Distributed power transaction method under energy shortage environment

Abstract

The application provides a distributed power transaction method in an energy shortage environment, which comprises a new power dispatch framework combining a main power grid and distributed power generation, and under the framework, a discrete time algorithm combining estimation technology and mining method is designed. The optimization algorithm and the power dispatch framework provided by the application solve the power shortage problem in a distributed manner based on the aggregation game theory and the Cournot price model, and can increase the power supply of the power grid in the case of power shortage. In addition, the distributed algorithm provided in the application can not only provide privacy protection and information security of the algorithm, but also improve the scalability of the power grid. Through simulation result examples, it is shown that the algorithm has good performance and effectiveness in the numerical examples of the proposed power dispatch framework.

Description

Technical Field

[0001] This invention relates to the field of electricity trading technology, and in particular to a distributed electricity trading method under energy shortage conditions. Background Technology

[0002] With the rapid development of power grids, the actual situations they face are becoming increasingly complex. Furthermore, the rapid growth in power grid scale renders traditional centralized algorithms inadequate for practical power dispatching processes. Against this backdrop, designing an algorithm in a distributed manner has become a necessary choice to meet practical needs.

[0003] Generally, the main states of power grid operation can be divided into three categories: normal operation, power shortage, and power outage. Distributed algorithms have different applications under different power grid operating states. Under the normal operation state of a large-scale power grid, the primary objective is distributed economic dispatch. Significant progress has been made in this area. Early research used distributed algorithms to solve traditional economic dispatch problems constrained only by global supply and demand. Later, more constraints were considered within this given framework, including ramp rate constraints, transmission line limitations, and power loss constraints. Since game theory is the preferred method for describing the impact of human factors in power dispatch models, recent research has begun to consider game theory, which expands the scope of model description.

[0004] In the event of a power shortage, the primary task of the power grid is to immediately increase power supply; otherwise, the best option is to reduce load. However, in reality, increasing power supply is often not an easy task when the main power source cannot provide sufficient power. Due to the rapid emergence of distributed generators and energy sources, a natural idea has arisen: to encourage distributed generators to increase their output power during power shortages. Considering that distributed generators may belong to different companies or individuals, how to aggregate power through appropriate strategies becomes a challenging problem. Price incentives, as the most effective and widespread method, not only conform to market principles but also meet practical needs. The underlying logic is clear: increasing energy supply is beneficial when there is a power shortage. Several challenges exist in implementing this strategy, primarily stemming from the distributed environment of distributed generators, price setting, and algorithmic environment.

[0005] First, the sheer number of distributed generators makes it impossible for centralized algorithms to simultaneously schedule all distributed power sources to compensate for power shortages. Second, designing a suitable strategy that ensures the interests of all generators while maintaining the power operator's initiative (i.e., pricing power) is the main challenge in trading strategy design. Third, due to the distributed nature of distributed generators, there is no ideal basis for constructing algorithms using continuous-time algorithms; therefore, discrete-time algorithms become the preferred choice. Summary of the Invention

[0006] The purpose of this invention is to provide a distributed power trading method for energy shortage environments. This method includes a novel power dispatch framework combining the main power grid and distributed generation, and a discrete-time algorithm that integrates estimation techniques and mining methods. The power dispatch framework combines the main power grid and distributed generation. Based on the novel power dispatch framework proposed in this invention, the algorithm employs a distributed optimization method to solve power shortage problems in a distributed manner, thereby increasing power output even under power shortage conditions. Furthermore, the distributed algorithm proposed in this invention not only provides privacy protection and information security but also improves the scalability of the power grid.

[0007] This invention first describes the power shortage situation as a two-part aggregation game, with the main power grid as one party and distributed generators as the other. The pricing strategy is designed based on the Cournot price model, ensuring the interests of all generators from an economic and market perspective. Then, this invention designs a discrete-time algorithm combining a distributed estimation strategy and a mining method to solve the proposed problem in a distributed manner.

[0008] To achieve the above objectives, the technical solution of the present invention is as follows: a distributed electricity trading method under energy shortage conditions, characterized by comprising the following steps:

[0009] Step 1: Construct a power dispatch game theory model that combines the main power grid and distributed generators;

[0010] Step 2: Transform the proposed game theory problem using distributed optimization techniques;

[0011] Step 3: Design a discrete-time distributed algorithm that integrates estimation techniques and mining methods, obtain the distributed iterative formula, and solve iteratively.

