CN111668882A - Method and device for optimizing output of micro power supply in intelligent energy ring network - Google Patents
Method and device for optimizing output of micro power supply in intelligent energy ring network Download PDFInfo
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Abstract
A method and a device for optimizing the output of a micro power supply in an intelligent energy ring network are disclosed. In a preferred embodiment of the present application, the optimization method may include: establishing a corresponding power model aiming at a generator set in the intelligent energy ring network; the method comprises the steps that by means of power models of generator sets in the intelligent energy ring network, output of micro power sources in the intelligent energy ring network system and exchange power of the micro power sources and a power grid are optimized under the condition that preset system operation constraint conditions are met, and a target function with the lowest operation cost, the lowest load power consumption cost or the highest environmental benefit of the intelligent energy ring network system is established; and optimizing the output of each micro power supply in the intelligent energy ring network through a particle swarm optimization algorithm based on the target function and the system operation constraint condition. The embodiment of the application can optimize the output power of the micro power supply in the intelligent energy ring network, thereby reducing the investment and the loss of power transmission and transformation, fully utilizing renewable energy sources and reducing the emission of greenhouse gases.
Description
Technical Field
The application relates to the technical field of electric power, in particular to a method and a device for optimizing output of a micro power supply in an intelligent energy ring network.
Background
The intelligent ring network is a small, independent, and distributed system, which includes a load, a distributed power generation unit, and an energy storage unit, and is connected to the main network through a Point of Common Coupling (PCC). The intelligent energy ring network is mainly provided with renewable energy sources, and conversion of electric energy is achieved through power electronic equipment.
The output of renewable energy power generation fluctuates with changing environmental conditions, which has a significant impact on the uncertainty of grid operation. Therefore, how to alleviate the fluctuation characteristic of the renewable energy output is very important for the stable operation of the load and the main power grid.
In an islanded microgrid (for example, a smart energy ring network) including fossil fuel type distributed power generation, how to optimize the output of a micro power source to make the power imbalance between a power generation side and a user side caused by the output fluctuation of renewable energy sources is a technical problem to be solved urgently.
Disclosure of Invention
In order to solve the above technical problems, it is desirable to provide a method and a device for optimizing the output of a micro power source in an intelligent energy ring network, so that the power between a power generation side and a user side caused by the output fluctuation of renewable energy sources can be effectively balanced.
According to an aspect of the application, a method for optimizing output of a micro power supply in an intelligent energy ring network is provided, which includes:
establishing a corresponding power model aiming at a generator set in the intelligent energy ring network;
the method comprises the steps that by means of power models of generator sets in the intelligent energy ring network, output of micro power sources in the intelligent energy ring network system and exchange power of the micro power sources and a power grid are optimized under the condition that preset system operation constraint conditions are met, and a target function with the lowest operation cost, the lowest load power consumption cost or the highest environmental benefit of the intelligent energy ring network system is established;
and optimizing the output of each micro power supply in the intelligent energy ring network through a particle swarm optimization algorithm based on the target function and the system operation constraint condition.
According to an aspect of the present application, a little power output optimization device in wisdom can looped netowrk is provided, includes:
the power model building module is configured to build a corresponding power model for the generator set in the intelligent energy ring network;
the target function building module is configured to optimize the output of each micro power supply in the intelligent energy ring network system and the exchange power of each micro power supply and a power grid under the condition of meeting the preset system operation constraint condition by using the power model of each generator set in the intelligent energy ring network, and build a target function with the lowest operation cost, the lowest load power consumption cost or the highest environmental benefit of the intelligent energy ring network system;
and the optimization module is configured to optimize the output of each micro power supply in the intelligent energy ring network through a particle swarm optimization algorithm based on the target function and the system operation constraint condition.
In the embodiment of the application, the output power of the micro power supply in the intelligent energy ring network can be optimized, the power between the power generation side and the user side caused by the output fluctuation of the renewable energy sources in the operation process of the intelligent energy ring network system can be effectively balanced, the power transmission and transformation investment and the network loss are reduced, the renewable energy sources can be fully utilized, the emission of greenhouse gases is reduced, and the environmental protection benefit is outstanding.
Drawings
Fig. 1 is a flowchart of a method for optimizing output of a micro power source in an intelligent energy ring network according to an embodiment of the present application.
Fig. 2 is a schematic structural diagram of a micro power output optimization device in an intelligent energy ring network according to an embodiment of the present application.
