A Microgrid Economic Dispatch Method Based on the Broadcast Gossip Algorithm

Abstract

The present invention relates to the technical field of power dispatching, and particularly to a microgrid economic dispatching method based on a broadcast gossip algorithm, aiming to solve the problems in the construction of existing smart grids that cannot coordinate various parts and optimize the allocation of power resources. Through a charging and discharging price sensing mechanism, a multi-agent microgrid cost model based on an electric vehicle aggregator is designed, a peak shaving and valley filling cost model caused by the peak-valley difference of the microgrid electricity price is established, and the peak and valley of the microgrid electricity consumption are adjusted; in addition, considering the carbon emission problem generated by thermal power generation, a carbon emission governance cost model is established, so that while the microgrid achieves optimal economic dispatching, the purpose of reducing emissions is also achieved. In addition, an event-triggered mechanism is introduced, so that the node unit does not need to frequently change the output power, thereby saving communication resources and solving the problems in the construction of existing smart grids that cannot coordinate various parts and optimize the allocation of power resources.

Description

Technical Field

[0001] The present invention relates to the technical field of power dispatching, and in particular to a microgrid economic dispatching method based on a broadcast gossip algorithm. Background Art

[0002] The advantages of high efficiency and environmental protection of distributed integrated energy systems are further highlighted. Under this background, renewable energy sources such as wind energy and solar energy, electric vehicles connected to the microgrid through V2G technology, user loads, and thermal power generation, which is representative of traditional power generation, make the challenges faced by power grid dispatching extremely complex. The most essential feature of a smart grid is the two-way flow of electricity and information, and thereby establish a highly automated and widely distributed energy exchange network; introduce the advantages of distributed computing and communication into the power grid to achieve real-time information exchange and near-instantaneous supply-demand balance at the device level. A well-designed smart grid can efficiently and inclusively coordinate various parts of the smart grid, optimize the allocation of power resources, and minimize costs while being clean and low-carbon.

[0003] Therefore, how to coordinate various parts of the smart grid and optimize the allocation of power resources to minimize the total social cost of the smart grid is the key issue to be solved in the current construction of the smart grid. Summary of the Invention

[0004] To solve the problem that various parts cannot be coordinated and the allocation of power resources cannot be optimized in the existing smart grid construction, the present application provides a microgrid economic dispatching method based on a broadcast gossip algorithm.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A microgrid economic dispatching method based on a broadcast gossip algorithm, characterized by comprising the following steps:

[0007] Step 1: A model construction module establishes cost models for electric vehicles, user loads, wind energy, solar energy, and thermal power generation, which is representative of traditional power generation, under the background of V2G.

[0008] Step 2: Establish a total social cost function including the energy and loads of various parts of the microgrid.

[0009] Step 3: Decouple the total social cost function using the Lagrange multiplier method and the KKT conditions.

[0010] Step 4: According to the theory of distributed consensus algorithms, select the marginal cost as the consensus variable and use the local power mismatch as the feedback value.

[0011] Step 5: Define the trigger function for each agent and determine whether the state measurement error reaches the threshold.

[0012] Step 6: Design the update iteration relationship of the consensus variables of the agents in the microgrid according to the broadcast gossip algorithm, so that the comprehensive social cost function reaches the minimum value.

[0013] In the above preferred technical solution of the microgrid economic dispatch method based on the broadcast gossip algorithm, in step 1, a peak shaving and valley filling cost model caused by the peak-valley difference of the microgrid electricity price is further established.

[0014] In the above preferred technical solution of the microgrid economic dispatch method based on the broadcast gossip algorithm, in step 1, an environmental protection cost model brought about by the consumption of fuel during power generation is established.

[0015] In the above preferred technical solution of the microgrid economic dispatch method based on the broadcast gossip algorithm, in step 2, the total social cost is the sum of the costs of photovoltaic and wind power generation, thermal power generation, user load, and electric vehicles.

[0016] In the above preferred technical solution of the microgrid economic dispatch method based on the broadcast gossip algorithm, in step 3, the Lagrange multiplier method function is:

[0017]

[0018] where λ, γ i , are the corresponding Lagrange coefficients, C g , C d , C t , C PEV are the costs of photovoltaic and wind power generation, thermal power generation, user load, and electric vehicles respectively, p d , p m , p g , p t are the output powers of photovoltaic and wind power generation, thermal power generation, user load, and electric vehicles respectively; P i,min , P i,max are the lower and upper limits of the output power of the i-th node respectively.

