Large power grid in-situ balance regulation and control method combining load and new energy power prediction

Abstract

The invention provides a large power grid in-situ balance regulation and control method combining load and new energy power prediction, and the method comprises the steps: firstly, precisely predicting the renewable energy power generation power of the next day through employing a wind power and photovoltaic power generation calculation formula based on the weather data of sunlight intensity, wind speed and the like, thereby providing reliable data support for the regulation and control of a power grid; and the uncertainty caused by new energy power generation power fluctuation is effectively reduced. And secondly, by establishing a power distribution network and large power grid exchange power regulation model, quantitatively analyzing the operation characteristics of the power distribution network and the large power grid and the mutual relation thereof, determining the upper and lower limits of power exchange between the power distribution network and the large power grid, and optimizing the configuration of power resources. And finally, safe and stable operation of the power system is ensured, and meanwhile, on-site balance of the large power grid is realized, the operation cost of the large power grid is reduced, and the economic benefit is improved by optimizing scheduling and power exchange.

Description

Technical Field

[0001] The present invention belongs to the technical field of local balancing control of a large power grid with new energy, and in particular relates to a local balancing control method of a large power grid combined with load and new energy power prediction. Background Art

[0002] In today's power system, the safe and efficient operation of large power grids is an important issue related to national energy security and sustainable economic development. However, the scale of renewable energy grid connection and electricity demand are increasing, and the randomness and volatility of power generation and load power consumption have brought huge challenges to the real-time power balance guarantee of large power grids. Local power balance is the ideal state for large power grids to achieve safe and efficient operation. It plays a key role in reducing network losses and preventing power flow from exceeding the limit. The distribution network has flexible regulation capabilities. It is not only the downstream power system of the large power grid, but also an important power regulation resource of the large power grid. It helps to promote the local power balance of the large power grid, effectively prevent power flow from exceeding the limit and equipment overload, and improve the stability and reliability of the power grid.

[0003] In summary, the optimization scheme 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 respond to the many challenges faced by modern power systems and provide important technical support and solutions for the development of the power industry. Summary of the invention

[0004] The embodiment of the present invention provides a large power grid local balancing control method combining load and renewable energy power forecasting, which can achieve large power grid local balancing under the condition of combining load and renewable energy power forecasting.

[0005] The technical solution of the present invention is: a large power grid on-site balancing control method combined with load and new energy power prediction, comprising the following steps:

[0006] Step 1: Obtain the forecast data of the next day's sunshine intensity and wind speed for the large power grid and distribution network from the power grid dispatching center. On this basis, the forecast power of renewable energy generation on the day before is obtained according to the calculation formula of wind power and photovoltaic power generation:

[0007]

[0008] P t PV =P STC G AC,t [1+ε(T t -T r )] / G STC (2)

[0009] Where: P t W is the predicted wind power; ut is the wind speed at time t; u in ,u e and u out Represents cut-in wind speed, rated wind speed and cut-out wind speed; P W,e Represents the rated power of the fan; P t PV is the actual photovoltaic power generation; P STC G is the maximum test power output of the photovoltaic cell under standard test conditions; AC,t , G STC are the sunshine intensity at time t and under standard test conditions; ε is the power temperature coefficient; T t is the operating temperature of the solar panel at time t; T r is the reference temperature;

[0010] Step 2: Quantitatively analyze the operating characteristics of the distribution network and build a power regulation model for the exchange of power between the distribution network and the large power grid;

[0011] In order to explore the upper and lower limits of the exchange power between the distribution network and the large power grid, the objective function of minimizing the daily operation cost of the distribution network taking into account the exchange power is established as follows; since the maximum power generation and total load of the distribution network at time t are predicted, by adjusting the exchange power price incentive coefficient λ t (λ min ≤λ t ≤λ max ), that is, from λ max Gradually decrease to λ min When the power exchange between the distribution network and the large power grid is and

[0012]

[0013] Where: T is the scheduling period; f t DS represents the operating cost of the distribution network at time t; and is the gas turbine energy consumption parameter; t is the price incentive coefficient; P t E is the exchange power between the distribution network and the large power grid at time t; and The upper and lower limits of the power exchanged between the distribution network and the large power grid at time t that need to be obtained;

[0014] The gas turbine operating constraints can be expressed as:

[0015]

