Large grid on-site balance regulation method combined with load and new energy power prediction

Abstract

The application provides a large power grid on-site balance regulation method combining load and new energy power prediction, first, based on meteorological data such as sunshine intensity and wind speed, the renewable energy power generation power of the next day is accurately predicted by using a wind power and photovoltaic power generation calculation formula, thereby providing reliable data support for power grid regulation, and effectively reducing the uncertainty caused by new energy power generation power fluctuation. Secondly, by establishing a power exchange regulation model of the distribution network and the large power grid, the operation characteristics and the mutual relationship of the distribution network and the large power grid are quantitatively analyzed, the upper and lower limits of the power exchange between the distribution network and the large power grid are determined, and the configuration of power resources is optimized. Finally, while ensuring the safe and stable operation of the power system, the on-site balance of the large power grid is realized through optimized scheduling and power exchange, the operation cost of the large power grid is reduced, and the economic benefit is improved.

Description

Technical Field

[0001] This invention belongs to the field of local balancing and control technology for large power grids with new energy sources, and specifically relates to a method for local balancing and control of large power grids that combines load and new energy power prediction. Background Technology

[0002] In today's power systems, the safe and efficient operation of large power grids is a crucial issue concerning national energy security and sustainable economic development. However, the increasing scale of renewable energy grid integration and electricity demand, coupled with the randomness and volatility of power generation and load consumption, pose significant challenges to ensuring real-time power balance in large power grids. Local power balance is the ideal state for the safe and efficient operation of large power grids, playing a key role in reducing network losses and preventing power flow overload. Distribution networks, with their flexible regulation capabilities, serve both as downstream power systems and as important power regulation resources for large power grids. They help promote local power balance, effectively prevent power flow overload and equipment overload, and improve the stability and reliability of the power grid.

[0003] In summary, optimization schemes based on the distribution network as the local balancing object of the large power grid can not only significantly improve the operating efficiency and energy utilization efficiency of the large power grid, but also effectively address many challenges faced by modern power systems, providing important technical support and solutions for the development of the power industry. Summary of the Invention

[0004] The embodiments of the present invention provide a method for local balancing and control of a large power grid that combines load and renewable energy power forecasting, which can achieve local balancing of the large power grid under the condition of taking into account load and renewable energy power forecasting.

[0005] The technical solution of this invention is: a method for local balancing and control of a large power grid that combines load and renewable energy power forecasting, comprising the following steps:

[0006] Step 1: Obtain the predicted solar irradiance and wind speed data for the next day from the power grid dispatch center. Based on this, calculate the predicted daily renewable energy power generation using the formulas for wind and solar power generation.

[0007] (1)

[0008] (2)

[0009] In the formula: This is the predicted wind power output; It is the wind speed at time t; , and Represents the cut-in wind speed, rated wind speed, and cut-out wind speed; This represents the rated power of the fan; This refers to the actual power generation of the photovoltaic system. This represents the maximum test power output of the photovoltaic cell under standard test conditions. , They are respectively Solar intensity under standard test conditions at any given time; The power temperature coefficient; for Monitor the operating temperature of the solar panel at all times; For reference temperature;

[0010] Step 2: Construct a power exchange regulation model between the distribution network and the main power grid by quantitatively analyzing the operating characteristics of the distribution network;

[0011] To explore the upper and lower limits of the power exchange between the distribution network and the main power grid, an objective function for minimizing the daily operating cost of the distribution network, taking into account the power exchange, is established as follows; since the maximum power generation and total load of the distribution network at time t are predicted, the incentive coefficient for the power exchange price is adjusted accordingly. That is, from Gradually decrease to At that time, the power exchanged between the distribution network and the main power grid can be obtained. and ;

[0012] (3)

[0013] (4)

[0014] (5)

[0015] In the formula: It is the scheduling cycle; This represents the operating cost of the distribution network at time t; and These are the energy consumption parameters of the gas turbine; It is the price incentive coefficient; It is the exchange power between the distribution network and the main power grid at time t; and It is the upper and lower limits of the power exchanged between the distribution network and the main power grid at time t that needs to be obtained;

[0016] Gas turbine operating constraints can be expressed as:

[0017] (6)

[0018] (7)

[0019] In the formula, and These are the operating power and rated power of the gas turbine at time t, respectively. This is the start-up indicator for the gas turbine at time t hours; and These represent the maximum uphill and downhill ramp rates of the gas turbine, respectively.

