CN111614087B - Energy storage double-layer optimization configuration method participating in power grid peak shaving - Google Patents

Abstract

The present invention is a two-layer optimal allocation method of energy storage participating in power grid peak regulation, which is characterized in that the outer layer model is used as an optimal configuration model, and the economic index of energy storage peak regulation and the system technical index are used as a multi-objective optimal configuration function to obtain The optimal configuration scheme of the energy storage system that takes both economical and technical aspects into account, and since the current price mechanisms for energy storage to participate in peak shaving have not yet been linked, in the economic index model, only the direct economics of the energy storage system are considered indicators, ignoring the social benefits generated by them; while the calculation of each indicator in the outer model depends on the output parameters of the inner model; Comprehensively considering the peak-shaving effect of the energy storage system and the deep peak-shaving effect of the thermal power unit, with the goal of minimizing the total peak-shaving cost of the system, optimize the output of energy storage and thermal power unit. It can reduce the cost of system peak regulation, reduce the generation of abandoned wind, and help to improve the pressure of power grid peak regulation.

Description

A two-layer optimal configuration method for energy storage participating in power grid peak regulation

technical field

The invention relates to the field of deep peak regulation of thermal power units assisted by energy storage, and is an energy storage double-layer optimal configuration method participating in power grid peak regulation.

Background technique

The development trend of new energy is getting faster and faster, but it also brings new problems to the stable operation of the power grid. Taking wind power as an example, the anti-peaking characteristics of wind power increase the peak-to-valley difference of the power grid, which brings difficulties to the stable operation of the power grid. In order to alleviate the above contradictions, it is necessary to develop deep peak regulation methods for thermal power units, but deep peak regulation increases unit operating costs, and how to balance the relationship between economy and peak regulation performance is a key factor in determining unit operation. The energy storage technology has a faster response speed, can optimize the power structure, increase the peak-shaving capacity of the system, and the participation of energy storage-assisted thermal power units in grid peak-shaving can improve the peak-shaving pressure of the grid and reduce the generation of abandoned wind in areas with high wind power penetration. However, the high cost of energy storage technology is one of the key factors affecting its development. Therefore, how to determine a suitable energy storage system configuration scheme to ensure economy and have a good peak-shaving effect is a hot spot in current research.

Contents of the invention

The purpose of the present invention is to overcome the deficiencies of the prior art, to provide a scientific and reasonable, highly applicable storage system that can reduce the system peak regulation cost, reduce the generation of abandoned wind, and help improve the peak regulation pressure of the power grid. Two-layer optimization configuration method

The technical solution adopted to achieve the purpose of the present invention is a two-layer optimal configuration method for energy storage participating in power grid peak regulation, which is characterized in that it includes the following content:

1) Build a two-tier optimized configuration model structure

① Establish a candidate set of energy storage system configuration schemes in the outer model;

② Select an energy storage system configuration alternative as a constraint condition. According to the input system load and wind power data, in the inner model, the energy storage system and thermal power unit are used for deep peak regulation, and the operation of the energy storage system is considered during the peak regulation process. Cost, coal consumption cost of thermal power unit operation, additional cost of deep peak regulation of thermal power unit, deep compensation income of thermal power unit, and system risk cost factors, with the goal of minimizing the total cost of system peak regulation, optimize the economics of system peak regulation, and obtain the energy storage system The charging and discharging power of the energy storage system, the output of thermal power units, and the amount of wind power received under the configuration alternatives;

③According to the charging and discharging power of the energy storage system output by the inner model, the output of thermal power units, and the amount of wind power received, calculate the various cost benefits of the energy storage system, the standard deviation of the output of thermal power units, and the amount of newly added wind power, and calculate the total life of the energy storage system The net income in the cycle is an economic index, and the improvement of the standard deviation of the thermal power unit output and the new wind power acceptance are used as technical indicators to form a multi-objective optimal configuration function. The figure of merit is the result of optimal configuration of the energy storage system;

2) Optimizing the model objective function

(a) Outer model objective function

The outer model optimizes the configuration of the energy storage system with the goal of maximizing the net income in the entire life cycle of the energy storage system, the standard deviation improvement of the output of thermal power units after the energy storage system is added, and the maximum acceptance of new wind power. The sub-objective functions are as follows:

In the formula: I _ALL is the net income in the whole life cycle of the energy storage system, SD is the average value of the standard deviation improvement of the thermal power unit output, E _wind is the average value of the new wind power acceptance; I _BZ,d is the storage capacity of the d-th day Energy arbitrage income, I _BP,d is energy storage compensation income on day d, C _BY,d is energy storage operation cost on day d, C _BI is energy storage investment cost, SD _GS,d is thermal power on day d The improvement of the standard deviation of unit output, E _windnew,d is the new wind power received on day d, and D is the entire life cycle of the energy storage system;

(b) Inner model objective function

The inner model considers the operating cost of the energy storage system, the coal consumption cost of thermal power unit operation, the additional cost of unit deep regulation and compensation income, and the risk cost of the system, and aims at the lowest total cost of system peak regulation. The objective function of charging and discharging power of the system, thermal power unit output and wind power acceptance is as follows:

为机组出力最大值；

为机组常规调峰最低出力值，

为机组不投油深度调峰最低出力值，

为机组投油深度调峰最低出力值；In the formula: C _G,i is the energy consumption cost function of the deep peak shaving stage of the unit, I _G,i is the compensation income function of the deep peak shaving stage of the unit,

is the maximum output of the unit;

is the minimum output value of the unit for conventional peak regulation,

is the minimum output value of the deep peak regulation without oil input of the unit,

It is the minimum output value of peak regulation for unit oil injection depth;

3) Optimizing the model solution method

(a) Solving method of the outer layer model

In the outer multi-objective optimal configuration model, firstly, ΔE and ΔP are used as the basic units of energy storage system capacity and power configuration, and the energy storage system capacity candidate set [ΔE, 2ΔE,...,MΔE] and energy storage system power are set Alternative set [ΔP, 2ΔP,...,NΔP] to form M×N alternatives, calculate the multi-objective function value under each option by iterative method, and select the configuration option corresponding to the optimal value as the storage System configuration results;

(b) Inner model solution method

The CPLEX solver in matlab is used to solve the optimal scheduling model for peak regulation of thermal power units assisted by inner layer energy storage, so as to obtain the energy storage charging and discharging power, thermal power unit output and wind power acceptance under different energy storage system configuration alternative values;

4) Constraints

(a) Energy storage system operating constraints

The energy storage system needs to meet the limit constraints of charging and discharging power and the limit constraints of state of charge.

In the formula: P _C,t is the charging power of the energy storage system, P _D,t is the discharge power of the energy storage system, P _B is the rated power of the energy storage system, η _C is the charging efficiency of the energy storage system, E _SOC,t is the energy storage system The state of charge of the system, E _B is the rated capacity of the energy storage system, E _SOC,min is the lower limit of the state of charge of the energy storage system, E _SOC,max is the upper limit of the state of charge of the energy storage system, E _SOC,start is the initial The state of charge of the energy storage system at all times; E _SOC,end is the state of charge of the energy storage system at the end time;

(b) Operation constraints of thermal power units

The thermal power unit needs to meet the power upper and lower limit constraints, ramp rate constraints and start-stop constraints of the thermal power unit.

为第i台火电机组出力的最小值，

为第i台火电机组出力的最大值，

为第i台火电机组最大向下爬坡量，

为第i台火电机组最大向上爬坡量，v_i,t为火电机组运行状态；T_on,i为第i台火电机组的最小连续运行时间，T_off,i为第i台火电机组的最小连续停机时间；T_on,i,t为第i台机组在时段t内的持续运行时间，T_off,i,t为第i台机组在时段t内的持续停机时间。In the formula:

is the minimum output value of the i-th thermal power unit,

is the maximum output value of the i-th thermal power unit,

is the maximum downward climbing amount of the i-th thermal power unit,

is the maximum climbing amount of the i-th thermal power unit, v _i,t is the running state of the i-th thermal power unit; T _on,i is the minimum continuous running time of the i-th thermal power unit, T _off,i is the minimum Continuous shutdown time; T _on,i,t is the continuous running time of the i-th unit in the period t, T _off,i,t is the continuous shutdown time of the i-th unit in the period t.

