Rural distributed integrated energy system multi-objective optimization scheduling method

Abstract

The invention relates to a multi-target optimization scheduling method for a rural distributed integrated energy system. The method comprises the following steps: establishing an hour-level day-ahead optimization scheduling model by taking the minimum demand response scheduling cost as a target; solving the day-ahead optimal scheduling model by using a Pareto method, and seeking an optimal solution to obtain a day-ahead scheduling plan; establishing a minute-level intra-day optimization scheduling model by taking the minimum net load fluctuation as a target; and solving the intra-day optimal scheduling model to obtain an intra-day scheduling plan. According to the invention, through multi-objective optimization scheduling, the stability and reliability of overall power supply are improved; the requirement for stable operation of the system is met through adjustment of the flexible resources, and compared with a mode of starting and stopping a unit in a traditional method, flexible resource scheduling is more flexible and applicable; on the basis of meeting the power supply reliability of the power grid, load fluctuation can be reduced, the operation efficiency of various devices of the power grid is improved, various demand responses of a user side are effectively utilized, and the flexible development demand of the power grid in future is met.

Description

Technical Field

[0001] The invention relates to the technical field of electric power systems, and in particular to a multi-objective optimization dispatching method for a rural distributed integrated energy system. Background Art

[0002] Integrated energy has received much attention in recent years. As a clean and efficient way of energy utilization, it will be a key path to promote the development of clean energy and non-fossil energy in the future, and thus become a key path to promote the revolution of energy supply and demand. In the future energy supply and demand network, electric energy will inevitably become a link between primary and secondary energy and play an important role.

[0003] At present, due to the large-scale access of distributed renewable energy to the power grid, its random fluctuation characteristics have brought unstable factors and influences to the power grid, causing the net load peak-to-valley difference to further increase, and even the power reverse transmission in the power generation peak-to-valley. Therefore, it is very important to optimize the system with multiple objectives to effectively promote the consumption of renewable energy and reduce the peak-to-valley difference. At present, at this stage, the optimization and scheduling are mainly focused on the research on how to adjust the output of each unit with the goal of minimizing operating costs and carbon emissions, and have failed to give full play to the flexibility resources on the user side. However, the cost of frequently starting and stopping units is relatively high, which may increase maintenance costs, and for some types of units, operation under non-optimal conditions will lead to reduced efficiency. Summary of the invention

[0004] In order to solve the problem of insufficient application of user-side flexibility resources in the prior art, the purpose of the present invention is to provide a multi-objective optimization scheduling method for rural distributed integrated energy systems that can reduce load fluctuations, improve the operating efficiency of various types of equipment in the power grid, effectively utilize various types of demand responses on the user side, and meet the future development needs of power grid flexibility on the basis of meeting the power supply reliability of the power grid.

[0005] To achieve the above object, the present invention adopts the following technical solution: a multi-objective optimization scheduling method for a rural distributed integrated energy system, the method comprising the following steps in order:

[0006] (1) Establish an hourly day-ahead optimal scheduling model with the goal of minimizing the demand response scheduling cost;

[0007] (2) Solving the day-ahead optimization scheduling model using the Pareto method to find the optimal solution and obtain a day-ahead scheduling plan, which includes a load reduction time period and a corresponding reduction capacity in demand response, and a load shifting time period and a corresponding shifting capacity in demand response;

[0008] (3) Establish a minute-level intraday optimization scheduling model with the goal of minimizing net load fluctuation;

[0009] (4) According to the day-ahead dispatch plan, the intraday optimization dispatch model is solved to obtain the intraday dispatch plan, which includes the energy storage output period and output duration.

