CN111355232A - Marginal cost-based distributed optimization method in virtual power plant - Google Patents

Abstract

The invention provides a Marginal Cost (MCs) based distributed optimization method in a virtual power plant. The method comprises the following steps: step 1, establishing a virtual power plant power network model: based on the geographic factors of all distributed power supplies, comprehensively considering the factors of control autonomy and communication cost, and constructing a virtual power plant power network model; step 2, establishing a virtual power plant partition global optimization objective function: the method comprises the steps that a virtual power plant partition global optimization objective function is established according to the response of an upper-layer instruction of an active distribution system and the demand of local autonomy, and the objective function is enabled to reach the minimum value, namely the total cost of power transmission in a virtual power plant is enabled to be the minimum; step 3, distributed optimization solution: when the marginal cost converges to average consistency, the objective function can reach the optimum; step 4, adjusting and distributing power: and distributing the power reference value to the local control layers of various devices according to the optimization result to realize the optimization control in the virtual power plant subarea.

Description

Marginal cost-based distributed optimization method in virtual power plant

Technical Field

The invention relates to the technical field of power distribution network control partitions, in particular to a distributed optimization method in a virtual power plant based on marginal cost.

Background

The randomness, the intermittence, the uncertainty, the bidirectional power flow characteristic and the fluctuation characteristic of diversified loads of various DGs in the active power distribution system and the customization demand of a user on high-quality power supply and utilization make the operation control of the active power distribution system have higher requirements on instantaneity, flexibility and adaptability. Therefore, the distributed power supply must be effectively controlled, the safety and stability risks are reduced, the economy of the distributed power supply is improved, and the full social resource benefit is maximized.

At present, a micro-grid concept is mostly adopted in an electric power system as a grid-connected form of distributed generation resources, the technical contradiction between a large power grid and the distributed generation resources can be well coordinated, and meanwhile, the micro-grid has a certain energy management function, but the micro-grid takes local application of the distributed generation resources as a main control target and is limited by a geographical region, so that the micro-grid has certain limitations on effective utilization of multi-region and large-scale distributed generation resources and large-scale benefits in an electric power market. In order to realize better integration of the distributed power generation resources, experts and scholars at home and abroad put forward a concept of a virtual power plant in recent years, and the virtual power plant can be regarded as a special power plant to participate in power grid operation by aggregating and optimizing the distributed power generation resources in a power distribution network through an advanced communication technology and a software management system so as to coordinate contradictions between the power grid and a demand side power supply. Therefore, research for regulating and controlling the power generation of the distributed power supply based on economic benefits in the virtual power plant is developed, and the method has positive significance for flexible management and control of the distributed power supply in a future power grid. At present, the research on the distributed optimization control of the virtual power plant in China is still in a starting stage. To realize distributed optimal control in a virtual power plant, the following problems need to be solved: 1) how a virtual power plant network model is constructed; 2) how to evaluate the distributed optimization in the virtual power plant and know whether the economic benefit of the virtual power plant is ideal.

Disclosure of Invention

The invention aims to provide a distributed optimization method in a virtual power plant based on marginal cost, and in order to make up for the deficiency of the prior art and achieve the aim, the invention provides the following technical scheme, which comprises the following steps:

step 1, establishing a virtual power plant power network model;

the establishment of the virtual power plant power network model takes the geographical position, communication conditions, system branch information, node information and other factors of each distributed power supply into consideration. The method is very close to the actual situation and has higher practical significance. The method specifically comprises the following steps: the geographical position of each distributed power supply takes the spatial relationship of each node of the virtual power plant into consideration; the communication condition takes the connection relation of each node of the virtual power plant into consideration; the system branch information considers the line resistance and inductance of the virtual power plant; and the system node information considers the node load of the virtual power plant and the node distributed power supply capacity.