[0020] The power flow constraints of the distribution network are represented by (8) to (14). Among them, (8) to (9) represent the node power balance expression, (10) represents the constraint relationship between the voltage difference between the nodes at both ends of the line and the line impedance parameters and power flow, (11) to (13) represent the line power transmission constraints, and (14) represents the node voltage constraints, where the gas turbine node voltage value is a fixed constant.

[0021] (8)

[0022] (9)

[0023] (10)

[0024] (11)

[0025] (12)

[0026] (13)

[0027] (14)

[0028] In the formula: , Represents the distribution network node at time t Flow to Node The magnitude of active and reactive power; , Represents the node at time t The active and reactive power values ​​of the load; , , and These represent the magnitudes of reactive power exchanged between the gas turbine, wind turbine, photovoltaic power, and mains power grid at a distribution network node at time t, respectively. , Indicates the voltage value at a distribution network node; , These represent the distribution network lines ( , The equivalent resistance and equivalent reactance reference values; This indicates the node voltage value of the gas turbine; Indicates the line transmission power limit; , These represent the upper and lower limits of the node voltage, respectively. Indicates distribution network lines ( , A set of ) Represents the set of nodes in a distribution network; Represents the set of gas turbine nodes;

[0029] At the same time, record hour, .and, For other values, ;

[0030] Step 3: By quantitatively analyzing the relationship between distribution network operating costs and switching power, a switching power adjustment cost model is obtained; first, calculation... The change in distribution network cost during adjustment is shown in equation (15); then, the polyfit function in MATLAB software is used for fitting. Power exchange between distribution networks and the main power grid The relationship is as shown in equation (16);

[0031] (15)

[0032] (16)

[0033] In the formula: This represents the power switching regulation cost of the large power grid at time t; , , and This represents the coefficient of the power exchange regulation cost curve of the large power grid at time t;

[0034] Step 4: Establish a local power balance model for the large power grid by quantitatively analyzing the operating characteristics of the large power grid and the power exchange regulation cost model;

[0035] The objective function is established with the goal of minimizing the operating cost of the large power grid within the scheduling cycle:

[0036] (17)

[0037] In the formula: , , This indicates the energy consumption parameters of thermal power units;

[0038] The operating constraints of thermal power units can be expressed as:

[0039] (18)

[0040] (19)

[0041] In the formula: Indicates thermal power unit Active power at time t; , They represent thermal power units The lower and upper limits of output; Indicates thermal power unit The start / stop status; , Indicates thermal power unit The upper and lower limits of the climbing rate;

[0042] Branch power flow distribution factor constraints can be expressed as:

[0043] (20)

[0044] In the formula: , , , , These represent thermal power, wind power, photovoltaic power, power exchanged with the distribution network, and conventional load at the nodes, respectively. , , , , For the line The node output power transfer distribution factor; , They represent the lines respectively. The upper and lower limits of the effective current trend;

[0045] Step 5: Since there are many nonlinear constraints and variables in the optimization scheduling model, a linearization method is adopted to transform the optimization scheduling model into a classic mixed-integer linear programming problem. On this basis, the optimization problem is solved by calling the commercial solver Gurobi using the yalmip software on the MATLAB platform.

[0046] The optimal scheduling model is solved in two stages. In the first stage, equations (1) to (16) are solved to obtain the output of the gas turbine, the upper and lower limits of the power exchange between the distribution network and the main power grid, and the specific... Value The value is calculated, and the power regulation cost of the large power grid is obtained. The value of the large power grid switching power regulation cost curve coefficient , , and The value of (17) to (20) is obtained in the second stage; the output of thermal power units in the large power grid and the exchange power between the large power grid and the distribution network are obtained by solving (17) to (20). The value is used to achieve local balance of the large power grid.

[0047] The beneficial effects of this invention are:

[0048] First, based on meteorological data such as solar radiation intensity and wind speed, the renewable energy power generation capacity for the following day is accurately predicted using wind and photovoltaic power generation calculation formulas. This provides reliable data support for grid regulation and effectively reduces the uncertainty caused by fluctuations in renewable energy power generation capacity. Second, by establishing a power exchange regulation model between the distribution network and the main grid, the operating characteristics and interrelationships of the distribution network and the main grid are quantitatively analyzed. The upper and lower limits of power exchange between the distribution network and the main grid are determined, optimizing the allocation of power resources. Finally, while ensuring the safe and stable operation of the power system, local balancing of the main grid is achieved through optimized dispatching and power exchange, reducing the operating costs of the main grid and improving economic efficiency. Attached Figure Description

[0049] Figure 1 This is a diagram of the IEEE-33 node distribution network structure in an embodiment of the present invention;

[0050] Figure 2 This is a diagram of the IEEE-30 large power grid structure in an embodiment of the present invention;

[0051] Figure 3 This is a feasible domain diagram of the power exchange between the main power grid and the distribution network in an embodiment of the present invention;

[0052] Figure 4 This is an example of a 24-hour optimized power curve of a large power grid in an embodiment of the present invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] The embodiments of the present invention provide a method for local balancing and control of a large power grid that combines load and renewable energy power forecasting, which can achieve local balancing of the large power grid under the condition of taking into account load and renewable energy power forecasting.

