CN111400857B - Energy optimization method considering battery attenuation cost in micro-grid - Google Patents
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Abstract
The invention discloses an energy optimization method taking battery attenuation cost into consideration in a micro-grid, which comprises the following steps: step one, constructing a battery attenuation cost model; step two, constructing an electricity charge expense cost model; step three, establishing an objective function for operation optimization of the micro-grid system; establishing constraint conditions for inducing and optimizing an objective function; and fifthly, carrying out global optimization solving and step six of the model, and running the example. Aiming at the defects of energy optimization in a micro-grid, the invention considers the attenuation cost of a battery, establishes a perfect mathematical model according to the requirement, decomposes a complex model into a relatively simple model in solving, solves the simple model, finally analyzes and obtains an energy optimization scheme with minimum economic expenditure, and improves the energy efficiency and the safety in the micro-grid.
Description
Technical Field
The invention relates to the technical field of energy management, in particular to an energy optimization method for considering battery attenuation cost in a micro-grid.
Background
The continuous growth of energy demand and greenhouse gas emissions has become an important issue of worldwide concern, and one of the promising approaches to solve these problems is to use smart grids as future power systems. Smart grids may improve energy efficiency in various ways, such as price policies and Demand Response (DR) control methods. Smart grid facilitated demand response may improve energy efficiency and security by coordinating the balance between energy supply and energy demand. Due to the randomness of the power load, a difference between the predicted value and the actual value is unavoidable. Energy storage batteries act as bi-directional regulators, together with the grid, improving energy efficiency, often integrated into the DR to compensate for power scheduling.
However, frequent charging and discharging in real-time schedulers can greatly reduce battery life. Therefore, the attenuation cost of the battery needs to be considered in energy optimization, and the solving of the mathematical model after modeling is a big problem.
For example, an "energy optimization management method of an adaptive micro-grid energy storage system" disclosed in chinese patent literature, it announces No. CN105098810B, the method includes the following steps: A. determining the energy which can be used for peak shaving more than the previous day in a battery in the micro-grid energy storage system; B. and adjusting the discharge threshold of the battery in the micro-grid energy storage system according to the ratio of the energy which can be used for peak shaving more than the previous day of the battery to the lower limit of the energy storage capacity of the battery, wherein the discharge threshold is reduced when the ratio is greater than 1, and the discharge threshold is increased when the ratio is less than 1. The method only considers the fluctuation of the power load, but does not consider the problem that the service life of the battery is reduced due to frequent charging and discharging in a real-time scheduling program, and has the defect of energy optimization.
Disclosure of Invention
The invention aims to overcome the defects of energy optimization in a micro-grid in the prior art and needs to consider the technical problem of battery attenuation cost, and provides an energy optimization method for considering battery attenuation cost in the micro-grid.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
an energy optimization method for considering battery attenuation cost in a micro-grid, comprising the following steps:
step one, constructing a battery attenuation cost model;
step two, constructing an electricity charge expense cost model;
step three, establishing an objective function for operation optimization of the micro-grid system: combining the first step and the second step, an optimized objective function with the least one-day electricity expense and the least battery attenuation cost in the micro-grid can be established;
establishing constraint conditions for inducing and optimizing an objective function: comprehensively considering the electric energy supply and demand balance constraint, the battery attenuation cost constraint, the battery working constraint and the constraint of the switch control equipment to obtain constraint conditions of the micro-grid system optimization;
step five, global optimization solving of the model: solving by adopting an interor-point algorithm and a cultural algorithm cultural algorithm according to the objective function determined in the step three and combining the constraint conditions given in the step four;
step six, running an example: and D, obtaining a power grid interactive electric energy and battery charge-discharge quantity curve according to the optimal variable parameters obtained in the step five, obtaining optimal operation of the storage battery and the switch control equipment from the power grid interactive electric energy and the battery charge-discharge quantity curve, and obtaining the minimum economic expenditure.
