CN114421536A - Multi-energy interactive regulation and control method based on energy storage - Google Patents
Multi-energy interactive regulation and control method based on energy storage Download PDFInfo
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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
- H02J3/38—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
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
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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
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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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
- H02J3/28—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
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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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
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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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
Abstract
The invention discloses a multi-energy interactive regulation and control method based on energy storage, and belongs to the technical field of multi-energy interactive regulation and control. The method comprises the following steps: step 1: establishing a new energy output model, and performing power calculation to obtain the output power of the unit; step 2: establishing an energy storage mathematical model meeting different energy sources; and step 3: establishing an objective function and a constraint condition; and 4, step 4: solving a target function through a particle swarm optimization algorithm to obtain the annual total cost and the output power of the solar wind power and the photovoltaic unit under the condition of the invention; and 5: according to the obtained objective function solving result, the reasonability of the method is further determined through comparative analysis; according to the technical method provided by the invention, the stability and reliability of the power system are enhanced through the established multi-energy interactive regulation and control model based on the stored energy, the output power of various energy sources is reasonably regulated and controlled, the new energy daily output power of the multi-energy system is improved, and the cost of the energy system is reduced.
Description
Technical Field
The invention belongs to a multi-energy interactive regulation and control method based on energy storage, and particularly relates to a multi-energy interactive regulation and control method based on new energy and traditional energy of energy storage.
Background
With the rapid development of new energy such as wind power, photovoltaic and the like, the proportion of clean energy accessed by a power grid is increased year by year. Because wind power generation and photovoltaic power generation output have uncertainty and volatility is great, the electric energy can't be used completely during the electricity generation peak period, leads to the generated energy of clean energy can't in time be absorbed, can effectually alleviate the absorption problem through rationally using energy storage equipment. In the traditional multi-energy system, the planning and the operation of various energy supply systems such as electricity, gas, cold, heat and the like are relatively independent, the coordination and complementation capability among various energy sources is difficult to exert, and the new power grid operation requirement cannot be met. Therefore, with the access of clean energy such as wind power generation and photovoltaic power generation output to the power grid, power balance is realized only by multi-source coordination in a new power market development environment, and the requirements of power grid peak regulation, frequency modulation and various power generation and power utilization can not be met.
Therefore, the power grid-based multi-energy scheduling mode with the aim of reducing the wind and light electricity abandoning amount of the power grid is significant in the direction of utilizing renewable energy, promoting energy conservation and emission reduction and reducing power consumption cost.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a multi-energy interactive regulation and control method based on energy storage, which can enhance the stability and reliability of a power system, reasonably regulate and control the output power of various energy sources, improve the output power of a multi-energy system of new energy sources and traditional energy sources and reduce the cost of an energy system.
The technical scheme of the invention is as follows:
a multi-energy interactive regulation and control method based on energy storage is characterized in that:
the method comprises the following steps:
step 1: establishing a new energy output model, and performing power calculation to obtain the output power of the new energy unit; the multi-energy comprises traditional energy and new energy, the power supply mode mainly comprises the new energy, and due to the uncertainty of the new energy in power generation, when the generated energy of the new energy is excessive, the redundant electric energy is stored in the energy storage device through the conversion equipment; when the generated energy of the new energy is insufficient, starting the traditional energy to generate electricity for supplement;
step 2: establishing an energy storage mathematical model meeting different energy sources, wherein the energy storage mathematical model comprises an electricity storage model and a heat storage model;
and step 3: establishing an objective function and a constraint condition;
and 4, step 4: and solving the objective function based on particle swarm optimization to obtain the lowest annual running total cost and the highest output power of the new energy unit.
