization of renewable energy sources such as wind and light and is not suitable for the national large strategic target of energy conservation. Facing great challenges, new energy optimization methods are urgently needed.
Disclosure of Invention
The invention aims to overcome at least one of the defects of the prior art, and provides a method for optimizing the operation of a park energy system. Meanwhile, uncertainty of wind-solar renewable energy output is fully considered, the park energy system is optimized by taking the lowest total cost of the running cost, the carbon emission cost, the photoelectric cost, the user comfort cost and the like of the comprehensive system as an optimization target, and simulation results show that the running optimization model provided by the method has robustness, and can improve the utilization of renewable energy and reduce carbon emission.
The invention adopts the following technical scheme:
a method for optimizing operation of a park energy system comprises the following steps:
s1, dividing a park energy system into a supply side and a user side, wherein the supply side comprises a gas boiler, a CHP unit, a photovoltaic unit and a wind turbine unit, and the user side comprises energy storage equipment, an electric heat pump, an absorption refrigeration unit, air conditioning equipment and an electric automobile;
s2, on the supply side, considering the uncertainty of the output of the photovoltaic unit and the wind turbine unit, and establishing a hub energy flow model and a carbon emission flow model of the supply side; on the user side, considering the randomness of electric automobile access, and establishing an energy hub matrix model on the user side;
s3, calculating the total cost of the supply side according to the supply side hub energy flow model and the carbon emission flow model obtained in the step S2 and the user side energy hub matrix model, wherein the total cost comprises the operation cost, the carbon emission cost and the wind and solar energy abandoning cost, and the objective function of the operation optimization of the park energy system is the minimum total cost;
s4, solving the objective function in the step S3 to obtain an optimal solution, and optimizing the operation of the energy system of the park according to the optimal solution.
As described in any possible implementation manner, there is further provided an implementation manner, and step S2 specifically includes:
s2.1 the energy junction model of the supply side is as follows:
Figure BDA0003386694330000021
in the formula, Pe、PhRespectively outputting the electric quantity and the heat quantity of a node at the supply side; etaT、
Figure BDA0003386694330000022
ηGBThe efficiency of the transformer, the heating efficiency of the CHP unit and the efficiency of the gas boiler are respectively;
Figure BDA0003386694330000023
respectively a CHP unit gas distribution coefficient and a gas boilerGas distribution coefficient of the unit, Ee、Eg、EhElectric energy, air quantity, heat energy, P, respectively purchased from park energy system to superior networkwt、PpvRespectively representing the actual wind power utilization value and the actual photoelectric utilization value;
s2.2 the energy hub matrix model at the user side is as follows:
Figure BDA0003386694330000024
in the formula: l ise、LhRespectively inputting the electric quantity and the heat quantity of a node at a user side; de、Dh、DcRespectively user side electrical load, thermal load and cold load;
Figure BDA0003386694330000025
the energy distribution coefficients of the transformer, the electric heat pump, the air conditioner, the heat exchanger and the absorption refrigerator are respectively; etaEHP、ηAC、 ηHE、ηAFRespectively the efficiency of an electric heat pump, the efficiency of an air conditioner, the efficiency of heat exchange and the efficiency of an absorption refrigerator.
As with any of the possible implementations described above, there is further provided an implementation in which, in step S2.1,
wind power and photoelectric output predicted values are obtained according to historical output data of the wind turbine generator and the photovoltaic generator;
the mean value of errors of predicted values of wind power and photoelectric output is 0, and the standard deviation is normally distributed as follows:
Figure BDA0003386694330000026
in the formula: sigmawt、σpvRespectively the standard deviation of the wind power output predicted value error and the standard deviation of the photoelectric output predicted value error; lambda [ alpha ]wt、 λpvStandard deviation coefficient and photoelectric output prediction for wind power output prediction value errorA standard deviation coefficient of value error;
Figure BDA0003386694330000027
Figure BDA0003386694330000028
respectively obtaining a wind power output predicted value and a photoelectric output predicted value;
the space of renewable energy on the internet is limited, so the actual use value P of wind powerwtAnd a photoelectric actual use value PpvFluctuating within an interval less than the predicted value plus the prediction error;
Figure BDA0003386694330000031
in the formula: deltawt、δpvRespectively wind power predicted value error and photoelectric predicted value error.
In any of the above possible implementation manners, there is further provided an implementation manner, in step S2.2, the user side load is obtained through historical data; the user side loads include electrical loads, thermal loads and cold loads;
the user side load error follows a normal distribution with a mean value of 0 and standard deviation as follows:
Figure BDA0003386694330000032
in the formula: sigmae、σh、σcRespectively is the standard deviation of the electric load error, the standard deviation of the heat load error and the standard deviation of the cold load error; lambda [ alpha ]ed、λhd、λcdRespectively is a standard deviation coefficient of an electric load error, a standard deviation coefficient of a heat load error and a standard deviation coefficient of a cold load error;
according to the demand characteristics of the electric automobile, the electric automobile is divided into a rigid EV and a flexible EV, wherein the flexible EV is divided into a fast charging flexible EV and a slow charging flexible EV; the electric automobile model is described by adopting a one-dimensional matrix:
Ω＝[L G Sn Se Ts Tc]；
in the formula: l represents the type of the electric vehicle load, and numbers 1,2 and 3 represent rigid EV load, slow charge flexible EV charge-discharge load and fast charge flexible EV charge-discharge load respectively; g represents a charging and discharging identifier of the electric automobile, the charging mode is 1, the discharging mode is-1, and the rest moments are 0; snAnd SeRespectively representing the charge quantity when the EV stops and the charge quantity expected by a user when the EV leaves the network; t issAnd TcRespectively representing the network access time and the network leaving time of the electric automobile;
the rigid EV does not participate in power grid dispatching; flexible EV participates in power grid dispatching;
establishing a flexible EV charge-discharge model:
the internal charge amount of the flexile EV satisfies the following expression:
SOCt＝SOCt-1+Pc-Pd；
in the formula: pcCharging power for a flexible EV; pdDischarge power for flexible EV; SOCtAmount of charge, SOC, for Flexible EV at time tt-1For the amount of charge of the agile EV at time t-1, time t-1 represents the last metering time at time t.