[0010] In step (1), the objective function of the day-ahead optimization scheduling model is as follows:

[0011] minF1=min{C XJ,t +C PY,t}

[0012] Among them, C PY，t Represents a shiftable load, which can transfer the power curve as a whole between different time periods. The shiftable load includes industrial load and electric vehicle load; C XJ，t Indicates that the load can be reduced. The load that can be reduced is the temperature-controlled adjustable load. C PY，t and C XJ，t The formulas are:

[0013]

[0014] Where q is the original starting time period of the translatable load, and p is the original ending time period of the translatable load; and They are the start time and end time of load translation respectively; u tPY is the translation state of the load; t PY is the starting time after load translation; C time,PY and C power,PY are the unit compensation standards for shift time and shift capacity respectively; i represents the number of users with class A load reduction, N A represents the maximum number of users that can reduce load in Class A; m represents the number of load levels that can be reduced in Class A, M A Indicates the maximum number of gears that can reduce load in Category A; and They are the unit compensation standards for class A and class B loads that can be reduced; They represent the actual response of the translatable load, the A-type reducible load and the B-type reducible load, respectively; k represents the level of the B-type reducible load, K B Indicates the maximum number of load reduction levels for Class B; Class A is the temperature control load for residential users; Class B is the power load for shopping malls and office spaces.

[0015] In step (1), the constraints of the day-ahead optimization scheduling model include a demand response scheduling lower limit constraint, a demand response scheduling upper limit constraint, a total number of times that load can be cut and a total number of times that load can be shifted;

[0016] The formula for the demand response scheduling lower limit constraint is:

[0017] P min <P DG ≤100%

[0018] 0<F load ≤F max

[0019] Among them, P DG is the new energy consumption rate, P min The minimum consumption rate of new energy required by the power grid; F load is the grid load factor, F max The maximum load factor specified for the power grid;

[0020] The formula for the upper limit constraint of demand response scheduling is:

[0021] 0≤L i ≤L imax

[0022] 0≤L ev ≤L evmax

[0023] 0≤L tc ≤L tcmax

[0024] Among them, L i , L ev , L tc They are industrial load, electric vehicle load, and temperature control load, respectively. imax , L evmax , L tcmax They are the maximum adjustable upper limits of industrial load, electric vehicle load and temperature control load respectively;

[0025] The formula for the total number of load reduction constraints is:

[0026]

[0027] Among them, x t1 It is the load shedding signal; N max is the maximum number of interruptions within the interval time period, t1 is the start time of the interval time period, and T1 is the end time of the interval time period;

[0028] The formula for the translation times constraint of the translatable load is:

[0029]

[0030] Where q is the original starting time period of the translatable load, and p is the original ending time period of the translatable load; and They are the start time and end time of load translation respectively; u tPYis the translation state of the load; t PY is the starting time after load translation.

[0031] In step (3), the objective function of the intraday optimization scheduling model is:

[0032]

[0033] In the formula, C means that energy storage can transfer the power curve after day-ahead scheduling between different time periods as a whole. represents the average net load in a scheduling cycle, t and N are the start and end time of the scheduling cycle respectively; P pl is the net load after day-ahead dispatch, and its formula is:

[0034] P pl =P DG +P load -P DR +P ES

[0035] Where P DG , P load , P DR , P ES They are respectively the wind and solar power output forecast curve, the load output forecast curve, the demand response dispatch value obtained in the day-ahead dispatch plan, and the energy storage dispatch plan value.

[0036] In step (3), the constraints of the intraday optimization scheduling model include energy storage charging power constraints and energy storage discharging power constraints;

[0037] The formula for the energy storage charging power constraint is:

[0038]

[0039] The formula for the energy storage discharge power constraint is:

[0040]

[0041] Where P c,t and P d,t They represent the actual charging power and actual discharging power of the energy storage system respectively. and They respectively represent the rated charging power and rated discharging power of the energy storage system.