The invention aims at the defects of energy optimization in a micro-grid, establishes a perfect mathematical model according to the requirements, takes the least one-day electric charge expense and the least battery attenuation cost in the micro-grid as an optimization objective function, establishes constraint conditions of the optimal mathematical model, solves a complex model into a relatively simple model, solves the simple model, and finally solves the original problem.
Preferably, the battery attenuation model in the first step is: c (C) batt (t), i.e., the battery's decay costs at time t;the electricity fee cost model in the second step is as follows: [ T ] elec (t)*p grid (T), wherein [ T ] elec (t) represents the electricity price at time t, p grid (t) represents the amount of power exchange between the time t and the main grid, if positive, the user purchases power from the main grid, and if negative, the user sells the power to the main grid; in the third step, the optimization objective function is shown as an expression (1):
wherein: f represents one-day economic expense, T u Representing a scheduling period.
Preferably, the electric energy supply and demand balance constraint in the fourth step is as shown in expression (3) and expression (2):
p grid (t)＝p inte (t)+p batt (t)+p pv (t)+p wt (t)+p must-run (t) (2)
wherein: p in expression (3) grid (t) represents the amount of interaction between the moment t and the grid,representing minimum and maximum electric quantity interacted with the power grid respectively, P in expression (2) inte (t) represents the electricity consumption of the switch control device at the t-th moment, P batt (t) represents the charge/discharge amount of the storage battery device at time t, P pv (t) represents the power generation amount of the solar photovoltaic at the moment t, P wt (t) represents the power generation amount of the wind energy at the moment t, p must-run And (t) represents the electricity consumption of the basic load at the moment t.
Preferably, the battery attenuation cost constraint in the fourth step is as shown in expression (4), expression (5), expression (6), expression (7) and expression (8):
I c (DOD batt (t))＝a*(DOD batt (t)) -b (5)
DOD batt (t)＝1-SOC batt (t) (6)
SOC min ≤SOC(t)≤SOC max (8)
the expressions (4) to (8) C batt,cap Representing the cost of operation of the battery, Δt represents the time interval, E batt,t Representing the total capacity of the battery, I c (DOD batt (t)) represents the depth of discharge of the battery, DOD batt (t) represents the depth of discharge, η, of the battery at time t batt Represents the working efficiency of the battery, a and b are coefficients, DOD batt (t) represents the depth of discharge of the battery at time t, SOC batt (t) represents the state of remaining battery level at time t, p batt,ch (t),p batt,dch (t) represents the charge and discharge amounts of the battery at time t,and->Representing the charge and discharge efficiency of the battery, and SOC (t) represents the state of charge of the battery at time t min Representing the minimum charge ratio of the battery, SOC max Representing the maximum charge ratio of the battery. In the present invention C batt,cap 600$/kWh; Δt is 1h; e (E) batt,t 12kWh；η batt 0.95; a and b are coefficients, 4980,1.98 respectively; />And->Both 0.95.
Preferably, the battery operation constraint in the fourth step is as shown in expression (9), expression (10), expression (11), expression (12) and expression (13):
SOC min ≤SOC(t)≤SOC max (13)
the expressions (9) to (13): p (P) batt,ch (t) represents the charge amount of the battery at the time t, P batt,dch (t) represents the discharge amount of the battery at the time t,represents the maximum discharge capacity of the accumulator at time t, < >>Represents the maximum charge quantity eta of the storage battery at the moment t ch Representing the efficiency, eta of battery charging dch Representing the efficiency of the discharge of the battery E batt,t Representing the total capacity of the storage battery, and SOC (t) representing the state of charge, SOC of the storage battery at time t batt (t) represents the state of remaining battery level at time t, SOC min Representing the minimum charge ratio of the battery, SOC max Represents the maximum charge ratio of the battery, and Δt represents the time periodAnd (3) separating.
Preferably, the constraint of the switch control device in the fourth step is as shown in expression (14), expression (15) and expression (16):
the expressions (14) to (16): alpha a ,β a Representing the operating area, delta, of the controllable device a a,t0 Indicating that controllable device a is at current t 0 Operating state at moment, delta a,t Representing the operating state of the controllable device a at time t, delta a,τ Indicating the operating state of the load a at the time tau, H a Indicating the length of work that the load a needs to complete,indicating the operating state before time t.
