Power control method for networking type optical storage station cluster

Abstract

The invention provides a power control method for a network construction type optical storage field station cluster, and the method comprises the steps: building an upper-layer scheduling scheme from a scheduling center to each optical storage field station according to the load demand of a power grid, the predicted power of a photovoltaic field station, and the real-time electric quantity information of an energy storage module, solving the upper-layer scheduling scheme, issuing an instruction to each optical storage station according to a solving result; establishing a lower-layer optimization scheme of internal power distribution of the optical storage station in combination with the issued instruction, and solving the lower-layer optimization scheme to obtain a power reference value; and inputting the power reference value into the network-forming inverter to carry out power control. Grid-connected power fluctuation is balanced through a double-layer scheduling scheme, a power grid has high anti-interference capacity in the operation process, meanwhile, photovoltaic real-time power changes are considered, and model prediction control is adopted to replace traditional PI control so as to adapt to dynamic parameter changes.

Description

Technical Field

[0001] The present application belongs to the field of renewable energy power generation technology, and specifically, relates to a power control method for a grid-based photovoltaic storage station cluster. Background Art

[0002] With the solid advancement of the carbon peak and carbon neutrality plans, the proportion of new energy installed capacity and power generation has increased year by year, and the power grid has shown the "double high" characteristics of a high proportion of new energy and a high proportion of power electronic equipment. In order to achieve the active power support function, the grid-type converter that can adapt to the weak power grid has become the research focus in related fields. The grid-type converter uses virtual synchronous machine technology that imitates the swing process of the synchronous machine rotor. After subtracting the active power command value and the actual value, it generates the reference frequency of the control link through the virtual inertia and damping link, which adds active power support capabilities to many power generation and energy storage equipment that do not have physical inertia.

[0003] In response to the output uncertainty of photovoltaic and storage station cluster systems and the power fluctuations caused by grid connection, existing research has too simplistically considered the uncertainty factors in their operation. If a more flexible and systematic scheduling strategy is adopted, it can better meet the real-time balance and optimized operation needs of the power grid. Summary of the invention

[0004] The technical problem solved by the present application is: how to provide a power control method for a grid-connected photovoltaic storage station cluster that can balance the power fluctuations of the grid to improve the anti-interference capability of the grid.

[0005] The present application provides a power control method for a networked optical storage station cluster, the power control method comprising:

[0006] According to the grid load demand, the predicted power of the photovoltaic station and the real-time power information of the energy storage module, an upper-level scheduling plan from the dispatch center to each photovoltaic storage station is established, the upper-level scheduling plan is solved, and instructions are issued to each photovoltaic storage station according to the solution results;

[0007] Combined with the issued instructions, establish the lower-level optimization scheme for the internal power distribution of the photovoltaic storage station, solve the lower-level optimization scheme, and obtain the power reference value;

[0008] The power reference value is input into the grid-connected inverter to perform power control.

[0009] Optionally, the method for establishing an upper-level dispatching scheme from the dispatching center to each photovoltaic storage station according to the grid load demand, the predicted power of the photovoltaic station and the real-time power information of the energy storage module includes:

[0010] The function is constructed with the goal of minimizing the variance of the active power difference fluctuation of the photovoltaic and storage station cluster. The expression is:

[0011]

[0012] Wherein: P i are respectively the active power of the real-time scheduling of the i-th photovoltaic and energy storage station, is the predicted active power of the i-th photovoltaic and energy storage station, k is the active power fluctuation difference coefficient in the optimization objective (0 < k < 1), and n is the number of photovoltaic and energy storage stations in the region;

[0013] Construct the following constraint conditions according to the operating conditions of the photovoltaic and energy storage station cluster:

[0014]

[0015] Wherein: P L is the load power in the formulated power generation plan, α is the curtailment constraint coefficient (0 < α < 1), β is the photovoltaic change control coefficient (0 < β < 1), is the maximum output of the photovoltaic and energy storage station, is the rated capacity of the photovoltaic and energy storage station.

[0016] Optionally, the method for establishing the lower-layer optimization scheme of the internal power distribution of the photovoltaic and energy storage station by combining the issued instructions includes:

[0017] Construct a multi-objective optimization function according to the energy storage charge and discharge loss, the total cost of the energy storage device, and the cost of the photovoltaic device. The expression is:

[0018]

[0019] Wherein: P sv is the active power of the photovoltaic output, is the charge and discharge power of the energy storage, C is the unit charge and discharge loss cost coefficient of the energy storage, C w is the capacity cost coefficient of the energy storage battery, N a is the charge and discharge cycle times of the energy storage over the entire life cycle, D dod is the maximum charge and discharge depth of the energy storage;

[0020] Construct constraint conditions according to the operating conditions of the internal equipment of the photovoltaic and energy storage station. The expression is:

[0021]

[0022] Wherein: P i is the station output power instruction issued by the dispatching center, P bm is the maximum charge and discharge power of the energy storage, is the maximum value of the photovoltaic active power output, S(t) is the SOC value of the energy storage at time t, S min is the lower limit of the energy storage SOC, S max is the upper limit of the energy storage SOC.

