CN110943487B - Energy optimization method and device for park energy system - Google Patents

Energy optimization method and device for park energy system Download PDF

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CN110943487B
CN110943487B CN201911419816.7A CN201911419816A CN110943487B CN 110943487 B CN110943487 B CN 110943487B CN 201911419816 A CN201911419816 A CN 201911419816A CN 110943487 B CN110943487 B CN 110943487B
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photovoltaic
value
fan
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power
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CN110943487A (en
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周步祥
夏海东
邹家惠
刘治凡
李祖钢
陈鑫
杨明通
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Sichuan University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

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Abstract

The application provides a method and a device for energy optimization of a park energy system, and relates to the field of energy. The method comprises the following steps: according to the user side load demand predicted value, the photovoltaic output power predicted value, the fan output power predicted value and the optimization constraint condition, calculating to obtain theoretical optimal values of respective output powers of the photovoltaic and the fan, according to the theoretical optimal values of the respective output powers of the photovoltaic and the fan, calculating to obtain the operation cost of the park energy system and the actual optimal values of the respective output powers of the photovoltaic and the fan, and according to the actual optimal values of the respective output powers of the photovoltaic and the fan, controlling the actual output powers of the photovoltaic and the fan so that the utilization rate of the distributed power supply is optimal and the operation cost of the park energy system is the minimum. The method of the invention maximizes the utilization rate of the distributed power supply while minimizing the cost, reduces the wind abandoning rate and the light abandoning rate, optimizes the energy of the park energy system on the whole, and has higher practical value.

Description

Energy optimization method and device for park energy system
Technical Field
The invention relates to the field of energy, in particular to a method and a device for optimizing energy of a park energy system.
Background
The ' source-network-load-store ' is an important way for realizing the energy Internet, and the operation mode of the ' source-network-load-store ' is an operation mode of a unified integral solution comprising ' power supply ', ' power grid ', ' load ' and ' energy storage ', and the mode mainly comprises ' source-source ' complementation, ' source-network ' coordination ' and ' network-load-store ' interaction. The energy internet-based source-network-load-storage mode can be applied to the whole wide energy industry, and the information interaction is more diversified.
The park micro-grid is a typical energy Internet 'source-grid-load-storage' mode, is provided with an autonomous system with self control and self energy management, and can be operated in a grid-connected mode with an external power grid or in an isolated mode. The park energy system comprises a schedulable unit, an energy storage system and a demand side management system, and the balance of energy supply and demand in the park energy system is guaranteed.
However, the energy coordination optimization degree of the existing park microgrid is insufficient, and the problems that the park energy system is high in operation cost, the utilization rate of Distributed Generation (DG) in a park is low, the wind and light abandoning rate is high and the like exist.
Disclosure of Invention
In view of the above problems, the present invention provides a method and an apparatus for energy optimization of a park energy system, which reduces the operating cost of the park energy system and improves the utilization rate of DG.
The embodiment of the invention provides a method for optimizing energy of a park energy system, which is applied to an upper-level operation server, wherein the upper-level operation server is used for controlling the operation of the park energy system, and the park energy system comprises the following steps: distributed power source and shared energy storage system, distributed power source charges to the shared energy storage system, the shared energy storage system provides the electric power energy source for the user, distributed power source includes: photovoltaic and wind turbine, the method comprises:
step 1: according to a user side load demand predicted value, a photovoltaic output power predicted value, a fan output power predicted value and an optimization constraint condition, calculating to obtain theoretical optimal values of respective output powers of a photovoltaic power source and a fan through a distributed power source utilization rate formula and a fuzzy genetic algorithm, wherein the distributed power source utilization rate formula is a formula for calculating the optimal utilization rate of the distributed power source;
step 2: according to the theoretical optimal values of the output power of the photovoltaic and the fan, calculating to obtain the operation cost of the park energy system and the actual optimal values of the output power of the photovoltaic and the fan through a cost minimum formula and the fuzzy genetic algorithm, wherein the cost minimum formula is a formula for calculating the minimum operation cost of the park energy system;
and step 3: and controlling the actual output power of the photovoltaic and the actual output power of the fan according to the actual optimal values of the respective output power of the photovoltaic and the fan, so that the utilization rate of the distributed power supply is optimal and the operation cost of the park energy system is minimum.
Optionally, calculating, according to a user side load demand predicted value, a photovoltaic output power predicted value, a fan output power predicted value, and an optimization constraint condition, a distributed power utilization formula and a fuzzy genetic algorithm to obtain theoretical optimal values of respective output powers of the photovoltaic and the fan, including:
step 10: taking the user side load demand predicted value, the photovoltaic output power predicted value and the fan output power predicted value as initial solutions of the fuzzy genetic algorithm, and setting the optimization constraint condition as parameters of the fuzzy genetic algorithm;
step 20: setting an evolution algebra counter and a maximization algebra T;
step 30: calculating to obtain a generation theoretical optimal value of the output power of the photovoltaic power generator and the output power of the fan through the distributed power source utilization rate formula on the basis of the initial solution;
step 40: calculating the user side load demand predicted value and the first-generation theoretical optimal value to obtain a corresponding mass center;
step 50: propagating according to the centroid to produce a new population;
step 60: generating two offspring through crossing according to each pair of fuzzy sets in the population generated by propagation;
step 70: if q is the probability of mutation, the operation of mutation is completed by randomly replacing a certain element on the fuzzy set;
step 80: under the condition that T is smaller than T, taking the mutated first-generation theoretical optimal value as the initial solution, and returning to the step 30;
step 90: and under the condition that T is greater than or equal to T, the varied first-generation theoretical optimal value is the theoretical optimal value of the respective output power of the photovoltaic and the fan.
Optionally, the method further comprises:
the formula of the utilization rate of the distributed power supply is as follows:
Figure GDA0003022859510000031
in this formula: in the formula, PL,1(t) represents the user side load demand prediction value, PGv(t) represents the predicted photovoltaic output power at time t, PGwAnd (t) represents a predicted value of the output power of the fan at the time t.
Optionally, the park energy system further comprises: a large power grid; the optimization constraints include: the method comprises the following steps of active balance constraint conditions, tie line power constraint conditions and park energy system operation constraint conditions, and further comprises the following steps:
the active balance constraint conditions are as follows:
Figure GDA0003022859510000032
in the formula: n is a radical ofDGRepresenting the number of the distributed power supplies; pGi(t) the output power of the ith micro source at the time t is represented, wherein the micro source is the fan or the photovoltaic; pESS,n(t) represents the charge-discharge power of the shared energy storage system in a period t, and the selection of the sign of the charge-discharge power is related to the charge and discharge state of the energy storage equipment; pl(t) represents the system load demand power for a period t; pPgridRepresenting the power purchased by the park energy system to the large power grid in the period of t, IPgridAnd ISgridEach represents a constant; pSgridRepresenting the time period t from the park energy system to the large power gridSelling electricity power; Δ t represents a time period;
the tie line power constraint conditions are as follows:
IPgrid(t)+ISgrid(t)≤1 (1)
PPgridmin≤PPgrid(t)≤PPgridmax (2)
PSgridmin≤PSgrid(t)≤PSgridmax (3)
wherein, the formula (1) represents that the park energy system only purchases or sells electricity to the large power grid in one of the situations, or the park energy system does not purchase or sell electricity to the large power grid; the formula (2) represents the upper limit and the lower limit of the power purchased by the park energy system to the large power grid; formula (3) represents the upper and lower limits of the power sold to the large power grid by the park energy system;
the operation constraint conditions of the park energy system are as follows:
Socmin≤Soc(t)≤Socmax (4)
EBmin≤EB(t)≤EBmax (5)
Figure GDA0003022859510000041
wherein, formula (4) represents the charge quantity S of the shared energy storage system at time toc(t) is required to be at a maximum of SocmaxMinimum SocminBetween the charge amounts of; equation (5) represents the remaining energy storage capacity E of the shared energy storage systemB(t) must satisfy no more than the limit value EBmin、EBmax(ii) a Formula (6) represents the condition that the charge-discharge power of the shared energy storage system needs to meet, PESS.eA rated value of charge and discharge power for the shared energy storage system; etaddIs the bidirectional DC-DC converter efficiency; pddeThe power rating of the bidirectional DC-DC converter is obtained.
