CN110265991B - Distributed coordination control method for direct-current micro-grid - Google Patents

Abstract

The invention relates to a distributed coordination control method of a direct-current micro-grid, which comprises the following steps: s1: according to a distributed power supply in a direct-current micro-grid system, combining supply-demand balance conditions and capacity limitation, and establishing a total power generation cost function of the system; s2: each power generation unit and an intelligent agent controller thereof acquire local information and initialize the local information; s3: solving a power generation cost function by adopting an improved multi-agent consistency algorithm to obtain the cost micro-increment rate and the output power of each unit, and stabilizing the voltage of a direct current bus; s4: performing algorithm iteration at the next moment, processing the obtained information, and finally outputting the optimal power; s5: and carrying out distributed coordination scheduling control on the direct current micro-grid by utilizing the optimal power. Compared with the prior art, the invention has the advantages of simultaneously realizing the multi-objective control of minimum power generation cost, effectively stabilizing the bus voltage, keeping the power balance and maximally utilizing the renewable energy.

Description

A distributed coordinated control method for DC microgrid

Technical Field

The present invention relates to the technical field of energy coordinated control of a direct current microgrid, and in particular to a distributed coordinated control method of a direct current microgrid.

Background Art

With the increasing penetration of a large number of renewable energy power generation in traditional power grids, microgrid technology has emerged. Microgrid technology is a small power generation and distribution system that organically integrates distributed power sources, loads, energy storage devices, etc. At present, most of the research on microgrids is focused on AC microgrids, but most of the electricity generated by renewable energy power generation units such as photovoltaic and wind power generation is DC power. The use of DC microgrids not only eliminates the AC/DC conversion device, reduces costs and losses, but also does not have frequency stability and reactive power problems in the power grid. Therefore, research on DC microgrid systems is receiving widespread attention. Bus voltage is a key indicator of stable operation and power balance of the reaction system. When the power in the system is unbalanced, the bus voltage will fluctuate. Excessive bus voltage indicates that there is excess active power in the system, and vice versa, the power in the system is insufficient. Therefore, controlling the stability of the DC bus voltage is usually an important goal of DC microgrids.

Energy management system is a necessary means of microgrid power flow management, and its management methods mainly include planning management and optimization management. Among them, optimization management takes into account the economic benefits of system operation, and thus has attracted widespread attention at home and abroad.

The control methods for DC microgrids are mainly divided into centralized control and distributed control. Centralized control uses the controllable units in the system to send and receive status instructions to the central controller in a unified manner. It has high requirements on the stability of the communication network and leads to high system operating costs. The high penetration rate of distributed power sources and the scalability of microgrids make traditional centralized optimization and coordination management lack flexibility and scalability. In comparison, distributed control has stronger adaptability and can better meet the plug-and-play requirements of distributed power sources.

As a kind of distributed structure, multi-agent system has good inspiration and autonomy, and is particularly suitable for complex microgrid energy management. In the distributed control of multi-agent system, the most basic problem is the consistency of multi-agent system. The consistency optimization algorithm of multi-agent multi-agent system can realize the distributed optimization operation of power system. This distributed control structure only needs to obtain the information of local agent and its neighboring agents, with low network communication pressure and plug-and-play. Controllable power equipment realizes information exchange through communication network, and exchanges information with other agents through local communication network to realize the coordinated optimization operation of the whole microgrid system.

In the current research on energy management strategies for microgrids, most of the research is on traditional AC microgrids. In the research on DC microgrids, there are few studies involving economic goals such as maximizing the utilization of renewable energy and minimizing the cost of power generation. Therefore, it is very necessary to propose a multi-objective control strategy method that can achieve the lowest operating cost in the DC microgrid, reasonably allocate the output power of each unit, and quickly achieve bus voltage stability and grid power balance.

Summary of the invention

The purpose of the present invention is to provide a distributed coordinated control method for a DC microgrid in order to overcome the defects of the above-mentioned prior art.