[0042] It can be seen from the above technical scheme that the beneficial effects of the present invention are: first, distributed energy is intermittent and unstable, and the fluctuation of distributed new energy will have different impacts on the operation of the power grid. For example, the distributed photovoltaic output is relatively large at noon. For areas with high distributed photovoltaic penetration and low load, photovoltaic power is very likely to be delivered at noon, which will affect the line and the upper transformer. Therefore, the present invention can avoid power reverse transmission and other situations through multi-objective optimization scheduling, and improve the stability and reliability of the overall power supply; second, the present invention can meet the requirements of stable operation of the system through the adjustment of flexible resources, and the flexible resource scheduling is more flexible and applicable than the traditional method of starting and stopping units; third, on the basis of meeting the reliability of power supply of the power grid, the present invention can reduce load fluctuations, improve the operating efficiency of various equipment in the power grid, effectively utilize various demand responses on the user side, and meet the future development needs of power grid flexibility; fourth, the present invention not only improves the stable operation of the power grid, but also can effectively absorb new energy, increase the absorption rate of new energy, and meet the needs of energy conservation and emission reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flow chart of the method of the present invention;

[0044] Figure 2 It is a workflow diagram of the day-ahead scheduling model in the present invention;

[0045] Figure 3 It is the workflow diagram of the intraday scheduling model in the present invention. DETAILED DESCRIPTION

[0046] like Figure 1 As shown, a multi-objective optimization scheduling method for a rural distributed integrated energy system includes the following steps in sequence:

[0047] (1) Establish an hourly day-ahead optimal scheduling model with the goal of minimizing the demand response scheduling cost;

[0048] (2) Solving the day-ahead optimization scheduling model using the Pareto method to find the optimal solution and obtain a day-ahead scheduling plan, which includes a load reduction time period and a corresponding reduction capacity in demand response, and a load shifting time period and a corresponding shifting capacity in demand response;

[0049] (3) Establish a minute-level intraday optimization scheduling model with the goal of minimizing net load fluctuation;

[0050] (4) According to the day-ahead dispatch plan, the intraday optimization dispatch model is solved to obtain the intraday dispatch plan, which includes the energy storage output period and output duration.

[0051] like Figure 2As shown, in step (1), the objective function of the day-ahead optimization scheduling model is as follows:

[0052] minF1=min{C XJ,t +C PY,t}

[0053] Among them, C PY，t Represents a shiftable load, which can transfer the power curve as a whole between different time periods. The shiftable load includes industrial load and electric vehicle load; C XJ，t Indicates that the load can be reduced. The load that can be reduced is the temperature-controlled adjustable load. C PY，t and C XJ，t The formulas are:

[0054]

[0055] Where q is the original starting time period of the translatable load, and p is the original ending time period of the translatable load; and They are the start time and end time of load translation respectively; u tPY is the translation state of the load; t PY is the starting time after load translation; C time,PY and C power,PY are the unit compensation standards for shift time and shift capacity respectively; i represents the number of users with load reduction in category A, N A represents the maximum number of users of Class A that can reduce load; m represents the number of load levels that Class A can reduce load. A Indicates the maximum number of gears that can reduce load in Category A; and They are the unit compensation standards for class A and class B loads that can be reduced; They represent the actual response of the translatable load, the A-type reducible load and the B-type reducible load, respectively; k represents the level of the B-type reducible load, K B Indicates the maximum number of load reduction levels for Class B; Class A is the temperature control load for residential users; Class B is the power load for shopping malls and office spaces.

[0056] On the basis of increasing the consumption rate of new energy, reducing the load rate of the power grid, and making its related indicators meet the requirements of the power grid, the demand response scheduling cost is guaranteed to be minimized. Based on the prediction of wind and solar output and load curves, the source-load coupling prediction curve is obtained, and the price-based demand response is combined for optimized scheduling. The consumption rate of new energy and the load rate of the power grid are used as the lower limit of the demand response adjustment range, and the maximum adjustable capacity of each demand response is used as the upper limit of the demand response adjustment. Each demand response is adjusted within this range, and the demand response scheduling cost is minimized.