The invention is a two-layer optimal configuration method of energy storage participating in power grid peak regulation. The outer layer model is the optimal configuration model, and the economic index of energy storage peak regulation and the system technical index are multi-objective optimal configuration functions, so as to obtain both economical efficiency and The optimal configuration scheme of the technical energy storage system, and since the current price mechanisms for energy storage to participate in peak regulation have not yet been linked, in the economic index model, only the direct economic index of the energy storage system is considered, and other The social benefits generated; while the calculation of the indicators of the outer model depends on the output parameters of the inner model; the inner model is an optimal scheduling model. In order to make full use of the system’s peak-shaving capacity and reduce the generation of abandoned wind, the model comprehensively considers energy storage The peak shaving function of the system and the deep peak shaving function of the thermal power unit, with the goal of minimizing the total peak shaving cost of the system, optimize the energy storage and the output of the thermal power unit. The invention can reduce the system peak regulation cost, reduce the generation of abandoned wind, and help to improve the peak regulation pressure of the power grid. It has the advantages of being scientific and reasonable, strong applicability and good effect.

Description of drawings

Fig. 1 The present invention is a model structure diagram of an energy storage double-layer optimal configuration method participating in power grid peak regulation;

Fig. 2 The flow chart of solving the outer layer configuration model;

Figure 3 Schematic diagram of wind power and load data for seven days;

Figure 4. Schematic diagram of charging and discharging power and state of charge of the energy storage system;

Fig. 5. Cumulative diagram of the output of each unit and the received power of wind power.

detailed description

The following is a further description of a double-layer optimal configuration method of energy storage that participates in power grid peak regulation by using the drawings and embodiments.

Referring to Fig. 1 and Fig. 2, a double-layer optimal configuration method of energy storage participating in power grid peak regulation proposed by the present invention includes the following contents:

1) Build a two-tier optimized configuration model structure

① Establish a candidate set of energy storage system configuration schemes in the outer model;

② Select an energy storage system configuration alternative as a constraint condition. According to the input system load and wind power data, in the inner model, the energy storage system and thermal power unit are used for deep peak regulation, and the operation of the energy storage system is considered during the peak regulation process. Cost, coal consumption cost of thermal power unit operation, additional cost of deep peak regulation of thermal power unit, deep compensation income of thermal power unit and system risk cost factors, with the goal of minimizing the total cost of system peak regulation, optimize system peak regulation economy, and obtain the energy storage system The charging and discharging power of the energy storage system, the output of thermal power units, and the amount of wind power received under the configuration alternatives;

③According to the charging and discharging power of the energy storage system output by the inner model, the output of thermal power units, and the amount of wind power received, calculate the various cost benefits of the energy storage system, the standard deviation of the output of thermal power units, and the amount of newly added wind power, and calculate the total life of the energy storage system The net income in the cycle is an economic index, and the improvement of the standard deviation of thermal power unit output and the new wind power acceptance are used as technical indicators to form a multi-objective optimization configuration and function, and calculate the value of the multi-objective optimization function under the configuration alternatives, select The optimal value is taken as the result of optimal configuration of the energy storage system;

2) Optimizing the model objective function

(a) Outer model objective function

The outer model optimizes the configuration of the energy storage system with the goal of maximizing the net income in the entire life cycle of the energy storage system, the standard deviation improvement of the output of thermal power units after the energy storage system is added, and the maximum acceptance of new wind power. The sub-objective functions are as follows:

In the formula: I _ALL is the net income in the whole life cycle of the energy storage system, SD is the average value of the standard deviation improvement of the thermal power unit output, E _wind is the average value of the new wind power acceptance; I _BZ,d is the storage capacity of the d-th day Energy arbitrage income, I _BP,d is the energy storage compensation income on the d-th day, C _BY,d is the energy storage operation on the d-th day, C _BI is the energy storage investment cost, SD _GS,d is the thermal power unit on the d-th day Output standard deviation improvement, E _windnew,d is the new wind power acceptance on day d, and D is the whole life cycle of the energy storage system;

(b) Inner model objective function

The inner model considers the operating cost of the energy storage system, the coal consumption cost of thermal power unit operation, the additional cost of unit deep regulation and compensation income, and the risk cost of the system, and aims at the lowest total cost of system peak regulation. The objective function of charging and discharging power of the system, thermal power unit output and wind power acceptance is as follows:

为机组出力最大值；

为机组常规调峰最低出力值，

为机组不投油深度调峰最低出力值，

为机组投油深度调峰最低出力值；In the formula: C _G,i is the energy consumption cost function of the deep peak shaving stage of the unit, I _G,i is the compensation income function of the deep peak shaving stage of the unit,

is the maximum output of the unit;

is the minimum output value of the unit for conventional peak regulation,

is the minimum output value of the deep peak regulation without oil input of the unit,

It is the minimum output value of peak regulation for unit oil injection depth;

3) Optimizing the model solution method

(a) Solving method of the outer layer model

In the outer multi-objective optimal configuration model, firstly, ΔE and ΔP are used as the basic units of energy storage system capacity and power configuration, and the energy storage system capacity candidate set [ΔE, 2ΔE,...,MΔE] and energy storage system power are set Alternative set [ΔP, 2ΔP,...,NΔP] to form M×N alternatives, calculate the multi-objective function value under each option by iterative method, and select the configuration option corresponding to the optimal value as the storage System configuration results;

(b) Inner model solution method

The CPLEX solver in matlab is used to solve the optimal scheduling model for peak regulation of thermal power units assisted by inner layer energy storage, so as to obtain the energy storage charging and discharging power, thermal power unit output and wind power acceptance under different energy storage system configuration alternative values;

4) Constraints

(a) Energy storage system operating constraints

The energy storage system needs to meet the limit constraints of charging and discharging power and the limit constraints of state of charge.

In the formula: P _C,t is the charging power of the energy storage system, P _D,t is the discharge power of the energy storage system, P _B is the rated power of the energy storage system, η _C is the charging efficiency of the energy storage system, and η _D is the discharge power of the energy storage system Efficiency, E _SOC,t is the state of charge of the energy storage system, E _B is the rated capacity of the energy storage system, E _SOC,min is the lower limit of the state of charge of the energy storage system, E _SOC,max is the upper limit of the state of charge of the energy storage system Limit value, E _SOC,start is the state of charge of the energy storage system at the initial moment; E _SOC,end is the state of charge of the energy storage system at the end moment;

(b) Operation constraints of thermal power units

The thermal power unit needs to meet the power upper and lower limit constraints, ramp rate constraints and start-stop constraints of the thermal power unit.

为第i台火电机组出力的最小值，

为第i台火电机组出力的最大值，

为第i台火电机组最大向下爬坡量，

为第i台火电机组最大向上爬坡量，v_i,t为火电机组运行状态；T_on,i为第i台火电机组的最小连续运行时间，T_off,i为第i台火电机组的最小连续停机时间；T_on,i,t为第i台机组在时段t内的持续运行时间，T_off,i,t为第i台机组在时段t内的持续停机时间。In the formula:

is the minimum output value of the i-th thermal power unit,

is the maximum output value of the i-th thermal power unit,

is the maximum downward climbing amount of the i-th thermal power unit,

is the maximum climbing amount of the i-th thermal power unit, v _i,t is the running state of the i-th thermal power unit; T _on,i is the minimum continuous running time of the i-th thermal power unit, T _off,i is the minimum Continuous shutdown time; T _on,i,t is the continuous running time of the i-th unit in the period t, T _off,i,t is the continuous shutdown time of the i-th unit in the period t.

The embodiment adopts a lithium iron phosphate battery with a configuration of 200MW/800MWh and a thermal power unit with a total installed capacity of 3200MW. The parameters of each thermal power unit are shown in Table 1.

Table 1 Parameters of thermal power units

The wind power data and load power data of the local grid for seven days are shown in Figure 3.

The charging and discharging power of the energy storage system, the state of charge of the energy storage, the output of each thermal power unit and the amount of wind power received by the inner model solution method are shown in Figure 4 and Figure 5 below.

Combining Figure 4 and Figure 5, it can be seen that through reasonable charging and discharging of the energy storage system, the output curve of the thermal power unit during the load valley and peak period is smoother, the adjustment amount of the daily peak-valley difference of the unit is reduced, and the energy storage system itself is charged. Status is always kept within limits.

In order to compare and analyze the solution method of the inner layer model of the present invention, three different peak-shaving scheduling schemes are set respectively.