Preferably, the global optimization solution of the model comprises the steps of:
step 5.1, initializing relevant parameters needed by optimizing a mathematical model, and randomly generating N particles to form particle subgroups, wherein each particle structure is as follows:
[x grid1 ...x grid48 ,x batt1 ...x batt48 ,x 1 inte1 ......x 1 inte48 ......x 5 inte1 ......x 5 inte48 ]
step 5.2, constructing a new expression group according to constraint limiting condition expressions (2) to (13) of each electrical device and the objective function expression (1) as follows:
min expression (1);
constraint limit expression (2-13); the random number generated in the step 5.1 is used as an initial value, and the expression is solved through an interor-point algorithm;
and 5.3, updating the new solution obtained by the interor-point algorithm in the step 5.2, wherein the updated algorithm adopts a cultural algorithm cultural algorithm, and a new objective function is established as an expression (17):
b (x, μ) =expression (1) - μ Σlog (g (x)) (17)
Wherein B (X, μ) is the target value, g (X) represents the value as X 0 When the conditions in the expressions (2) to (16) are not satisfied, μ represents a penalty factor;
step 5.4, repeating the steps 5.2 and 5.3, performing iterative calculation, taking the minimum value of all particles as an optimal value, namely the sum of all parameter values in all particles is minimum as the minimum value of all particles, until the variation of the optimal value is less than 0.001, namely the sum of all parameter value variations of the particles of the optimal value is less than 0.001, ending the iterative calculation, taking the particle with the minimum value finally obtained as an optimal variable parameter, and taking the electric energy output power x of the fuel cell in the particle with the optimal variable parameter grid Charge and discharge capacity x of storage battery batt And state x of the switch control device inte 。
The initialization parameters in step 5.1 areT u ,C batt,cap ,Δt,E batt,t ,η batt ,a，b，η ch ,η dch ，SOC min ，SOC max And parameters of 5 switch control devices, the particle group N formed by randomly generating N particles can take different values.
Preferably, each particle comprises an interaction power p with the power grid grid Charge/discharge capacity p of storage battery batt And 5 sets of state parameters of the switch control device. Each set of parameters contained 48 parameter values for a total dimension of 536.
Preferably, each group of statesThe control parameter is each value, x, of the control parameter which changes in units of one hour within 48 hours grid1 ...x grid48 Expressed as the interaction power with the power grid in 48 hours, x batt1 ...x batt48 Expressed as the charge and discharge amount of the battery in 48 hours,representing the switching states of the 5 switching control devices in 48 hours. Wherein the group control parameter [ x ] grid1 ...x grid48 ,x batt1 ...x batt48 ]Is a continuous value that is a function of the value,is a discrete value.
The beneficial effects of the invention are as follows: aiming at the defects of energy optimization, a perfect mathematical model is established according to the requirements by considering the attenuation cost of a battery, a complex model is decomposed into a relatively simple model in solving, the simple model is solved, the optimal operation of a storage battery and a switch control device is finally known, the minimum economic expenditure is obtained, and the balance between energy supply and energy demand is coordinated to improve the energy efficiency and the safety.
Drawings
Fig. 1 is a flow chart of a method of the present invention.
Fig. 2 is a graph of the price of electricity over 48 hours for the present invention.
Fig. 3 is a graph of the power usage, photovoltaic power generation and wind power generation of a base load over a 48 hour period of the present invention.
Fig. 4 is a graph of grid interaction power and battery charge and discharge of the present invention.
Fig. 5 is a graph of five switching variables operation after optimization in accordance with the present invention.
Detailed Description
The technical scheme of the invention is further specifically described below through examples and with reference to the accompanying drawings. Examples: an energy optimization method for considering battery attenuation cost in a micro-grid of the present embodiment, as shown in fig. 1, includes the following steps:
step one, constructing a battery attenuation cost model: c (C) batt (t), i.e. the decay expense of the battery at time t.