Optionally, the calculating, according to the theoretical optimal values of the respective output powers of the photovoltaic and the wind turbine, the actual optimal values of the respective output powers of the photovoltaic and the wind turbine and the operation cost of the park energy system through a cost minimization formula and the fuzzy genetic algorithm includes:
step 10: taking the theoretical optimal values of the output power of the photovoltaic generator and the output power of the fan as an initial solution of a fuzzy genetic algorithm;
step 20: setting an evolution algebra counter and a maximization algebra T;
step 30: on the basis of the initial solution, calculating to obtain a first-generation actual optimal value of the output power of the photovoltaic and the fan and a first-generation minimum value of the operation cost of the park energy system through the cost minimum formula;
step 40: calculating a first-generation actual optimal value of the output power of the photovoltaic power generator and the output power of the fan and a first-generation minimum value of the operation cost of the park energy system to obtain a corresponding mass center;
step 50: propagating according to the centroid to produce a new population;
step 60: generating two offspring through crossing according to each pair of fuzzy sets in the population generated by propagation;
step 70: if q is the probability of mutation, the operation of mutation is completed by randomly replacing a certain element on the fuzzy set;
step 80: under the condition that T is smaller than T, taking the variant first-generation actual optimal value and the variant first-generation minimum value as initial solutions, and returning to the step 30;
step 90: and under the condition that T is greater than or equal to T, the mutated first-generation theoretical optimal value is the actual optimal value of the output power of the photovoltaic and the fan, and the mutated first-generation minimum value is the minimum value of the operation cost of the park energy system.
Optionally, the method further comprises:
the cost minimization formula is:
Figure GDA0003022859510000051
in the formula, minF is a value corresponding to the minimum running cost of the park energy system;CIN(t) is a depreciation cost function; cG(t) is a cost function for purchasing and selling electricity;
the depreciation cost function is:
Figure GDA0003022859510000052
in the formula, n represents the number of the micro sources; pi(t) represents the output power of the micro-source at time t; n isiRepresenting an investment payback period; r isiRepresenting the fixed annual interest rate of the ith micro source; cin,iExpressing the unit capacity construction cost; k is a radical ofiRepresenting annual utilization coefficient, ni、ri、kiThe values of (a) are respectively a first preset value, a second preset value and a third preset value;
the electricity purchasing and selling cost function is as follows:
Figure GDA0003022859510000053
in the formula, CP(t) represents the price of electricity purchased during the period t; cSAnd (t) represents the electricity selling price in the t period, and the electricity selling price and the electricity purchasing price are respectively divided into 3 periods of peak, valley and average.
Optionally, the method further comprises:
establishing the shared energy storage system through an electric energy charging and discharging model according to the charging and discharging efficiency of a storage battery in the shared energy storage system;
the electric energy charging and discharging model comprises the following steps:
Figure GDA0003022859510000054
in the formula, SOC(t) represents the charge of the shared energy storage system at time t;
Figure GDA0003022859510000061
and
Figure GDA0003022859510000062
respectively representing the charge and discharge power of the user n in a t period; etainRepresenting the charging efficiency, η, of the shared energy storage systemoutRepresents the discharge efficiency of the shared energy storage system, and is more than or equal to 0 and less than or equal to etain≤1，0≤ηout≤1。
An embodiment of the present invention further provides a device for optimizing energy of a park energy system, where the device is applied to a higher-level operation server, the higher-level operation server is configured to control operation of the park energy system, and the park energy system includes: distributed power source and shared energy storage system, distributed power source charges to the shared energy storage system, the shared energy storage system provides the electric power energy source for the user, distributed power source includes: photovoltaic, fan, the device includes:
the calculation theoretical optimal value module is used for calculating theoretical optimal values of the output power of the photovoltaic power supply and the fan respectively according to a user side load demand predicted value, a photovoltaic output power predicted value, a fan output power predicted value and an optimization constraint condition through a distributed power supply utilization rate formula and a fuzzy genetic algorithm, wherein the distributed power supply utilization rate formula is a formula for calculating the optimal utilization rate of the distributed power supply;
a module for calculating a cost and an actual optimal value, which is used for calculating the operation cost of the park energy system and the actual optimal value of the output power of the photovoltaic and the fan through a cost minimum formula and the fuzzy genetic algorithm according to the theoretical optimal value of the output power of the photovoltaic and the fan, wherein the cost minimum formula is a formula for calculating the operation cost of the park energy system to be minimum;
and the control module is used for controlling the actual output power of the photovoltaic and the fan according to the actual optimal value of the output power of the photovoltaic and the fan, so that the utilization rate of the distributed power supply is optimal and the operation cost of the park energy system is minimum.
Optionally, the module for calculating the theoretical optimization value is specifically configured to implement the following steps:
step 10: taking the user side load demand predicted value, the photovoltaic output power predicted value and the fan output power predicted value as initial solutions of the fuzzy genetic algorithm, and setting the optimization constraint condition as parameters of the fuzzy genetic algorithm;
step 20: setting an evolution algebra counter and a maximization algebra T;
step 30: calculating to obtain a generation theoretical optimal value of the output power of the photovoltaic power generator and the output power of the fan through the distributed power source utilization rate formula on the basis of the initial solution;
step 40: calculating the user side load demand predicted value and the first-generation theoretical optimal value to obtain a corresponding mass center;
step 50: propagating according to the centroid to produce a new population;
step 60: generating two offspring through crossing according to each pair of fuzzy sets in the population generated by propagation;
step 70: if q is the probability of mutation, the operation of mutation is completed by randomly replacing a certain element on the fuzzy set;
step 80: under the condition that T is smaller than T, taking the mutated first-generation theoretical optimal value as the initial solution, and returning to the step 30;
step 90: under the condition that T is greater than or equal to T, the varied first-generation theoretical optimal value is the theoretical optimal value of the output power of the photovoltaic generator and the fan;
wherein the distributed power source utilization formula is as follows:
Figure GDA0003022859510000071
in this formula: in the formula, PL,1(t) represents the user side load demand prediction value, PGv(t) represents the predicted photovoltaic output power at time t, PGwAnd (t) represents a predicted value of the output power of the fan at the time t.