As in any of the possible implementations described above, there is further provided an implementation that, in step S3,
s3.1, the method for calculating the carbon emission cost at the supply side of the energy system of the park comprises the following steps:
s3.1.1, determining the uncompensated carbon emission quota of the supply side of the park energy system by adopting a reference line method:
Figure BDA0003386694330000041
in the formula: CE' is the uncompensated carbon emission quota of the park energy system; beta is aCHPECarbon emission quota, beta, obtained for a unit of power supply of a CHP plantCHPH、βGBCarbon emission quotas obtained for the unit heat supply of the CHP unit and the gas boiler unit respectively; beta is aEGridFor purchasing unit electric quantity from upper-grade power gridCarbon emission quota of (c);
s3.1.2 when the carbon emission of the park energy system is higher than the uncompensated carbon emission quota, purchasing the carbon emission right to the carbon trading market, wherein the larger the carbon emission is, the higher the corresponding carbon trading price is;
s3.1.3 calculating the carbon emission cost C of the park energy system supply sideco2: the carbon emission cost is calculated by adopting a step carbon price;
the carbon emissions of the park energy system are calculated by the following formula:
CE＝βeEe+βgEg
in the formula: CE is the carbon emission, beta, of the energy system of the parkeCarbon emission, beta, generated for the unit generation of the upper level gridgCarbon emissions per unit of natural gas consumed;
carbon emission cost C of supply side of park energy systemco2Comprises the following steps:
Figure BDA0003386694330000042
cco2carbon price per carbon emission;
s3.2 operating cost C of energy supply side of parkbThe calculation method comprises the following steps:
Figure BDA0003386694330000043
s3.3 abandoned wind and light cost C of energy system supply side of parkpThe calculation method comprises the following steps:
Figure BDA0003386694330000044
in the formula: cb、CpRespectively the running cost and the wind and light abandoning cost; c. Ce、cg、chRespectively the electricity price, the gas price and the heat price; c. CpAbandoning the wind and light cost for a unit; pwt、PpvAre respectively asThe actual wind power utilization value and the actual photoelectric utilization value,
Figure BDA0003386694330000045
respectively a wind power predicted value and a photoelectric predicted value.
In any of the above possible implementations, there is further provided an implementation that, in step S3, the total cost of the supply side further includes a user comfort cost, where the user comfort cost is a cost generated when an offset occurs between an actual electrical load, a thermal load, a cold load and energy provided by the park energy system; skew refers to the difference in energy supply and load that results from the park energy system failing to meet the actual load demands of the customer.
In any of the above possible implementations, there is further provided an implementation, in step S4, the objective function is solved by using the zun sea squirt group algorithm.
As mentioned above, any possible implementation manner further provides an implementation manner, and the specific method for solving the objective function by using the zun sea squirt group algorithm is as follows:
s4.1, setting a search space to be an Euclidean space of NxD, wherein D is a space dimension and N is the number of the populations; nth population Xn＝[Xn1,Xn2,…,XnD]TRepresenting the output of each device at the supply side and the user side in the nth scene, including the output of a CHP unit, the output of a GB unit, the output of an electric heat pump, the output of an air conditioner and the output of an absorption refrigeration unit; fnRepresenting the total cost of the nth population Xn; n-1, 2,3, …, N; the upper bound of the search space is ub ═ ub1,ub2,…,ubD]Lower bound lb ═ lb1,lb2,…,lbD]；ub1、ub2、……、ubnRespectively the upper limit value of the output of each device at the supply side and the user side; lb1、 lb2、……、lbnRespectively the lower limit value of the output of each device at the supply side and the user side;
s4.2, initializing a population; initializing a goblet sea sheat group with the scale of NxD according to the upper bound and the lower bound of each dimension of the search space;
s4.3, calculating the total cost Fn of each group Xn; selecting food: sorting the populations Xn from small to large according to the value of Fn, setting the population with the smallest total cost Fn ranked at the top as the current food, and recording X0Is the current food; selecting a leader and a follower: except for current food X0In addition, the rest N-1 populations in the population are treated as leaders and the rest populations are treated as followers according to the sequence of the goblet and sea squirt populations;
s4.4: leader location update
During the movement and foraging of the chain of goblet sea squirts, the position update of the leader is expressed as:
Figure BDA0003386694330000051
in the formula:
Figure BDA0003386694330000052
x0drespectively the d-th dimensional equipment output of the nth group and the d-th dimensional equipment output of food; ubdAnd lbdUpper and lower bounds for the corresponding d-th dimension of device contribution, respectively; c. C2、c3Is a control parameter; c. C1Is a convergence factor in the optimization algorithm;
c1the expression of (a) is:
Figure BDA0003386694330000053
wherein iter is the current iteration number; maximum is the maximum number of iterations;
s4.5 follower location update
In the process of moving and foraging of the goblet sea squirt chain, the followers sequentially advance in a chain shape through mutual influence between the front and the rear individuals; their displacement follows the newton law of motion, the displacement of the follower is:
Figure BDA0003386694330000061
wherein a is acceleration, and the calculation formula is a ═ vfinalA/iter; and are each and every
Figure BDA0003386694330000062
After simplification, the expression is:
Figure BDA0003386694330000063
in the formula:
Figure BDA0003386694330000064
respectively the d-dimension equipment output of two followers which are closely connected with each other before updating;
Figure BDA0003386694330000065
the output of the equipment in the d dimension of the updated follower is obtained;
s4.6, calculating the total cost of each updated population, comparing the total cost of each updated population with the total cost of the current food, and if the total cost of a certain updated population is less than the total cost of the current food, taking the population with the minimum total cost as the new food;
s4.7, repeating the steps S4.4-S4.6 until a certain iteration number is reached or the total cost reaches a termination threshold, and after a termination condition is met, the current food corresponds to the optimal solution with the minimum total cost.