Detailed Description
Hereinafter, embodiments of the present application will be described in detail with reference to the accompanying drawings. It should be noted that, in the present application, the embodiments and the features thereof may be arbitrarily combined with each other without conflict.
The embodiment of the application is applicable to island micro-grids such as intelligent energy looped netowrk. Particularly suitable for use in islanded microgrids involving fossil fuel-based distributed generation.
An exemplary implementation of the embodiments of the present application is described in detail below.
Fig. 1 illustrates an exemplary optimization method for micro power output in an intelligent energy ring network according to an embodiment of the present disclosure. As shown in fig. 1, the exemplary method for optimizing the output of the micro power source in the smart energy ring network may include the following steps:
step S101, a power model corresponding to the generator set in the intelligent energy ring network is established;
in the step, an intelligent energy ring network composed of a steam turbine unit, a wind turbine unit, a lithium iron phosphate battery and a photovoltaic array is taken as an example, and a corresponding power model can be constructed for each generator unit in the intelligent energy ring network.
Firstly, the following photovoltaic array power model can be constructed for the photovoltaic array in the intelligent energy ring network:
PPV＝UPV*IPV(1)
in the formulae (1) and (2), IlRepresents photocurrent (in units of a); i isoShowing reverse saturation current (in units of A), q the amount of electron charge (1.6 × 1019C), K the Boltzmann constant (which may be, for example, 1.38 × 10-23J/K), T the absolute temperature (in units of K), A the diode factor, Rs the series resistance (in units of omega), P the absolute temperature (in units of K), andPVrepresenting the power of the photovoltaic array, UPVRepresenting the voltage of the photovoltaic array, IPVRepresenting the current of the photovoltaic array.
Secondly, the following fan power model can be constructed for the wind turbine in the intelligent energy ring network:
in the formulas (3), (4) and (5), v is the wind speed before the incoming flow, P is the air pressure, T is the temperature, R is the gas constant, A is the swept area of the wind wheel, Cp is the wind energy utilization coefficient, theta is the pitch angle,
C1-C6the fan type is a basic parameter representing the fan characteristic.
Thirdly, the following lithium iron phosphate battery power model is constructed aiming at the lithium iron phosphate battery in the intelligent energy ring network:
in the formula (6), the reaction mixture is,efor energy storage charge-discharge efficiency, SOC (t) is the state of charge at time t, SOC (t-1) is the state of charge at time t-1, Pe tCharge and discharge power representing stored energy at time t, EeRepresents the capacity of stored energy, and Δ t represents the difference between t and (t-1).
Step S102, optimizing the output of each micro power supply in the intelligent energy ring network system and the exchange power of each micro power supply and the power grid under the condition of meeting the preset system operation constraint condition by using the power model of each generator set in the intelligent energy ring network, and establishing a target function with the lowest operation cost, the lowest load power consumption cost or the highest environmental benefit of the intelligent energy ring network system.
In this step, still taking an intelligent energy ring network composed of a steam turbine set, a wind turbine set, a lithium iron phosphate battery and a photovoltaic array as an example, two following objective functions can be established:
1) the first objective function with the lowest running cost:
in the formula (7), F1For the unit operation cost of the intelligent energy ring network system, T is the scheduling period of the system, generally 24h, T is the time, and N is the micro-scale in the intelligent energy ring network systemTotal number of sources, FiFor each micro-power consumption cost, PGi(t) Power of each micro-Power supply at time t, MiFor the operating maintenance costs of the individual micro-power supplies at time t, EbPrice for buying and selling electricity for public power grid, PbAnd (t) buying and selling the power of the electricity at the moment t.
2) A second objective function with the lowest load electricity cost:
in the formula (8), F2Cost of power consumption per unit load, S, for intelligent energy ring network systemiThe other variables are the same as (7) for the electricity selling cost of the ith power supply at the time t.
In this step, still taking an intelligent energy ring network composed of a steam turbine set, a wind turbine set, a lithium iron phosphate battery and a photovoltaic array as an example, the system constraint conditions may include but are not limited to one or more of the following constraint conditions:
1) and (3) system power balance constraint conditions in a grid-connected state:
in the above formula (9), M is the number of lithium iron phosphate battery devices in the intelligent ring network system, Ploss(t) loss, P, of the intelligent energy ring network systemloadAnd (t) is the load of the intelligent energy ring network system at the moment t.
