CN111884266A - Gas turbine intraday rolling unit combination optimization method - Google Patents

Abstract

The invention discloses a method for optimizing the combination of gas turbine rolling units within a day. The steps are as follows: predicting the output of renewable energy after the current period; establishing and solving a combination model of the rolling gas turbine units within the day, and obtaining the decision and output power of the rolling unit combination within the current and subsequent periods. ; Repeat the preceding steps until the optimization period ends. The present invention takes into account the rolling adjustment of the combination decision of the gas turbine unit by intraday scheduling, and can give full play to the flexibility of the gas turbine and the ability to track the fluctuation of the renewable energy, thereby effectively dealing with the severe fluctuation of the renewable energy.

Description

A method for optimizing the combination of gas turbine rolling units within a day

technical field

The invention belongs to the field of power system scheduling, and particularly relates to a combined optimization method for gas turbine units.

Background technique

With the rapid development of society, the acceleration of economic globalization and industrialization, the demand for energy has risen sharply. According to the current energy demand of human society development, non-renewable energy such as fossil energy will gradually be difficult to support the sustainable development of human society. It is desirable to develop clean energy to gradually replace traditional fossil energy. In recent years, a large number of renewable energy sources have been connected to the grid. According to the International Energy Outlook released by the U.S. Energy Information Administration in 2019, the incremental renewable energy generation will be the first between 2020 and 2050 (up to an average annual growth rate of 3.6%), and by 2025, renewable energy generation will replace Coal-fired power generation has become the primary energy source for power generation.

However, the randomness and intermittency of renewable energy output bring great challenges to the safe and stable operation of the power system. As a fast-response unit, gas turbine can well smooth the fluctuation of renewable energy output. At present, the research on the combination of gas turbine units generally refers to the traditional coal-fired units, that is, the combination scheduling of gas turbine units is considered. The scheduling strategy fixes the unit combination decision of the gas turbine a few days ago, and considers that the unit combination decision remains unchanged during the day. However, the flexibility of gas turbines is much greater than that of traditional coal-fired units, and it will be difficult to make full use of the flexibility and rapid response capabilities of gas turbines in the decision of the unit combination of fixed gas turbines in intraday scheduling. As the penetration rate of renewable energy continues to increase, this strategy is no longer sufficient to cope with the drastic fluctuations in renewable energy.

SUMMARY OF THE INVENTION

In order to solve the technical problems raised by the above-mentioned background art, the present invention provides a method for optimizing the combination of gas turbine rolling units within a day.

In order to realize the above-mentioned technical purpose, the technical scheme of the present invention is:

A method for optimizing the combination of gas turbine rolling units within a day, comprising the following steps:

(1) Predict the renewable energy output after the current period;

(2) Establish and solve the gas turbine rolling unit combination model within the day, and obtain the decision and output power of the gas turbine rolling unit combination within the current and subsequent periods;

(3) Repeat steps (1)-(2) until the optimization period ends.

Further, in step (1), a time series method, an artificial neural network or a support vector machine is used to predict the renewable energy output after c period, where c is the current period.

Further, the concrete process of step (2) is as follows:

(2-1) Establish the objective function of the gas turbine daily rolling unit combination model:

分别为燃气轮机e的启动、停止、固定和单位发电成本；机组组合变量u_e,c、v_e,c、x_e,c分别表示当前时段燃气轮机e是否启动、停止、工作，是则置1，否则置0；

为当前时段燃气轮机e的输出功率；机组组合变量u_e,t、v_e,t、x_e,t分别表示t时段燃气轮机e是否启动、停止、工作；

为t时段燃气轮机e的输出功率；t为优化时段；In the above formula,

are the start, stop, fixed and unit power generation costs of the gas turbine e respectively; the unit combination variables ue _,c , ve _,c and x _e,c respectively indicate whether the gas turbine e is started, stopped and working in the current period, if it is, it is set to 1, Otherwise set to 0;

is the output power of the gas turbine e in the current period; the unit combination variables u _e,t , ve _,t , x _e,t respectively indicate whether the gas turbine e is started, stopped or worked in the t period;

is the output power of the gas turbine e in the period t; t is the optimization period;