The purpose of the present invention can be achieved by the following technical solutions:

A distributed coordinated control method for a DC microgrid comprises the following steps:

Step 1: Based on the distributed power sources in the DC microgrid system, combined with the supply and demand balance conditions and capacity constraints, establish the total power generation cost function model of the system;

Step 2: Each power generation unit and its intelligent controller obtain local information and initialize;

Step 3: Use the improved multi-agent consensus algorithm to solve the power generation cost function, obtain the cost increment rate and output power of each unit, and stabilize the DC bus voltage;

Step 4: Perform algorithm iteration at the next moment, process the obtained information, and finally output the optimal power;

Furthermore, the total power generation cost function model in step 1 is described by the formula:

Where P _Bi is the output power of energy storage unit i, _SB is the set of controllable energy storage units, a _i , b _i , c _i are the corresponding function coefficients, i is a natural number, and C _i represents the operating cost of power generation unit i.

Furthermore, the supply-demand balance condition in step 1 is described by the formula:

Where _PG is the output power of all distributed generation units, _SL is the set of all load units, and _PDi is the local active power demand of energy storage unit i.

Furthermore, the capacity limit in step 1 is described by the formula:

P_B.i(k)、

分别对应为k时刻储能单元i的输出功率最小值、实际值和最大值，

SOC_B.i(k)、

分别对应为k时刻储能单元i的剩余电量最小值、实际值和最大值。In the formula,

P _Bi (k),

They correspond to the minimum, actual and maximum output power of energy storage unit i at time k respectively.

SOC _Bi (k),

They correspond to the minimum, actual and maximum remaining power of energy storage unit i at time k respectively.

Furthermore, the initialization process in step 2 includes the following sub-steps:

Step 201: Obtain the local load information and active power measured by the energy storage unit i agent, and obtain the cost increment rate of each unit;

Step 202: Form a Laplacian matrix and an adjacency matrix according to the topological graph.

Furthermore, the algorithm improvement of the multi-agent consensus algorithm in step 3 includes defining auxiliary variables and further defining the output power of the energy storage unit and introducing a voltage stabilization function and further setting a modified consistency protocol.

Furthermore, the calculation formula for the output power of the energy storage unit is:

和

分别表示t时刻智能体i和j的辅助变量，d_ic为储能单元i与其本地需求有功负荷之间的权重系数，P_D.c表示与权重系数对应的本地有功需求。In the formula, _aij represents the communication topology weight between agents i and j, _Ni represents the set of neighboring units of energy storage unit i,

and

They represent the auxiliary variables of agents i and j at time t respectively, _dic is the weight coefficient between energy storage unit i and its local required active load, and _Pdc represents the local active demand corresponding to the weight coefficient.

Furthermore, the modified consistency protocol is described as follows:

λ(t)为t时刻可控运行单元成本，

为t时刻可控运行单元成本的导数，ε为直流母线电压误差因子，U_dc为直流母线电压的设定值，U为直流母线电压的实际值。Where L represents the Laplace matrix,

λ(t) is the controllable operating unit cost at time t,

is the derivative of the controllable operation unit cost at time t, ε is the DC bus voltage error factor, U _dc is the set value of the DC bus voltage, and U is the actual value of the DC bus voltage.

Furthermore, the processing of the obtained information in step 4 includes the following sub-steps:

Step 401: when the output power of a certain power generation unit exceeds its limit value after the consistency algorithm is adopted, modify the power limit of the output power and set the output power constraint of the controllable energy storage unit;

Step 402: After the consistency algorithm iteration is completed, it is determined whether the output power exceeds the limit and the corresponding active power is output according to the output power constraint of the controllable energy storage unit.

Furthermore, the output power constraint of the controllable energy storage unit in step 401 is specifically described by the formula:

和

分别为储能单元i的输出功率最小值和最大值。Where λ ^* is the optimal incremental cost,

and

are the minimum and maximum output power of energy storage unit i respectively.

Compared with the prior art, the present invention has the following advantages:

(1) The present invention adopts an improved multi-agent consensus algorithm to solve the DC microgrid operation cost model. The improved consensus algorithm has good convergence and fast speed, and can stably converge to the maximum power point. Each agent exchanges incremental cost information with its neighboring agents and obtains its own DC bus voltage value. Compared with traditional centralized control, the communication burden is small.