On the other hand, the invention also provides an information data processing terminal for realizing the operation optimization method of the park energy system.
In another aspect, the present invention also provides a computer-readable storage medium, which includes instructions that, when executed on a computer, cause the computer to perform the above-mentioned campus energy system operation optimization method.
The invention has the beneficial effects that:
1. the park energy system considers the uncertainty of user energy behaviors and the random access of the electric automobile on the user side, and considers the uncertainty of the output of the fan and the photovoltaic unit on the supply side.
2. The operation optimization strategy provided by the invention combines the energy flow and the carbon emission flow model, simultaneously considers the carbon emission, the operation cost, the wind and light abandoning cost and the user comfort cost, takes the lowest total cost as the optimization target, and ensures the full utilization of the renewable energy source and reduces the carbon emission.
3. The invention adopts a goblet sea squirt group algorithm to solve the model and provides a plurality of scene comprehensive analysis solutions, and simulation results show that the operation optimization model provided by the invention has robustness.
Drawings
Fig. 1 is a flow chart of the algorithm of the sea squirt group algorithm in the embodiment.
Fig. 2 is a graph showing the change in electricity prices (within 24 hours a day) in the examples.
Fig. 3 is a graph showing the change in electricity prices in the example.
FIG. 4 is a diagram of wind-solar predictive power generation in an embodiment.
FIG. 5 shows predicted values of electric heating load in the examples.
Fig. 6 is a schematic diagram showing electric power balance of scenario 2 in the embodiment.
FIG. 7 is a schematic diagram showing thermal power balance of scenario 2 in the example.
Figure 8 shows a block diagram of the park energy system according to an embodiment.
Detailed Description
Specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that technical features or combinations of technical features described in the following embodiments should not be considered in isolation, and they may be combined with each other to achieve better technical effects.
The embodiment of the invention provides a method for optimizing operation of a park energy system, the structure of the park energy system is shown in figure 8, and the method comprises the following steps:
s1, dividing a park energy system into a supply side and a user side, wherein the user is a multi-energy user, in order to increase the response capability of the user to energy requirements, the user side is provided with energy storage equipment, an electric heat pump, an absorption refrigeration unit, air conditioning equipment and an electric automobile, the supply side is provided with a gas boiler, a CHP unit, a photovoltaic unit and a wind turbine unit, and the energy storage equipment, the electric heat pump, the absorption refrigeration unit, the air conditioning equipment and the electric automobile can interact with a superior power grid;
s2, on the supply side, considering the uncertainty of the output of the photovoltaic unit and the wind turbine unit, and establishing a hub energy flow model and a carbon emission flow model of the supply side; on the user side, considering the randomness of electric automobile access, and establishing an energy hub matrix model on the user side;
s3, calculating the total cost of the supply side according to the supply side hub energy flow model and the carbon emission flow model obtained in the step S2 and the user side energy hub matrix model, wherein the total cost comprises the operation cost, the carbon emission cost and the wind and solar energy abandoning cost, and the objective function of the operation optimization of the park energy system is the minimum total cost; the total cost may also include a user comfort cost;
s4, solving the objective function in the step S3 to obtain an optimal solution, and optimizing the operation of the energy system of the park according to the optimal solution.
In one embodiment, the specific steps of step S2 are:
s2.1, the multi-energy load of a user is met by purchasing energy such as electricity, heat, natural gas and the like from a superior level and managing energy conversion equipment; the supply side hub energy flow model and the carbon emission flow model are as follows:
Figure BDA0003386694330000081
in the formula, Pe、PhRespectively outputting node electric power and thermal power for the supply side; etaT、
Figure BDA0003386694330000082
ηGBThe efficiency of the transformer, the heating efficiency of the CHP unit and the efficiency of the gas boiler are respectively;
Figure BDA0003386694330000083
respectively a CHP unit gas distribution coefficient, a gas boiler unit gas distribution coefficient, Ee、Eg、EhElectric, gas and heat energy P purchased from the park energy system to the superior networkwt、PpvRespectively representing the actual wind power utilization value and the actual photoelectric utilization value;
s2.2, on the energy demand side, a user purchases electric energy and heat energy resources from a system operator, and the energy consumption requirements of electricity, heat and cold are met through various energy consumption devices, so that an energy hub matrix model of the user can be established as follows:
Figure BDA0003386694330000084
in the formula: l ise、LhRespectively inputting the electric quantity and the heat quantity of a node at a user side; de、Dh、DcRespectively the user side electricity demand, the heat demand and the cold demand;
Figure BDA0003386694330000085
the energy distribution coefficients of the transformer, the electric heat pump, the air conditioner, the heat exchanger and the absorption refrigerator are respectively; etaEHP、ηAC、 ηHE、ηAFRespectively the efficiency of an electric heat pump, the efficiency of an air conditioner, the efficiency of heat exchange and the efficiency of an absorption refrigerator.