Step 2, establishing a virtual power plant partition global optimization objective function;

and (3) according to the virtual power plant power network model established in the step (1), aiming at various types of DGs and controllable loads in the virtual power plant, establishing a power generation cost model and a load regulation cost model, and establishing an optimal control objective function with the minimum power generation cost. The smaller the objective function is, the more economical and load interactive performance of power generation in the virtual power plant partition is considered while the comprehensive response of the superior power generation instruction and the local disturbance self-suppression are shown.

The step 2 comprises the following three steps:

step 2.1: and (3) establishing an equality constraint condition: to satisfy active balancing, power equality constraints are first formulated to ensure that the VPP provides active power equal to the active output tasks allocated to the VPP. The calculation formula is shown as formula (1):

p in formula (1)_TOTRepresenting the power required by the load in the ADN; p_rRepresenting the output power of the r-th VPP.

Step 2.2: establishing a power generation cost function:

C^P＝u_iP²+σ_iP+λ_iformula (2)

C in formula (2)^PFor cost of electricity generation, mu_i，σ_iAnd λ_iAre all constants and P is active power.

Step 2.3: establishing a power generation cost objective function: aiming at various distributed power sources and controllable loads in a virtual power plant, constructing a minimum optimal control objective function of power generation cost, wherein the calculation formula is as follows (2):

c in formula (3)^P(P_r，i) In the form of a quadratic cost function; p_r，iRepresenting the output power of the ith distributed power supply on the r-th VPP partition. In this control mode, each VPP has its own minimum C_totalAs a target.

Step 3, distributed optimization solution;

C^P(P_r，i) Expressed by a quadratic cost function form, establishing a power generation cost model and a load regulation cost model, as shown in formula (4):

in the formula (4), the reaction mixture is,

representing the active marginal cost of the ith controllable unit in the r virtual power plant zone; mu.s_iAnd σ_iAre all constants. According to the distributed optimization theory, when the marginal cost converges to the average consistency, the marginal cost converges to

Then the objective function defined in equation (3) will be optimal.

Step 4, adjusting and distributing power;

when the average consistency converges, the corresponding active power reference value will be adjusted as shown in formula (5):

controllable units such as distributed generators in the virtual power plant subareas can control and adjust the power output of the controllable units according to the formula (5), so that power balance is realized, upper-layer instructions are comprehensively responded, internal optimization control is realized, and the instruction requirements of the upper layer are met while the economy is ensured.

Compared with the closest prior art, the invention has the beneficial effects that:

1. the marginal cost-based distributed optimization control in the virtual power plant comprehensively considers the virtual synchronous power generation characteristic, the marginal cost optimization characteristic and the communication topology change adaptability of the virtual power plant partition, and improves the comprehensiveness and scientificity of the optimization result;

2. the invention realizes the distributed optimal distribution of various DGs and controllable loads in the virtual power plant subareas, comprehensively responds to the superior power generation instruction and the local disturbance self-suppression, and simultaneously considers the economy and the load interactivity of the power generation in the virtual power plant subareas.

Drawings

FIG. 1 is a flow chart of the present invention.

Fig. 2 is a system block diagram employed in an embodiment of the present invention.

FIG. 3 is a graph showing the results of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

As shown in fig. 1, a flow of the distributed optimization method in a virtual power plant based on marginal cost in the present invention is shown in fig. 1, and specifically includes the following steps:

step 1, establishing a virtual power plant power network model;

the establishment of the virtual power plant power network model takes the geographical position, communication conditions, system branch information, node information and other factors of each distributed power supply into consideration. The method is very close to the actual situation and has higher practical significance. The method specifically comprises the following steps: the geographical position of each distributed power supply takes the spatial relationship of each node of the virtual power plant into consideration; the communication condition takes the connection relation of each node of the virtual power plant into consideration; the system branch information considers the line resistance and inductance of the virtual power plant; and the system node information considers the node load of the virtual power plant and the node distributed power supply capacity.

Step 2, establishing a virtual power plant partition global optimization objective function;

and (3) according to the virtual power plant power network model established in the step (1), aiming at various types of DGs and controllable loads in the virtual power plant, establishing a power generation cost model and a load regulation cost model, and establishing an optimal control objective function with the minimum power generation cost. The smaller the objective function is, the more economical and load interactive performance of power generation in the virtual power plant partition is considered while the comprehensive response of the superior power generation instruction and the local disturbance self-suppression are shown.