[0057] like Figure 2As shown, in step (1), the constraints of the day-ahead optimization scheduling model include a demand response scheduling lower limit constraint, a demand response scheduling upper limit constraint, a total number of load reduction constraints, and a shifting number of load shifting constraints;

[0058] The formula for the demand response scheduling lower limit constraint is:

[0059] P min <P DG ≤100%

[0060] 0<F load ≤F max

[0061] Among them, P DG is the new energy consumption rate, P min The minimum consumption rate of new energy required by the power grid; F load is the grid load factor, F max The maximum load factor specified for the power grid;

[0062] The formula for the upper limit constraint of demand response scheduling is:

[0063] 0≤L i ≤L imax

[0064] 0≤L ev ≤L evmax

[0065] 0≤L tc ≤L tcmax

[0066] Among them, L i , L ev , L tc They are industrial load, electric vehicle load, and temperature control load, respectively. imax , L evmax , L tcmax They are the maximum adjustable upper limits of industrial load, electric vehicle load and temperature control load respectively;

[0067] The formula for the total number of load reduction constraints is:

[0068]

[0069] Among them, x t1 It is the load shedding signal; N max is the maximum number of interruptions within the interval time period, t1 is the start time of the interval time period, and T1 is the end time of the interval time period;

[0070] The formula for the translation times constraint of the translatable load is:

[0071]

[0072] Where q is the original starting time period of the translatable load, and p is the original ending time period of the translatable load; and They are the start time and end time of load translation respectively; u tPY is the translation state of the load; t PY is the starting time after load translation.

[0073] like Figure 3 As shown, in step (3), the objective function of the intraday optimization scheduling model is:

[0074]

[0075] In the formula, C means that energy storage can transfer the power curve after day-ahead scheduling between different time periods as a whole. represents the average net load in a scheduling cycle, t and N are the start and end time of the scheduling cycle respectively; P pl is the net load after day-ahead dispatch, and its formula is:

[0076] P pl =P DG +P load -P DR +P ES

[0077] Where P DG , P load , P DR , P ES They are respectively the wind and solar power output forecast curve, the load output forecast curve, the demand response dispatch value obtained in the day-ahead dispatch plan, and the energy storage dispatch plan value.

[0078] like Figure 3 As shown, in step (3), the constraints of the intraday optimization scheduling model include energy storage charging power constraints and energy storage discharging power constraints;

[0079] The formula for the energy storage charging power constraint is:

[0080]

[0081] The formula for the energy storage discharge power constraint is:

[0082]

[0083] Where P c,t and P d,t They represent the actual charging power and actual discharging power of the energy storage system respectively. and They respectively represent the rated charging power and rated discharging power of the energy storage system.

[0084] To sum up, the present invention can avoid power reverse transmission and other situations through multi-objective optimization scheduling, thereby improving the stability and reliability of the overall power supply; through the adjustment of flexible resources, the requirements for stable operation of the system can be met, and the flexible resource scheduling is more flexible and applicable than the traditional method of starting and stopping units; on the basis of meeting the reliability of power supply of the power grid, it can reduce load fluctuations, improve the operating efficiency of various equipment in the power grid, effectively utilize various demand responses on the user side, and meet the future development needs of power grid flexibility; it not only improves the stable operation of the power grid, but also can effectively absorb new energy, increase the new energy absorption rate, and meet the needs of energy conservation and emission reduction.

Claims

1. A multi-objective optimization scheduling method for a rural distributed integrated energy system, characterized by: The method comprises the following steps in order: (1) Establish an hourly day-ahead optimal scheduling model with the goal of minimizing the demand response scheduling cost; (2) Solving the day-ahead optimization scheduling model using the Pareto method to find the optimal solution and obtain a day-ahead scheduling plan, which includes a load reduction time period and a corresponding reduction capacity in demand response, and a load shifting time period and a corresponding shifting capacity in demand response; (3) Establish a minute-level intraday optimization scheduling model with the goal of minimizing net load fluctuation; (4) According to the day-ahead dispatch plan, the intraday optimization dispatch model is solved to obtain the intraday dispatch plan, which includes the energy storage output period and output duration.