In a specific embodiment, in step S2.1, the wind-solar output prediction error and the prediction error of the load are considered, the consumed wind-solar output is less than or equal to the predicted value + the prediction error, and the load is the predicted value + the prediction error of the load;
the wind and light output prediction value can be obtained from historical output data of the wind turbine generator and the photovoltaic generator, and in a specific embodiment, the wind and light output prediction is shown in FIG. 4.
The errors of the predicted values of the wind and light output are subjected to normal distribution with the mean value of 0 and the standard deviation as follows:
Figure BDA0003386694330000086
in the formula: sigmawt、σpvRespectively obtaining standard deviation of wind power output predicted value errors and standard deviation of photoelectric output predicted value errors; lambdawt、 λpvRespectively taking a standard deviation coefficient of the wind power output predicted value error and a standard deviation coefficient of the photoelectric output predicted value error;
Figure BDA0003386694330000087
Figure BDA0003386694330000088
respectively obtaining a wind power output predicted value and a photoelectric output predicted value;
the space of renewable energy on the internet is limited, so the actual use value P of wind powerwtAnd a photoelectric actual use value PpvFluctuating within an interval less than the predicted value plus the prediction error;
Figure BDA0003386694330000091
in the formula: deltawt、δpvRespectively a wind power predicted value error and a photoelectric predicted value error.
In step S2.2, the user side load is obtained through historical data; the user side loads comprise an electrical load, a thermal load and a cold load; for example, in one embodiment, the electrical, thermal, and cooling loads are predicted from historical loads, see FIG. 5 for detailed loads.
The user side load error follows a mean value of 0 and a positive distribution with standard deviation as follows:
Figure BDA0003386694330000092
in the formula: sigmae、σh、σcStandard deviation of electrical load error, standard deviation of thermal load error and standard deviation of cold load error, respectivelyA difference; lambda [ alpha ]ed、λhd、λcdThe standard deviation coefficient of the electrical load error, the standard deviation coefficient of the thermal load error and the standard deviation coefficient of the cold load error are respectively.
Due to the characteristics of fluctuation, randomness and the like of wind power output, large-scale wind power access brings huge challenges to the optimal scheduling of the power generation side of the power system. According to the difference of charging requirements of electric vehicles, Electric Vehicles (EV) are divided into three categories: the first type is rigid EV, the average driving time per day is relatively long, the requirements on the charging speed and the charging time are high, and the EV cannot participate in power grid interaction; the second type is quick charging flexible EV, the dispatching response is quick, the regional power grid load can be reduced in a short time, but the quick charging mode can generate huge current impact on the power battery pack, the battery heats seriously during charging, and the cycle life of the power battery pack is reduced. Meanwhile, a special charging pile for an operator is needed, and the charging cost can increase the service cost of the charging pile operator on the basis of the industrial electricity price; the third type is a slow charging flexible EV, a large amount of EV charging time periods can be transferred, and the slow charging power is small, so that the heating phenomenon during battery charging can be reduced, and the service life of the battery can be prolonged; the EV slow charging period generally occurs in the electricity utilization low peak period, a community charging pile or a private charging pile is used, and the charging electricity price is the electricity price for the residents;
the method and the device have the advantages that the demand characteristics of the electric automobile are described in a refined mode, and the electric automobile is divided into the rigid EV, the fast charging flexible EV and the slow charging flexible EV. The EV model is described using a one-dimensional matrix:
Ω＝[L G Sn Se Ts Tc]
in the formula: l represents the type of the electric vehicle load, and numbers 1,2 and 3 represent rigid EV load, slow charge flexible EV charge-discharge load and fast charge flexible EV charge-discharge load respectively; g represents a charging and discharging identifier of the electric automobile, the charging mode is 1, the discharging mode is-1, and the rest moments are 0; sn and Se respectively represent the state of charge when the EV stops running and the expected charge quantity of a user when the EV leaves the network; t issAnd TcRespectively representing the network access time of the electric automobile and the network leaving time of a user;
the method comprises the steps of obtaining a daily driving mileage prediction probability density curve of the electric automobile by analyzing driving data of the U.S. private cars and fitting the data, wherein the rigid cars are cars not involved in power grid dispatching, and the probability density curve of the process is approximate to normal distribution. And taking the time of finishing driving as the charging time to obtain a probability density curve of the time of accessing the power grid, wherein the time of finishing driving of the electric automobile is approximate to WeiBULL distribution. And (5) obtaining a rigid EV model by adopting Monte Carlo sampling. For the flexible EV charge-discharge model, taking the time when the flexible EV finishes running as the time of being merged into the power grid, the internal charge state of the electric automobile meets the following expression:
SOCt＝SOCt-1+Pc-Pd
in the formula: pcCharging power for a flexible EV; pdDischarge power for flexible EV; SOCtThe amount of charge, SOC, at time EVt-1The charge amount at time EV is t-1, and time EV 1 represents the last measurement time at time t.