The step 2 comprises the following three steps:

step 2.1: and (3) establishing an equality constraint condition: to satisfy active balancing, power equality constraints are first formulated to ensure that the VPP provides active power equal to the active output tasks allocated to the VPP. The calculation formula is shown as formula (1):

p in formula (1)_TOTRepresenting the power required by the load in the ADN; p_rRepresents the output power of the r-th VPP;

step 2.2: establishing a power generation cost function:

C^P＝u_iP²+σ_iP+λ_iformula (2)

C in formula (2)^PFor cost of electricity generation, mu_i，σ_iAnd λ_iAre all constants, and P is active power;

step 2.3: establishing a power generation cost objective function: aiming at various distributed power sources and controllable loads in a virtual power plant, constructing a minimum optimal control objective function of power generation cost, wherein the calculation formula is as follows (2):

c in formula (3)^P(P_r，i) In the form of a quadratic cost function; p_r，iRepresenting the output power of the ith distributed power supply on the r-th VPP partition. In this control mode, each VPP has its own minimum C_totalAs a target.

Step 3, distributed optimization solution;

C^P(P_r，i) Expressed by a quadratic cost function form, establishing a power generation cost model and a load regulation cost model, as shown in formula (4):

in the formula (4), the reaction mixture is,

representing the active marginal cost of the ith controllable unit in the r virtual power plant zone; mu.s_iAnd σ_iAre all constants; according to the distributed optimization theory, when the marginal cost converges to the average consistency, the marginal cost converges to

Then the objective function defined in equation (3) will be optimal.

Step 4, adjusting and distributing power;

when the average consistency converges, the corresponding active power reference value will be adjusted as shown in formula (5):

controllable units such as distributed generators in the virtual power plant subareas can control and adjust the power output of the controllable units according to the formula (5), so that power balance is realized, upper-layer instructions are comprehensively responded, internal optimization control is realized, and the instruction requirements of the upper layer are met while the economy is ensured.

Example of the implementation

1) Establishing a virtual power plant power network model:

FIG. 2 is a model of a virtual power plant power network. At the beginning of the simulation phase, the system is in a steady state. When t is 0.02s, the system load changes. Before the load changes, the total output power is 1200KW, and the new load power is 100 KW. The node-mounted distributed power capacity is shown in table 1.

TABLE 1 node distributed Power installation Capacity

Distributed power source installation location	Distributed power installation capacity/KVar
		A1.1	250
A1.2	270
		A1.3	100
A2.1	200
		A2.2	230
A2.3	120
		A2.4	200
A2.5	100

The results of constructing a virtual power plant power network model by using factors such as the geographic position, the communication condition, the system branch information and the node information of the distributed power supply are shown in fig. 2.

2) Establishing a virtual power plant partition global optimization objective function:

and (3) aiming at the active power balance of the system, and performing distributed optimal control on the virtual power plant based on the marginal cost consistency of all the distributed power supplies.

(1) Establishing an active power constraint condition:

P_TOTrepresenting the power required by the load in the ADN; p_rRepresenting the output power of the r-th VPP.

(2) Establishing a power generation cost function:

C^P＝u_iP²+σ_iP+λ_iformula (7)

In the formula C^PFor cost of electricity generation, mu_i，σ_iAnd λ_iAre all alwaysP is the active power. The cost function parameters and the initial output active power of each distributed power supply are shown in table 2 and table 3:

TABLE 2 parameters of VPPA1

TABLE 3 parameters of VPPA2

(3) Constructing a minimum optimal control objective function of the power generation cost:

in the formula C^P(P_r，i) In the form of a quadratic cost function; p_r，iRepresenting the output power of the ith controllable distributed power supply on the r-th VPP partition. In this control mode, each VPP has its own minimum C_totalAs a target.

