Micro-grid multi-target scheduling method considering energy storage attenuation

Abstract

The invention relates to a micro-grid multi-target scheduling method considering energy storage attenuation. The method comprises the following steps: acquiring day-ahead operation data of a micro-grid; establishing a system objective function and constraint conditions according to the acquired data; and solving a pareto solution of the objective function by using a multi-objective optimization algorithm, and taking the solution as a scheduling plan value. According to the method, the economical efficiency of system operation, the new energy utilization rate and attenuation generated by irregular discharge in the energy storage operation process are comprehensively considered, the pareto solution is solved through the multi-target optimization algorithm on the premise that the system operation constraint is met, and energy storage attenuation can be delayed while the economical efficiency and the energy utilization rate are considered.

Description

Technical Field

[0001] The invention belongs to the technical field of optimal dispatching of power systems, and in particular relates to a multi-objective dispatching method for a microgrid considering energy storage attenuation. Background Art

[0002] In recent years, wind power, photovoltaic power, and energy storage have developed rapidly. More and more large industrial modern parks are building park microgrids to reduce electricity costs and improve their own economy. The output of new energy represented by wind power and photovoltaic power is random and volatile. To improve the utilization efficiency of energy in the microgrid, the microgrid must adopt a suitable dispatching strategy for optimal dispatching. In a microgrid configured with wind-solar-diesel-storage, when formulating a dispatching strategy, its optimization problem is generally a single-objective optimization that only considers economy or energy utilization rate, ignoring the energy storage attenuation caused by different discharge depths. Summary of the Invention

[0003] The purpose of the invention is to overcome the deficiencies of the prior art and propose a multi-objective dispatching method for a microgrid considering energy storage attenuation, which comprehensively considers the economy of system operation, the utilization rate of new energy, and the attenuation caused by irregular discharge during the operation of energy storage. On the premise of meeting the system operation constraints, a multi-objective optimization algorithm is used to solve the Pareto solution, which can delay the attenuation of energy storage while taking into account economy and energy utilization rate.

[0004] The technical problem of the invention is solved by adopting the following technical solutions:

[0005] A multi-objective dispatching method for a microgrid considering energy storage attenuation includes the following steps:

[0006] Step 1, obtain the day-ahead operation data of the microgrid;

[0007] Step 2, establish a system objective function and constraint conditions according to the data obtained in Step 1;

[0008] Step 3, use a multi-objective optimization algorithm to solve the Pareto front of the system objective function in Step 2. The dispatcher selects a set of appropriate solutions according to the current system operation situation and the external environment, and uses this solution as the dispatching plan value, where the Pareto front can be solved using the non-dominated sorting genetic algorithm-II.

[0009] Moreover, the operation data in Step 1 includes load prediction, wind power output prediction, photovoltaic power output prediction, and the current operation state of energy storage.

[0010] Moreover, the objective function in Step 2 includes the lowest operation cost, the minimum energy storage attenuation, and the maximum utilization rate of energy storage and new energy, where the lowest operation cost is:

[0011] min f1 = C1 + C2 + C3

[0012] Among them, C1, C2, and C3 are the operating costs of diesel generators, the operating costs of wind and light, and the daily life loss cost of energy storage, respectively;

[0013] The minimum energy storage attenuation is:

[0014]

[0015] Among them, N toa (D OD (i)) is the number of battery cycles corresponding to the i-th depth of discharge; n is the number of cycles experienced;

[0016] The maximum utilization rate of energy storage and new energy is:

[0017]

[0018] Among them, P bess,t is the output power of the energy storage at time t.

[0019] Moreover, the operating cost of the diesel generator is:

[0020]

[0021] Among them, α, β, and γ are the fuel cost coefficients of the diesel generator; P d,t is the output power of the diesel generator at time t; T is the scheduling period.

[0022] Moreover, the operating cost of the wind and light:

[0023]

[0024] Among them, ρ w and ρ pv are the operating cost coefficients of wind power and photovoltaic power respectively; P w,t and P pv,t are the output powers of wind power and photovoltaic power at time t respectively.

[0025] Moreover, the daily life loss cost of the energy storage:

[0026]

[0027] Among them, C pay is the initial investment cost of the energy storage battery; T toa is the operating life of the energy storage battery; r is the annual interest rate; C man is the annual operating cost of the energy storage power station.