Step two, constructing an electricity expense cost model: [ T ] elec (t)*p grid (T), wherein [ T ] elec (t) represents the electricity price at time t, p grid And (t) represents the power exchange quantity between the moment t and the main power grid, and if the positive number represents that the user purchases the electric quantity from the main power grid, the negative number represents that the user sells the electric quantity to the main power grid.
Step three, establishing an objective function for operation optimization of the micro-grid system: combining the first step and the second step, an optimized objective function with the least one-day electric charge expense and the least battery attenuation cost in the micro-grid can be established, and the optimized objective function is shown as an expression (1):
wherein: f represents one-day economic expense, T u Representing a scheduling period. In the present invention, two days, namely 48 hours, are taken as one scheduling period.
Establishing constraint conditions for inducing and optimizing an objective function: and comprehensively considering the electric energy supply and demand balance constraint, the battery attenuation cost constraint, the battery working constraint and the constraint of the switch control equipment to obtain the constraint condition of the micro-grid system optimization, wherein the constraint condition comprises basic electric appliance constraint, the basic electric appliance constraint comprises the electric energy supply and demand balance, the storage battery equipment constraint and the switch control equipment constraint, and the storage battery equipment constraint comprises the battery attenuation cost constraint and the battery working constraint.
And in the fourth step, the electric energy supply and demand balance constraint is shown as an expression (2) and an expression (3):
p grid (t)＝p inte (t)+p batt (t)+p pv (t)+p wt (t)+p must-run (t) (2)
wherein: p in expression (3) grid (t) represents the amount of interaction between the moment t and the grid,representing minimum and maximum electric quantity interacted with the power grid respectively, P in expression (2) inte (t) represents the electricity consumption of the switch control device at the t-th moment, P batt (t) represents the charge/discharge amount of the storage battery device at time t, P pv (t) represents the power generation amount of the solar photovoltaic at the moment t, P wt (t) represents the power generation amount of the wind energy at the moment t, p must-run And (t) represents the electricity consumption of the basic load at the moment t.
The battery attenuation cost constraint in the fourth step is shown as an expression (4), an expression (5), an expression (6), an expression (7) and an expression (8):
I c (DOD batt (t))＝a*(DOD batt (t)) -b (5)
DOD batt (t)＝1-SOC batt (t) (6)
SOC min ≤SOC(t)≤SOC max (8)
c in expressions (4) to (8) batt,cap Representing the cost of operation of the battery, Δt represents the time interval, E batt,t Representing the total capacity of the battery, I c (DOD batt (t)) represents the depth of discharge of the battery, DOD batt (t) represents the depth of discharge, η, of the battery at time t batt Represents the working efficiency of the battery, a and b are coefficients, DOD batt (t) represents the depth of discharge of the battery at time t, SOC batt (t) represents the state of remaining battery level at time t, pb att,ch (t),p batt,dch (t) represents the charge and discharge amounts of the battery at time t,and->Representing the charge and discharge efficiency of the battery, and SOC (t) represents the state of charge of the battery at time t min Representing the minimum charge ratio of the battery, SOC max Representing the maximum charge ratio of the battery.
In the fourth step, the battery operation constraint is as shown in expression (9), expression (10), expression (11), expression (12) and expression (13):
SOC min ≤SOC(t)≤SOC max (13)
expression (9) to expression (13): p (P) batt,ch (t) represents the charge amount of the battery at the time t, P batt,dch (t) represents the discharge amount of the battery at the time t,represents the maximum discharge capacity of the accumulator at time t, < >>Represents tMaximum charge, eta of the battery at the moment ch Representing the efficiency, eta of battery charging dch Representing the efficiency of the discharge of the battery E batt,t Representing the total capacity of the storage battery, and SOC (t) representing the state of charge, SOC of the storage battery at time t batt (t) represents the state of remaining battery level at time t, SOC min Representing the minimum charge ratio of the battery, SOC max Representing the maximum charge ratio of the battery, Δt represents the time interval.