[0055] To achieve the above objectives, the embodiments of this application adopt the following technical solutions:

[0056] The local balancing and control method for large power grids that combines load and renewable energy power forecasting includes the following steps:

[0057] 1. Obtain the next-day solar irradiance and wind speed forecast data for the main power grid and distribution network from the power grid dispatch center. The main power grid uses 30 MW of photovoltaic (PV) installed capacity and 60 MW of wind power installed capacity, while the distribution network uses 10 MW of PV installed capacity and 20 MW of wind power installed capacity. Based on this, the predicted daily renewable energy power generation is obtained according to the wind and PV power generation calculation formulas.

[0058] (1)

[0059] (2)

[0060] In the formula: This is the predicted wind power output; It is the wind speed at time t; , and Represents the cut-in wind speed, rated wind speed, and cut-out wind speed; This represents the rated power of the fan; This refers to the actual power generation of the photovoltaic system. This represents the maximum test power output of the photovoltaic cell under standard test conditions. , They are respectively Solar intensity under standard test conditions at any given time; The power temperature coefficient; for Monitor the operating temperature of the solar panel at all times; This is a reference temperature.

[0061] 2. By quantitatively analyzing the operating characteristics of the distribution network, a power exchange regulation model between the distribution network and the main power grid is constructed.

[0062] To explore the upper and lower limits of the power exchange between the distribution network and the main power grid, an objective function for minimizing the daily operating cost of the distribution network, taking into account the power exchange, is established as follows. Since the maximum power generation and total load of the distribution network at time t are predicted, the incentive coefficient for the power exchange price is adjusted accordingly. That is, from Gradually decrease to At that time, the power exchanged between the distribution network and the main power grid can be obtained. and .

[0063] (3)

[0064] (4)

[0065] (5)

[0066] In the formula: It is the scheduling cycle; This represents the operating cost of the distribution network at time t; and These are the energy consumption parameters of the gas turbine; It is the price incentive coefficient; It is the exchange power between the distribution network and the main power grid at time t; and It is the upper and lower limits of the power exchanged between the distribution network and the main power grid at time t that needs to be obtained.

[0067] Gas turbine operating constraints can be expressed as:

[0068] (6)

[0069] (7)

[0070] In the formula, and These are the operating power and rated power of the gas turbine at time t, respectively. This is the start-up indicator for the gas turbine at time t hours; and These represent the maximum uphill and downhill ramp rates of the gas turbine, respectively.

[0071] The parameters of the gas turbine in the power distribution network are shown in the table below:

[0072]

[0073] The power flow constraints of the distribution network are represented by (8) to (14). Among them, (8) to (9) represent the node power balance expression, (10) represents the constraint relationship between the voltage difference between the nodes at both ends of the line and the line impedance parameters and power flow, (11) to (13) represent the line power transmission constraints, and (14) represents the node voltage constraints, where the gas turbine node voltage value is a fixed constant.

[0074] (8)

[0075] (9)

[0076] (10)

[0077] (11)

[0078] (12)

[0079] (13)

[0080] (14)

[0081] In the formula: , Represents the distribution network node at time t Flow to Node The magnitude of active and reactive power; , Represents the node at time t The active and reactive power values ​​of the load; , , and These represent the magnitudes of reactive power exchanged between the gas turbine, wind turbine, photovoltaic power, and mains power grid at a distribution network node at time t, respectively. , Indicates the voltage value at a distribution network node; , These represent the distribution network lines ( , The equivalent resistance and equivalent reactance reference values; This indicates the node voltage value of the gas turbine; Indicates the line transmission power limit; , These represent the upper and lower limits of the node voltage, respectively. Indicates distribution network lines ( , A set of ) Represents the set of nodes in a distribution network; This represents the set of gas turbine nodes.

[0082] At the same time, record hour, .and, For other values, .

[0083] 3. By quantitatively analyzing the relationship between distribution network operating costs and switching power, a switching power adjustment cost model is obtained. First, the calculation... The change in distribution network cost during adjustment is shown in equation (15). Then, the polyfit function in MATLAB software is used for fitting. Power exchange between distribution networks and the main power grid The relationship is as shown in equation (16).