(2) Under the premise of satisfying the lowest operating cost of the controllable energy storage unit, the present invention simultaneously achieves the maximum absorption of distributed power sources, optimal power generation and stable DC bus voltage, thus realizing multi-objective coordinated control of the DC microgrid.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of the structure of a DC microgrid used in the present invention;

FIG2 is a flow chart of the control method proposed by the present invention;

FIG3 is a topological diagram of the communication network of each intelligent agent used in the present invention;

FIG4 is a convergence diagram of the cost micro-increase rate obtained in an embodiment of the present invention;

FIG5 is a graph showing the optimal output power convergence of each controllable unit obtained in an embodiment of the present invention;

FIG6 is a curve diagram of DC bus voltage variation in an embodiment of the present invention;

FIG. 7 is a comparison diagram of DC bus voltage variation curves under the embodiment of the present invention and the traditional centralized control method.

DETAILED DESCRIPTION

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 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 should fall within the scope of protection of the present invention.

Example

FIG1 is a DC microgrid structure adopted by the present invention. As can be seen from FIG1, the DC microgrid mainly includes distributed generation units, energy storage units and load units. The DC microgrid is connected to the main grid through a static transfer switch (STS), so the DC microgrid can work in two modes: grid-connected mode and island mode. The present invention only considers the DC microgrid working in island mode. The DC microgrid structure is divided into two layers: communication structure and physical structure. The communication structure is composed of the intelligent agent corresponding to the control unit, and the physical structure is composed of the control unit in the power grid. Each intelligent agent can receive and sample the instructions and information of the corresponding local physical unit and its neighboring units, and iterate in the intelligent agent after receiving the corresponding information, thereby updating the information of the local physical unit.

The specific steps of the control method of the present invention are shown in Figure 2, which includes the following steps:

Step 1: According to the distributed power sources in the DC microgrid system, combined with the supply and demand balance conditions and capacity constraints, establish the total power generation cost function of the system.

The total power generation cost function of the DC microgrid system is expressed as:

Where P _Bi is the output power of energy storage unit i, _SB is the set of controllable energy storage units, a _i , b _i , c _i are the corresponding function coefficients, i is a natural number, C _i represents the operating cost of power generation unit i, and the energy storage unit is divided into two states: charging and discharging. The sign is positive when discharging and negative when charging.

The supply and demand balance condition of the DC microgrid is:

Where _PG is the output power of all distributed generation units, _SL is the set of all load units, and _PDi is the local active power demand of energy storage unit i.

The operating capacity of the controllable energy storage unit is limited to:

P_B.i(k)、

分别对应为k时刻储能单元i的输出功率最小值、实际值和最大值，

SOC_B.i(k)、

分别对应为k时刻储能单元的剩余电量最小值、实际值和最大值。In the formula,

P _Bi (k),

They correspond to the minimum, actual and maximum output power of energy storage unit i at time k respectively.

SOC _Bi (k),

They correspond to the minimum, actual and maximum remaining power of the energy storage unit at time k respectively.

When the output power and remaining power limits are not considered, the Lagrange multiplier method can be used to transform the objective function into:

Where: η is the Lagrange multiplier. By taking the derivative of variables P _Bi and η, we can obtain the optimal condition of the objective function:

The derivative of the cost _Ci with respect to the output power P _Bi is defined as the cost increment rate _λi of the i-th energy storage unit. From the optimal condition, it can be seen that the optimal solution of the cost objective function is that the cost increment rate _λi of each energy storage unit is equal and the power balance is maintained, that is:

λ ₁ =λ ₂ =…=λ _i =λ ^* i∈S _B

Where: λ ^* is the optimal incremental cost. That is, when the “equal incremental rate principle” is met, the operating cost of the DC microgrid system is the minimum. At this time, the relationship between the corresponding optimal incremental cost and the optimal output power is:

为各单元的的最优输出功率，即：Where:

is the optimal output power of each unit, that is:

Step 2: Each power generation unit and its intelligent body controller obtains local information and initializes.

As shown in Figure 1, the structure of the DC microgrid is divided into two layers: communication structure and physical structure. The specific content of the initialization information includes:

(1) Agent i of unit i obtains the measured local load information P _Di and active power P _Bi and calculates the cost increment rate λ _i of each unit.

(2) Form a Laplace matrix based on the topological graph to form an adjacency matrix.