Option 1: Conventional peak regulation of thermal power units;

Option 2: Deep peak regulation of thermal power units;

Option 3: Energy storage plus deep peak regulation of thermal power units.

The optimization results of each scheme are shown in Table 2 below.

Table 2 Comparison table of peak shaving effect and economic efficiency of different schemes

When the thermal power unit is in deep peak regulation, compared with the first scheme, the curtailed wind power is reduced by 3639.9MWh, a drop of 36.59%. However, due to the deep peak regulation, the output value of the unit during the low load period is low, so the peak-to-valley difference adjustment of the unit has increased.

When using energy storage-assisted deep peak regulation of thermal power units, the curtailment of wind power has been significantly improved, a decrease of 6257.2MWh, or 62.90%, compared with Scheme 1, and a decrease of 2617.3MWh, or 41.50%, compared with Scheme 2. In addition, due to the peak-shaving and valley-filling effect of the energy storage system, the peak-to-valley difference of the system is equivalently reduced, and the peak-to-valley difference adjustment amount of the unit is eased. It can be seen from Table 2 that the peak-to-valley difference improvement of Scheme 3 compared with Scheme 1 is 90.08MW, and the improvement of peak-to-valley difference compared with Scheme 2 is 207.28MW.

In addition, from the perspective of system peak shaving economics, the deep peak shaving of thermal power units will generate additional high loss costs and oil investment costs, which will increase unit operating costs, but reduce system risk penalty costs. Therefore, the total peak shaving cost of Scheme 2 It is 1.5161 million yuan lower than that of Plan One.

After the energy storage system is added, it relieves the deep peak-shaving pressure of the unit, and reduces the output of the unit during the peak load period. It not only reduces the operating cost of the unit, but also greatly reduces the system risk penalty cost. The reduction is 1.9586 million yuan, which is 3.4747 million yuan less than that of Plan 1.

Through the above analysis, it can be seen that the deep peak regulation of thermal power units assisted by energy storage has certain advantages in terms of system peak regulation effect and system peak regulation economy.

Based on the effective solution method of the inner layer model, the optimal configuration scheme of the outer layer is analyzed. Through the analysis, it is found that the most effective way to make the energy storage system reach the economic balance point is to provide corresponding auxiliary service compensation. However, the above analysis only achieves the economic equilibrium point from unilateral price changes. In the future, with the improvement of policies and the reduction of the cost of the energy storage system itself, the economics of the energy storage system will be greatly improved.

The calculation conditions, legends, tables, etc. in the embodiments of the present invention are only used to further illustrate the present invention, and are not exhaustive, and do not constitute a limitation to the scope of protection of the claims. Those skilled in the art obtain enlightenment according to the embodiments of the present invention , and other substantially equivalent substitutions can be conceived without creative efforts, all of which are within the protection scope of the present invention.

Claims (1)

1. An energy storage double-layer optimization configuration method participating in power grid peak shaving is characterized by comprising the following contents:

1) Construction of a two-layer optimal configuration model structure

(1) Establishing an energy storage system configuration scheme alternative set in the outer layer model;

(2) selecting a certain energy storage system configuration alternative scheme as a constraint condition, according to input system load and wind power data, in an inner layer model, adopting the deep peak regulation effect of an energy storage system and a thermal power unit, considering the operation cost of the energy storage system in the peak regulation process, the operation coal consumption cost of the thermal power unit, the deep peak regulation additional cost of the thermal power unit, the deep compensation income of the thermal power unit and the risk cost factors of the system, optimizing the peak regulation economy of the system by taking the minimum total peak regulation cost of the system as a target, and obtaining the charging and discharging power of the energy storage system, the output power of the thermal power unit and the wind power acceptance under the energy storage system configuration alternative scheme;

(3) calculating various cost benefits of the energy storage system, the standard deviation of the output of the thermal power unit and the newly increased wind power acceptance according to the charging and discharging power of the energy storage system, the output of the thermal power unit and the wind power acceptance output of the inner layer model, taking the net benefit of the energy storage system in the whole life cycle as an economic index, taking the improved quantity of the standard deviation of the output of the thermal power unit and the newly increased wind power acceptance as technical indexes to form a multi-objective optimization configuration function, calculating a multi-objective optimization function value under the alternative configuration scheme, and selecting an optimal value as an optimization configuration result of the energy storage system;