[0084] (15)

[0085] (16)

[0086] In the formula: This represents the power switching regulation cost of the large power grid at time t; , , and This represents the coefficient of the power exchange regulation cost curve of the large power grid at time t.

[0087] 4. By quantitatively analyzing the operating characteristics of the large power grid and the power exchange adjustment cost model, a local power balance model for the large power grid is established.

[0088] The objective function is established with the goal of minimizing the operating cost of the large power grid within the scheduling cycle:

[0089] (17)

[0090] In the formula: , , This indicates the energy consumption parameters of thermal power units.

[0091] The operating constraints of thermal power units can be expressed as:

[0092] (18)

[0093] (19)

[0094] In the formula: Indicates thermal power unit Active power at time t; , They represent thermal power units The lower and upper limits of output; Indicates thermal power unit The start / stop status; , Indicates thermal power unit The upper and lower limits of the climbing rate.

[0095] The parameters of a conventional thermal power unit are shown in the table below:

[0096]

[0097] Branch power flow distribution factor constraints can be expressed as:

[0098] (20)

[0099] In the formula: , , , , These represent thermal power, wind power, photovoltaic power, power exchanged with the distribution network, and conventional load at the nodes, respectively. , , , , For the line The node output power transfer distribution factor; , They represent the lines respectively. The upper and lower limits of the trend.

[0100] 5. Due to the large number of nonlinear constraints and variables in the optimization scheduling model, a linearization approach is adopted to transform the optimization scheduling model into a classic mixed-integer linear programming problem. Based on this, the optimization problem is solved on the MATLAB platform using the yalmip software to call the commercial solver Gurobi.

[0101] The optimal scheduling model is solved in two stages. In the first stage, equations (1) to (16) are solved to obtain the output of the gas turbine, the upper and lower limits of the power exchange between the distribution network and the main power grid, and the specific... Value The value is calculated, and the power regulation cost of the large power grid is obtained. The value of the large power grid switching power regulation cost curve coefficient , , and The value of (17) to (20) is obtained in the second stage; the output of thermal power units in the large power grid and the exchange power between the large power grid and the distribution network are obtained by solving (17) to (20). The value is used to achieve local balance of the large power grid.

[0102] 6. Simulation Results

[0103] In this embodiment of the invention, an IEEE-33 node distribution network and an IEEE-30 node large power grid are used, with the following topology: Figure 1 and Figure 2 As shown. The upper and lower limits of the power exchange between the main power grid and the distribution network are obtained from the operation, as shown below. Figure 3 As shown, the optimization scheme for the 24-hour output of thermal power units and the power exchange between the distribution network and the main power grid is as follows: Figure 4 As shown, before using this method, the daily operating cost of the power grid was 365,370.9 yuan. After using this method, the daily operating cost of the large power grid was 336,141.23 yuan, increasing the economic percentage by 8%.

[0104] The above are preferred embodiments of the present invention. Those skilled in the art should understand that the embodiments of the present invention are not limited to the above content. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention should be considered equivalent substitutions and are included within the protection scope of the present invention.

Claims

1. A method for local balancing and control of large power grids that combines load and renewable energy power forecasting, characterized in that: Includes the following steps: Step 1: Obtain the predicted solar irradiance and wind speed data for the next day from the power grid dispatch center, and then obtain the predicted power of renewable energy generation for the day-ahead based on the calculation formulas for wind and photovoltaic power generation. Step 2: Construct a power exchange regulation model between the distribution network and the main power grid by quantitatively analyzing the operating characteristics of the distribution network; To explore the upper and lower limits of the power exchange between the distribution network and the main power grid, an objective function for minimizing the daily operating cost of the distribution network, taking into account the power exchange, is established as follows; since the maximum power generation and total load of the distribution network at time t are predicted, the incentive coefficient for the power exchange price is adjusted accordingly. , ≤ ≤ Soon from Gradually decrease to At that time, the power exchanged between the distribution network and the main power grid can be obtained. and ; (3) (4) (5) In the formula: It is the scheduling cycle; This represents the operating cost of the distribution network at time t; and These are the energy consumption parameters of the gas turbine; It is the operating power of the gas turbine at time t; This is the start-up indicator for the gas turbine at time t hours; It is the exchange power between the distribution network and the main power grid at time t; and It is the upper and lower limits of the power exchanged between the distribution network and the main power grid at time t that needs to be obtained; At the same time, record hour, ,and, For other values, ; Step 3: By quantitatively analyzing the relationship between distribution network operating costs and switching power, a switching power adjustment cost model is obtained; first, calculation... The change in distribution network cost during adjustment is shown in equation (15); then, the polyfit function in MATLAB software is used for fitting. Power exchange between distribution networks and the main power grid The relationship is as shown in equation (16); (15) (16) In the formula: This represents the power switching regulation cost of the large power grid at time t; , , and This represents the coefficient of the power exchange regulation cost curve of the large power grid at time t; Step 4: Establish a local power balance model for the large power grid by quantitatively analyzing the operating characteristics of the large power grid and the power exchange regulation cost model; Step 5: Since there are many nonlinear constraints and variables in the optimization scheduling model, a linearization method is adopted to transform the optimization scheduling model into a classic mixed-integer linear programming problem. On this basis, the optimization problem is solved by calling the commercial solver Gurobi using the yalmip software on the MATLAB platform. The optimal scheduling model is solved in two stages.