Further, the specific process based on particle swarm optimization is as follows:
(1) particle swarmInitializing, setting performance parameters of a particle swarm algorithm, wherein the number n of particles in the population is 100, and each particle has a decision variable X with 6 dimensionsn,1、Xn,2、Xn,3、Xn,4、Xn,5、Xn,6Wind turbine generator system output power, photovoltaic unit output power, light and heat unit output power, thermal power unit output power, power storage device charging power and heat storage device heat-retaining power respectively, the initial position and the initial speed of the particle swarm can be expressed as:
in the formula, X100Representing the power of each device in the system; v100Representing the power variation of each device after iteration;
(2) the method comprises the following steps of (1) constraining particle positions, namely, constraining upper and lower limits of solving of optimized variables of each dimension of particles, namely, constraining upper and lower optimized limits of wind power, photovoltaic, photothermal and thermal power generating units, electricity storage devices and heat storage devices;
(3) establishing a fitness function for judging the quality of particles in the population so as to find out the optimal particles in the particle population;
(4) updating the particles, wherein the learning formula of the particles is as follows:
in the formula, ω represents the inertial weight of the particle maintaining the last evolution speed;
representing the speed of the particles after the evolution is completed for s times, namely the power variation of the equipment after the iteration is completed for s times; r is1，r2Constant coefficients representing evolution between (0, 1); c. C1,c2Learning factors representing individual and groups of particles in the particle swarm optimization;
showing that the evolution is completed for s times, and the individual fitness of the historical particles is extreme;
showing that the evolution is completed for s times, and the historical population fitness is extreme;
the position of the particle after s evolutions, i.e. the power of the device after s iterations.
(5) And iteratively updating the particle swarm, comparing the individual extreme value of each particle with the global extreme value, and updating the global extreme value and the position of the global extreme value if the fitness of the individual extreme value is less than the fitness of the global extreme value.
(6) And (5) fully performing processes (2) to (5), stopping optimization when the iteration times are reached, and outputting results.
Further, the objective function is an economic objective function, and a calculation formula of the economic objective function is as follows:
wherein:
in the formula (I), the compound is shown in the specification,
representing the sum of the operation cost of each unit in the system at the moment t;
representing the lost load power of the system at the moment t; bSA cost factor representing the power of the system lost load; Δ t represents a unit time;
representing the running cost of photo-thermal at the time t;
representing the running cost of the wind power at the time t;
representing the operation cost of the photovoltaic at the moment t;
representing the operating cost of thermal power at time t.
Further, the new energy output model comprises a wind power generation model and a photovoltaic power generation model, and the mathematical expression of the wind power generation model is as follows:
in the formula, PWTRepresents the output power (kW) of the wind power generation; prRepresents the rated power (kW) of the wind turbine; v represents an actual wind speed (m/s); v. ofr、vci、vcoRespectively representing rated wind speed, cut-in wind speed and cut-out wind speed of the wind driven generator; and when the actual wind speed does not reach the cut-in wind speed, the unit keeps a standby state, and the output is 0.
Under the general standard condition, according to the solar irradiation intensity and the battery temperature, the photovoltaic power generation model has a mathematical expression:
in the formula, PPVRepresents the actual output power (kW) of the photovoltaic cell assembly; pSTCRepresents the maximum output power (kW) of the photovoltaic cell under standard conditions; gINGRepresenting the actual solar radiation intensity (W/square meter); gSTCThe irradiation intensity under the STC condition is 1000W/square meter; k represents a temperature power coefficient (%/DEG C); t isCRepresenting the actual working temperature (DEG C) of the panel; t isrIndicating the reference temperature, 25 ℃.
Further, the mathematical expression of the electricity storage model is as follows:
in the formula, Pc(t) represents a charging power (kW) at time t of the power storage device; pd(t) represents the discharge power (kW) at time t of the electric storage device; cE(t) represents the remaining capacity (kWh) of the electric storage device at time t; cE(0) Represents an initial amount of power of the power storage device; etacRepresents the charging efficiency of the electric storage device; etadIndicating the discharge efficiency of the electric storage device.
Further, the mathematical expression of the heat storage model is as follows:
RH(t)＝ηTRH(t-1)+ηTsHTs(t)-HTr(t)/ηTr
in the formula, HTs(t) represents the heat storage power (kW) at the moment t of the heat storage device; hTr(t) represents the heat release power (kW) at the moment t of the heat storage device; rH(t) represents the residual heat (J) of the heat storage device at time t; etaTsIndicating the heat storage efficiency of the heat storage device; etaTrIndicating the heat release efficiency of the heat storage device; etaTRepresents the heat loss rate (%) of the heat storage device.