In one embodiment, in step S3,
the method for calculating the carbon emission cost of the supply side of the park energy system comprises the following steps:
s3.1.1, determining the uncompensated carbon emission quota of the supply side of the park energy system by adopting a reference line method:
Figure BDA0003386694330000101
in the formula: CE' is the uncompensated carbon emission quota of the park energy system; beta is aCHPECarbon emission quota, beta, obtained for a unit of supply of the CHP unitCHPH、βGBCarbon emission quota obtained for heat supply of CHP unit and gas boiler unit; beta is aEGridA carbon emission quota for purchasing a unit of electricity from an upper level grid.
S3.1.2 when the carbon emission of the energy system is higher than the free carbon emission limit, purchasing the carbon emission right to the carbon trading market, the larger the carbon emission, the higher the corresponding carbon trading price; when the carbon emission of the park is lower than the carbon emission limit distributed freely, giving a certain subsidy (or not giving the subsidy, only free);
s3.1.3 cost C for carbon emission on the supply side of the park energy systemco2The carbon emission cost is calculated by adopting the price of step carbon; it is assumed herein that the electricity purchased to the upper grid comes from thermal power, the carbon emission of the park mainly comes from consumption of natural gas and the amount of electricity purchased causes carbon emission on the power generation side, and the carbon emission of the park is calculated by the following formula:
CE＝βeEe+βgEg
in the formula: CE is the carbon emission of the park, betaeCarbon emission, beta, generated for the unit generation of the upper level gridgCarbon emissions per unit of natural gas consumed.
Carbon emission cost Cco2Comprises the following steps:
Figure BDA0003386694330000102
cco2is the carbon price per carbon emission. The actual electrical, thermal and cold energy loads are offset from the energy supplied by the system when the user is involved in the response, and in order to compensate the user, a user comfort cost is defined in terms of the user energy offset.
The operation cost and the wind and light abandoning cost are mathematically described in the following formula.
Figure BDA0003386694330000111
Figure BDA0003386694330000112
In the formula: cb、CpRespectively the running cost, the wind and light abandoning cost, Ee、Eg、EhRespectively purchasing electricity, gas and heat, ce、cg、chRespectively the electricity price, the gas price and the heat price; c. CpAbandoning the wind and light cost for a unit; pwt、PpvIs the actual use value of wind power and photoelectricity,
Figure BDA0003386694330000113
respectively wind power and photoelectric predicted values.
In one embodiment, in step S4, the objective function is solved by using the zun sea squirt group algorithm.
S4.1, setting a search space to be an Euclidean space of NxD, wherein D is a space dimension and N is the number of the populations; nth population Xn＝[Xn1,Xn2,…,XnD]TRepresenting the output of each device at the supply side and the user side in the nth scene, including the output of a CHP unit, the output of a GB unit, the output of an electric heat pump, the output of an air conditioner and the output of an absorption refrigeration unit; fnRepresenting the total cost of the nth population Xn; n-1, 2,3, …, N; the upper bound of the search space is ub ═ ub1,ub2,…,ubD]Lower bound lb ═ lb1,lb2,…,lbD]；ub1、ub2、……、ubnRespectively the upper limit value of the output of each device at the supply side and the user side; lb1、 lb2、……、lbnRespectively the lower limit value of the output of each device at the supply side and the user side;
s4.2, initializing a population; initializing a goblet sea sheath group with the size of NxD according to the upper bound and the lower bound of each dimension of a search space;
s4.3, calculating the total cost Fn (population fitness) of each population Xn; selecting food: sorting the populations Xn according to the values of Fn from small to large, setting the population with the smallest total cost Fn ranked at the top as the current food, and recording X0Is the current food; selecting a leader and a follower: except for current food X0In addition, the rest N-1 populations in the population are treated as leaders and the rest populations are treated as followers according to the sequence of the goblet and sea squirt populations;
s4.4: leader location update
During the movement and foraging of the chain of goblet sea squirts, the position update of the leader is expressed as:
Figure BDA0003386694330000114
in the formula:
Figure BDA0003386694330000115
x0drespectively the equipment output of the d-th dimension of the nth group and the equipment output of the d-th dimension of the food; ubdAnd lbdUpper and lower bounds for the corresponding d-th dimension of device contribution, respectively; c. C2、c3Is a control parameter; c. C1Is a convergence factor in the optimization algorithm;
c1the expression of (a) is:
Figure BDA0003386694330000121
wherein iter is the current iteration number; maximum is the maximum number of iterations;
s4.5 follower location update
In the process of moving and foraging of the goblet sea squirt chain, the followers sequentially advance in a chain shape through mutual influence between the front and the rear individuals; their displacement follows the newton law of motion, the displacement of the follower is:
Figure BDA0003386694330000122
wherein a is acceleration, and the calculation formula is a ═ vfinalA/iter; and is and
Figure BDA0003386694330000123
after simplification, the expression is:
Figure BDA0003386694330000124
in the formula:
Figure BDA0003386694330000125
respectively the d-dimension equipment output of two followers which are closely connected with each other before updating;
Figure BDA0003386694330000126
the output of the equipment in the d dimension of the updated follower is obtained;
s4.6, calculating the total cost of each updated population, comparing the total cost of each updated population with the total cost of the current food, and if the total cost of a certain updated population is less than the total cost of the current food, taking the population with the minimum total cost as the new food;
s4.7, repeating the steps S4.4-S4.6 until a certain iteration number is reached or the total cost reaches a termination threshold, and after a termination condition is met, the current food corresponds to the optimal solution with the minimum total cost.