[0023] Optionally, the PI controller of the voltage and current control loop of the grid-connected inverter is replaced by a model predictive controller, and the rolling optimization objective function of the model predictive controller is:

[0024] min J(k)=||y PM (k)|| 2 Q+||Δu M (k)|| 2 R

[0025] Where: y PM (k) is the output state quantity of the system, Δu M (k) is the input increment of the system, Q and R are weight matrices.

[0026] Optionally, the methods for solving the upper-layer scheduling solution and the methods for solving the lower-layer optimization solution are both particle swarm optimization algorithms.

[0027] The present application provides a power control method for a grid-type photovoltaic storage station cluster, which has the following technical effects:

[0028] A double-layer dispatching scheme is used to balance the power fluctuations of the grid, so that the grid has a strong anti-interference ability during operation. At the same time, taking into account the real-time power changes of photovoltaics, model predictive control is used instead of traditional PI control to adapt to dynamic parameter changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A flow chart of the main steps of a power control method for a grid-type photovoltaic storage station cluster according to one or more embodiments;

[0030] Figure 2 A complete flow chart of a power control method for a grid-type photovoltaic storage station cluster according to one or more embodiments;

[0031] Figure 3 A schematic diagram of the working process of a grid-connected inverter according to one or more embodiments;

[0032] Figure 4 The figure is a schematic diagram of a working circuit of a voltage and current control loop of a grid-type inverter according to one or more embodiments. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0034] Before describing the various embodiments of the present application in detail, the technical concept of the present application will be briefly described first: At present, when solving the active power control strategy of the optical storage isolated network, the state of the SOC is not considered, and it is difficult to obtain a more accurate control strategy. Therefore, the present application provides a power control method for a network-forming optical storage station cluster. The following will describe the specific principle of the power control method for the network-forming optical storage station cluster of the present application in combination with more embodiments.

[0035] Specifically, as Figure 1 and Figure 2 shown, the power control method for the network-forming optical storage station cluster in the first embodiment of the present application includes:

[0036] Step S10: According to the grid load demand, the predicted power of the photovoltaic power station, and the real-time power information of the energy storage module, establish an upper-layer scheduling plan from the dispatching center to each optical storage station, solve the upper-layer scheduling plan, and issue instructions to each optical storage station according to the solution result;

[0037] Step S20: Combine the issued solution result to establish a lower-layer optimization plan for the internal power distribution of the optical storage station, solve the lower-layer optimization plan, and obtain a power reference value;

[0038] Step S30: Input the power reference value into the network-forming inverter for power control.

[0039] Specifically, in step S10, the method for establishing an upper-layer scheduling plan from the dispatching center to each optical storage station according to the grid load demand, the predicted power of the photovoltaic power station, and the real-time power information of the energy storage module includes:

[0040] Construct a function with the minimum variance of the active power difference fluctuation of the optical storage station cluster as the target, and the expression is:

[0041]

[0042] In the formula: P i are the active powers of the real-time scheduling of the i-th optical storage station respectively, is the predicted active power of the i-th optical storage station, k is the active power fluctuation difference coefficient in the optimization target (0 < k < 1), and n is the number of optical storage stations in the region;

[0043] Construct the following constraint conditions according to the operating conditions of the optical storage station cluster:

[0044]

[0045] In the formula: P L is the load power in the formulated power generation plan, α is the curtailment constraint coefficient (0 < α < 1), β is the photovoltaic change control coefficient (0 < β < 1), is the maximum output of the optical storage station, is the rated capacity of the photovoltaic storage station. Among them, the first constraint is the power balance constraint, which ensures the balance between power generation and power consumption in the system; the second constraint is the photovoltaic abandonment constraint, which maintains a certain proportion of photovoltaic consumption in the dispatching system; the third constraint is the photovoltaic control constraint, which controls the photovoltaic change to prevent its power from increasing / decreasing significantly and causing harm to the power grid.

[0046] In one or more embodiments, a method for establishing a lower-level optimization scheme for internal power allocation of a photovoltaic storage station in combination with the issued instructions includes:

[0047] A multi-objective optimization function is constructed based on energy storage charging and discharging losses, total cost of energy storage equipment, and photovoltaic equipment cost. The expression is:

[0048]

[0049] Where: P sv is the active power of photovoltaic output, is the charging and discharging power of energy storage, C is the unit charging and discharging loss cost coefficient of energy storage, C w is the capacity cost coefficient of energy storage battery, N a is the number of charge and discharge cycles in the entire life cycle of the energy storage, D dod It is the maximum charge and discharge depth of energy storage.

[0050] The constraint conditions are constructed based on the operating conditions of the internal equipment of the PV storage station, and the expression is:

[0051]

[0052] Where: P i is the station output power instruction issued by the dispatching center, P bm is the maximum charge and discharge power of energy storage, is the maximum value of the photovoltaic active power output, S(t) is the SOC value of the energy storage at time t, S min is the lower limit of energy storage SOC, S max It is the upper limit of energy storage SOC.