Optionally, the module for calculating the cost and the actual optimal value is specifically configured to implement the following steps:
step 10: taking the theoretical optimal values of the output power of the photovoltaic generator and the output power of the fan as an initial solution of a fuzzy genetic algorithm;
step 20: setting an evolution algebra counter and a maximization algebra T;
step 30: on the basis of the initial solution, calculating to obtain a first-generation actual optimal value of the output power of the photovoltaic and the fan and a first-generation minimum value of the operation cost of the park energy system through the cost minimum formula;
step 40: calculating a first-generation actual optimal value of the output power of the photovoltaic power generator and the output power of the fan and a first-generation minimum value of the operation cost of the park energy system to obtain a corresponding mass center;
step 50: propagating according to the centroid to produce a new population;
step 60: generating two offspring through crossing according to each pair of fuzzy sets in the population generated by propagation;
step 70: if q is the probability of mutation, the operation of mutation is completed by randomly replacing a certain element on the fuzzy set;
step 80: under the condition that T is smaller than T, taking the variant first-generation actual optimal value and the variant first-generation minimum value as initial solutions, and returning to the step 30;
step 90: under the condition that T is greater than or equal to T, the mutated first-generation theoretical optimal value is the actual optimal value of the output power of the photovoltaic and the fan, and the mutated first-generation minimum value is the minimum value of the operation cost of the park energy system;
wherein the cost minimization formula is:
Figure GDA0003022859510000081
in the formula, minF is a value corresponding to the minimum running cost of the park energy system; cIN(t) is a depreciation cost function; cG(t) is a cost function for purchasing and selling electricity;
the depreciation cost function is:
Figure GDA0003022859510000082
in the formula, n represents the number of the micro sources; pi(t) represents the output power of the micro-source at time t; n isiRepresenting an investment payback period; r isiRepresenting the fixed annual interest rate of the ith micro source; cin,iExpressing the unit capacity construction cost; k is a radical ofiRepresenting annual utilization coefficient, ni、ri、kiThe values of (a) are respectively a first preset value, a second preset value and a third preset value;
the electricity purchasing and selling cost function is as follows:
Figure GDA0003022859510000083
in the formula, CP(t) represents the price of electricity purchased during the period t; cSAnd (t) represents the electricity selling price in the t period, and the electricity selling price and the electricity purchasing price are respectively divided into 3 periods of peak, valley and average.
Optionally, the apparatus further comprises: the system establishing module is used for establishing the shared energy storage system through an electric energy charging and discharging model according to the charging and discharging efficiency of a storage battery in the shared energy storage system;
the electric energy charging and discharging model comprises the following steps:
Figure GDA0003022859510000084
in the formula, SOC(t) represents the charge of the shared energy storage system at time t;
Figure GDA0003022859510000085
and
Figure GDA0003022859510000086
respectively representing users n in the t periodThe charging and discharging power of (1); etainRepresenting the charging efficiency, η, of the shared energy storage systemoutRepresents the discharge efficiency of the shared energy storage system, and is more than or equal to 0 and less than or equal to etain≤1，0≤ηout≤1。
According to the energy optimization method for the park energy system, provided by the invention, the theoretical optimal values of the respective output powers of the photovoltaic and the fan are obtained through calculation by a distributed power utilization formula and a fuzzy genetic algorithm according to the user side load demand predicted value, the photovoltaic output power predicted value, the fan output power predicted value and the optimization constraint condition, the operation cost of the park energy system and the actual optimal values of the respective output powers of the photovoltaic and the fan are obtained through calculation by a cost minimum formula and the fuzzy genetic algorithm again according to the theoretical optimal values, and finally the actual output powers of the photovoltaic and the fan are controlled according to the actual optimal values, so that the utilization rate of the distributed power supply is optimal and the operation cost of the park energy system is minimum. According to the method, the distributed power supply is combined with the power load, the theoretical optimization value is calculated by combining the predicted value of the distributed power supply with the corresponding constraint condition, the cost is minimized by combining the cost factor, the utilization rate of the distributed power supply is maximized while the cost is minimized, the wind abandonment rate and the light abandonment rate are reduced, the energy of a park energy system is optimized integrally, and the method has high practical value.
Drawings
FIG. 1 is a flow chart of a method for energy optimization of a park energy system in accordance with an embodiment of the present invention;
FIG. 2 is a schematic diagram of a park energy system according to an embodiment of the present invention;
FIG. 3 is a graph of DG output power according to various aspects of the present invention;
figure 4 is a block diagram of an apparatus for energy optimization of a park energy system in accordance with an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below. It should be understood that the specific embodiments described herein are merely illustrative of the invention, but do not limit the invention to only some, but not all embodiments.
The inventor finds that the energy coordination optimization degree of the park microgrid at the present stage is insufficient, and the problems that the park energy system is high in operation cost, the utilization rate of Distributed Generation (DG) in a park is low, the wind and light abandoning rate is high and the like exist.
Specifically, in the use process of the park microgrid, the economic benefit of the operation of the energy system is considered unilaterally, the economic benefit is maximized, the cost is not considered, the cost is transferred to users, the difference between the electricity price of the park microgrid and the electricity price of the large power grid is not large, even the difference is higher than the electricity price of the large power grid, and the park microgrid is possibly not willing to be used by the users.
In addition, when the economic benefit is maximized as a target, the utilization rate of the DG is not fully considered, the photovoltaic power cannot output power in the daytime, the fan cannot output power in the daytime as much as possible, and the like, but the comprehensive consideration is not combined with the equipment cost, depreciation rate, output power and the like of the fan and the photovoltaic power, so that the problems of low utilization rate of the DG, high wind abandonment rate, high light abandonment rate and the like are caused.
Aiming at the problems, the inventor carries out deep research, combines a large number of practical tests and simulation experiments, creatively combines a fuzzy genetic algorithm, and comprehensively considers two directions of minimizing the cost and maximizing the DG utilization rate to solve the problems. The technical solution of the present invention is described in detail below.
Referring to fig. 1, a flowchart of a method for energy optimization of a park energy system according to an embodiment of the present invention is shown, where the method is applied to a higher-level operation server, and the higher-level operation server is used to control operation of the park energy system, where the park energy system includes: distributed generator and sharing energy storage system, distributed generator charges to sharing energy storage system, and sharing energy storage system provides the electric power energy for the user, and distributed generator includes: the structural schematic diagram of the photovoltaic system, the fan system and the park energy system is shown in fig. 2, and the energy optimization method of the park energy system comprises the following steps:
step 101: according to the user side load demand predicted value, the photovoltaic output power predicted value, the fan output power predicted value and the optimization constraint condition, theoretical optimal values of respective output powers of the photovoltaic and the fan are obtained through calculation through a distributed power utilization formula and a fuzzy genetic algorithm, wherein the distributed power utilization formula is a formula for calculating the utilization rate of the distributed power to be optimal.
In the embodiment of the invention, the energy system of the general park needs an operator to combine the scattered users with the distributed power supply for unified management and scheduling. The operator provides higher level's operation server, and higher level's operation server is used for controlling the operation of garden energy system, and the garden energy system includes: DG and shared energy storage system, DG charges to shared energy storage system, and shared energy storage system provides the electric power energy for the user, and DG includes: photovoltaic and fan.