Further, the constraint conditions are as follows:
0≤Pd(t)≤Pmax,0≤Pc(t)≤Pmax
CEmin≤CE(t)≤CEmax
Pd(t)Pc(t)＝0
0≤HTs(t)≤Hmax,0≤HTr(t)≤Hmax
0≤RH(t)≤VHS
in the formula, PmaxRepresents the maximum charge-discharge power of the electricity storage device; cEmin、CEmaxRepresenting the minimum and maximum amount of stored power (kWh) within the power storage device; pd(t)Pc(t) ═ 0 denotes the electric storage device complementary constraint preventing simultaneous charging and discharging of the device; hmaxRepresents the maximum heat storage power (kW) of the energy storage device; vHSRepresenting the maximum capacity (kWh) of the heat storage device.
Further, the fitness function is the total annual cost:
F＝min YTC＝f(x)
in the formula, YTC represents the total annual cost.
The invention has the advantages and effects that:
the stability and the reliability of the power system are enhanced, the output power of various energy sources is reasonably regulated, the output power of the multi-energy-source system is improved, and the cost of the energy source system is reduced.
Independent energy subsystems such as electric power, heat energy, photovoltaic power generation, wind power generation and the like in the system are coupled, so that interactive adjustment among the energy subsystems is realized. The multi-energy system can reduce the energy consumption cost, improve the utilization of renewable energy sources and promote energy conservation and emission reduction while meeting diversified energy consumption requirements. The energy storage technology plays an important role in improving the power grid regulation capacity, enhancing the flexibility of the power grid and meeting the requirements of various power generation and power utilization customers.
Drawings
FIG. 1 is a flow chart of a multi-energy interactive regulation and control method based on energy storage according to the present invention;
FIG. 2 is a graph comparing the annual total cost results of the present invention (corresponding to example 1);
fig. 3 is a graph of the output power of the solar wind power and photovoltaic module according to the invention (corresponding to example 1).
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Example 1
As shown in fig. 1, the energy storage-based multi-energy interactive regulation and control method specifically includes the steps of:
step 1: establishing a new energy output model, and performing power calculation to obtain the output power of the new energy unit;
the multi-energy comprises traditional energy and new energy, the power supply mode mainly comprises the new energy, and due to the uncertainty of the new energy in power generation, when the generated energy of the new energy is excessive, the redundant electric energy is stored in the energy storage device through the conversion equipment; when the generated energy of the new energy is insufficient, starting the traditional energy to generate electricity for supplement;
the new energy output model comprises a wind power generation model and a photovoltaic power generation model, and the mathematical expression of the wind power generation model is as follows:
in the formula, PWTRepresents the output power (kW) of the wind power generation; prRepresents the rated power (kW) of the wind turbine; v represents an actual wind speed (m/s); v. ofr、vci、vcoRespectively representing rated wind speed, cut-in wind speed and cut-out wind speed of the wind driven generator; and when the actual wind speed does not reach the cut-in wind speed, the unit keeps a standby state, and the output is 0.
Under the general standard condition, the new energy output model is based on the solar irradiation intensity and the battery temperature, wherein the mathematical expression of the photovoltaic power generation model is as follows:
in the formula, PPVRepresents the actual output power (kW) of the photovoltaic cell assembly; pSTCRepresents the maximum output power (kW) of the photovoltaic cell under standard conditions; gINGRepresenting the actual solar radiation intensity (W/square meter); gSTCThe irradiation intensity under the STC condition is 1000W/square meter; k represents a temperature power coefficient (%/DEG C); t isCRepresenting the actual working temperature (DEG C) of the panel; t isrIndicating the reference temperature, 25 ℃. The wind turbine generator and photovoltaic generator set equipment parameters are as follows, as shown in table 1.
TABLE 1
Step 2: establishing an energy storage mathematical model meeting different energy sources;
the energy storage mathematical model comprises an electricity storage model and a heat storage model, and the mathematical expression of the electricity storage model is as follows:
in the formula, Pc(t) represents a charging power (kW) at time t of the power storage device; pd(t) represents the discharge power (kW) at time t of the electric storage device; cE(t) represents the remaining capacity (kWh) of the electric storage device at time t; cE(0) Represents an initial amount of power of the power storage device; etacRepresents the charging efficiency of the electric storage device; etadIndicating the discharge efficiency of the electric storage device.