[0016] In the formula, and are the operating power and rated power of the gas turbine at time t respectively; is the start-up mark of the gas turbine at t hour; R u and R d are the upper and lower maximum operating ramp rates of the gas turbine respectively;

[0017] The power flow constraints of the distribution network are expressed by (8) to (14). Among them, (8) to (9) represent the node power balance expression, (10) represents the constraint relationship between the node voltage difference 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 constraint, where the gas turbine node voltage value is a fixed constant;

[0018]

[0019] U i,t -U j,t =(r ij P ij,t +x ij Q ij,t ) / U0 (10)

[0020]

[0021] -S ij,max ≤P ij,t ±Q ij,t ≤S ij,max (13)

[0022]

[0023] Where: P ij,t , Q ij,t It represents the active and reactive power flowing from node i to node j in the distribution network at time t; Represents the active and reactive power values ​​of the load at node i at time t; and They represent the reactive power of the gas turbine, wind power, photovoltaic power and the large power grid at the distribution network node at time t; U i,t , U j,t Represents the voltage value of the distribution network node; r ij 、x ij They represent the reference values ​​of the equivalent resistance and equivalent reactance of the distribution network line (i, j) respectively; U0 represents the voltage value of the gas turbine node; S ij,max Indicates the line transmission power limit; U i,max , U i,min They represent the upper and lower limits of node voltage respectively; Z represents the set of distribution network lines (i, j); S represents the set of distribution network nodes; M represents the set of gas turbine nodes;

[0024] At the same time, record λ t = 0, And, t For other values,

[0025] Step 3: By quantitatively analyzing the relationship between the distribution network operation cost and the exchange power, the exchange power regulation cost model is obtained; first, calculate λ t The distribution network cost changes during adjustment, as shown in formula (15); then, the polyfit function in MATLAB software is used to fit Δf t The power exchange between the distribution network and the large power grid P t E The relationship is as shown in formula (16);

[0026]

[0027]

[0028] Where: Δf t represents the power exchange adjustment cost of the large power grid at time t; and represents the coefficient of the large power grid exchange power regulation cost curve at time t;

[0029] Step 4: Establish a local power balance model of the large power grid by quantitatively analyzing the large power grid operation characteristics and the exchange power regulation cost model;

[0030] Taking the minimum operation cost of the large power grid within the dispatching period as the goal, the objective function is established:

[0031]

[0032] Where: Indicates the energy consumption parameters of thermal power units;

[0033] The operation constraints of thermal power units can be expressed as:

[0034]

[0035] Where: represents the active power of thermal power unit i at time t; They represent the lower and upper limits of the output of thermal power unit i respectively; Indicates the start and stop status of thermal power unit i; U i , D i Indicates the upper and lower limits of the ramp rate of thermal power unit i;

[0036] The branch flow distribution factor constraint can be expressed as:

[0037]

[0038] Where: They represent thermal power, wind power, photovoltaic power, power exchange with the distribution network, and conventional load at nodes α, β, θ, δ are the node output power transfer distribution factors for line l; They represent the upper and lower limits of the active power flow of line l respectively;

[0039] Step 5: Since there are a large number of nonlinear constraints and variables in the optimization scheduling model, the optimization scheduling model is transformed into a classic mixed integer linear programming problem by linearization. On this basis, the commercial solver Gurobi is called by yalmip software on the MATLAB platform to solve the optimization problem.

[0040] The optimization dispatch 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 large power grid, and the P with a specific λ value. t E value, and obtain the large power grid exchange power regulation cost Δf t The value of the large power grid exchange power regulation cost curve coefficient and The value of; In the second stage, (17) ~ (20) are solved to obtain the output of thermal power units in the large power grid and the exchange power P between the large power grid and the distribution network t E value to achieve local balancing of the large power grid.