In the actual using process, the superior operation server calculates theoretical optimal values of respective output powers of the photovoltaic power and the fan through a distributed power utilization formula and a fuzzy genetic algorithm according to a user side load demand predicted value, a photovoltaic output power predicted value, a fan output power predicted value and an optimization constraint condition, wherein the distributed power utilization formula is a formula for calculating the optimal utilization rate of the distributed power.
In the process, the user side load demand predicted value can be obtained according to historical big data and real-time data, and the photovoltaic output power predicted value and the fan output power predicted value can be obtained according to big data and weather forecast, for example: the weather forecast today is cloudy day, and wind power is great, then the fan output power can be some more, and the weather forecast today is sunny day, and wind power is less, then photovoltaic output power can be some more, in addition, the photovoltaic does not carry out power output after general night.
The DG also charges the shared energy storage system while outputting power to the user, and the shared energy storage system provides power energy to the user when the power output by the DG does not satisfy the load required by the user (i.e., the shared energy storage system outputs power to the user), and the optimization constraint condition is explained below and is not described herein again.
The formula of the utilization rate of the distributed power supply is as follows:
Figure GDA0003022859510000111
in this formula: in the formula, PL,1(t) represents a predicted value of the user side load demand, PGv(t) represents the predicted photovoltaic output power at time t, PGw(t) represents a predicted value of the fan output power at time t, and it should be noted that time t in the embodiment of the present invention means a certain time period, for example: the predicted value of the photovoltaic output power at the time t can specifically refer to 7: 00-8: 00, the predicted photovoltaic output power value in the period is not described in detail below; c1minThe difference between the DG output and the load consumption is expressed, and the smaller the value, the better.
The distributed power supply utilization rate formula needs to be combined with a fuzzy genetic algorithm to obtain the theoretical optimal values of the respective output powers of the photovoltaic power generator and the fan, and the method specifically comprises the following steps:
step 10: taking a user side load demand predicted value, a photovoltaic output power predicted value and a fan output power predicted value as initial solutions of the fuzzy genetic algorithm, and setting optimization constraint conditions as parameters of the fuzzy genetic algorithm;
step 20: setting an evolution algebra counter and a maximization algebra T;
step 30: on the basis of the initial solution, calculating a generation of theoretical optimal values of the respective output powers of the photovoltaic power generator and the fan through a distributed power supply utilization rate formula;
step 40: calculating a user side load demand predicted value and a first-generation theoretical optimal value to obtain a corresponding centroid;
step 50: propagating according to the centroid to produce a new population;
step 60: generating two offspring through crossing according to each pair of fuzzy sets in the population generated by propagation;
step 70: if q is the probability of mutation, the operation of mutation is completed by randomly replacing a certain element on the fuzzy set;
step 80: under the condition that T is smaller than T, taking the mutated first-generation theoretical optimal value as an initial solution, and returning to the step 30;
step 90: and under the condition that T is greater than or equal to T, the mutated first-generation theoretical optimal value is the theoretical optimal value of the respective output power of the photovoltaic and the fan.
Based on the steps, the theoretical optimal values of the respective output powers of the photovoltaic power generator and the fan can be obtained.
The park energy system of the embodiment of the invention further comprises: a large power grid; the optimization constraint conditions of the embodiment of the invention comprise: active balance constraint condition, tie-line power constraint condition, park energy system operation constraint condition, wherein, shared energy storage system is the system of storing the electric power energy among the park energy system, and it includes but not limited to: the shared energy storage system can store redundant power energy generated by the fan and the photovoltaic, and can be used for providing power energy for a user when the load required by the user is large and the power output by the fan and the photovoltaic cannot meet the load required by the user, so that the load required by the user is met.
The large power grid refers to an external power grid, for example: when a park energy system (power output by a fan and a photovoltaic and power output by a shared energy storage system) cannot meet the load required by a user, a power grid established by a national power grid company needs to purchase power from a large power grid to meet the load required by the user. It can be understood that if the output power of the fan and the photovoltaic in the park energy system is greater than the load required by the user and the charge amount in the shared energy storage system also reaches the specified value, the power output by the fan and the photovoltaic can be sold to a large power grid, so that the wind power resource and the light energy resource are not wasted, and the profitability of the park energy system is improved.
In addition, based on the particularity of the park energy system, the park energy system further satisfies an active power balance constraint condition, a tie line power constraint condition and a park energy system operation constraint condition, and specifically comprises the following steps:
the active power balance constraint conditions are as follows:
Figure GDA0003022859510000131
in the formula: n is a radical ofDGRepresents the number of distributed power supplies; pGi(t) the output power of the ith micro source at the time t is shown, and the micro source is a fan or a photovoltaic; pESS,n(t) the charging and discharging power of the shared energy storage system in a t period is represented, the selection of the sign of the charging and discharging power is related to the charging and discharging states of the energy storage equipment, the charging state of the energy storage equipment selects a positive sign, and the discharging state of the energy storage equipment selects a negative sign; pl(t) represents the system load demand power at time period t, i.e. the demand load power at the user side; pPgridIndicating that the park energy system purchases electric power from the large power grid at t period IPgridAnd ISgridEach represents a constant; pSgridRepresenting that the park energy system sells power to a large power grid at the time t; Δ t denotes a time period, for example: 6: a 2 hour period of 00-8: 00 points. I isPgridAnd ISgridThe expression may be:
Figure GDA0003022859510000132
the tie line power constraint conditions are as follows:
IPgrid(t)+ISgrid(t)≤1 (1)
PPgridmin≤PPgrid(t)≤PPgridmax (2)
PSgridmin≤PSgrid(t)≤PSgridmax (3)
the formula (1) shows that the park energy system only purchases or sells electricity to or from the large power grid, namely, the park energy system purchases or sells electricity to or from the large power grid at the same time, and other states except the electricity cannot exist; the formula (2) represents the upper and lower limits of the power purchased from the park energy system to the large power grid; formula (3) represents the upper and lower limits of the power sold to the large power grid by the park energy system, the upper and lower limits are set according to the factors of the bearing capacity of the line, and the line is marked under general conditions.
The operation constraint conditions of the park energy system are as follows:
Socmin≤Soc(t)≤Socmax (4)
EBmin≤EB(t)≤EBmax (5)
Figure GDA0003022859510000141
wherein, formula (4) represents the charge quantity S at the moment t of sharing the energy storage systemoc(t) is required to be at a maximum of SocmaxMinimum SocminThe charge quantity of the shared energy storage system cannot exceed the maximum value, damage equipment when the charge quantity exceeds the maximum value, and cannot be lower than the minimum value, and the demand load of a user side may not be met when the charge quantity is lower than the minimum value; equation (5) represents the remaining energy storage capacity E of the shared energy storage systemB(t) must satisfy no more than the limit value EBmin、EBmax(ii) a Formula (6) represents the condition that the charge-discharge power of the shared energy storage system needs to meet, PESS.eA rated value for the charge and discharge power of the shared energy storage system; etaddIs the bidirectional DC-DC converter efficiency; pddeThe power rating of the bidirectional DC-DC converter is obtained.
Step 102: and according to the theoretical optimal values of the respective output powers of the photovoltaic and the fan, calculating to obtain the operation cost of the park energy system and the actual optimal values of the respective output powers of the photovoltaic and the fan through a cost minimum formula and a fuzzy genetic algorithm, wherein the cost minimum formula is a formula for calculating the minimum operation cost of the park energy system.