(2-2) Constraints for establishing a gas turbine rolling unit combination model within a day:

a) Gas turbine constraints:

x _e,t -x _e,t-1 =u _e,t -v _e,t

x _e,τ ≥u _e,t

1-x _e,τ ≥ve _,t

分别为燃气轮机e的最大、最小输出功率；

分别为燃气轮机e的最大向上、向下爬坡率；

为t-1时段燃气轮机e的输出功率；机组组合变量x_e,t-1表示t-1时段燃气轮机e是否工作，是则置1，否则置0；b)配电网潮流约束：In the above formula,

are the maximum and minimum output power of the gas turbine e, respectively;

are the maximum upward and downward slope rates of the gas turbine e, respectively;

is the output power of the gas turbine e during the t-1 period; the unit combination variable x _{e, t-1} indicates whether the gas turbine e is working during the t-1 period, if it is, it is set to 1, otherwise it is set to 0; b) The power flow constraint of the distribution network:

V _j,t =V _i,t -(P _ij,t r _ij +Q _ij,t x _ij )/V ₀

分别为t时段节点j的有功、无功电源输出功率，其中，

包括节点j燃气轮机的输出功率和可再生能源出力；P_ij,t、Q_ij,t分别为t时段支路i-j的有功、无功功率；

为首端节点为j的所有支路集合；P_jk,t、Q_jk,t分别为t时段支路j-k的有功、无功功率；

分别为t时段节点j的有功、无功负荷；V_i,t、V_j,t分别为t时段节点i、j的电压幅值；r_ij、x_ij分别为支路i-j的电阻、电抗；V₀为电压基准值；In the above formula,

are the active and reactive power output power of node j in t period, respectively, where,

Including the output power of the gas turbine at node j and the output of renewable energy; P _ij,t and Q _ij,t are the active and reactive power of branch ij in the t period;

is the set of all branches whose head-end node is j; P _jk,t and Q _jk,t are the active and reactive power of branch jk in t period;

are the active and reactive loads of node j in the t period respectively; V _i,t and V _j,t are the voltage amplitudes of the nodes i and j in the t period respectively; r _ij and x _ij are the resistance and reactance of the branch ij respectively; V ₀ is the voltage reference value;

(2-3) Use the modeling software to solve the rolling unit combination model of the gas turbine within a day, and obtain the combination decision and output power of the gas turbine unit in the period c and later.

Further, only the unit combination decision and output power in period c are used in the actual dispatch, and the unit combination decision and output power in the period after c need to be adjusted in real time according to the latest forecast information.

Further, the modeling software for solving the intraday rolling unit combination model of the gas turbine includes CPLEX modeling software and GAMS modeling software.

Further, in step (3), the optimization time window is moved backward by a period of time, that is, c=c+1; the renewable energy output after period c is repeatedly predicted and the gas turbine rolling unit combination model is solved in the day until the end of the optimization period. .

The beneficial effects brought by the above technical solutions:

The present invention rolling forecasts the output of renewable energy in intraday scheduling, and continuously optimizes the decision-making and output power of the gas turbine unit combination, realizes the real-time adjustment of the combination decision of the gas turbine unit and the real-time tracking of the output of the renewable energy, so as to effectively deal with the renewable energy. Situations where energy fluctuates wildly.

Description of drawings

Fig. 1 is the method flow chart of the present invention;

2 is a schematic diagram of an IEEE33 node distribution network test system in an embodiment;

FIG. 3 is a schematic diagram of the wind power forecasted output, intraday rolling forecasted output and actual output data in the embodiment.

Detailed ways

The technical solutions of the present invention will be described in detail below with reference to the accompanying drawings.

The present invention designs a method for optimizing the combination of gas turbine rolling units within a day, as shown in FIG. 1 .

The IEEE33 node distribution network test system is used as an embodiment, and its schematic diagram is shown in Figure 2. The renewable energy units considered are wind turbines. The gas turbine GT1, the gas turbine GT2, and the wind turbine WT are connected to the distribution network nodes 17, 32, and 21, respectively. The parameters of the gas turbine are shown in Table 1. The scheduling period is 1 day, divided into 24 time periods. The time series method is used to forecast the wind power output.

Table 1 Gas turbine parameters

The day-ahead unit combination model and the intra-day rolling unit combination model of the gas turbine are solved by GAMS software, and the obtained gas turbine unit combination decision x _e,t (starting state) is shown in Table 2. It can be seen that in the intraday rolling unit combination scheduling strategy, the gas turbine GT1 has more start-up periods. This is because the intraday forecast is more accurate. Therefore, the scheduling strategy increases the start-up period of GT1 with a high maximum generating capacity to cope with the higher real-time wind power output. In the low case, the real-time tracking of wind power output is realized.