3) And (3) distributed optimization solution:

hypothesis C^P(P_r，i) The method can be expressed in a quadratic cost function form, and a power generation cost model and a load regulation cost model are established:

in the formula (9), the reaction mixture is,

representing the active marginal cost of the ith controllable unit in the r virtual power plant zone; mu.s_iAnd σ_iAre all constants. According to the distributed optimization theory, when the marginal cost converges to the average consistency, the marginal cost converges to

The objective function defined in equation (8)An optimum will be reached.

4) Adjusting and distributing power:

when the average consistency converges, the corresponding active power reference value will be adjusted as shown in equation (10):

controllable units such as distributed generators in the virtual power plant subareas can control and adjust the power output of the controllable units according to the formula (10), so that power balance is realized, upper-layer instructions are comprehensively responded, internal optimization control is realized, and the instruction requirements of the upper layer are met while the economy is ensured. The results are schematically shown in FIG. 3.

Although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that various changes, modifications, substitutions, and improvements can be made without departing from the spirit and scope of the invention.

Claims (1)

1. A marginal cost based distributed optimization method within a virtual power plant, the method comprising:

step 1, establishing a virtual power plant power network model;

the establishment of the virtual power plant power network model takes the geographical position, communication conditions, system branch information, node information and other factors of each distributed power supply into consideration; the method is very close to the actual situation and has higher practical significance; the method specifically comprises the following steps: the geographical position of each distributed power supply takes the spatial relationship of each node of the virtual power plant into consideration; the communication condition takes the connection relation of each node of the virtual power plant into consideration; the system branch information considers the line resistance and inductance of the virtual power plant; the system node information considers the node load of the virtual power plant and the node distributed power supply capacity;

step 2, establishing a virtual power plant partition global optimization objective function;

according to the virtual power plant power network model established in the step 1, aiming at various types of DGs and controllable loads in the virtual power plant, establishing a power generation cost model and a load regulation cost model, and establishing an optimal control objective function with the minimum power generation cost; the smaller the objective function is, the more economical and load interactive performance of power generation in the virtual power plant partition is considered while the comprehensive response of the superior power generation instruction and the local disturbance self-suppression are shown;

the step 2 comprises the following 3 steps:

step 2.1: and (3) establishing an equality constraint condition: in order to meet active balance, power equation constraint is firstly established, and active power provided by a VPP is ensured to be equal to active output tasks distributed to the VPP; the calculation formula is shown as formula (1):

p in formula (1)_TOTRepresenting the power required by the load in the ADN; p_rRepresents the output power of the r-th VPP;

step 2.2: establishing a power generation cost function:

C^P＝u_iP²+σ_iP+λ_iformula (2)

C in formula (2)^PFor cost of electricity generation, mu_i，σ_iAnd λ_iAre all constants, and P is active power;

step 2.3: establishing a power generation cost objective function: aiming at various distributed power sources and controllable loads in a virtual power plant, constructing a minimum optimal control objective function of power generation cost, wherein the calculation formula is as follows (2):

c in formula (3)^P(P_r，i) In the form of a quadratic cost function; p_r，iRepresenting the output power of the ith distributed power supply on the r-th VPP partition; in this control mode, each VPP has its own minimum C_totalAs a target;

step 3, distributed optimization solution;

C^P(P_r，i) Expressed by a quadratic cost function form, establishing a power generation cost model and a load regulation cost model, as shown in formula (4):

in the formula (4), the reaction mixture is,

representing the active marginal cost of the ith controllable unit in the r virtual power plant zone; mu.s_iAnd σ_iAre all constants; according to the distributed optimization theory, when the marginal cost converges to the average consistency, the marginal cost converges to

Then, the objective function defined in equation (3) will be optimized;

step 4, adjusting and distributing power;

when the average consistency converges, the corresponding active power reference value will be adjusted as shown in formula (5):

controllable units such as distributed generators in the virtual power plant subareas can control and adjust the power output of the controllable units according to the formula (5), so that power balance is realized, upper-layer instructions are comprehensively responded, internal optimization control is realized, and the instruction requirements of the upper layer are met while the economy is ensured.

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