The stored energy model comprises an electricity storage model and a heat storage model, and the mathematical expression of the heat storage model is as follows:
RH(t)＝ηTRH(t-1)+ηTsHTs(t)-HTr(t)/ηTr
in the formula, HTs(t) represents the heat storage power (kW) at the moment t of the heat storage device; hTr(t) represents the heat release power (kW) at the moment t of the heat storage device; rH(t) represents the residual heat (J) of the heat storage device at time t; etaTsIndicating the heat storage efficiency of the heat storage device; etaTrIndicating the heat release efficiency of the heat storage device; etaTRepresents a heat loss rate (%) of the heat storage device; the relevant parameters of the electric storage device and the heat storage device are shown in table 2.
TABLE 2
And step 3: establishing an economic objective function and constraint conditions;
the constraint conditions are as follows:
0≤Pd(t)≤Pmax,0≤Pc(t)≤Pmax
CEmin≤CE(t)≤CEmax
Pd(t)Pc(t)＝0
0≤HTs(t)≤Hmax,0≤HTr(t)≤Hmax
0≤RH(t)≤VHS
in the formula, Pmax、PminRepresents the maximum (1000kW) and minimum (200kW) charge and discharge power (kW) of the electricity storage device; cEmin、 CEmaxRepresents the minimum and maximum stored electricity (kWh) within the electricity storage device, 150kWh, 3000 kWh; pd(t)Pc(t) ═ 0 denotes the electric storage device complementary constraint preventing simultaneous charging and discharging of the device; hmaxRepresents the maximum heat storage power (kW), 800kW, of the heat storage device; vHSRepresents the maximum capacity (kWh) of the heat storage device, 2400 kWh; the energy storage device parameters are shown in table 3.
TABLE 3
The operation formula of the economic objective function is as follows:
wherein:
in the formula (I), the compound is shown in the specification,
representing the sum of the operation cost of each unit in the system at the moment t;
the lost load power of the system at the moment t is represented, and is 3.5 kW; bSCost coefficient representing system lost load power, 0.08;
represents the operation cost of photo-thermal at the time t, and is 0.04 ten thousand yuan/kW;
the running cost of the wind power at the time t is represented, and is 0.07 ten thousand yuan/kW;
representing the operation cost of the photovoltaic at the time t, and 0.05 ten thousand yuan/kW;
representing the operation cost of thermal power at the moment t, and 0.06 ten thousand yuan/kW; Δ t represents a unit time.
And 4, step 4: solving an economic objective function based on a particle swarm optimization algorithm to obtain the lowest annual running total cost and the highest daily wind power and the output power of the photovoltaic unit;
the particle swarm optimization algorithm is based on the following solving process:
(1) initializing a particle swarm, setting performance parameters of a particle swarm algorithm, wherein the population comprises 100 particles, and each particle has a decision variable X with 6 dimensionsn,1、Xn,2、Xn,3、Xn,4、Xn,5、Xn,6Wind turbine generator system output power, photovoltaic unit output power, light and heat unit output power, thermal power unit output power, power storage device charging power and heat storage device heat-retaining power respectively, the initial position and the initial speed of the particle swarm can be expressed as:
in the formula, X100Representing the power of each device in the system; v100Representing the amount of power change of each device after iteration.
(2) And (3) constraining the positions of the particles, and constraining the upper limit and the lower limit of the solution of each dimension optimization variable of the particles, namely constraining the upper limit and the lower limit of the optimization of the wind power, the photovoltaic, the photothermal and the thermal power generating unit, the electricity storage device and the heat storage device.
(3) And establishing a fitness function for judging the quality of the particles in the population so as to find the optimal particles in the particle population.
F＝min YTC＝f(x)
In the formula, YTC represents the total annual cost.
(4) Updating the particles, wherein the learning formula of the particles is as follows:
in the formula, ω represents the inertial weight of the particle maintaining the last evolution speed;
representing the speed of the particles after the evolution is completed for s times, namely the power variation of the equipment after the iteration is completed for s times; r is1，r2Constant coefficients representing evolution between (0, 1); c. C1,c2Learning factors representing individual and groups of particles in the particle swarm optimization;
show the last s evolutions and the historical particle sizeA body fitness extreme value;
expressing the extreme value of the historical population fitness after s evolutions;
the position of the particle after s evolutions, i.e. the power of the device after s iterations.
(5) And iteratively updating the particle swarm, comparing the individual extreme value of each particle with the global extreme value, and updating the global extreme value and the position of the global extreme value if the fitness of the individual extreme value is less than the fitness of the global extreme value.