[0053] In one or more embodiments, in step S30, Figure 3 and Figure 4 As shown in the figure, the PI controller of the voltage and current control loop of the grid-connected inverter is replaced by a model predictive controller, and the rolling optimization objective function of the model predictive controller is:

[0054] min J(k)=||y PM (k)|| 2 Q+||Δu M (k)|| 2 R

[0055] Where: y PM (k) is the output state quantity of the system, Δu M (k) is the input increment of the system, Q and R are weight matrices. After each optimization calculation, the predicted control quantity at the first moment in the future is taken as the control signal and input into the control object. At the next moment, a similar optimization problem is formed again to find the predicted control signal at the next moment, forming an online rolling optimization calculation.

[0056] The power control method for a grid-connected photovoltaic storage station cluster provided in this embodiment balances the power fluctuations of the grid through a two-layer scheduling scheme, so that the power grid has a strong anti-interference ability during operation. At the same time, considering the real-time power changes of photovoltaics, model predictive control is used instead of traditional PI control to adapt to dynamic parameter changes.

[0057] The specific implementation methods of the present application are described in detail above. Although some embodiments have been shown and described, those skilled in the art should understand that these embodiments can be modified and improved without departing from the principles and spirit of the present application whose scope is defined by the claims and their equivalents. These modifications and improvements should also be within the scope of protection of the present application.

Claims

1. A power control method for a grid-type photovoltaic storage station cluster, characterized in that: The power control method comprises: According to the grid load demand, the predicted power of the photovoltaic station and the real-time power information of the energy storage module, an upper-level scheduling plan from the dispatch center to each photovoltaic storage station is established, the upper-level scheduling plan is solved, and instructions are issued to each photovoltaic storage station according to the solution results; Combined with the issued instructions, establish the lower-level optimization scheme for the internal power distribution of the photovoltaic storage station, solve the lower-level optimization scheme, and obtain the power reference value; The power reference value is input into the grid-connected inverter to perform power control.

2. The power control method of a grid-type photovoltaic storage station cluster according to claim 1, characterized in that: The method for establishing an upper-level dispatching scheme from the dispatching center to each photovoltaic storage station according to the grid load demand, the predicted power of the photovoltaic station and the real-time power information of the energy storage module includes: The function is constructed with the goal of minimizing the variance of the active power difference fluctuation of the photovoltaic and storage station cluster. The expression is: Where: P i are the active power of the real-time dispatch of the i-th photovoltaic and energy storage station respectively, is the predicted active power of the i-th photovoltaic and energy storage station, k is the active power fluctuation difference coefficient in the optimization target (0 < k < 1), and n is the number of photovoltaic and energy storage stations in the region; The following constraints are constructed based on the operating conditions of the PV-storage station cluster: Where: P L To formulate the load power in the power generation plan, α is the constraint coefficient of abandoned light (0<α<1), β is the control coefficient of photovoltaic change (0<β<1), is the maximum output of the photovoltaic storage station, is the rated capacity of the photovoltaic storage station.

3. The power control method of a grid-type photovoltaic storage station cluster according to claim 2, characterized in that: The method for establishing a lower-level optimization scheme for internal power distribution of a photovoltaic storage station in combination with the issued instructions includes: A multi-objective optimization function is constructed based on energy storage charging and discharging losses, total cost of energy storage equipment, and photovoltaic equipment cost. The expression is: Where: P sv is the active power of photovoltaic output, is the charging and discharging power of energy storage, C is the unit charging and discharging loss cost coefficient of energy storage, C w is the capacity cost coefficient of energy storage battery, N a is the number of charge and discharge cycles in the entire life cycle of the energy storage, D dod is the maximum charge and discharge depth of energy storage; The constraint conditions are constructed based on the operating conditions of the internal equipment of the PV storage station, and the expression is: Where: P i is the station output power instruction issued by the dispatching center, P bm is the maximum charge and discharge power of energy storage, is the maximum value of the photovoltaic active power output, S(t) is the SOC value of the energy storage at time t, S min is the lower limit of energy storage SOC, S max It is the upper limit of energy storage SOC.

4. The power control method of the networked photovoltaic storage station cluster according to claim 3 is characterized in that: The PI controller of the voltage and current control loop of the grid-connected inverter is replaced by a model predictive controller, and the rolling optimization objective function of the model predictive controller is: min J(k)=||y PM (k)|| 2 Q+||Δu M (k)|| 2 R Where: y PM (k) is the output state quantity of the system, Δu M (k) is the input increment of the system, Q and R are weight matrices.

5. The power control method of a grid-type photovoltaic storage station cluster according to claim 1, characterized in that: The methods for solving the upper-level scheduling scheme and the lower-level optimization scheme are both particle swarm optimization algorithms.

Images