[0028] Moreover, the constraint conditions in step 2 include system power balance constraint, wind power and photovoltaic output constraint, diesel generator output constraint, energy storage battery charge and discharge power constraint, and energy storage battery SOC constraint.

[0029] The advantages and positive effects of the present invention are as follows:

[0030] The present invention obtains the day-ahead operation data of the microgrid; establishes a system objective function and constraint conditions according to the obtained data; uses a multi-objective optimization algorithm to solve the pareto solution of the objective function, and takes the solution as the dispatching plan value. The present invention comprehensively considers the economy of system operation, the utilization rate of new energy, and the attenuation generated by the irregular discharge during the operation of the energy storage. On the premise of meeting the system operation constraints, the multi-objective optimization algorithm is used to solve the pareto solution, which can delay the attenuation of the energy storage while taking into account economy and energy utilization rate. Brief Description of the Drawings

[0031] Figure 1 is a flowchart of the present invention;

[0032] Figure 2 is a schematic diagram of the patero front of the multi-objective optimization problem of the present invention. Detailed Embodiments

[0033] The following further details the present invention with reference to the drawings.

[0034] A multi-objective scheduling method for a microgrid considering energy storage attenuation, as Figure 1 shown, includes the following steps:

[0035] Step 1, obtain the day-ahead operation data of the microgrid, where the day-ahead operation data of the microgrid includes load prediction, wind power output prediction, photovoltaic output prediction, and the current operation state of the energy storage.

[0036] Step 2, establish a system objective function and constraint conditions.

[0037] Among them, the three objective functions include the lowest operating cost, the smallest energy storage attenuation, and the largest utilization rate of energy storage and new energy.

[0038] Objective function 1, the lowest operating cost, and its expression is:

[0039] min f1 = C1 + C2 + C3

[0040] Wherein, C1, C2, and C3 are respectively the operating cost of the diesel generator, the operating cost of wind and light, and the daily life loss cost of the energy storage.

[0041] The mathematical expression of the operating cost of the diesel generator is:

[0042]

[0043] Among them, α, β, and γ are the fuel cost coefficients of the diesel generator; P d,t is the output power of the diesel generator at time t; T is the scheduling period.

[0044] The mathematical expression for the operation cost of wind and light is:

[0045]

[0046] Among them, ρ w and ρ pv are the operation cost coefficients of wind power and photovoltaic power respectively; P w,t and P pv,t are the output powers of wind power and photovoltaic power at time t respectively.

[0047] The mathematical expression for the daily life loss cost of energy storage is:

[0048]

[0049] Among them, C pay is the initial investment cost of the energy storage battery; T toa is the operation years of the energy storage battery; r is the annual interest rate; C man is the annual operation cost of the energy storage power station.

[0050] The operation years T toa of the energy storage battery is:

[0051]

[0052] Among them, N is the number of cycles when the energy storage battery is charged and discharged at 100% depth of discharge, and its specific value can be provided by the manufacturer; E eq is the cycle conversion coefficient of the energy storage battery under standard working days, and its value can be solved according to the following formula:

[0053]

[0054] Among them, (DOD t ) -a is the cycle life of the energy storage battery at the depth of discharge in time t, and its value can be provided by the manufacturer of the energy storage battery.

[0055] Objective function 2: Minimum energy storage attenuation:

[0056]

[0057] Among them, N toa (D OD (i)) is the number of battery cycles corresponding to the i-th depth of discharge; n is the number of cycles experienced.

[0058] Objective function 3: Maximum utilization rate of energy storage and new energy:

[0059]

[0060] Among them, P bess,t is the output power of the energy storage at time t, and P d,t is the output power of the diesel generator at time t.

[0061] The system constraint conditions include system power balance constraint, wind power and photovoltaic output constraint, diesel generator output constraint, energy storage battery charge and discharge power constraint, and energy storage battery SOC constraint.

[0062] The system power balance constraint is:

[0063] P w,t +P pv,t +P bess,t +P d,t =P load,t

[0064] Among them, P load,t is the load value at time t.