[0019] In the above preferred technical solution of the microgrid economic dispatch method based on the broadcast gossip algorithm, it is characterized in that in step 4, the consensus variable is:

[0020]

[0021] where C g , C d , C t , C PEV are the costs of photovoltaic and wind power generation, thermal power generation, user load, and electric vehicles respectively.

[0022] In the preferred technical solution of the above microgrid economic dispatch method based on the broadcast gossip algorithm, the update rules for the marginal cost and the power mismatch degree are as follows:

[0023] The marginal cost and its corresponding iteration formula are:

[0024] When node n is activated at the k-th iteration

[0025] λ n (k + 1) = λ n (k) + εy n (k)

[0026]

[0027] For its neighbor node j

[0028] λ j (k + 1) = (1 - α i,j )λ j (k) + α i,j λ i (k) + θγ ij ξ j (k) + εy j (k)

[0029]

[0030] For non - adjacent node l

[0031] λ l (k + 1) = λ l (k) + εy l (k)

[0032]

[0033] In the above formula, ξ i , i ∈ V respectively represent the auxiliary values of the consensus variable and the power mismatch degree, and α i,j , β i,j , γ i,j are all algorithm parameter values;

[0034] The iteration formula for the power mismatch degree is:

[0035] When node n is activated at the k - th iteration

[0036] y n (k + 1) = y n (k) - [P n (k + 1) - P n (k)]

[0037] ζ n(k+1)=0-[δ n (k+1)-δ n (k)]

[0038] Its neighbor node j

[0039] y j (k+1)=(1-α i,j )y j (k)+α i,j y i (k)+θγ ij ζ j (k)-[P j (k+1)-P j (k)]

[0040] ζ j (k+1)=ζ j (k)+β i,j ζ j (k)+y j (k)-y(k+1)-[δ j (k+1)-δ j (k)]

[0041] Non-adjacent nodes

[0042] y l (k+1)=y l (k)-[P l (k+1)-P l (k)]

[0043] ζ l (k+1)=ζ l (k)-[δ l (k+1)-δ l (k)]

[0044] In the preferred technical solution of the microgrid economic dispatch method based on the broadcast rumor algorithm, each agent i can Monitor your own communication information. It is called the trigger time sequence, and its iteration is defined as:

[0045]

[0046] in is the trigger function, and its expression is:

[0047]

[0048] In the preferred technical solution of the microgrid economic dispatch method based on the broadcast rumor algorithm, in step 5, the corresponding cost parameter α is input. i, β i , the marginal cost and the optimal output power of each unit can be obtained through algorithm iteration and update:

[0049]

[0050] Due to the adoption of the above technical solution, the technical progress achieved by the present invention is:

[0051] Regarding the energy scheduling problem of a distributed microgrid containing multiple energy sources, first, through a charge-discharge price sensing mechanism, a multi-agent microgrid cost model based on an electric vehicle aggregator is designed, a peak-shaving and valley-filling cost model caused by the peak-valley difference of the microgrid electricity price is established, and the peak-valley of the microgrid electricity consumption is adjusted; in addition, considering the carbon emission problem generated by thermal power generation, a carbon emission control cost model is established to achieve the purpose of reducing emissions while the microgrid reaches the optimal economic dispatch; finally, a broadcast gossip algorithm capable of solving the economic dispatch in the microgrid is proposed, and the power mismatch degree is used as the feedback gain to minimize the total social cost. In addition, an event-triggered mechanism is introduced, so that the node units do not need to frequently change the output power, thus saving communication resources and solving the problem that the existing smart grid construction cannot coordinate each part and optimize the power resource allocation. Description of the Drawings

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings;

[0053] Figure 1 It is the IEEE11 standard bus structure diagram of the microgrid;

[0054] Figure 2 It is the convergence diagram of the marginal cost of electric vehicle discharging;

[0055] Figure 3 It is the convergence diagram of the marginal cost of electric vehicle charging;

[0056] Figure 4 It is the power output diagram of each node of electric vehicle discharging;

[0057] Figure 5 It is the power output diagram of each node of electric vehicle charging;

[0058] Figure 6 It is the convergence diagram of the local power mismatch of electric vehicle discharging;

[0059] Figure 7Convergence diagram of partial power mismatch for electric vehicle charging;

[0060] Figure 8 Discharge trigger interval diagram for electric vehicles;

[0061] Figure 9 Charging trigger interval diagram for electric vehicle charging stations. Specific implementation manner

[0062] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with specific implementation manners and with reference to the accompanying drawings. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. In addition, in the following descriptions, the descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present invention.