The Laplace matrix is defined as follows:

A为描述节点和边之间关系的邻接矩阵。如果在节点V_i和V_j间存在通信路径，则V_i和V_j是彼此的邻居，即认为两者之间存在一条无向边，该连接表示为(V_i,V_j)，且(V_i,V_j)∈E，特别地在无向图中，(V_i,V_j)∈E等同于(V_j,V_i)∈E；其在邻接矩阵A＝{a_ij}中对应的邻接矩阵元素，a_ij＝a_ji＞0，否则a_ij＝a_ji＝0，对角线元素a_ii＝0。因此，定义顶点的邻居集合为N_i＝{j∈v：(v_j，v_i)∈E}。节点V_i对应的入度为

由d_i组成的度矩阵D＝diag{d_ij}，则图G的Laplace矩阵定义为L＝D-A，其中l_ij＝-a_ij，

The nodes of the DC microgrid exchange information through the communication network. The communication network graph can be represented by G = {V, E, A}. The graph G is defined as an undirected network, where V = {v ₁ , v ₂ , …v _n } is a node set, n is the total number of nodes; E is the set of edges between nodes,

A is an adjacency matrix that describes the relationship between nodes and edges. If there is a communication path between nodes _Vi and _Vj , then _Vi and _Vj are neighbors of each other, that is, it is considered that there is an undirected edge between the two. The connection is represented by ( _Vi , _Vj ), and ( _Vi , _Vj )∈E. In particular, in an undirected graph, ( _Vi , _Vj )∈E is equivalent to ( _Vj , _Vi )∈E; its corresponding adjacency matrix element in the adjacency matrix A＝{ _aij } is _aij ＝ _aji ＞0, otherwise _aij ＝ _aji ＝0, and the diagonal element _aii ＝0. Therefore, the neighbor set of the vertex is defined as _Ni ＝{j∈v：( _vj , _vi )∈E}. The in-degree corresponding to node _Vi is

The degree matrix D = diag{d _ij } composed of d _i , then the Laplace matrix of the graph G is defined as L = DA, where l _ij = -a _ij ,

Step 3: Use the improved multi-agent consensus algorithm to solve the power generation cost function, obtain the cost increment rate and output power of each unit, and stabilize the DC bus voltage.

The consensus algorithm has a wide range of applications in group control, complex dynamic networks, coordinated control, etc. As a method for finding the optimal economic operation point of a DC microgrid, it has the characteristics of fast convergence speed and simple convergence conditions.

In the traditional multi-agent consensus algorithm, assuming there are n agents, the dynamic equation is defined as:

In the formula, _ui (t) is the control variable, _xi (t) is the state variable representing the state of different agents in the network, and the superscript · represents the derivative. The specific control rules of the control variables are as follows:

Written in matrix form:

The algorithm exchanges information between each agent and its adjacent agents, and continuously reduces the state difference between two agents until the consistency of all agents is achieved. This method does not require global information, but only needs to obtain adjacent local information.

定义储能单元的输出功率为：The improved multi-agent consensus algorithm adopted by the present invention adopts λ as the consistency variable to solve the economic optimization problem. When each agent iterates to the optimal incremental cost, the optimal output power can be obtained at the same time. Considering that the cost increment rate of each controllable unit is different, the upper and lower limits of the output power are different, and the delay in the distributed consensus algorithm communication network cannot be ignored, that is, to ensure the robustness of the strategy. Assume that the communication time between agents is represented by t, define the auxiliary variable

The output power of the energy storage unit is defined as:

和

分别表示t时刻智能体i和j的辅助变量，d_ic为储能单元i与其本地需求有功负荷之间的权重系数，如果单元有本地有功需求，则取值为1，否则为0，P_D.c表示与权重系数对应的本地有功需求，j，c为自然数。In the formula, _aij represents the communication topology weight between agents i and j, _Ni represents the set of neighboring units of energy storage unit i,

and

They represent the auxiliary variables of agents i and j at time t respectively, _dic is the weight coefficient between energy storage unit i and its local required active load, if the unit has local active demand, the value is 1, otherwise it is 0, P _Dc represents the local active demand corresponding to the weight coefficient, j, c are natural numbers.

The communication protocol proposed by the present invention is as follows:

λ _i (0) = a _i P _Bi (0) + _bi

Where λ _i (0) and P _Bi (0) are the initial values of λ and P _Bi respectively; the superscript · represents the derivative value; the output power iteration of each unit satisfies:

It can be seen that the consistency protocol can iterate to the optimal output power on the basis of satisfying the power balance condition, and at the same time satisfy the equal incremental rate criterion, and can obtain the minimum cost of the controllable operating unit.