[0065] The wind power and photovoltaic output constraints are:

[0066]

[0067] Among them, P w,max and P pv,max are the predicted outputs of wind power and photovoltaic respectively.

[0068] The diesel generator output constraint is:

[0069] P dmin ≤P d,t ≤P dmax

[0070] Among them, P dmin and P dmax are the minimum and maximum allowable powers of the diesel generator respectively.

[0071] The energy storage battery charge and discharge power constraint is:

[0072] -P cmax ≤P bess,t ≤P dmax

[0073] Among them, P cmax and P dmax are the maximum charge and maximum discharge powers of the energy storage system respectively.

[0074] The energy storage battery SOC constraint is:

[0075] SOC min ≤ SOC t ≤ SOC max

[0076] Among them, SOC t is the SOC value of the energy storage battery at time t; SOC min , SOC max are respectively the minimum and maximum SOC values allowed for the energy storage battery.

[0077] Step 3: Use the multi-objective optimization algorithm to solve the pareto front of the system objective function in Step 2. The dispatcher selects a set of appropriate solutions according to the current system operating conditions and the external environment, and uses this solution as the dispatching plan value, where the pareto front can be solved using the non-dominated sorting genetic algorithm-II.

[0078] It should be emphasized that the embodiments described in the present invention are illustrative rather than restrictive. Therefore, the present invention includes but is not limited to the embodiments described in the specific embodiments. Any other embodiments obtained by those skilled in the art based on the technical solutions of the present invention also fall within the scope of protection of the present invention.

Claims

1. A multi-objective scheduling method for a microgrid considering energy storage degradation, characterized in that: It includes the following steps: Step 1: Obtain the day-ahead operation data of the microgrid; Step 2: Establish the system objective function and constraints based on the data obtained in Step 1; Step 3: Use the multi-objective optimization algorithm to solve the Pareto front of the system objective function in Step 2. The dispatcher selects a set of appropriate solutions according to the current system operation situation and the external environment, and takes this solution as the dispatching plan value, where the Pareto front can be solved using the non-dominated sorting genetic algorithm-II.

2. A multi-objective scheduling method for a microgrid considering energy storage attenuation according to claim 1, characterized in that: The day-ahead operation data in Step 1 includes load prediction, wind power output prediction, photovoltaic power output prediction, and the current operation state of the energy storage.

3. A multi-objective scheduling method for a microgrid considering energy storage attenuation according to claim 1, characterized in that: The system objective function in Step 2 includes the lowest operation cost, the minimum energy storage attenuation, and the maximum utilization rate of the energy storage and new energy. Among them, the lowest operation cost is: min f1 = C1 + C2 + C3 where C1, C2, and C3 are the operation costs of the diesel generator, the operation costs of wind and light, and the daily life loss cost of the energy storage respectively; The minimum energy storage attenuation is: Among them, N toa (D OD (i)) is the number of battery cycles corresponding to the i-th depth of discharge; n is the number of cycles experienced; The maximum utilization rate of the energy storage and new energy is: Among them, P bess,t is the output power of energy storage in period t.

4. The multi-objective scheduling method for a microgrid considering energy storage attenuation according to claim 3, characterized in that: The operation cost of the diesel generator is: where α, β, and γ are the fuel cost coefficients of the diesel generator respectively; P d,t is the output power of the diesel generator at time period t; T is the scheduling period.

5. A multi-objective scheduling method for a microgrid considering energy storage attenuation according to claim 3, characterized in that: The operation costs of wind and light: Among them, ρ w and ρ pv are the operating cost coefficients of wind power and photovoltaic respectively; P w,t and P pv,t are the output powers of wind power and photovoltaic at time t respectively.

6. A multi-objective scheduling method for a microgrid considering energy storage attenuation according to claim 3, characterized in that: The daily life loss cost of the energy storage: Among them, C pay is the initial investment cost of the energy storage battery; T toa is the operation years of the energy storage battery; r is the annual interest rate; C man is the annual operation cost of the energy storage power station.

7. A multi-objective scheduling method for a microgrid considering energy storage attenuation according to claim 1, characterized in that: The constraints in Step 2 include system power balance constraints, wind power and photovoltaic power output constraints, diesel generator power output constraints, energy storage battery charge and discharge power constraints, and energy storage battery SOC constraints.

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