Constraint of the switching control device in the fourth step is as shown in expression (14), expression (15) and expression (16):
expression (14) to expression (16): alpha a ,β a Representing the operating area, delta, of the controllable device a a,t0 Indicating that controllable device a is at current t 0 Operating state at moment, delta a,t Representing the operating state of the controllable device a at time t, delta a,τ Indicating the operating state of the load a at the time tau, H a Indicating the length of work that the load a needs to complete,indicating the operating state before time t.
Step five, global optimization solving of the model:
step 5.1, initializing relevant parameters needed by optimizing a mathematical model, and randomly generating N particles to form particle subgroups, wherein each particle structure is as follows:
[x grid1 ...x grid48 ,x batt1 ...x batt48 ,x 1 inte1 ......x 1 inte48 ......x 5 inte1 ......x 5 inte48 ]
initialization ofT u ＝48,C batt,cap ＝600$/kWh,Δt＝1h,E batt,t ＝12kWh,η batt ＝0.95,a＝4980，b＝1.98，η ch ,η dch ＝0.95，SOC min ＝0.1，SOC max Values of = 0.9,5 switch control devices are as in table 1:
TABLE 1
The N in the random generation of N particles forming the particle swarm can take different values, each particle comprising an interaction power p with the power grid grid Charge/discharge capacity p of storage battery batt And 5 sets of state parameters of the switch control device; each set of parameters contained 48 parameter values for a total dimension of 536.
The electricity price in 48 hours is shown in fig. 2, and the electricity consumption of the base load, the photovoltaic power generation amount and the wind power generation amount in 48 hours are shown in fig. 3. Each set of control parameters is a respective value of the control parameter that varies in units of one hour within 48 hours, wherein the set of control parameters [ x ] grid1 ...x grid48 ,x batt1 ...x batt48 ]Is a continuous value, x grid1 ...x grid48 Expressed as the interaction power with the power grid in 48 hours, x batt1 ...x batt48 Expressed as the charge and discharge of the battery in 48 hours. The 5 sets of state parameters are expressed asAnd is a discrete value, which represents the switching state of 5 switching control devices in 48 hours, and the switching state value adopts [0,1 ]]Binary state.
Step 5.2, constructing a new expression group according to constraint limiting condition expressions (2) to (13) of each electrical device and the objective function expression (1) as follows:
min expression (1);
constraint limit expressions (2-13); and solving the expression by an interor-point algorithm by using the random number generated in the step 5.1 as an initial value, wherein each particle in the obtained new solution keeps the original 5 groups of state parametersFive new sets of control parameters are generated +.>The original 5 groups of state parameters are discrete values, and the new five groups of control parameters are continuous values.
And 5.3, updating the new solution obtained by the interor-point algorithm in the step 5.2, wherein the updated algorithm adopts a cultural algorithm cultural algorithm, and a new objective function is established as an expression (17):
b (x, μ) =expression (1) - μ Σlog (g (x)) (17)
Wherein B (X, μ) is the target value, g (X) represents the value as X 0 In the case where the conditions in the expressions (2) to (16) are not satisfied, μ represents a penalty factor, which is a fixed value and may be 500.
Step 5.4, repeating the steps 5.2 and 5.3, performing iterative calculation, taking the minimum value of all particles as an optimal value, namely the sum of all parameter values in all particles is minimum as the minimum value of all particles, until the variation of the optimal value is less than 0.001, namely the sum of all parameter value variations of the particles of the optimal value is less than 0.001, ending the iterative calculation, taking the particle with the minimum value finally obtained as an optimal variable parameter, and taking the electric energy output power x of the fuel cell in the particle with the optimal variable parameter grid Charge and discharge capacity x of storage battery batt And state x of the switch control device inte 。
Step six, running an example: and D, obtaining a power grid interactive electric energy and battery charge-discharge quantity curve according to the optimal variable parameters obtained in the step five, obtaining optimal operation of the storage battery and the switch control equipment from the power grid interactive electric energy and the battery charge-discharge quantity curve, and obtaining the minimum economic expenditure.
Analyzing the influence of initial particle number on algorithm performance, and when random particle number is converted from 1 to 20, obtaining target value results as shown in Table 2:
table 2: target values at different initial values
When the initial value is 20, the result value after the system optimization is solved is as shown in fig. 4:
FIG. 4 shows x within 48 hours after optimization grid And x batt From this it can be seen that the battery and the grid charge cooperate to achieve the least economic expenditure.