[0041] Beneficial effects of the present invention:

[0042] First, based on meteorological data such as sunshine intensity and wind speed, the wind power and photovoltaic power generation calculation formulas are used to accurately predict the renewable energy power generation of the next day, thereby providing reliable data support for power grid regulation and effectively reducing the uncertainty caused by the fluctuation of renewable energy power generation. Secondly, by establishing a power exchange regulation model between the distribution network and the large power grid, the operating characteristics of the distribution network and the large power grid and their mutual relationship are quantitatively analyzed, the upper and lower limits of 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 large power grid is balanced on the spot by optimizing scheduling and power exchange, reducing the operating cost of the large power grid and improving economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a structural diagram of IEEE-33 node distribution network in an embodiment of the present invention;

[0044] Figure 2 It is a structural diagram of the IEEE-30 large power grid in an embodiment of the present invention;

[0045] Figure 3 A diagram showing a feasible region for exchanging power between a large power grid and a distribution network in an embodiment of the present invention;

[0046] Figure 4 This is a 24-hour optimized power curve diagram of a large power grid in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] The embodiment of the present invention provides a large power grid local balancing control method combining load and renewable energy power forecasting, which can achieve large power grid local balancing under the condition of combining load and renewable energy power forecasting.

[0049] In order to achieve the above objectives, the embodiments of the present application adopt the following technical solutions:

[0050] The local balancing control method of a large power grid combined with load and new energy power forecasting includes the following steps:

[0051] 1. Obtain the forecast data of the next day's sunshine intensity and wind speed for the large power grid and distribution network from the power grid dispatching center. The large power grid adopts 30MW of photovoltaic installed capacity and 60MW of wind power installed capacity, and the distribution network adopts 10MW of photovoltaic installed capacity and 20MW of wind power installed capacity. On this basis, the forecast power of renewable energy generation on the day before is obtained according to the calculation formula of wind power and photovoltaic power generation:

[0052]

[0053] P t PV =P STC G AC,t [1+ε(T t -T r )] / G STC (2)

[0054] Where: P t W is the predicted wind power; u t is the wind speed at time t; u in ,u e and u out Represents cut-in wind speed, rated wind speed and cut-out wind speed; P W,e Represents the rated power of the fan; Pt PV is the actual photovoltaic power generation; P STC G is the maximum test power output of the photovoltaic cell under standard test conditions; AC,t , G STC are the sunshine intensity at time t and under standard test conditions; ε is the power temperature coefficient; T t is the operating temperature of the solar panel at time t; T r is the reference temperature.

[0055] 2. By quantitatively analyzing the operating characteristics of the distribution network, a power exchange regulation model between the distribution network and the large power grid is constructed.

[0056] In order to find the upper and lower limits of the exchange power between the distribution network and the large power grid, the objective function of minimizing the daily operation cost of the exchange power distribution network is established as follows. Since the maximum power generation and total load of the distribution network at time t are predicted, by adjusting the exchange power price incentive coefficient λ t (λ min ≤λ t ≤λ max ), that is, from λ max Gradually decrease to λ min When the power exchange between the distribution network and the large power grid is and

[0057]

[0058] Where: T is the scheduling period; f t DS represents the operating cost of the distribution network at time t; and is the gas turbine energy consumption parameter; t is the price incentive coefficient; P t E is the exchange power between the distribution network and the large power grid at time t; and It is the upper and lower limits of the power exchanged between the distribution network and the large power grid at time t that needs to be obtained.

[0059] The gas turbine operating constraints can be expressed as:

[0060]

[0061] In the formula, and are the operating power and rated power of the gas turbine at time t respectively; is the start-up mark of the gas turbine at t hour; R u and R d are the upper and lower maximum operating ramp rates of the gas turbine respectively.

[0062] The parameters of the gas turbine for the distribution network are shown in the following table:

[0063]

[0064] The power flow constraints of the distribution network are expressed by (8) to (14). Among them, (8) to (9) represent the node power balance expression, (10) represents the constraint relationship between the node voltage difference 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 constraint, where the gas turbine node voltage value is a fixed constant.

[0065]

[0066] U i,t -U j,t =(r ij P ij,t +x ij Q ij,t ) / U0 (10)

[0067]

[0068]

[0069] -S ij,max ≤P ij,t ±Q ij,t ≤S ij,max (13)

[0070]

[0071] Where: P ij,t , Q ij,t It represents the active and reactive power flowing from node i to node j in the distribution network at time t; Represents the active and reactive power values ​​of the load at node i at time t; and They represent the reactive power of the gas turbine, wind power, photovoltaic power and the large power grid at the distribution network node at time t; U i,t , U j,t Represents the voltage value of the distribution network node; r ij 、x ij They represent the reference values ​​of the equivalent resistance and equivalent reactance of the distribution network line (i, j) respectively; U0 represents the voltage value of the gas turbine node; S ij,max Indicates the line transmission power limit; U i,max , U i,min They represent the upper and lower limits of node voltage respectively; Z represents the set of distribution network lines (i, j); S represents the set of distribution network nodes; and M represents the set of gas turbine nodes.