[0063] Based on the technical problems proposed in the background art, the present application provides a microgrid economic dispatch method based on the broadcast gossip algorithm. Through a charging and discharging price sensing mechanism, a multi-agent microgrid cost model based on an electric vehicle aggregator is designed, a peak shaving and valley filling cost model caused by the peak-valley difference of the microgrid electricity price is established, and the peak-valley of the microgrid electricity consumption is adjusted. In addition, considering the carbon emission problem caused by thermal power generation, a carbon emission control cost model is established, so that the microgrid achieves the optimal economic dispatch while achieving the purpose of reducing emissions. In addition, an event trigger mechanism is introduced, so that the node unit does not need to frequently change the output power, thereby saving communication resources and solving the problem that various parts cannot be coordinated in the construction of the existing smart grid and optimizing the allocation of power resources.

[0064] Combined with the accompanying drawings of the specification, a microgrid economic dispatch method provided by the present application is introduced, where Figure 1 Microgrid IEEE11 standard bus structure diagram; Figure 2 Convergence diagram of the marginal cost of electric vehicle discharging; Figure 3 Convergence diagram of the marginal cost of electric vehicle charging; Figure 4 Power output diagram of each node of electric vehicle discharging; Figure 5 Power output diagram of each node of electric vehicle charging; Figure 6 Convergence diagram of partial power mismatch for electric vehicle discharging; Figure 7 Convergence diagram of partial power mismatch for electric vehicle charging; Figure 8 Discharge trigger interval diagram for electric vehicles; Figure 9 Charging trigger interval diagram for electric vehicle charging stations.

[0065] A microgrid economic dispatch method based on the broadcast gossip algorithm includes the following steps:

[0066] Step 1: The model construction module establishes the cost models of five parts: electric vehicles, user loads, wind energy, solar energy, and the representative thermal power generation of traditional power generation under the background of V2G.

[0067] Step 2: Establish the total social cost function including the energy and loads of each part of the microgrid.

[0068] Step 3: Decouple the total social cost function using the Lagrange multiplier method and KKT conditions.

[0069] Step 4: According to the theory of distributed consensus algorithm, select the marginal cost as the consensus variable and use the local power mismatch degree as the feedback value.

[0070] Step 5: Define the trigger function of each agent to determine whether the state measurement error reaches the threshold.

[0071] Step 6: If it reaches, update the state value of the agent according to the broadcast gossip algorithm, and iterate until each agent reaches consensus, so that the cost function of the comprehensive social cost obtains the minimum value.

[0072] Furthermore, in Step 1, the cost functions of each part of the microgrid unit are all expressed in the form of quadratic functions. Among them, electric vehicles adopt the form of aggregators and access the power grid through V2G technology; in the process of thermal power generation, the consumption of fuel is related to the power generation power, and the total cost generated is the sum of the operating cost and the environmental protection cost.

[0073] The cost of electric vehicles mainly consists of two parts: the battery cost and the cost of peak shaving and valley filling.

[0074] Battery cost: Define the charging and discharging cost C of the electric vehicle aggregator m is proportional to the charging and discharging power P m and inversely proportional to the cyclic charging and discharging times of the electric vehicle, that is:

[0075]

[0076] l m (dod m ) = 649·(dod m ) -0.795

[0077]

[0078] Cost of peak shaving and valley filling: In order to enable the electric vehicle aggregator to achieve the peak shaving and valley filling peak regulation benefit, increase the charging or discharging demand of the aggregator at the peak or valley of the power grid and give them corresponding compensation.

[0079] The cost function of the charging incentive for electric vehicles at the peak is:

[0080] F I = T Im (η 1 P m 2 + η 2 |P m | + η 2 |P m |μ Im )

[0081] In the formula, T Im represents the state of the m-th electric vehicle. When T Im = 0, it means the m-th electric vehicle does not participate in the peak shaving and valley filling scheduling. When T Im = 1, it means the m-th electric vehicle participates in the peak shaving and valley filling scheduling; η 1 , η 2 are incentive coefficients, and μ Im is the willingness factor for the m-th electric vehicle to charge.