FIG. 5 shows 5 x after optimization inte And the switch variable works.
As can be seen from fig. 5, 5 switching devices achieve reduced economic costs by scheduling time periods of higher electricity prices (16:00-18:00, 37:00-38:00) to be avoided.
The invention aims at the defects of energy optimization in a micro-grid, considers the attenuation cost of the battery, establishes a perfect mathematical model according to the requirements, takes the one-day electricity charge expense and the battery attenuation cost in the micro-grid as the optimal objective function, establishes constraint conditions of the optimal mathematical model, decomposes a complex model into a relatively simple model on solving, solves the simple model, finally realizes the solving of the original problem, namely, knows the optimal operation of a storage battery and a switch control device, obtains the least economic expense, and coordinates the balance between energy supply and energy demand to improve the energy efficiency and the safety.
Claims (5)
1. An energy optimization method for considering battery attenuation cost in a micro-grid, comprising the following steps:
step one, constructing a battery attenuation cost model; the battery attenuation cost model in the first step is as follows: c (C) batt (t), i.e., the battery's decay costs at time t;
step two, constructing an electricity charge expense cost model; the electricity fee cost model in the second step is as follows: t (T) elec (t)*p grid (T), wherein T elec (t) represents the electricity price at time t, p grid (t) represents the amount of power exchange between the time t and the main grid, if positive, the user purchases power from the main grid, and if negative, the user sells the power to the main grid;
step three, establishing an objective function for operation optimization of the micro-grid system: combining the first step and the second step, an optimized objective function with the least one-day electricity expense and the least battery attenuation cost in the micro-grid can be established; in the third step, the optimization objective function is shown as an expression (1):
wherein: f represents one-day economic expense, T u Representing a scheduling period;
establishing constraint conditions for inducing and optimizing an objective function: comprehensively considering the electric energy supply and demand balance constraint, the battery attenuation cost constraint, the battery working constraint and the constraint of the switch control equipment to obtain constraint conditions of the micro-grid system optimization;
the battery attenuation cost constraint in the fourth step is shown as an expression (4), an expression (5), an expression (6), an expression (7) and an expression (8):
I c (DOD batt (t))＝a*(DOD batt (t)) -b (5)
DOD batt (t)＝1-SOC batt (t) (6)
SOC min ≤SOC(t)≤SOC max (8)
the expressions (4) to (8) C batt,cap Representing the cost of operation of the battery, Δt represents the time interval, E batt,t Represents the total capacity of the battery, eta batt Represents the working efficiency of the battery, a and b are coefficients, DOD batt (t) represents the depth of discharge of the battery at time t, SOC batt (t) represents the state of remaining battery level at time t, P batt (t) represents the charge/discharge amount of the storage battery device at time t, p batt,ch (t),p batt,dch (t) represents the charge and discharge amounts of the battery at time t,and->Representing the charge and discharge efficiency of the battery, and SOC (t) represents the state of charge of the battery at time t min Representing the minimum charge ratio of the battery, SOC max Representing the maximum charge ratio of the battery;
step five, global optimization solving of the model: solving by adopting an interor-point algorithm and a cultural algorithm cultural algorithm according to the objective function determined in the step three and combining the constraint conditions given in the step four;
step six, running an example: and D, obtaining a power grid interactive electric energy and battery charge-discharge quantity curve according to the optimal variable parameters obtained in the step five, obtaining optimal operation of the storage battery and the switch control equipment from the power grid interactive electric energy and the battery charge-discharge quantity curve, and obtaining the minimum economic expenditure.