(6) And (5) fully performing processes (2) to (5), stopping optimization when the iteration times are reached, and outputting results.
Solving an objective function through a multi-objective optimization algorithm, wherein the number of particles is set to be 100; the initial individual learning factor and the population learning factor are respectively 2 and 3; the inertia weight omega is 0.5; the maximum number of iterations is set to 100. The method adopts a particle swarm algorithm-based optimization target of lowest annual total cost and highest output power of the solar wind power and the photovoltaic unit to obtain a relational graph of the annual total cost and the output power of the solar wind power and the photovoltaic unit under the condition of the embodiment, as shown in fig. 2 and fig. 3.
And 5: and according to the obtained objective function solving result, comparing and analyzing to further determine the rationality of the method.
As can be seen from fig. 2, after the method of the present invention is applied to a multi-energy system including energy storage, the operation cost of each energy system is significantly reduced through iterative computation contrast screening, and the annual total cost of the method of the present invention is saved by 56.38 ten thousand yuan compared with the conventional multi-energy interactive adjustment method.
As can be seen from fig. 3, the daily output power of the wind power generation unit and the photovoltaic power generation unit is obviously increased and relatively stable, which further illustrates that the output power of the wind power generation unit and the photovoltaic power generation unit is more stable and reliable after the method is adopted.
In conclusion, the stability and the reliability of the power system are enhanced through the established multi-energy interactive regulation and control model based on the energy storage, the output power of various energy sources is reasonably regulated and controlled, the output power of the multi-energy system is improved, and the cost of the energy system is reduced.
The above description is only exemplary of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A multi-energy interactive regulation and control method based on energy storage is characterized in that:
the method comprises the following steps:
step 1: establishing a new energy output model, and performing power calculation to obtain the output power of the new energy unit; the multi-energy comprises traditional energy and new energy, the power supply mode mainly comprises the new energy, and due to the uncertainty of the new energy in power generation, when the generated energy of the new energy is excessive, the redundant electric energy is stored in the energy storage device through the conversion equipment; when the generated energy of the new energy is insufficient, starting the traditional energy to generate electricity for supplement;
step 2: establishing an energy storage mathematical model meeting different energy sources, wherein the energy storage mathematical model comprises an electricity storage model and a heat storage model;
and step 3: establishing an objective function and a constraint condition;
and 4, step 4: and solving the objective function based on particle swarm optimization to obtain the lowest annual running total cost and the highest output power of the new energy unit.
2. The energy storage-based multi-energy interactive regulation and control method according to claim 1, characterized in that:
the specific process based on particle swarm optimization is as follows:
(1) initializing a particle swarm, setting performance parameters of a particle swarm algorithm, wherein the number n of common particles in the particle swarm is 100, and each particle has a decision variable X with 6 dimensionsn,1、Xn,2、Xn,3、Xn,4、Xn,5、Xn,6Wind turbine generator system output power, photovoltaic unit output power, light and heat unit output power, thermal power unit output power, power storage device charging power and heat storage device heat-retaining power respectively, the initial position and the initial speed of the particle swarm can be expressed as:
in the formula, X100Representing the power of each device in the system; v100Representing the power variation of each device after iteration;
(2) the method comprises the following steps of (1) constraining particle positions, namely, constraining upper and lower limits of solving of optimized variables of each dimension of particles, namely, constraining upper and lower optimized limits of wind power, photovoltaic, photothermal and thermal power generating units, electricity storage devices and heat storage devices;
(3) establishing a fitness function for judging the quality of particles in the population so as to find out the optimal particles in the particle population;
(4) updating the particles, wherein the learning formula of the particles is as follows:
in the formula, ω represents the inertial weight of the particle maintaining the last evolution speed;
representing the speed of the particles after the evolution is completed for s times, namely the power variation of the equipment after the iteration is completed for s times; r is1，r2Constant coefficient representing evolution, (0)1) above; c. C1,c2Learning factors representing individual and groups of particles in the particle swarm optimization;
showing that the evolution is completed for s times, and the individual fitness of the historical particles is extreme;
showing that the evolution is completed for s times, and the historical population fitness is extreme;
representing the position of the particles after the evolution is completed for s times, namely the power of the equipment after the iteration is completed for s times;
(5) iteratively updating the particle swarm, comparing the individual extreme value of each particle with the global extreme value, and updating the global extreme value and the position thereof if the fitness of the individual extreme value is less than the fitness of the global extreme value;
(6) and (5) fully performing processes (2) to (5), stopping optimization when the iteration times are reached, and outputting results.