2. The method according to claim 1, characterized in that, In step one, the predicted power generation of renewable energy is obtained based on the calculation formulas for wind and photovoltaic power generation, specifically as follows: P t PV =P STC G AC,t [1+ε(T t -T r )] / G STC (2) In the formula: This is the predicted wind power output; v t The wind speed at time t; v in v e and v out Represents the cut-in wind speed, rated wind speed, and cut-out wind speed; P W,e P represents the rated power of the fan; t PV This refers to the actual power generation of the photovoltaic system; P STC G represents the maximum test power output of the photovoltaic cell under standard test conditions. AC,t G STC ε represents the solar radiation intensity at time t under standard test conditions; ε is the power temperature coefficient; T t T represents the operating temperature of the solar panel at time t. r This is a reference temperature.

3. The method according to claim 2, characterized in that, Step two also includes: Gas turbine operating constraints: (6) (7) In the formula, It is the rated power of the gas turbine at time t; and These represent the maximum uphill and downhill ramp rates of the gas turbine, respectively. The power flow constraints of the distribution network are: (8) (9) (10) (11) (12) (13) (14) Equations (8) to (9) represent the node power balance expression, equation (10) represents the constraint relationship between the voltage difference between the nodes at both ends of the line and the line impedance parameters and power flow, equations (11) to (13) represent the line power transmission constraint, and equation (14) represents the node voltage constraint, where the gas turbine node voltage value is a fixed constant; where: , Represents the distribution network node at time t Flow to Node The magnitude of active and reactive power; , Represents the node at time t The active and reactive power values ​​of the load; , , and These represent the magnitudes of reactive power exchanged between the gas turbine, wind turbine, photovoltaic power, and mains power grid at a distribution network node at time t, respectively. , Indicates the voltage value at a distribution network node; , These represent the distribution network lines ( , The equivalent resistance and equivalent reactance reference values; This indicates the node voltage value of the gas turbine; Indicates the line transmission power limit; , These represent the upper and lower limits of the node voltage, respectively. Indicates distribution network lines ( , A set of ) Represents the set of nodes in a distribution network; This represents the set of gas turbine nodes.

4. The method according to claim 3, characterized in that, Step four also includes: The objective function is established with the goal of minimizing the operating cost of the large power grid within the scheduling cycle: (17) In the formula: , , This indicates the energy consumption parameters of thermal power units; The operating constraints of thermal power units can be expressed as: (18) (19) In the formula: Indicates thermal power unit Active power at time t; , They represent thermal power units The lower and upper limits of output; Indicates thermal power unit The start / stop status; , Indicates thermal power unit The upper and lower limits of the climbing rate; Branch power flow distribution factor constraints can be expressed as: (20) In the formula: , , , , These represent thermal power, wind power, photovoltaic power, power exchanged with the distribution network, and conventional load at the nodes, respectively. , , , , For the line The node output power transfer distribution factor; , They represent the lines respectively. The upper and lower limits of the trend.

5. The method according to claim 4, characterized in that, Step five involves a two-stage solution process for the optimized scheduling model, including: The first stage involves solving equations (1) to (16) to obtain the output of the gas turbine, the upper and lower limits of the power exchange between the distribution network and the main power grid, and specific... Value The value is calculated, and the power regulation cost of the large power grid is obtained. The value of the large power grid switching power regulation cost curve coefficient , , and The value of (17) to (20) is obtained in the second stage; the output of thermal power units in the large power grid and the exchange power between the large power grid and the distribution network are obtained by solving (17) to (20). The value is used to achieve local balance of the large power grid.

Images