The simplified matrix form is:

In view of the possible fluctuations of bus voltage in DC microgrid, a voltage stability function is introduced on the basis of consistency protocol, and the DC bus voltage value is controlled to the rated value by using consistency protocol, so that the DC bus voltage can converge to the rated value set by the system. The modified consistency protocol is:

λ(t)为t时刻可控运行单元成本，

为t时刻可控运行单元成本的导数，ε为直流母线电压误差因子，U_dc为直流母线电压的设定值，U为直流母线电压的实际值，当直流母线电压的设定值大于直流母线电压的实际值时，输出功率增加；若相反则需要减小，当个智能体单元的增量成本迭代达到一致时，直流母线电压实际值达到额定设定值。Where L represents the Laplace matrix,

λ(t) is the controllable operating unit cost at time t,

is the derivative of the cost of the controllable operating unit at time t, ε is the DC bus voltage error factor, U _dc is the set value of the DC bus voltage, and U is the actual value of the DC bus voltage. When the set value of the DC bus voltage is greater than the actual value of the DC bus voltage, the output power increases; otherwise, it needs to decrease. When the incremental cost iterations of the intelligent units reach a consensus, the actual value of the DC bus voltage reaches the rated set value.

Step 4: Perform algorithm iteration at the next moment, process the obtained information, and finally output the optimal power.

When the consistency algorithm is used, it is possible that the output power of a certain power generation unit exceeds its limit value. In this case, the power limit of the output power should be modified. Considering the output power limit range, the output power constraint of the controllable energy storage unit can be modified as follows:

和

分别为储能单元i的输出功率最小值和最大值。Where λ ^* is the optimal incremental cost,

and

are the minimum and maximum output power of energy storage unit i respectively.

When the consistency algorithm iteration is completed, it is determined whether the output power exceeds the limit, and then the corresponding active power is output according to the power constraint.

In order to prove the effectiveness of the coordinated control strategy method of the present invention, this embodiment builds a structural simulation model of a DC microgrid, as shown in Figure 1. The microgrid model operates in an island state, and the DC bus voltage rating is 380V; the simulation system includes two groups of photovoltaic power generation units, with maximum powers of 100kW and 150kW respectively; three groups of energy storage units, with a capacity of 30kWh, and the initial SOC of each unit is 70%, 65%, and 60% respectively. The range of SOC selected in this article is 20% to 90%; the initial load of the system includes a 200kW DC load.

The communication topology structure adopted in this embodiment is shown in FIG3 , and the corresponding adjacency matrix is:

The cost parameters and control parameters of each energy storage unit are shown in Table 1 and Table 2.

Table 1 Cost parameters

Table 2 Control parameters

In order to verify the stabilizing effect of the consistency algorithm of the present invention on the DC bus voltage, for the operation of the DC microgrid, the DC load 1 of 80kW is suddenly reduced at 20s, and the AC load 2 of 100kW is increased at 35s. The obtained results are compared with the traditional droop control method.

Figure 4 shows the optimal cost increment rate obtained by the consistency algorithm used in the model of the present invention. The cost increment rates λ of all controllable energy storage units in the system converge to the same value within 5s, and the optimal cost increment rate λ ^* = 12.68 yuan/kW.

Figure 5 shows the optimal output power obtained by the consistency algorithm used in the model of the present invention. Through the analysis of Figure 5, it is found that the output power of each energy storage converges to the optimal output power within 11 seconds. The optimal output power of energy storage 1, energy storage 2, and energy storage 3 are P _B.1 = 18.11kW, P _B.2 = 17.61kW, and P _B.3 = 30.7kW, respectively, which completes the optimal energy management of the microgrid.

Figure 6 shows the control of the DC bus voltage change by the consistency algorithm used in the model of the present invention. When the microgrid is running stably, the DC load is reduced by 80kW at 20s, and the AC load is increased by 100kW at 35s. The DC bus voltage waveform is shown in Figure 7. It can be seen that within 5s after the load fluctuates, the improved consistency algorithm can respond and stabilize the fluctuating DC bus voltage to the rated value again.

Figure 7 is a comparison between the control of DC bus voltage change by the consistency algorithm used in the model of the present invention and the traditional droop control method. By analyzing Figure 7, it can be seen that the method proposed in the present invention has a more significant effect on stabilizing the DC bus voltage.