2) Constraints of the exchange power:
0≤|Pb(t)|≤Plmax(10)
in the above formula (10), PlmaxThe intelligent energy ring network system exchanges the upper limit value of power with a public power grid.
3) Energy storage operation power constraint conditions:
in the formula (11), Pe，maxFor energy storage systemsThe upper limit value of the charge and discharge power is stored,
respectively the discharge power and the charging power of the stored energy.
4) Constraint conditions of the energy storage operation state of charge:
in the formula (12), SOCiThe charge state of the energy storage system is in an i-period; SOC0The charge state is the initial moment of energy storage; xt、YtThe energy storage charging and discharging state is set as 0 or 1, for example, the value is set as 0 during charging and 1 during discharging;ethe energy storage charge-discharge efficiency.
5) Energy storage running state constraint conditions:
SOCmin≤SOCi≤SOCmax(13)
in the formula (13), SOCminIs the minimum value of the state of charge of the stored energy, SOCmaxThe maximum value of the energy storage state of charge.
Step S103, based on the objective function and the constraint condition of the system operation, optimizing the output of each micro power source (for example, the power of the micro power source) in the smart energy ring network by using a particle swarm optimization algorithm to achieve the objective of the smart energy ring network system operation, where the objective may include, but is not limited to, that the power between the power generation side and the user side can be effectively balanced due to the output fluctuation of renewable energy.
In this step, the particle swarm optimization algorithm may be a particle swarm optimization algorithm that seeks an optimal solution in terms of economy. In the embodiment of the present application, the particle swarm optimization algorithm may be any particle swarm optimization algorithm that is applicable to the smart energy ring network and has been disclosed at present.
The embodiment of the application also provides a micro power output optimization device in the intelligent energy ring network. As shown in fig. 2, the apparatus for optimizing the output of the micro power source in the intelligent energy ring network may include: a power model building module 21, an objective function building module 22 and an optimization module 23.
The power model building module 21 is configured to build a power model corresponding to each generator set in the intelligent energy ring network, and determine parameters in the power models.
The objective function building module 22 is configured to optimize the output of each micro power source in the intelligent energy ring network system and the exchange power between the output of each micro power source and the power grid under the condition of meeting the predetermined system operation constraint condition by using the power model of each generator set in the intelligent energy ring network, and build an objective function with the lowest operation cost, the lowest load power consumption cost or the highest environmental benefit of the intelligent energy ring network system.
The optimization module 23 is configured to optimize, based on the objective function and the constraint condition of the system operation, the output of each micro power source (for example, the power of the micro power source) in the smart energy ring network through a particle swarm optimization algorithm, so as to achieve the target of the smart energy ring network system operation, where the target may include, but is not limited to, that the power between the power generation side and the user side can be effectively balanced due to the output fluctuation of the renewable energy.
The specific technical details of the micro-power output optimization device in the intelligent energy ring network in the embodiment of the present application can refer to the description of the above method, and are not repeated.
The output optimization method and device for the micro power supply in the intelligent energy ring network can optimize the output power of the micro power supply in the intelligent energy ring network, can effectively balance the power between the power generation side and the user side caused by the output fluctuation of renewable energy sources in the operation process of the intelligent energy ring network system, reduce the investment and the network loss of power transmission and transformation, fully utilize the renewable energy sources, reduce the emission of greenhouse gases, and have outstanding environmental protection benefits.
The method of the embodiment of the application can be implemented through power electronic equipment in the intelligent energy ring network, for example, the method can be implemented through equipment or a system in the intelligent energy ring network, which is responsible for managing the output of each generator set.
The above description is only for the preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. A micro power output optimization method in an intelligent energy ring network comprises the following steps:
establishing a corresponding power model aiming at a generator set in the intelligent energy ring network;
the method comprises the steps that by means of power models of generator sets in the intelligent energy ring network, output of micro power sources in the intelligent energy ring network system and exchange power of the micro power sources and a power grid are optimized under the condition that preset system operation constraint conditions are met, and a target function with the lowest operation cost, the lowest load power consumption cost or the highest environmental benefit of the intelligent energy ring network system is established;
and optimizing the output of each micro power supply in the intelligent energy ring network through a particle swarm optimization algorithm based on the target function and the system operation constraint condition.