[0082] The cost function for discharging compensation of electric vehicles at valley value is:

[0083] F c = T cm (ρ 1 P m 2 + ρ 2 P m + ρ 2 P m μ cm )

[0084] In the formula, T cm represents the state of the m-th electric vehicle. When T cm = 0, it means the m-th electric vehicle does not participate in the peak shaving and valley filling scheduling. When T cm = 1, it means the m-th electric vehicle participates in the peak shaving and valley filling scheduling; ρ 1 , ρ 2 are compensation coefficients, and μ cm is the willingness factor for the m-th electric vehicle to discharge.

[0085] For thermal power generation units, they not only need to consider the operating cost C to (P t ), but also need to consider the environmental protection cost brought by CO 2 generated during the power generation process. Its operating cost model can be expressed as:

[0086] C to (P t ) = α t P t 2+β t P t +γ t

[0087] where α t , β t , γ t are respectively the operating cost parameters, and the CO 2 (P t ) generated during the thermal power generation process is related to the power generation

[0088]

[0089] In the formula are respectively the carbon emission parameters, is the emission of CO 2 , is the treatment cost per unit volume. Then, the total cost C t of thermal power generation is defined as the sum of the operating cost and the environmental protection cost, that is:

[0090]

[0091] Furthermore, in step 2, the total social cost function is:

[0092]

[0093] In the formula, C g , C d , C t , C PEV are respectively the costs of photovoltaic and wind power generation, thermal power generation, user load, and electric vehicle. The total social cost function is defined as the sum of the costs of these four parts: photovoltaic and wind power generation, thermal power generation, user load, and electric vehicle. When the power distribution at each node reaches the optimum, the total social cost reaches the minimum.

[0094] The power balance constraint is

[0095]

[0096] Furthermore, in step 3, the Lagrange multiplier method function is defined as:

[0097]

[0098] In the formula, λ, γ i , are respectively the corresponding Lagrange coefficients, p d , p m , p g , p t are respectively the output powers of photovoltaic and wind power generation, thermal power generation, user load, and electric vehicle. P i,min,P i,max They are the lower and upper limits of the output power of the \(i\)-th node respectively. According to the KKT conditions, we can get:

[0099]

[0100] Furthermore, in step 4, the consistency variable \(\lambda\) is defined as the marginal cost of each node, that is:

[0101]

[0102] When each node reaches consistency, the problem obtains the optimal solution.

[0103] Furthermore, in step 5, each agent \(i\) can monitor its own communication information at the trigger time , which is called the trigger time series, and its iteration is defined as:

[0104]

[0105] where is the trigger function, and its expression is:

[0106]

[0107] Furthermore, in step 6, the gossip algorithm utilizes the inherent broadcasting advantage of wireless sensors in the smart grid and auxiliary variables to enable all neighbors of a node to update information at each iteration. Define In the above formula, \(\xi\), \(\delta\) represent the consistency variable, the power mismatch degree, and the auxiliary value of the output power respectively. The update matrix \(W\) of the gossip algorithm i can be written as:

[0108]

[0109] where \(L = diag(A_1 n ) - A\), \(L i = diag(A i 1 n ) - A i ,

[0110] Define \(U(k)\) as the update matrix at the \(k\)-th iteration. They are completely independent and identically distributed, that is:

[0111]

[0112] Then the matrix form of the above consistency algorithm is:

[0113] ​

[0114] Input the corresponding cost parameter α i , β i . After iterative update by the algorithm, the marginal cost tends to the same value, and at this time the corresponding total social cost reaches the minimum.

[0115] The structure diagram of the IEEE 11 standard bus of the microgrid is as Figure 1 shown. Bus 1 and 2 are for wind power generation, bus 3 and 4 are for photovoltaic power generation, bus 6 is for thermal power generation, bus 11 is for the electric vehicle unit, and the rest are load units. According to the cost models of each node unit shown in Step 1, input the cost parameters of each node of the microgrid as shown in the following table to obtain the initial value of the marginal cost.

[0116]

[0117] Then, perform iterative update through the gossip algorithm. Select the marginal cost as the consensus variable and use the local power mismatch degree as the feedback value. Each iteration of the gossip algorithm randomly activates a node. The activated node maintains its previous state, while its auxiliary state for maintaining consensus is reset to zero. The neighbor nodes of the activated node and their auxiliary values start to update their states, and the non-adjacent nodes and their auxiliary values remain unchanged. Each node can monitor its communication information at the trigger time. From the perspective of network resource utilization, in order to reduce resource consumption, it is required that the controllers of each node update their information as little as possible. Only when the node iteration satisfies the event trigger function shown in Step 5 does the node update the marginal cost information. Finally, after multiple iterative updates, the marginal costs of all nodes converge to the same value, as Figure 2 and Figure 3 shown. At this time, the total social cost of the smart grid reaches the minimum, and the power output value is as Figure 4 and Figure 5 shown. The power output reaches stability, and at the same time the local power mismatch degree converges to 0, as Figure 6 and Figure 7 shown. The overall smart grid reaches charge-discharge balance, and the trigger intervals of each node are as Figure 8 and Figure 9 shown. The number of times each agent exchanges information is significantly reduced.