In order to verify the operation optimization method of the park energy system provided by the application, 2 scenarios are provided below for comparison and verification.
The initial data are shown in fig. 2-5.
In this embodiment, 100 electric vehicles are extracted by adopting monte carlo sampling, and two scenes are set:
the scenario 1 is that the electric vehicle does not participate in the dispatching plan, that is, when the electric quantity of the vehicle owner at the end of the journey is less than the electric quantity expected by the second journey, the electric vehicle is charged, the charging start time is the time when the vehicle owner ends the journey, and the end time is the time when the charging reaches the expected electric quantity or the time when the vehicle owner starts the second journey.
Scenario 2 is an electric vehicle participation scheduling plan. The electric automobile starts to be connected to the grid when the automobile owner finishes the journey, the off-grid time is the second journey starting time, the electric automobile participates in scheduling and serves as virtual energy storage no matter the residual electric quantity of the automobile is larger than or smaller than the expected electric quantity, and the electric quantity is larger than or equal to the expected electric quantity of the automobile owner when each automobile is off-grid in a constrained mode.
Fig. 3 shows the electricity price variation of scene 2, and after the electric vehicle participates in scheduling, the electric vehicle is optimized on the basis of the original electricity price.
Fig. 6 is a power balance curve of scenario 2, where it can be seen that most electric vehicles are charged from 23 to 5 in the morning, the price at this time is relatively cheap, the power consumption of the user is relatively small, the output of renewable energy is relatively high, and the electric vehicles can effectively consume renewable energy during charging. In the afternoon, the load of the user is high, the electric automobile discharges to meet the user requirement, and the electric automobile plays a role in energy storage, so that the running cost is reduced.
Fig. 7 is a heat load power balance curve of scenario 2, in which the heat load is mainly supplied by the upper heat supply network, the gas boiler, and the CHP unit, in which the output of the CHP unit is affected by the electrical load, and when the electrical load is low, the power supply amount of the CHP unit is reduced, and at the same time, the heat supply amount is also reduced, and the heat load is mainly supplied by the upper unit. When the electric load is higher, the output of the CHP unit is increased, the heating capacity of the CHP unit is increased, and the heat load is mainly supplied by the CHP unit.
The data of the operation results are shown in Table 1.
TABLE 1 running costs for each scene
Scene Cost of energy purchase Cost of abandoning wind and light Cost of comfort Cost of carbon emissions Total cost of
Scene 1 4511 35 0 507 5053
Scene 2 4110 0 0 441 4551
As can be seen from table 1, the total cost of scenario 2 is significantly lower than scenario 1, which indicates that participation of the electric vehicle in scheduling increases the capacity of the virtual energy storage system of the system, improves the consumption of renewable energy by the system, and thus reduces the energy purchase cost and reduces the carbon emission.
While several embodiments of the present invention have been presented herein, it will be appreciated by those skilled in the art that changes may be made to the embodiments herein without departing from the spirit of the invention. The above-described embodiments are merely exemplary and should not be taken as limiting the scope of the invention.

Claims (10)

1. A method for optimizing operation of a park energy system, the method comprising the steps of:
s1, dividing a park energy system into a supply side and a user side, wherein the supply side comprises a gas boiler, a CHP unit, a photovoltaic unit and a wind turbine unit, and the user side comprises energy storage equipment, an electric heat pump, an absorption refrigeration unit, air conditioning equipment and an electric automobile;
s2, at the supply side, considering output uncertainty of a photovoltaic unit and a wind turbine unit, and establishing a hub energy flow model and a carbon emission flow model of the supply side; on the user side, considering the randomness of electric automobile access, and establishing an energy hub matrix model on the user side;
s3, calculating the total cost of the supply side according to the supply side hub energy flow model and the carbon emission flow model obtained in the step S2 and the user side energy hub matrix model, wherein the total cost comprises the operation cost, the carbon emission cost and the wind and light abandoning cost, and the objective function of the operation optimization of the park energy system is the minimum total cost;
s4, solving the objective function in the step S3 to obtain an optimal solution, and optimizing the operation of the energy system of the park according to the optimal solution.
2. The operation optimization method for the park energy system according to claim 1, wherein the step S2 is specifically:
s2.1 the energy junction model of the supply side is as follows:
Figure FDA0003386694320000011
in the formula, Pe、PhRespectively outputting the electric quantity and the heat quantity of a node at the supply side; etaT、
Figure FDA0003386694320000012
ηGBThe efficiency of the transformer, the heating efficiency of the CHP unit and the efficiency of the gas boiler are respectively;
Figure FDA0003386694320000013
respectively a CHP unit gas distribution coefficient, a gas boiler unit gas distribution coefficient, Ee、Eg、EhElectric energy, air quantity, heat energy P purchased from park energy system to superior networkwt、PpvRespectively representing the actual wind power utilization value and the actual photoelectric utilization value;
s2.2 the energy hub matrix model at the user side is as follows:
Figure FDA0003386694320000014
in the formula: l ise、LhAre respectively provided withInputting the electric quantity and the heat quantity of the node for a user side; de、Dh、DcRespectively user side electrical load, thermal load and cold load;
Figure FDA0003386694320000015
the energy distribution coefficients of the transformer, the electric heat pump, the air conditioner, the heat exchanger and the absorption refrigerator are respectively; etaEHP、ηAC、ηHE、ηAFRespectively the efficiency of an electric heat pump, the efficiency of an air conditioner, the efficiency of heat exchange and the efficiency of an absorption refrigerator.