In the embodiment of the invention, after the theoretical optimal values of the respective output powers of the photovoltaic and the fan are obtained, the superior operation server further needs to calculate the operation cost of the park energy system and the actual optimal values of the respective output powers of the photovoltaic and the fan through a cost minimum formula and a fuzzy genetic algorithm, so that the effects of minimizing the operation cost and optimizing the utilization rate of the DG are achieved, wherein the cost minimum formula is a formula for calculating the operation cost of the park energy system, and the cost minimum formula also needs to be combined with the fuzzy genetic algorithm to obtain the operation cost of the park energy system and the actual optimal values of the respective output powers of the photovoltaic and the fan, and the specific steps include:
step 10: taking the theoretical optimal values of the respective output powers of the photovoltaic and the fan as the initial solution of the fuzzy genetic algorithm;
step 20: setting an evolution algebra counter and a maximization algebra T;
step 30: on the basis of the initial solution, calculating to obtain a first-generation actual optimal value of the respective output power of the photovoltaic and the fan and a first-generation minimum value of the operation cost of the park energy system through a cost minimum formula;
step 40: calculating the first-generation actual optimal value of the respective output power of the photovoltaic and the fan and the first-generation minimum value of the operation cost of the park energy system to obtain the corresponding mass center;
step 50: propagating according to the centroid to produce a new population;
step 60: generating two offspring through crossing according to each pair of fuzzy sets in the population generated by propagation;
step 70: if q is the probability of mutation, the operation of mutation is completed by randomly replacing a certain element on the fuzzy set;
step 80: under the condition that T is smaller than T, taking the variant first-generation actual optimal value and the variant first-generation minimum value as initial solutions, and returning to the step 30;
step 90: and under the condition that T is greater than or equal to T, the mutated first-generation theoretical optimal value is the actual optimal value of the respective output power of the photovoltaic and the fan, and the mutated first-generation minimum value is the minimum value of the operation cost of the energy system of the park.
The cost minimum formula is specifically as follows:
Figure GDA0003022859510000151
in the formula, minF is a value corresponding to the minimum running cost of the park energy system; cIN(t) is a depreciation cost function; cGAnd (t) is a power purchase and sale cost function. The depreciation cost belongs to the fixed cost of the power generation cost of the park energy system, wherein the depreciation costs of the fan, the photovoltaic panel and the energy storage system are included; the electricity purchasing and selling cost is the cost of purchasing or selling electricity from the park energy system to the large power grid.
Wherein the depreciation cost function is:
Figure GDA0003022859510000152
in the formula, n represents the number of micro sources (fans or photovoltaics); pi(t) represents the output power of the micro-source at time t; n isiRepresenting an investment payback period; r isiRepresenting the fixed annual interest rate of the ith micro source; cin,iExpressing the unit capacity construction cost; k is a radical ofiRepresenting annual utilization coefficient, ni、ri、kiThe values of the first preset value, the second preset value and the third preset value are respectively obtained by referring to relevant documents;
the electricity purchase and sale cost function is as follows:
Figure GDA0003022859510000153
in the formula, CP(t) represents the price of electricity purchased during the period t; cSAnd (t) represents the electricity selling price and the electricity purchasing price in the t time period, wherein the electricity selling price and the electricity purchasing price are divided into 3 time periods of peak, valley and average.
The shared energy storage system in the embodiment of the invention is established through an electric energy charging and discharging model according to the charging and discharging efficiency of a storage battery in the shared energy storage system, wherein the electric energy charging and discharging model is as follows:
Figure GDA0003022859510000161
in the formula, SOC(t) represents the charge capacity of the shared energy storage system at the moment t;
Figure GDA0003022859510000162
and
Figure GDA0003022859510000163
respectively representing the charge and discharge power of the user n in a t period; etainRepresenting charge efficiency, η, of a shared energy storage systemoutRepresents the discharge efficiency of the shared energy storage system, and is more than or equal to 0 and less than or equal to etain≤1，0≤ηout≤1。
The user side load demand predicted value is obtained according to a user power load model, the user power load model comprises user household electricity, an electric automobile and the like, and the user load demand predicted value predicted by applying an inspection technology is as follows:
le,n＝[le,n,1,...,le,n,t,...,le,n,T]
wherein: le,n,tRepresenting the load demand of the user n in the t period; t represents all time periods of a whole day.
Step 103: and controlling the actual output power of the photovoltaic and the fan according to the actual optimal value of the respective output power of the photovoltaic and the fan, so that the utilization rate of the distributed power supply is optimal and the operation cost of the park energy system is minimum.
In the embodiment of the invention, after the actual optimal values of the respective output powers of the photovoltaic power system and the fan are determined, the upper operation server controls the actual output powers of the photovoltaic power system and the fan to be the actual optimal values, so that the minimum operation cost of the park energy system is achieved, and the optimal utilization rate of the distributed power supply is achieved.
It should be noted that, in the embodiment of the present invention, the method for optimizing energy of the park energy system is explained and illustrated by taking an electric energy as an example, but in practical application, the park energy system further includes energy sources such as heat energy and gas energy, for example: the energy sources such as solar heating, heat pump, methane tank and biomass gasification can be coordinated and used together, but the general idea is the same as that of electric energy sources, so the method of the invention does not represent only the optimization of the electric energy sources.
In the following, experimental simulation verification is performed on the method in the embodiment of the present invention with the park energy system only having electric energy as a research object.
Assume that the time of use electricity price data is as follows: the peak time period (10: 00-15: 00, 18: 00-22: 00) is 1.055 yuan/(kWh); the flat time period (6: 00-10: 00, 15: 00-18: 00) is 0.633 yuan/(kWh); the valley period (22: 00-6: 00) is 0.291 yuan/(kWh).
The upper limit of the fan is set at 10kW, the upper limit of the photovoltaic is set at 10kW, the upper limit of the energy storage system is set at 8kW, and the lower limit is-8 kW. The data of the fan, the photovoltaic output power prediction and the user power consumption prediction are shown in the following table:
Figure GDA0003022859510000171
to verify the effectiveness of the method of the invention, 3 different protocols were compared, respectively:
scheme 1: in a traditional energy storage mode, a 'spontaneous self-use and surplus internet access' mode is adopted;
scheme 2: in a shared energy storage mode, a 'spontaneous self-use and margin internet access' mode is adopted;
scheme 3: the method of the invention.
The cost for each of the three protocols is shown in the following table:
Figure GDA0003022859510000172
Figure GDA0003022859510000181
comparing the economic cost of the three schemes in the table above, scheme 3 is obtained, namely, the cost of the optimized method of the invention is the lowest, so the economic efficiency is the best.
In addition, to realize the maximum utilization of DG in a general park energy system, the utilization rate of DG is an important index, and fig. 3 shows the output power of DG under different schemes.
In fig. 3, the horizontal axis represents a time period, dividing 24 hours a day into 48 time segments; the vertical axis represents the output power of the DGs, the curve composed of the short transverse lines is the output power of the DGs under the scheme 1, the curve composed of the short transverse lines and the points is the output power of the DGs under the scheme 2, the curve composed of the points is the output power of the DGs under the scheme 3, and the curve composed of the implementation is the sum of the distributed energy sources.