[0118] The embodiments described above are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A microgrid economic dispatch method based on the broadcast gossip algorithm, characterized in that, it includes the following steps: Step 1: The model construction module establishes cost models for electric vehicles, user loads, wind energy, solar energy, and traditional power generation (represented by thermal power) under the background of V2G; Step 2: Establish a social total cost function including the energy and loads of each part of the microgrid; Step 3: Decouple the social total cost function using the Lagrange multiplier method and the KKT conditions; In Step 3, the Lagrange multiplier method function is: where λ, γ i , θ i are the corresponding Lagrangian coefficients, C g , C d , C t , C PEV are the costs of photovoltaic and wind power generation, thermal power generation, user load, and electric vehicle respectively, p d , p m , p g , p t are the output powers of photovoltaic and wind power generation, thermal power generation, user load, and electric vehicle respectively; P i,min , P i,max are the lower and upper limits of the output power of the i-th node respectively; Step 4: According to the theory of the distributed consensus algorithm, select the marginal cost as the consensus variable and use the local power mismatch degree as the feedback value; In Step 4, define the consensus variable λ as the marginal cost of each node, that is: Step 5: Define the trigger function of each agent to judge whether the state measurement error reaches the threshold; Step 6: Design the update and iteration relationship of the consensus variable of the agents in the microgrid according to the broadcast gossip algorithm, so that the comprehensive social cost function obtains the minimum value; In step 6, input the corresponding cost parameters α i , β i , and the marginal cost and the optimal output power of each unit can be obtained through algorithm iteration and update:

2. A microgrid economic dispatch method based on the broadcast gossip algorithm according to claim 1, characterized in that, in Step 1, a peak shaving and valley filling cost model caused by the peak-valley difference of the microgrid electricity price is further established.

3. A microgrid economic dispatch method based on the broadcast gossip algorithm according to claim 1, characterized in that, in Step 1, an environmental protection cost model brought about by the consumption of fuel during power generation is further established.

4. A microgrid economic dispatch method based on the broadcast gossip algorithm according to claim 1, characterized in that, in Step 2, the social total cost function is the sum of the costs of photovoltaic and wind power generation, thermal power generation, user loads, and electric vehicles.

5. A microgrid economic dispatch method based on the broadcast gossip algorithm according to claim 1, characterized in that, the update rules of the marginal cost and the power mismatch degree are respectively: The corresponding iteration formula of the marginal cost is: When node n is activated at the k-th iteration λ n (k + 1) = λ n (k) + εy n (k) its neighbor node j λ j (k + 1) = (1 - α i,j )λ j (k) + α i,j λ i (k) + θγ ij ξ j (k) + εy j (k) non-adjacent node l λ l (k + 1) = λ l (k) + εy l (k) In the above formula, ξ i , i ∈ V respectively represent the auxiliary values of the consistency variable and the power mismatch degree, α i,j , β i,j , γ i,j are all algorithm parameter values; The corresponding iteration formula of the power mismatch degree is: When node n is activated at the k-th iteration y n (k + 1) = y n (k) - [P n (k + 1) - P n (k)] ζ n (k + 1) = 0 - [δ n (k + 1) - δ n (k)] its neighbor node j y j (k + 1) = (1 - α i,j )y j (k) + α i,j y i (k) + θγ ij ζ j (k) - [P j (k + 1) - P j (k)] ζ j (k + 1) = ζ j (k) + β i,j ζ j (k) + y j (k) - y(k + 1) - [δ j (k + 1) - δ j (k)] non-adjacent node l y l (k + 1) = y l (k) - [P l (k + 1) - P l (k)] ζ l (k + 1) = ζ l (k) - [δ l (k + 1) - δ l (k)].

6. A microgrid economic dispatch method based on the broadcast gossip algorithm according to claim 1, characterized in that, In step 5, each agent i can monitor its own communication information at the triggering moment which is called the triggering time series, and its iteration is defined as: which is called the triggering time series, and its iteration is defined as: Among them is a trigger function, and its expression is:

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