[0072] At the same time, record λ t = 0, And, t For other values,

[0073] 3. By quantitatively analyzing the relationship between the distribution network operation cost and the exchange power, the exchange power regulation cost model is obtained. First, calculate λ t The distribution network cost changes during adjustment, as shown in formula (15). Then, the polyfit function in MATLAB software is used to fit Δf t The power exchange between the distribution network and the large power grid P t E The relationship is as shown in formula (16).

[0074]

[0075] Where: Δf t represents the power exchange adjustment cost of the large power grid at time t; and It represents the coefficient of the large power grid exchange power regulation cost curve at time t.

[0076] 4. Establish a local power balance model of the large power grid by quantitatively analyzing the operating characteristics of the large power grid and the exchange power regulation cost model.

[0077] Taking the minimum operation cost of the large power grid within the dispatching period as the goal, the objective function is established:

[0078]

[0079] Where: Represents the energy consumption parameters of thermal power units.

[0080] The operation constraints of thermal power units can be expressed as:

[0081]

[0082] Where: represents the active power of thermal power unit i at time t; They represent the lower and upper limits of the output of thermal power unit i respectively; Indicates the start and stop status of thermal power unit i; U i , D i Indicates the upper and lower limits of the ramp rate of thermal power unit i.

[0083] The parameters of conventional thermal power units are shown in the following table:

[0084]

[0085] The branch flow distribution factor constraint can be expressed as:

[0086]

[0087] Where: They represent thermal power, wind power, photovoltaic power, power exchange with the distribution network, and conventional load at nodes α, β, θ, δ are the node output power transfer distribution factors for line l; They represent the upper and lower limits of the active power flow of line l respectively.

[0088] 5. Since there are a large number of nonlinear constraints and variables in the optimization scheduling model, the optimization scheduling model is transformed into a classic mixed integer linear programming problem by linearization. On this basis, on the MATLAB platform, the commercial solver Gurobi is called by yalmip software to solve the optimization problem.

[0089] The optimization dispatch 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 large power grid, and the P with a specific λ value. t E value, and obtain the large power grid exchange power regulation cost Δf t The value of the large power grid exchange power regulation cost curve coefficient and The value of; In the second stage, (17) ~ (20) are solved to obtain the output of thermal power units in the large power grid and the exchange power P between the large power grid and the distribution network t E value to achieve local balancing of the large power grid.

[0090] 6. Simulation results

[0091] In the embodiment of the present invention, an IEEE-33 node distribution network and an IEEE-30 node large power grid are used, and the topology is as follows: Figure 1 and Figure 2 The upper and lower limits of the power exchange between the large power grid and the distribution network are shown in Figure 3 As shown in the figure, the optimization scheme of the 24-hour thermal power unit output of the large power grid and the exchange power between the distribution network and the large power grid is as follows Figure 4 Before using this method, the daily operation cost of the power grid was 365370.9 yuan. After using this method, the daily operation cost of the large power grid was 336141.23 yuan, which increased the economic percentage by 8%.