Table 3 shows the DC microgrid operating costs obtained by the consistency algorithm used in the model of the present invention and the traditional droop control method. The operating cost of the DC microgrid system obtained by the distributed consistency algorithm designed by the present invention is 13% lower than the operating cost of the droop control method.

It can be seen that the multi-agent consensus algorithm proposed in the model of the present invention effectively reduces the operating cost of the DC microgrid, realizes the reasonable allocation of the output power of each unit on the basis of the lowest operating cost, and can quickly realize multi-objective control of DC bus voltage stability and grid power balance.

Table 3 Operating costs under different control methods

The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or substitutions within the technical scope disclosed by the present invention, and these modifications or substitutions should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims (8)

1. A distributed coordinated control method for a DC microgrid, comprising the following steps: Step 1: Based on the distributed power sources in the DC microgrid system, combined with the supply and demand balance conditions and capacity constraints, establish the total power generation cost function model of the system; Step 2: Each power generation unit and its intelligent controller obtain local information and initialize; Step 3: Use the improved multi-agent consensus algorithm to solve the power generation cost function, obtain the cost increment rate and output power of each unit, and stabilize the DC bus voltage; Step 4: Perform algorithm iteration at the next moment, process the obtained information, and finally output the optimal power; Step 5: Use the optimal power to perform distributed coordinated dispatching control on the DC microgrid; The algorithm improvement of the multi-agent consensus algorithm in step 3 includes defining auxiliary variables and further defining the output power of the energy storage unit and introducing a voltage stability function and further setting a modified consensus protocol; The modified consistency protocol is described as follows:

Where L represents the Laplace matrix,

λ(t) is the controllable operating unit cost at time t,

is the derivative of the controllable operation unit cost at time t, ε is the DC bus voltage error factor, U _dc is the set value of the DC bus voltage, and U is the actual value of the DC bus voltage. 2. A distributed coordinated control method for a DC microgrid according to claim 1, characterized in that the total power generation cost function model in step 1 is described by the formula:

Where P _Bi is the output power of energy storage unit i, _SB is the set of controllable energy storage units, a _i , b _i , c _i are the corresponding function coefficients, i is a natural number, and C _i represents the operating cost of power generation unit i. 3. A distributed coordinated control method for a DC microgrid according to claim 1, characterized in that the supply and demand balance condition in step 1 is described by the formula:

Where _PG is the output power of all distributed generation units, _SL is the set of all load units, and _PDi is the local active power demand of energy storage unit i. 4. A distributed coordinated control method for a DC microgrid according to claim 1, characterized in that the capacity limitation in step 1 is described by the formula:

In the formula,

P _Bi (k),

They correspond to the minimum, actual and maximum output power of energy storage unit i at time k respectively.

SOC _Bi (k),

They correspond to the minimum, actual and maximum remaining power of energy storage unit i at time k respectively. 5. The distributed coordinated control method of a DC microgrid according to claim 1, wherein the initialization process in step 2 comprises the following sub-steps: Step 201: Obtain the local load information and active power measured by the energy storage unit i agent, and obtain the cost increment rate of each unit; Step 202: Form a Laplacian matrix and an adjacency matrix according to the topological graph. 6. The distributed coordinated control method of a DC microgrid according to claim 1, characterized in that the calculation formula of the output power of the energy storage unit is:

In the formula, _aij represents the communication topology weight between agents i and j, _Ni represents the set of neighboring units of energy storage unit i,

and

They represent the auxiliary variables of agents i and j at time t respectively, _dic is the weight coefficient between energy storage unit i and its local required active load, and _Pdc represents the local active demand corresponding to the weight coefficient. 7. A distributed coordinated control method for a DC microgrid according to claim 1, characterized in that the processing of the obtained information in step 4 comprises the following sub-steps: Step 401: when the output power of a certain power generation unit exceeds its limit value after the consistency algorithm is adopted, modify the power limit of the output power and set the output power constraint of the controllable energy storage unit; Step 402: After the consistency algorithm iteration is completed, it is determined whether the output power exceeds the limit and the corresponding active power is output according to the output power constraint of the controllable energy storage unit. 8. A distributed coordinated control method for a DC microgrid according to claim 7, characterized in that the output power constraint of the controllable energy storage unit in step 401 is specifically described by the formula:

Where λ ^* is the optimal incremental cost,

and

are the minimum and maximum output power of energy storage unit i respectively.

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