It can be seen that scheme 3, i.e., scheme 2, has the largest DG power output and scheme 1, has the smallest DG power output, so the highest DG utilization is achieved by the method of the present invention.
By integrating the above 3 points, the technical scheme of the invention achieves the purposes of minimum operation cost and maximum DG utilization rate.
Referring to fig. 4, a block diagram of an apparatus for energy optimization of a park energy system according to an embodiment of the present invention is shown, where the apparatus is applied to a superior operation server, and the superior operation server is used to control operation of the park energy system, and the park energy system includes: distributed generator and sharing energy storage system, distributed generator charges to sharing energy storage system, and sharing energy storage system provides the electric power energy for the user, and distributed generator includes: the device for optimizing the energy of the photovoltaic system, the fan system and the park energy system comprises a photovoltaic system, a fan system and a park energy system;
a theoretical optimal value calculating module 310, configured to calculate, according to the user-side load demand predicted value, the photovoltaic output power predicted value, the fan output power predicted value, and the optimization constraint condition, a theoretical optimal value of respective output powers of the photovoltaic and the fan through a distributed power utilization formula and a fuzzy genetic algorithm, where the distributed power utilization formula is a formula for calculating a utilization rate of the distributed power to be optimal;
a module 320 for calculating the cost and the actual optimal value, which is used for calculating the operation cost of the park energy system and the actual optimal value of the output power of each photovoltaic and the fan according to the theoretical optimal value of the output power of each photovoltaic and the fan through a cost minimum formula and a fuzzy genetic algorithm, wherein the cost minimum formula is a formula for calculating the operation cost of the park energy system to be minimum;
and the control module 330 is configured to control the actual output powers of the photovoltaic and the wind turbine according to the actual optimal values of the respective output powers of the photovoltaic and the wind turbine, so that the utilization rate of the distributed power supply is optimal and the operation cost of the park energy system is the minimum.
Optionally, the module 310 for calculating a theoretical optimization value is specifically configured to implement the following steps:
step 10: taking a user side load demand predicted value, a photovoltaic output power predicted value and a fan output power predicted value as initial solutions of the fuzzy genetic algorithm, and setting optimization constraint conditions as parameters of the fuzzy genetic algorithm;
step 20: setting an evolution algebra counter and a maximization algebra T;
step 30: on the basis of the initial solution, calculating a generation of theoretical optimal values of the respective output powers of the photovoltaic power generator and the fan through a distributed power supply utilization rate formula;
step 40: calculating a user side load demand predicted value and a first-generation theoretical optimal value to obtain a corresponding centroid;
step 50: propagating according to the centroid to produce a new population;
step 60: generating two offspring through crossing according to each pair of fuzzy sets in the population generated by propagation;
step 70: if q is the probability of mutation, the operation of mutation is completed by randomly replacing a certain element on the fuzzy set;
step 80: under the condition that T is smaller than T, taking the mutated first-generation theoretical optimal value as an initial solution, and returning to the step 30;
step 90: under the condition that T is greater than or equal to T, the mutated first-generation theoretical optimal value is the theoretical optimal value of the respective output power of the photovoltaic power and the fan;
the formula of the utilization rate of the distributed power supply is as follows:
Figure GDA0003022859510000191
in this formula: in the formula, PL,1(t) represents a predicted value of the user side load demand, PGv(t) represents the predicted photovoltaic output power at time t, PGwAnd (t) represents a predicted value of the output power of the fan at the time t.
Optionally, the module 320 for calculating the cost and the actual optimal value is specifically configured to implement the following steps:
step 10: taking the theoretical optimal values of the respective output powers of the photovoltaic and the fan as the initial solution of the fuzzy genetic algorithm;
step 20: setting an evolution algebra counter and a maximization algebra T;
step 30: on the basis of the initial solution, calculating to obtain a first-generation actual optimal value of the respective output power of the photovoltaic and the fan and a first-generation minimum value of the operation cost of the park energy system through the cost minimum formula;
step 40: calculating the first-generation actual optimal value of the respective output power of the photovoltaic and the fan and the first-generation minimum value of the operation cost of the park energy system to obtain the corresponding mass center;
step 50: propagating according to the centroid to produce a new population;
step 60: generating two offspring through crossing according to each pair of fuzzy sets in the population generated by propagation;
step 70: if q is the probability of mutation, the operation of mutation is completed by randomly replacing a certain element on the fuzzy set;
step 80: under the condition that T is smaller than T, taking the variant first-generation actual optimal value and the variant first-generation minimum value as initial solutions, and returning to the step 30;
step 90: under the condition that T is greater than or equal to T, the first-generation theoretical optimal value after variation is the actual optimal value of the output power of each of the photovoltaic and the fan, and the first-generation minimum value after variation is the minimum value of the operation cost of the park energy system;
wherein, the cost minimum formula is:
Figure GDA0003022859510000201
in the formula, minF is a value corresponding to the minimum running cost of the park energy system; cIN(t) is a depreciation cost function; cG(t) is a cost function for purchasing and selling electricity;
the depreciation cost function is:
Figure GDA0003022859510000202
in the formula, n represents the number of micro sources; pi(t) represents the output power of the micro-source at time t; n isiRepresenting an investment payback period; r isiRepresenting the fixed annual interest rate of the ith micro source; cin,iExpressing the unit capacity construction cost; k is a radical ofiRepresenting annual utilization coefficient, ni、ri、kiThe values of (a) are respectively a first preset value, a second preset value and a third preset value;
the electricity purchase and sale cost function is as follows:
Figure GDA0003022859510000211
in the formula, CP(t) represents the price of electricity purchased during the period t; cSAnd (t) represents the electricity selling price in the t time period, and the electricity selling price and the electricity purchasing price are respectively divided into 3 time periods of peak, valley and average.