2) Optimizing a model objective function

(a) Outer model objective function

The outer layer model optimizes the configuration of the energy storage system according to the maximum target of the net gain of the energy storage system in the whole life cycle, the improvement of the standard deviation of the output of the thermal power generating unit after the energy storage system is added and the acceptance of newly added wind power, and each sub-objective function is as follows:

in the formula: I.C. A _ALL SD is the average value of the improvement of the standard deviation of the output of the thermal power generating unit for the net gain in the whole life cycle of the energy storage system, E _wind The wind power acceptance is the average value of the newly added wind power acceptance; i is _BZ,d Profit for the stored energy on day d, I _BP,d Compensation for energy storage gain on day d, C _BY,d The energy storage running cost on day d, C _BI For energy storage investment cost, SD _GS,d The improvement amount of the standard deviation of the output of the thermal power generating unit on the d day, E _windnew,d The wind power acceptance is newly increased on the day D, and D is the life cycle of the energy storage system;

(b) Inner layer model objective function

The inner layer model considers the operation cost of the energy storage system, the coal consumption cost of the thermal power unit, additional cost of deep power regulation of the unit, compensation income and risk cost of the system, the lowest peak regulation total cost of the system is taken as a target, the charge-discharge power of the energy storage system, the output of the thermal power unit and the wind power receiving capacity which are optimized under each energy storage configuration scheme are obtained, and the target functions are as follows:

in the formula: c _G,i For a unit deep peak shaving grading energy consumption cost function, I _G,i A gain function is compensated for the unit depth peak regulation grading,

the maximum value of the unit output is obtained;

the lowest output value for the conventional peak regulation of the unit,

the lowest peak load value is adjusted for the depth without oil feeding of the unit,

the lowest peak load value is regulated for the oil feeding depth of the unit;

3) Optimization model solving method

(a) Outer layer model solving method

In the outer-layer multi-objective optimization configuration model, firstly, setting an energy storage system capacity alternative set [ delta E,2 delta E, say, M delta E ] and an energy storage system power alternative set [ delta P,2 delta P, say, N delta P ] by taking delta E and delta P as capacity and power configuration basic units of an energy storage system, thereby forming M multiplied by N alternative schemes, calculating multi-objective function values under each scheme by an iterative method, and selecting a configuration scheme corresponding to an optimal value as an energy storage system configuration result;

(b) Inner layer model solving method

Solving by adopting a CPLEX solver in matlab in the internal-layer energy storage assisting thermal power generating unit peak regulation optimizing scheduling model, so as to obtain energy storage charging and discharging power, thermal power generating unit output and wind power receiving capacity under different configuration alternative values of the energy storage system;

4) Constraint conditions

(a) Energy storage system operating constraints

The energy storage system needs to satisfy the charging and discharging power limit constraint and the charging state limit constraint,

in the formula: p is _C,t Charging power for energy storage systems, P _D,t Discharging power, P, for energy storage systems _B Rated power, eta, of the energy storage system _C For charging efficiency of energy storage systems, E _SOC,t To the state of charge of the energy storage system, E _B Rated capacity for energy storage systems, E _SOC,min Is the lower limit of the state of charge of the energy storage system, E _SOC,max Is the upper limit value of the state of charge of the energy storage system, E _SOC,start The initial moment is the charge state of the energy storage system; e _SOC,end The energy storage system charge state at the end moment;

(b) Thermal power generating unit operation constraints

The thermal power generating unit needs to meet the power upper and lower limit constraint, the climbing rate constraint and the start-stop constraint of the thermal power generating unit,

in the formula:

the minimum value of the output of the ith thermal power generating unit,

the maximum value of the output of the ith thermal power generating unit,

the maximum downward climbing amount of the ith thermal power generating unit,

the maximum upward climbing amount v of the ith thermal power generating unit _i,t The operation state of the thermal power generating unit is set; t is _on,i Minimum continuous operation time, T, of the ith thermal power generating unit _off,i Is the ithMinimum continuous downtime of the thermal power generating unit; t is _on,i,t For the duration of time, T, of the ith unit in time period T _off,i,t The continuous shutdown time of the ith unit in the time period t.

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