Table 2 Combination decision comparison of gas turbine units

The total load shedding of the day-ahead unit combination model and the intraday rolling unit combination model is shown in Table 3. It can be seen that in the day-ahead unit combination scheduling strategy, since the unit combination decision remains unchanged within the day, the gas turbine cannot track the fluctuation of wind power output in real time, which leads to serious load shedding when the actual wind power output is low. The intraday rolling unit combination scheduling strategy can well stabilize the fluctuation of wind power output by adjusting the decision of the gas turbine unit combination, thereby effectively avoiding the situation of load shedding.

Table 3 Comparison of objective function values of different methods

day-ahead unit combination scheduling Intraday rolling unit combination scheduling Load cut capacity/(MW) 5.708 0

The embodiment is only to illustrate the technical idea of the present invention, and cannot limit the protection scope of the present invention. Any changes made on the basis of the technical solution according to the technical idea proposed by the present invention all fall within the protection scope of the present invention. .

Claims (6)

1. a method for optimizing the combination of gas turbine rolling units within a day, is characterized in that, comprises the following steps: (1) Predict the renewable energy output after the current period; (2) Establish and solve the gas turbine rolling unit combination model within the day, and obtain the decision and output power of the gas turbine rolling unit combination within the current and subsequent periods; (3) Repeat steps (1)-(2) until the optimization period ends. 2. according to the described gas turbine day rolling unit combination optimization method of claim 1, it is characterized in that, in step (1), adopt time series method, artificial neural network or support vector machine to predict the renewable energy output after c period, c for the current period. 3. according to the described gas turbine day rolling unit combination optimization method of claim 2, it is characterized in that, the concrete process of step (2) is as follows: (2-1) Establish the objective function of the gas turbine daily rolling unit combination model:

In the above formula,

are the start, stop, fixed and unit power generation costs of the gas turbine e respectively; the unit combination variables ue _,c , ve _,c and x _e,c respectively indicate whether the gas turbine e is started, stopped and working in the current period, if it is, it is set to 1, Otherwise set to 0;

is the output power of the gas turbine e in the current period; the unit combination variables u _e,t , ve _,t , x _e,t respectively indicate whether the gas turbine e is started, stopped or worked in the t period;

is the output power of the gas turbine e in the period t; t is the optimization period; (2-2) Constraints for establishing a gas turbine rolling unit combination model within a day: a) Gas turbine constraints: x _e,t -x _e,t-1 =u _e,t -v _e,t x _e,τ ≥u _e,t 1-x _e,τ ≥ve _,t

In the above formula,

are the maximum and minimum output power of the gas turbine e, respectively;

are the maximum upward and downward slope rates of the gas turbine e, respectively;

is the output power of the gas turbine e during the t-1 period; the unit combination variable x _{e, t-1} indicates whether the gas turbine e is working during the t-1 period, if it is, it is set to 1, otherwise it is set to 0; b) The power flow constraint of the distribution network:

V _j,t =V _i,t -(P _ij,t r _ij +Q _ij,t x _ij )/V ₀ In the above formula,

are the active and reactive power output power of node j in t period, respectively, where,

Including the output power of the gas turbine at node j and the output of renewable energy; P _ij,t and Q _ij,t are the active and reactive power of branch ij in the t period;

is the set of all branches whose head-end node is j; P _jk,t and Q _jk,t are the active and reactive power of branch jk in t period;

are the active and reactive loads of node j in the t period respectively; V _i,t and V _j,t are the voltage amplitudes of the nodes i and j in the t period respectively; r _ij and x _ij are the resistance and reactance of the branch ij respectively; V ₀ is the voltage reference value; (2-3) Use the modeling software to solve the rolling unit combination model of the gas turbine within a day, and obtain the combination decision and output power of the gas turbine unit in the period c and later. 4. The intraday rolling unit combination optimization method of a gas turbine according to claim 3, characterized in that, only the unit combination decision and output power in the c period is applied in the actual dispatch, and the unit combination decision and output power in the period after c need to be based on The latest forecast information is adjusted in real time. 5 . The method for optimizing the combination of gas turbine rolling units within a day according to claim 3 , wherein the modeling software for solving the combination model of gas turbine rolling units within a day includes CPLEX modeling software and GAMS modeling software. 6 . 6. The method for optimizing the combination of gas turbine rolling units within a day according to claim 2, characterized in that, in step (3), the optimization time window is moved backward by a period of time, that is, c=c+1; Renewable energy output and solving the gas turbine rolling unit combination model within the day until the end of the optimization period.

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