2. The method of claim 1, wherein the step of constructing the power model corresponding to the generator set in the intelligent ring network comprises:
the method comprises the following steps of constructing a photovoltaic array power model aiming at a photovoltaic array in the intelligent energy ring network:
PPV＝UPv*IPV
wherein, IlRepresents a photocurrent; i isoRepresents reverse saturation current; q represents an electron charge amount, and K is a boltzmann constant; t represents an absolute temperature;a denotes the diode factor, Rs denotes the series resistance, PPVRepresenting the power of the photovoltaic array, UPVRepresenting the voltage of the photovoltaic array, IPVRepresenting the current of the photovoltaic array.
3. The method of claim 1, wherein the step of constructing the power model corresponding to the generator set in the intelligent ring network comprises:
the following fan power model is built for the wind turbine group in the intelligent energy ring network:
wherein v is the wind speed before the incoming flow, P is the air pressure, T is the temperature, R is the gas constant, A is the swept area of the wind wheel, Cp is the wind energy utilization coefficient, theta is the pitch angle,
C1-C6related to the type of the fan, all represent basic parameters of the fan characteristics.
4. The method of claim 1, wherein the step of constructing the power model corresponding to the generator set in the intelligent ring network comprises:
the following lithium iron phosphate battery power model is constructed aiming at the lithium iron phosphate battery in the intelligent energy ring network:
in the formula (6), the reaction mixture is,efor energy storage charge-discharge efficiency, SOC (t) is the state of charge at time t, SOC (t-1) is the state of charge at time t-1,
charge and discharge power representing energy storage at time t, EeRepresents the capacity of stored energy, and Δ t represents the difference between t and (t-1).
5. The intelligent energy ring network micro power output optimization method according to claim 1, wherein the system operation constraint includes at least one of the following constraints:
and (3) system power balance constraint conditions in a grid-connected state:
constraints of the exchange power: p is more than or equal to 0b(t)|≤Plmax；
Energy storage operation power constraint conditions:
constraint conditions of the energy storage operation state of charge:
energy storage running state constraint conditions: SOCmin≤SOCi≤SOCmax；
Wherein M is the number of iron phosphate lithium battery devices in the intelligent energy ring network system, Ploss(t) loss, P, of the intelligent energy ring network systemload(t) is the load of the intelligent energy ring network system at time t, PlmaxFor the upper limit value, P, of the exchange power between the intelligent energy ring network system and the public power gride，maxFor storing the upper limit value of charging and discharging power of the energy storage system,
discharge power and charging power, SOC, respectively, for stored energyiThe charge state of the energy storage system is in an i-period; SOC0The charge state is the initial moment of energy storage; xt、YtThe energy storage charging and discharging state is set as 0 or 1, for example, the value is set as 0 during charging and 1 during discharging;ethe energy storage charge-discharge efficiency is obtained; SOCminIs the minimum value of the state of charge of the stored energy, SOCmaxThe maximum value of the energy storage state of charge.
6. The method of claim 1, wherein the objective function comprises a first objective function with the lowest operation cost as follows:
wherein, F1For the unit operation cost of the intelligent energy ring network system, T is the scheduling period of the system, generally 24h, T is the time, N is the total number of micro sources in the intelligent energy ring network system, FiFor each micro-power consumption cost, PGi(t) Power of each micro-Power supply at time t, MiFor the operating maintenance costs of the individual micro-power supplies at time t, EbPrice for buying and selling electricity for public power grid, PbAnd (t) buying and selling the power of the electricity at the moment t.
7. The intelligent energy ring network micro power output optimization method according to claim 6, wherein the objective function further comprises a second objective function with the lowest load electricity cost as follows:
wherein, F2Is a unit of intelligent energy ring network systemCost of load power consumption, SiThe electricity selling cost of the ith power supply at the time t is shown.
8. The utility model provides a little power output optimization device in wisdom can looped netowrk, includes:
the power model building module is configured to build a corresponding power model for the generator set in the intelligent energy ring network;
the target function building module is configured to optimize the output of each micro power supply in the intelligent energy ring network system and the exchange power of each micro power supply and a power grid under the condition of meeting the preset system operation constraint condition by using the power model of each generator set in the intelligent energy ring network, and build a target function with the lowest operation cost, the lowest load power consumption cost or the highest environmental benefit of the intelligent energy ring network system;
and the optimization module is configured to optimize the output of each micro power supply in the intelligent energy ring network through a particle swarm optimization algorithm based on the target function and the system operation constraint condition.
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