2. The energy optimizing method considering the battery attenuation cost in the micro-grid according to claim 1, wherein the electric energy supply and demand balance constraint in the fourth step is as shown in the expression (2) and the expression (3):
p grid (t)＝p inte (t)+p batt (t)+p pv (t)+p wt (t)+p must-run (t) (2)
wherein: p in expression (2) grid (t) represents the amount of interaction between the moment t and the grid,representing minimum and maximum electric quantity interacted with the power grid respectively, P in expression (2) inte (t) represents the electricity consumption of the switch control device at the t-th moment, P batt (t) represents the charge/discharge amount of the storage battery device at time t, P pv (t) represents the power generation amount of the solar photovoltaic at the moment t, P wt (t) represents the power generation amount of the wind energy at the moment t, p must-run And (t) represents the electricity consumption of the basic load at the moment t.
3. The energy optimization method considering the battery attenuation cost in the micro-grid according to claim 2, wherein the battery operation constraint in the fourth step is as shown in expression (9), expression (10), expression (11), expression (12) and expression (13):
SOC min ≤SOC(t)≤SOC max (13)
the expressions (9) to (13): p (P) batt,ch (t) represents the charge amount of the battery at the time t, P batt,dch (t) represents the discharge amount of the battery at the time t,represents the maximum discharge capacity of the accumulator at time t, < >>Represents the maximum charge quantity eta of the storage battery at the moment t ch Representing the efficiency, eta of battery charging dch Representing the efficiency of the discharge of the battery E batt,t Representing the total capacity of the storage battery, and SOC (t) representing the state of charge, SOC of the storage battery at time t batt (t) represents the state of remaining battery level at time t, SOC min Representing the minimum charge ratio of the battery, SOC max Representing the maximum charge ratio of the battery, Δt represents the time interval.
4. A method of energy optimization in a microgrid according to claim 3, wherein the constraints of the switching control device in the fourth step are as shown in expression (14), expression (15) and expression (16):
the expressions (14) to (16): alpha a ,β a Representing the operating area, delta, of the controllable device a a,t0 Indicating that controllable device a is at current t 0 Operating state at moment, delta a,t Representing the operating state of the controllable device a at time t, delta a,τ Indicating the operating state of the load a at the time tau, H a Indicating the length of work that the load a needs to complete,indicating the operating state before time t.
5. The method for optimizing energy in a microgrid according to claim 4, wherein the global optimization solution of the model in the fifth step comprises the steps of:
step 5.1, initializing relevant parameters needed by optimizing a mathematical model, and randomly generating N particles to form particle subgroups, wherein each particle structure is as follows:
each particle comprises an interaction electric quantity p between the particle and a power grid grid Charge/discharge capacity p of storage battery batt And 5 sets of state parameters of the switch control device;
each set of state control parameters is a respective value, x, of the control parameters that vary in units of one hour over 48 hours grid1 ...x grid48 Expressed as the interaction power with the power grid in 48 hours, x batt1 ...x batt48 Expressed as the charge and discharge amount of the battery in 48 hours,representing the switching states of the 5 switching control devices in 48 hours;
step 5.2, constructing a new expression group according to constraint limiting condition expressions (2) to (13) of each electrical device and the objective function expression (1) as follows:
min expression (1);
constraint and constraint expressions (2-13); the random number generated in the step 5.1 is used as an initial value, and the expression is solved through an interor-point algorithm;
and 5.3, updating the new solution obtained by the interor-point algorithm in the step 5.2, wherein the updated algorithm adopts a cultural algorithm cultural algorithm, and a new objective function is established as an expression (17):
b (x, μ) =expression (1) - μ Σlog (g (x)) (17)
Wherein B (X, μ) is the target value, g (X) represents the value as X 0 When the conditions in the expressions (2) to (16) are not satisfied, μ represents a penalty factor;
step 5.4, repeating the steps 5.2 and 5.3, performing iterative calculation, taking the minimum value of all particles as an optimal value, namely the sum of all parameter values in all particles is minimum as the minimum value of all particles, until the variation of the optimal value is less than 0.001, namely the sum of all parameter value variations of the particles of the optimal value is less than 0.001, ending the iterative calculation, taking the particle with the minimum value finally obtained as an optimal variable parameter, and taking the electric energy output power x of the fuel cell in the particle with the optimal variable parameter grid Charge and discharge capacity x of storage battery batt And state x of the switch control device inte 。
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