3. The energy storage-based multi-energy interactive regulation and control method according to claim 1, characterized in that:
the objective function is an economic objective function, and the calculation formula of the economic objective function is as follows:
wherein:
in the formula (I), the compound is shown in the specification,
representing the sum of the operation cost of each unit in the system at the moment t; pt STo representthe lost load power of the system at the moment t; bSA cost factor representing the power of the system lost load; Δ t represents a unit time;
representing the running cost of photo-thermal at the time t;
representing the running cost of the wind power at the time t;
representing the operation cost of the photovoltaic at the moment t;
representing the operating cost of thermal power at time t.
4. The energy storage-based multi-energy interactive regulation and control method according to claim 1, characterized in that:
the new energy output model comprises a wind power generation model and a photovoltaic power generation model, and the mathematical expression of the wind power generation model is as follows:
in the formula, PWTRepresents the output power (kW) of the wind power generation; prRepresents the rated power (kW) of the wind turbine; v represents an actual wind speed (m/s); v. ofr、vci、vcoRespectively representing rated wind speed, cut-in wind speed and cut-out wind speed of the wind driven generator; when the actual wind speed does not reach the cut-in wind speed, the unit keeps a standby state, and the output is 0;
under the general standard condition, according to the solar irradiation intensity and the battery temperature, the photovoltaic power generation model has a mathematical expression:
in the formula, PPVRepresents the actual output power (kW) of the photovoltaic cell assembly; pSTCRepresents the maximum output power (kW) of the photovoltaic cell under standard conditions; gINGRepresenting the actual solar radiation intensity (W/square meter); gSTCThe irradiation intensity under the STC condition is 1000W/square meter; k represents a temperature power coefficient (%/DEG C); t isCRepresenting the actual working temperature (DEG C) of the panel; t isrIndicating the reference temperature, 25 ℃.
5. The energy storage-based multi-energy interactive regulation and control method according to claim 1, characterized in that:
the mathematical expression of the electricity storage model is as follows:
in the formula, Pc(t) represents a charging power (kW) at time t of the power storage device; pd(t) represents the discharge power (kW) at time t of the electric storage device; cE(t) represents the remaining capacity (kWh) of the electric storage device at time t; cE(0) Represents an initial amount of power of the power storage device; etacRepresents the charging efficiency of the electric storage device; etadIndicating the discharge efficiency of the electric storage device.
6. The energy storage-based multi-energy interactive regulation and control method according to claim 1, characterized in that: the mathematical expression of the heat storage model is as follows:
RH(t)＝ηTRH(t-1)+ηTsHTs(t)-HTr(t)/ηTr
in the formula, HTs(t) represents the heat storage power (kW) at the moment t of the heat storage device; hTr(t) represents the heat release power (kW) at the moment t of the heat storage device; rH(t) represents the residual heat (J) of the heat storage device at time t; etaTsTo representThe heat storage efficiency of the heat storage device; etaTrIndicating the heat release efficiency of the heat storage device; etaTRepresents the heat loss rate (%) of the heat storage device.
7. The energy storage based multi-energy interactive regulation and control method according to claims 4-6, characterized in that: the constraint conditions are as follows:
0≤Pd(t)≤Pmax,0≤Pc(t)≤Pmax
CEmin≤CE(t)≤CEmax
Pd(t)Pc(t)＝0
0≤HTs(t)≤Hmax,0≤HTr(t)≤Hmax
0≤RH(t)≤VHS
in the formula, PmaxRepresents the maximum charge-discharge power of the electricity storage device; cEmin、CEmaxRepresenting the minimum and maximum amount of stored power (kWh) within the power storage device; pd(t)Pc(t) ═ 0 denotes the electric storage device complementary constraint preventing simultaneous charging and discharging of the device; hmaxRepresents the maximum heat storage power (kW) of the energy storage device; vHSRepresenting the maximum capacity (kWh) of the heat storage device.
8. The energy storage-based multi-energy interactive regulation and control method according to claim 2, characterized in that: the fitness function is the annual total cost:
F＝minYTC＝f(x)
in the formula, YTC represents the total annual cost.
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