[0092] The above is a preferred embodiment of the present invention. Those skilled in the art should understand that the embodiments of the present invention are not limited to the above contents, and any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A large power grid on-site balancing control method combining load and new energy power forecasting, characterized by: The following steps are involved: Step 1: Obtain the forecast data of the next day's sunshine intensity and wind speed for the large power grid and distribution network from the power grid dispatching center. On this basis, the forecast power of renewable energy generation on the day before is obtained according to the calculation formula of wind power and photovoltaic power generation: P t PV =P STC G AC,t [1+ε(T t -T r )] / G STC (2) Where: is the predicted wind power; u t is the wind speed at time t; u in ,u e and u out Represents cut-in wind speed, rated wind speed and cut-out wind speed; P W,e Represents the rated power of the fan; P t PV is the actual photovoltaic power generation; P STC G is the maximum test power output of the photovoltaic cell under standard test conditions; AC,t , G STC are the sunshine intensity at time t and under standard test conditions; ε is the power temperature coefficient; T t is the operating temperature of the solar panel at time t; T r is the reference temperature; Step 2: Quantitatively analyze the operating characteristics of the distribution network and build a power regulation model for the exchange of power between the distribution network and the large power grid; In order to explore the upper and lower limits of the exchange power between the distribution network and the large power grid, the objective function of minimizing the daily operation cost of the distribution network taking into account the exchange power is established as follows; since the maximum power generation and total load of the distribution network at time t are predicted, by adjusting the exchange power price incentive coefficient λ t (λ min ≤λ t ≤λ max ), that is, from λ max Gradually decrease to λ min When the power exchange between the distribution network and the large power grid is and Where: T is the scheduling period; f t DS represents the operating cost of the distribution network at time t; and is the gas turbine energy consumption parameter; t is the price incentive coefficient; P t E is the exchange power between the distribution network and the large power grid at time t; and The upper and lower limits of the power exchanged between the distribution network and the large power grid at time t that need to be obtained; The gas turbine operating constraints can be expressed as: In the formula, and are the operating power and rated power of the gas turbine at time t respectively; is the start-up mark of the gas turbine at t hour; R u and R d are the upper and lower maximum operating ramp rates of the gas turbine respectively; The power flow constraints of the distribution network are expressed by (8) to (14). Among them, (8) to (9) represent the node power balance expression, (10) represents the constraint relationship between the node voltage difference 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 constraint, where the gas turbine node voltage value is a fixed constant; -S ij,max ≤P ij,t ±Q ij,t ≤S ij,max (13) Where: P ij,t , Q ij,t It represents the active and reactive power flowing from node i to node j in the distribution network at time t; Represents the active and reactive power values ​​of the load at node i at time t; and They represent the reactive power of the gas turbine, wind power, photovoltaic power and the large power grid at the distribution network node at time t; U i,t , U j,t Represents the voltage value of the distribution network node; r ij 、x ij They represent the reference values ​​of the equivalent resistance and equivalent reactance of the distribution network line (i, j) respectively; U0 represents the voltage value of the gas turbine node; S ij,max Indicates the line transmission power limit; U i,max , U i,min They represent the upper and lower limits of node voltage respectively; Z represents the set of distribution network lines (i, j); S represents the set of distribution network nodes; M represents the set of gas turbine nodes; At the same time, record λ t = 0, And, t For other values, Step 3: By quantitatively analyzing the relationship between the distribution network operation cost and the exchange power, the exchange power regulation cost model is obtained; first, calculate λ t The distribution network cost changes during adjustment, as shown in formula (15); then, the polyfit function in MATLAB software is used to fit Δf t The power exchange between the distribution network and the large power grid P t E The relationship is as shown in formula (16); Where: Δf t represents the power exchange adjustment cost of the large power grid at time t; and represents the coefficient of the large power grid exchange power regulation cost curve at time t; Step 4: Establish a local power balance model of the large power grid by quantitatively analyzing the large power grid operation characteristics and the exchange power regulation cost model; Taking the minimum operation cost of the large power grid within the dispatching period as the goal, the objective function is established: Where: Indicates the energy consumption parameters of thermal power units; The operation constraints of thermal power units can be expressed as: Where: represents the active power of thermal power unit i at time t; They represent the lower and upper limits of the output of thermal power unit i respectively; Indicates the start and stop status of thermal power unit i; U i , D i Indicates the upper and lower limits of the ramp rate of thermal power unit i; The branch flow distribution factor constraint can be expressed as: Where: They represent thermal power, wind power, photovoltaic power, power exchange with the distribution network, and conventional load at nodes α, β, θ, δ are the node output power transfer distribution factors for line l; They represent the upper and lower limits of the active power flow of line l respectively; Step 5: Since there are a large number of nonlinear constraints and variables in the optimization scheduling model, the optimization scheduling model is transformed into a classic mixed integer linear programming problem by linearization. On this basis, the commercial solver Gurobi is called by yalmip software on the MATLAB platform to solve the optimization problem. The optimization dispatch 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 large power grid, and the P with a specific λ value. t E value, and obtain the large power grid exchange power regulation cost Δf t The value of the large power grid exchange power regulation cost curve coefficient and The value of; In the second stage, (17) ~ (20) are solved to obtain the output of thermal power units in the large power grid and the exchange power P between the large power grid and the distribution network t E value to achieve local balancing of the large power grid.

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