2. The multi-objective optimization scheduling method for rural distributed integrated energy systems according to claim 1 is characterized by: In step (1), the objective function of the day-ahead optimization scheduling model is as follows: minF1=min{C XJ,t +C PY,t } Among them, C PY，t Represents a shiftable load, which can transfer the power curve as a whole between different time periods. The shiftable load includes industrial load and electric vehicle load; C XJ，t Indicates that the load can be reduced. The load that can be reduced is the temperature-controlled adjustable load. C PY，t and C XJ，t The formulas are: Where q is the original starting time period of the translatable load, and p is the original ending time period of the translatable load; and They are the start time and end time of load translation respectively; u tPY is the translation state of the load; t PY is the starting time after load translation; C time,PY and C power,PY are the unit compensation standards for shift time and shift capacity respectively; i represents the number of users with class A load reduction, N A represents the maximum number of users that can reduce load in Class A; m represents the number of load levels that can be reduced in Class A, M A Indicates the maximum number of gears that can reduce load in Category A; and They are the unit compensation standards for class A and class B loads that can be reduced; They represent the actual response of the translatable load, the A-type reducible load and the B-type reducible load, respectively; k represents the level of the B-type reducible load, K B Indicates the maximum number of load reduction levels for Class B; Class A is the temperature control load for residential users; Class B is the power load for shopping malls and office spaces.

3. The multi-objective optimization scheduling method for rural distributed integrated energy systems according to claim 1 is characterized by: In step (1), the constraints of the day-ahead optimization scheduling model include a demand response scheduling lower limit constraint, a demand response scheduling upper limit constraint, a total number of times that load can be cut and a total number of times that load can be shifted; The formula for the demand response scheduling lower limit constraint is: P min <P DG ≤100% 0<F load ≤F max Among them, P DG is the new energy consumption rate, P min The minimum consumption rate of new energy required by the power grid; F load is the grid load factor, F max The maximum load factor specified for the power grid; The formula for the upper limit constraint of demand response scheduling is: 0≤L i ≤L imax 0≤L ev ≤L evmax 0≤L tc ≤L tcmax Among them, L i , L ev , L tc They are industrial load, electric vehicle load, and temperature control load, respectively. imax , L evmax , L tcmax They are the maximum adjustable upper limits of industrial load, electric vehicle load and temperature control load respectively; The formula for the total number of load reduction constraints is: Among them, x t1 It is the load shedding signal; N max is the maximum number of interruptions within the interval time period, t1 is the start time of the interval time period, and T1 is the end time of the interval time period; The formula for the translation times constraint of the translatable load is: Where q is the original starting time period of the translatable load, and p is the original ending time period of the translatable load; and They are the start time and end time of load translation respectively; u tPY is the translation state of the load; t PY is the starting time after load translation.

4. The multi-objective optimization scheduling method for rural distributed integrated energy systems according to claim 1 is characterized by: In step (3), the objective function of the intraday optimization scheduling model is: In the formula, C means that energy storage can transfer the power curve after day-ahead scheduling between different time periods as a whole. represents the average net load in a scheduling cycle, t and N are the start and end time of the scheduling cycle respectively; P pl is the net load after day-ahead dispatch, and its formula is: P pl =P DG +P load -P DR +P ES Where P DG , P load , P DR , P ES They are respectively the wind and solar power output forecast curve, the load output forecast curve, the demand response dispatch value obtained in the day-ahead dispatch plan, and the energy storage dispatch plan value.

5. The multi-objective optimization scheduling method for rural distributed integrated energy systems according to claim 1 is characterized by: In step (3), the constraints of the intraday optimization scheduling model include energy storage charging power constraints and energy storage discharging power constraints; The formula for the energy storage charging power constraint is: The formula for the energy storage discharge power constraint is: Where P c,t and P d,t They represent the actual charging power and actual discharging power of the energy storage system respectively. and They respectively represent the rated charging power and rated discharging power of the energy storage system.

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