3. The park energy system operation optimization method of claim 2, wherein in step S2.1, the wind power actual usage value PwtAnd a photoelectric actual use value PpvFluctuating in an interval less than the predicted value plus the prediction error:
Figure FDA0003386694320000021
in the formula:
Figure FDA0003386694320000022
respectively obtaining a wind power output predicted value and a photoelectric output predicted value; deltawt、δpvRespectively a wind power predicted value error and a photoelectric predicted value error;
the wind power output predicted value and the photoelectric output predicted value are respectively obtained according to historical output data of the wind turbine generator and the photovoltaic generator;
the obeying mean value of the wind power output predicted value error and the photoelectric output predicted value error is 0, and the standard deviation is normally distributed as follows:
Figure FDA0003386694320000023
in the formula: sigmawt、σpvAre respectively windStandard deviation of the error of the predicted value of the electric output and standard deviation of the error of the predicted value of the photoelectric output; lambda [ alpha ]wt、λpvThe standard deviation coefficient of the wind power output predicted value error and the standard deviation coefficient of the photoelectric output predicted value error are respectively.
4. The park energy system operation optimization method of claim 2, wherein in step S2.2, the user side load is derived from historical data; the user side loads comprise an electric load, a heat load and a cold load;
the user side load error follows a normal distribution with a mean value of 0 and standard deviation as follows:
Figure FDA0003386694320000024
in the formula: sigmae、σh、σcRespectively, the standard deviation of the electric load error, the standard deviation of the heat load error and the standard deviation of the cold load error; lambda [ alpha ]ed、λhd、λcdRespectively is a standard deviation coefficient of an electric load error, a standard deviation coefficient of a heat load error and a standard deviation coefficient of a cold load error;
according to the demand characteristics of the electric automobile, the electric automobile is divided into a rigid EV and a flexible EV, wherein the flexible EV is divided into a fast charging flexible EV and a slow charging flexible EV; the electric automobile model is described by adopting a one-dimensional matrix:
Ω＝[L G Sn Se Ts Tc]；
in the formula: l represents the type of the electric vehicle load, and numbers 1,2 and 3 represent rigid EV load, slow charge flexible EV charge-discharge load and fast charge flexible EV charge-discharge load respectively; g represents a charging and discharging identifier of the electric automobile, the charging mode is 1, the discharging mode is-1, and the rest moments are 0; snAnd SeRespectively representing the charge quantity when the EV stops and the charge quantity expected by a user when the EV leaves the network; t issAnd TcRespectively representing the network access time and the network leaving time of the electric automobile;
the rigid EV does not participate in power grid dispatching; flexible EV participates in power grid dispatching;
establishing a flexible EV charge-discharge model:
the internal charge amount of the flexile EV satisfies the following expression:
SOCt＝SOCt-1+Pc-Pd；
in the formula: pcCharging power for a flexible EV; pdDischarge power for flexible EV; SOCtAmount of charge, SOC, for Flexible EV at time tt-1For the amount of charge of the agile EV at time t-1, time t-1 represents the last metering time at time t.
5. The campus energy system operation optimizing method of claim 2 wherein, in step S3,
s3.1, the method for calculating the carbon emission cost at the supply side of the energy system of the park comprises the following steps:
s3.1.1, determining the uncompensated carbon emission quota of the supply side of the park energy system by adopting a reference line method:
Figure FDA0003386694320000031
in the formula: CE' is the uncompensated carbon emission quota of the park energy system; beta is aCHPECarbon emission quota, beta, obtained for a unit supply of the CHP unitCHPH、βGBCarbon emission quotas obtained for the unit heat supply of the CHP unit and the gas boiler unit respectively; beta is aEGridA carbon emission quota obtained for purchasing a unit of electricity to an upper-level power grid;
s3.1.2 when the carbon emission of the park energy system is higher than the uncompensated carbon emission quota, purchasing the carbon emission right to the carbon trading market, wherein the larger the carbon emission is, the higher the corresponding carbon trading price is;
s3.1.3 calculating the carbon emission cost C of the park energy system supply sideco2: the carbon emission cost is calculated by adopting a step carbon price;
the carbon emissions of the park energy system are calculated by the following formula:
CE＝βeEe+βgEg；
in the formula: CE is the carbon emission, beta, of the energy system of the parkeCarbon emission, beta, generated for the unit generation of the upper level gridgCarbon emissions per unit of natural gas consumed;
carbon emission cost C of supply side of park energy systemco2Comprises the following steps:
Figure FDA0003386694320000032
cco2carbon price per carbon emission;
s3.2 operating cost C of energy supply side of parkbThe calculation method comprises the following steps:
Figure FDA0003386694320000033
s3.3 abandoned wind and light cost C of energy system supply side of parkpThe calculation method comprises the following steps:
Figure FDA0003386694320000041
in the formula: cb、CpRespectively the running cost and the wind and light abandoning cost; c. Ce、cg、chRespectively the electricity price, the gas price and the heat price; c. CpAbandoning the wind and light cost for a unit; pwt、PpvRespectively a wind power actual use value and a photoelectric actual use value,
Figure FDA0003386694320000042
respectively a wind power predicted value and a photoelectric predicted value.