Optionally, the apparatus further comprises: the system establishing module is used for establishing the shared energy storage system through an electric energy charging and discharging model according to the charging and discharging efficiency of the storage battery in the shared energy storage system;
the electric energy charging and discharging model comprises the following steps:
Figure GDA0003022859510000212
in the formula, SOC(t) represents the charge capacity of the shared energy storage system at the moment t;
Figure GDA0003022859510000213
and
Figure GDA0003022859510000214
respectively representing the charge and discharge power of the user n in a t period; etainRepresenting charge efficiency, η, of a shared energy storage systemoutRepresents the discharge efficiency of the shared energy storage system, and is more than or equal to 0 and less than or equal to etain≤1，0≤ηout≤1。
According to the embodiment, the theoretical optimal values of the output power of the photovoltaic and the fan are obtained through calculation according to the user side load demand predicted value, the photovoltaic output power predicted value, the fan output power predicted value and the optimization constraint condition through the distributed power supply utilization rate formula and the fuzzy genetic algorithm, the operation cost of the park energy system and the actual optimal values of the output power of the photovoltaic and the fan are obtained through calculation again through the cost minimum formula and the fuzzy genetic algorithm according to the theoretical optimal values, and finally the actual output power of the photovoltaic and the fan is controlled according to the actual optimal values, so that the utilization rate of the distributed power supply is optimal and the operation cost of the park energy system is minimum. According to the method, the distributed power supply is combined with the power load, the theoretical optimization value is calculated by combining the predicted value of the distributed power supply with the corresponding constraint condition, the cost is minimized by combining the cost factor, the utilization rate of the distributed power supply is maximized while the cost is minimized, the wind abandonment rate and the light abandonment rate are reduced, the energy of a park energy system is optimized integrally, and the method has high practical value.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The embodiments of the present invention have been described in connection with the accompanying drawings, and the principles and embodiments of the present invention are described herein using specific examples, which are provided only to help understand the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims (6)

1. A method for energy optimization of a park energy system, the method being applied to a superior operation server for controlling the operation of the park energy system, the park energy system comprising: distributed power source and shared energy storage system, distributed power source charges to the shared energy storage system, the shared energy storage system provides the electric power energy source for the user, distributed power source includes: photovoltaic and wind turbine, the method comprises:
step 1: according to a user side load demand predicted value, a photovoltaic output power predicted value, a fan output power predicted value and an optimization constraint condition, calculating to obtain theoretical optimal values of respective output powers of a photovoltaic power source and a fan through a distributed power source utilization rate formula and a fuzzy genetic algorithm, wherein the distributed power source utilization rate formula is a formula for calculating the optimal utilization rate of the distributed power source;
step 2: according to the theoretical optimal values of the output power of the photovoltaic and the fan, calculating to obtain the operation cost of the park energy system and the actual optimal values of the output power of the photovoltaic and the fan through a cost minimum formula and the fuzzy genetic algorithm, wherein the cost minimum formula is a formula for calculating the minimum operation cost of the park energy system;
and step 3: controlling the actual output power of the photovoltaic and the fan according to the actual optimal values of the respective output power of the photovoltaic and the fan, so that the utilization rate of the distributed power supply is optimal and the operation cost of the park energy system is minimum;
according to a user side load demand predicted value, a photovoltaic output power predicted value, a fan output power predicted value and an optimization constraint condition, calculating to obtain theoretical optimal values of respective output powers of the photovoltaic and the fan through a distributed power utilization formula and a fuzzy genetic algorithm, wherein the method comprises the following steps:
step 10: taking the user side load demand predicted value, the photovoltaic output power predicted value and the fan output power predicted value as initial solutions of the fuzzy genetic algorithm, and setting the optimization constraint condition as parameters of the fuzzy genetic algorithm;
step 20: setting an evolution algebra counter and a maximization algebra T;
step 30: calculating to obtain a generation theoretical optimal value of the output power of the photovoltaic power generator and the output power of the fan through the distributed power source utilization rate formula on the basis of the initial solution;
step 40: calculating the user side load demand predicted value and the first-generation theoretical optimal value to obtain a corresponding mass center;
step 50: propagating according to the centroid to produce a new population;
step 60: generating two offspring through crossing according to each pair of fuzzy sets in the population generated by propagation;
step 70: if q is the probability of mutation, the operation of mutation is completed by randomly replacing a certain element on the fuzzy set;
step 80: under the condition that T is smaller than T, taking the mutated one-generation theoretical optimal value as the initial solution, and returning to the step 30, wherein T refers to a time period;
step 90: under the condition that T is greater than or equal to T, the varied first-generation theoretical optimal value is the theoretical optimal value of the output power of the photovoltaic generator and the fan;
the method further comprises the following steps:
the formula of the utilization rate of the distributed power supply is as follows:
Figure FDA0003022859500000021
in the formula, C1minA difference value representing the distributed power output and load consumption; pL,1(t) represents the user side load demand prediction value, PGv(t) represents the predicted photovoltaic output power at time t, PGw(t) representing a predicted value of the output power of the fan at the time t;
the method further comprises the following steps:
the cost minimization formula is:
Figure FDA0003022859500000022
in the formula, min F is a value corresponding to the minimum operating cost of the park energy system; cIN(t) is a depreciation cost function; cG(t) is a cost function for purchasing and selling electricity;
the depreciation cost function is:
Figure FDA0003022859500000023
in the formula, n represents the number of micro sources, and the micro sources are the fan or the photovoltaic; pi(t) represents the output power of the micro-source at time t; n isiRepresenting an investment payback period; r isiRepresenting the fixed annual interest rate of the ith micro source; cin,iExpressing the unit capacity construction cost; k is a radical ofiRepresenting annual utilization coefficient, ni、ri、kiThe values of (a) are respectively a first preset value, a second preset value and a third preset value;
the electricity purchasing and selling cost function is as follows:
CG(t)＝IPgrid(t)CP(t)PPgrid(t)△t-ISgrid(t)CS(t)PSgrid(t)△t
Figure FDA0003022859500000031
in the formula, CP(t) represents the price of electricity purchased during the period t; cSAnd (t) represents the electricity selling price in the t period, and the electricity selling price and the electricity purchasing price are respectively divided into 3 periods of peak, valley and average.
2. The method of claim 1, wherein the campus energy system further comprises: a large power grid; the optimization constraints include: the method comprises the following steps of active balance constraint conditions, tie line power constraint conditions and park energy system operation constraint conditions, and further comprises the following steps:
the active balance constraint conditions are as follows:
Figure FDA0003022859500000032
in the formula: n is a radical ofDGRepresenting the number of the distributed power supplies; pGi(t) the output power of the ith micro-source at the time t; pESS,n(t) represents the charge-discharge power of the shared energy storage system in a period t, and the selection of the sign of the charge-discharge power is related to the charge and discharge state of the energy storage equipment; pl(t) represents the system load demand power for a period t; pPgridRepresenting the power purchased by the park energy system to the large power grid in the period of t, IPgridAnd ISgridEach represents a constant; pSgridRepresenting the time period t, wherein the park energy system sells electric power to the large power grid; Δ t represents a time period;
the tie line power constraint conditions are as follows:
IPgrid(t)+ISgrid(t)≤1 (1)
PPgridmin≤PPgrid(t)≤PPgridmax (2)
PSgridmin≤PSgrid(t)≤PSgridmax (3)
wherein, the formula (1) represents that the park energy system only purchases or sells electricity to the large power grid in one of the situations, or the park energy system does not purchase or sell electricity to the large power grid; the formula (2) represents the upper limit and the lower limit of the power purchased by the park energy system to the large power grid; formula (3) represents the upper and lower limits of the power sold to the large power grid by the park energy system;
the operation constraint conditions of the park energy system are as follows:
Socmin≤Soc(t)≤Socmax (4)
EBmin≤EB(t)≤EBmax (5)
Figure FDA0003022859500000041
wherein, formula (4) represents the charge quantity S of the shared energy storage system at time toc(t) is required to be at a maximum of SocmaxMinimum SocminBetween the charge amounts of; equation (5) represents the remaining energy storage capacity E of the shared energy storage systemB(t) must satisfy no more than the limit value EBmin、EBmax(ii) a Formula (6) represents the condition that the charge-discharge power of the shared energy storage system needs to meet, PESS.eA rated value of charge and discharge power for the shared energy storage system; etaddIs the bidirectional DC-DC converter efficiency; pddeThe power rating of the bidirectional DC-DC converter is obtained.