6. The campus energy system operation optimizing method of claim 2, wherein in step S3, the total cost of the supply side further includes a user comfort cost, the user comfort cost is a cost generated when a deviation occurs between an actual electric load, a thermal load, a cold load and energy supplied from the campus energy system; skew refers to the difference in energy supply and load that the park energy system fails to meet the actual load demand of the user.
7. The method for optimizing operation of a park energy system according to claim 2, wherein in step S4, the objective function is solved using the zun sea squirt group algorithm.
8. The park energy system operation optimization method of claim 7, wherein the specific method for solving the objective function by using the zun sea squirt group algorithm is as follows:
s4.1, setting a search space to be an Euclidean space of NxD, wherein D is a space dimension and N is the number of the populations; nth population Xn＝[Xn1,Xn2,…,XnD]TRepresenting the output of each device at the supply side and the user side in the nth scene, including the electrical output of a CHP unit, the thermal output of the CHP unit, the output of a GB unit, the output of an electric heat pump, the output of an air conditioner and the output of an absorption refrigeration unit; fnRepresenting the total cost of the nth population Xn; n-1, 2,3, …, N; the upper bound of the search space is ub ═ ub1,ub2,…,ubD]Lower bound lb ═ lb1,lb2,…,lbD]；ub1、ub2、……、ubnRespectively the upper limit value of the output of each device at the supply side and the user side; lb1、lb2、……、lbnRespectively the lower limit value of the output of each device at the supply side and the user side;
s4.2, initializing a population; initializing a goblet sea squirt group with the scale of NxD according to the upper bound and the lower bound of each dimension of the search space;
s4.3, calculating the total cost Fn of each group Xn; selecting food: sorting the populations Xn from small to large according to the value of Fn, setting the population with the smallest total cost Fn ranked at the top as the current food, and recording X0Is the current food; selecting a leader and a follower: except for current food X0In addition, the rest N-1 groups are arranged according to the goblet sea squirt groupThe first half of the population is regarded as a leader, and the rest of the population is regarded as followers;
s4.4: leader location update
During the movement and foraging of the chain of goblet sea squirts, the position update of the leader is expressed as:
Figure FDA0003386694320000051
in the formula:
Figure FDA0003386694320000052
x0drespectively the equipment output of the d-th dimension of the nth group and the equipment output of the d-th dimension of the food; ubdAnd lbdUpper and lower bounds for the corresponding d-th dimension of device contribution, respectively; c. C2、c3Is a control parameter; c. C1Is a convergence factor in the optimization algorithm;
c1the expression of (a) is:
Figure FDA0003386694320000053
wherein iter is the current iteration number; maximum is the maximum number of iterations;
s4.5 follower location update
In the process of moving and foraging of the goblet sea squirt chain, the followers sequentially advance in a chain shape through mutual influence between the front and the rear individuals; their displacement follows the newton law of motion, the displacement of the follower is:
Figure FDA0003386694320000054
wherein a is acceleration, and the calculation formula is a ═ vfinalA/iter; and is and
Figure FDA0003386694320000055
after simplification, the expression is:
Figure FDA0003386694320000056
in the formula:
Figure FDA0003386694320000057
respectively the d-dimension equipment output of two followers which are closely connected with each other before updating;
Figure FDA0003386694320000058
the output of the equipment in the d dimension of the updated follower is obtained;
s4.6, calculating the total cost of each updated population, comparing the total cost of each updated population with the total cost of the current food, and if the total cost of a certain population is less than the total cost of the current food after updating, taking the population with the minimum total cost as the new current food;
s4.7, repeating the steps S4.4-S4.6 until a certain iteration number is reached or the total cost reaches a termination threshold, and after the termination condition is met, the current food corresponds to the optimal solution with the minimum total cost.
9. An information data processing terminal for implementing the method for optimizing the operation of a park energy system according to any one of claims 1 to 8.
10. A computer-readable storage medium comprising instructions which, when executed on a computer, cause the computer to perform the method of optimizing the operation of a park energy system according to any one of claims 1 to 8.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115347572A (en) * 2022-10-18 2022-11-15 武汉再来科技有限公司 Intelligent park energy control method
CN115496302A (en) * 2022-11-15 2022-12-20 宏景科技股份有限公司 Distributed automatic control method and system for zero-carbon park
CN116128690A (en) * 2022-12-08 2023-05-16 浙江正泰智维能源服务有限公司 Carbon emission cost value calculation method, device, equipment and medium
CN117172389A (en) * 2023-11-01 2023-12-05 山东建筑大学 Regional comprehensive energy optimization operation method and system considering wind-light uncertainty

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115347572A (en) * 2022-10-18 2022-11-15 武汉再来科技有限公司 Intelligent park energy control method
CN115496302A (en) * 2022-11-15 2022-12-20 宏景科技股份有限公司 Distributed automatic control method and system for zero-carbon park
CN116128690A (en) * 2022-12-08 2023-05-16 浙江正泰智维能源服务有限公司 Carbon emission cost value calculation method, device, equipment and medium
CN116128690B (en) * 2022-12-08 2024-03-05 浙江正泰智维能源服务有限公司 Carbon emission cost value calculation method, device, equipment and medium
CN117172389A (en) * 2023-11-01 2023-12-05 山东建筑大学 Regional comprehensive energy optimization operation method and system considering wind-light uncertainty
CN117172389B (en) * 2023-11-01 2024-02-02 山东建筑大学 Regional comprehensive energy optimization operation method and system considering wind-light uncertainty

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