3. The method of claim 1, wherein calculating the operating cost of the park energy system and the actual optimal values of the respective output powers of the photovoltaic and the wind turbine from the theoretical optimal values of the respective output powers of the photovoltaic and the wind turbine through a cost minimization formula and the fuzzy genetic algorithm comprises:
step 10: taking the theoretical optimal values of the output power of the photovoltaic generator and the output power of the fan as an initial solution of a fuzzy genetic algorithm;
step 20: setting an evolution algebra counter and a maximization algebra T;
step 30: on the basis of the initial solution, calculating to obtain a first-generation actual optimal value of the output power of the photovoltaic and the fan and a first-generation minimum value of the operation cost of the park energy system through the cost minimum formula;
step 40: calculating a first-generation actual optimal value of the output power of the photovoltaic power generator and the output power of the fan and a first-generation minimum value of the operation cost of the park energy system to obtain a corresponding mass center;
step 50: propagating according to the centroid to produce a new population;
step 60: generating two offspring through crossing according to each pair of fuzzy sets in the population generated by propagation;
step 70: if q is the probability of mutation, the operation of mutation is completed by randomly replacing a certain element on the fuzzy set;
step 80: under the condition that T is smaller than T, taking the variant first-generation actual optimal value and the variant first-generation minimum value as initial solutions, and returning to the step 30;
step 90: and under the condition that T is greater than or equal to T, the mutated first-generation theoretical optimal value is the actual optimal value of the output power of the photovoltaic and the fan, and the mutated first-generation minimum value is the minimum value of the operation cost of the park energy system.
4. The method of claim 1, further comprising:
establishing the shared energy storage system through an electric energy charging and discharging model according to the charging and discharging efficiency of a storage battery in the shared energy storage system;
the electric energy charging and discharging model comprises the following steps:
Figure FDA0003022859500000051
in the formula, SOC(t) represents the charge of the shared energy storage system at time t;
Figure FDA0003022859500000052
and
Figure FDA0003022859500000053
respectively representing the charge and discharge power of the user n in a t period; etainRepresenting the charging efficiency, η, of the shared energy storage systemoutRepresents the discharge efficiency of the shared energy storage system, and is more than or equal to 0 and less than or equal to etain≤1，0≤ηout≤1。
5. An energy optimization device for a park energy system, wherein the energy optimization device is applied to a superior operation server, the superior operation server is used for controlling the operation of the park energy system, and the park energy system comprises: distributed power source and shared energy storage system, distributed power source charges to the shared energy storage system, the shared energy storage system provides the electric power energy source for the user, distributed power source includes: photovoltaic, fan, the device includes:
the calculation theoretical optimal value module is used for calculating theoretical optimal values of the output power of the photovoltaic power supply and the fan respectively according to a user side load demand predicted value, a photovoltaic output power predicted value, a fan output power predicted value and an optimization constraint condition through a distributed power supply utilization rate formula and a fuzzy genetic algorithm, wherein the distributed power supply utilization rate formula is a formula for calculating the optimal utilization rate of the distributed power supply;
a module for calculating a cost and an actual optimal value, configured to calculate, according to theoretical optimal values of respective output powers of the photovoltaic and the wind turbine, an operation cost of the park energy system and an actual optimal value of respective output powers of the photovoltaic and the wind turbine through a cost minimization formula and the fuzzy genetic algorithm, where the cost minimization formula is a formula for minimizing the operation cost of the park energy system, and the cost minimization formula is:
Figure FDA0003022859500000061
in the formula, min F is a value corresponding to the minimum operating cost of the park energy system; cIN(t) is a depreciation cost function; cG(t) is a cost function for purchasing and selling electricity;
the depreciation cost function is:
Figure FDA0003022859500000062
in the formula, n represents the number of micro sources, and the micro sources are the fan or the photovoltaic; pi(t) represents the output power of the micro-source at time t; n isiRepresenting an investment payback period; r isiRepresenting the fixed annual interest rate of the ith micro source; cin,iExpressing the unit capacity construction cost; k is a radical ofiRepresenting annual utilization coefficient, ni、ri、kiThe values of (a) are respectively a first preset value, a second preset value and a third preset value;
the electricity purchasing and selling cost function is as follows:
Figure FDA0003022859500000063
in the formula, CP(t) represents the price of electricity purchased during the period t; cS(t) represents the price of electricity sold in the t time period, and the price of electricity sold in the t time period and the price of electricity purchased in the t time period are respectively divided into 3 time periods of peak, valley and average;
the control module is used for controlling the actual output power of the photovoltaic and the fan according to the actual optimal value of the output power of the photovoltaic and the fan, so that the utilization rate of the distributed power supply is optimal and the operation cost of the park energy system is minimum;
the module for calculating the theoretical optimization value is specifically configured to implement the following steps:
step 10: taking the user side load demand predicted value, the photovoltaic output power predicted value and the fan output power predicted value as initial solutions of the fuzzy genetic algorithm, and setting the optimization constraint condition as parameters of the fuzzy genetic algorithm;
step 20: setting an evolution algebra counter and a maximization algebra T;
step 30: calculating to obtain a generation theoretical optimal value of the output power of the photovoltaic power generator and the output power of the fan through the distributed power source utilization rate formula on the basis of the initial solution;
step 40: calculating the user side load demand predicted value and the first-generation theoretical optimal value to obtain a corresponding mass center;
step 50: propagating according to the centroid to produce a new population;
step 60: generating two offspring through crossing according to each pair of fuzzy sets in the population generated by propagation;
step 70: if q is the probability of mutation, the operation of mutation is completed by randomly replacing a certain element on the fuzzy set;
step 80: under the condition that T is smaller than T, taking the mutated one-generation theoretical optimal value as the initial solution, and returning to the step 30, wherein T refers to a time period;
step 90: under the condition that T is greater than or equal to T, the varied first-generation theoretical optimal value is the theoretical optimal value of the output power of the photovoltaic generator and the fan;
wherein the distributed power source utilization formula is as follows:
Figure FDA0003022859500000071
in this formula: c1minA difference value representing the distributed power output and load consumption; pL,1(t) represents the user side load demand prediction value, PGv(t) represents the predicted value of the photovoltaic output power at time t,PGwand (t) represents a predicted value of the output power of the fan at the time t.
6. The apparatus according to claim 5, wherein the means for calculating the cost and the actual optimal value is configured to implement the steps of:
step 10: taking the theoretical optimal values of the output power of the photovoltaic generator and the output power of the fan as an initial solution of a fuzzy genetic algorithm;
step 20: setting an evolution algebra counter and a maximization algebra T;
step 30: on the basis of the initial solution, calculating to obtain a first-generation actual optimal value of the output power of the photovoltaic and the fan and a first-generation minimum value of the operation cost of the park energy system through the cost minimum formula;
step 40: calculating a first-generation actual optimal value of the output power of the photovoltaic power generator and the output power of the fan and a first-generation minimum value of the operation cost of the park energy system to obtain a corresponding mass center;
step 50: propagating according to the centroid to produce a new population;
step 60: generating two offspring through crossing according to each pair of fuzzy sets in the population generated by propagation;
step 70: if q is the probability of mutation, the operation of mutation is completed by randomly replacing a certain element on the fuzzy set;
step 80: under the condition that T is smaller than T, taking the variant first-generation actual optimal value and the variant first-generation minimum value as initial solutions, and returning to the step 30;
step 90: and under the condition that T is greater than or equal to T, the mutated first-generation theoretical optimal value is the actual optimal value of the output power of the photovoltaic and the fan, and the mutated first-generation minimum value is the minimum value of the operation cost of the park energy system.
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