[0012] The power dispatch model in step 1 is specifically referred to as model (1).

[0013]

[0014] Among them, P i and G i Let $\mathbf{i}$ represent the power output of distributed generator $i$ and the power obtained by the operator from node $i$ of the main grid, respectively, which meet the output constraints of the second and third constraints in model (1). The superscripts $min$ and $max$ represent the minimum and maximum values ​​of the corresponding variables. i This represents the load demand of operator node i and Represents the operator index set.

[0015] The first constraint in model (1) is the power balance limit, and the cost function of each operator i is defined as follows:

[0016]

[0017] Where a i ,b i ,c i ,α i ,β i and γ i It is a positive parameter, C i (P i G i ) represents the electricity cost of operator i.

[0018] The added value for operator i is described as follows:

[0019]

[0020] Both r and l are positive parameters adjusted by the main power grid.

[0021] The conversion process in step 2) is as follows:

[0022] In step 1), the power dispatch model (1) is a game theory problem, which is transformed into an unconstrained optimization problem to fit the framework of the distributed algorithm. First, the local objective function F of model (1) is defined. i (P i G i ), pseudo gradient The Lagrange dual function and its locally feasible set. Secondly, under specific assumptions, the original game problem is transformed into an unconstrained optimization problem. The specific process is as follows:

[0023] ① Provide the definitions required for model transformation:

[0024] The local objective function F of model (1) i (P i G i ) is defined as:

[0025] F i (P i G i ) = C i (P i G i )-R i (P1,…,P N (4)

[0026] P i and G i is the decision variable for operator i, and the decision variables for other operators are independent variables.

[0027] pseudo gradient definition:

[0028]

[0029] Where S = [P1, G1, ..., P N G N ] T .

[0030] The Lagrange dual function is defined as:

[0031]

[0032] Where S i =[P i G i ] T ,λ i Let d be the Lagrange multiplier of operator i. i (λ i ) indicates that it is only related to λ i The relevant function, Ω i The locally feasible set is defined as follows:

[0033]

[0034] ② Assumptions required for model transformation:

[0035] pseudo gradient Composed of the gradients of the local objective function in model (1), it satisfies the following monotonicity condition.

[0036]

[0037] Where σ>0 is a strongly monotonic parameter, and Ω represents the feasible set subject to all constraints in the power dispatch model (1).

[0038] ③ Transform the original game theory problem into an unconstrained optimization problem:

[0039] Based on Lagrange duality theory, the power dispatch model (1) proposed in step 1 has the same optimal solution as formula (9):

[0040]

[0041] If for all All Lagrange multipliers λ i If the values ​​are the same, then formula (9) is transformed into formula (10):

[0042]

[0043] Where λ=[λ1,…,λ N ] T The conjugate function (objective function) F i* (λ i The following is represented:

[0044]

[0045] Based on duality theory, equation (11) is transformed into an unconstrained optimization problem:

[0046]

[0047] Where θ represents the Lagrange multiplier with respect to the consistency constraint in formula (11). I and W represent the identity matrix and the non-negative birandom matrix, respectively.

[0048] The original game model (1) is then transformed into an unconstrained optimization formula (12).

[0049] The distributed algorithm in step 3) is specifically as follows:

[0050]

[0051]

[0052]

[0053]

[0054] Where δ>0 represents the step size of the designed algorithm. The initial value satisfies y i (0)=-S i (0), where argmin represents the variable that makes the corresponding function reach its minimum value. W ij (k) represents the element in the i-th row and j-th column of the time-varying random matrix W(k). Function Defined as:

[0055]

[0056] Compared with existing technologies, the advantages of this invention are as follows: 1) The discrete-time algorithm structure provides favorable characteristics for the power grid dispatching process in terms of privacy protection, information security and scalability, and the use of the Cournot price model brings distributed generator sets together in a feasible, market-determined framework; 2) The distributed power trading method proposed in this invention under energy shortage environment can solve the dilemma of energy shortage faced by the power system.

[0057] This invention proposes a novel power dispatch framework combining the main power grid and distributed generation. Based on this framework, a discrete-time algorithm incorporating distributed estimation strategies and mining methods is designed to efficiently and cost-effectively solve the problem of distributed power trading under energy shortage environments. Its objective function optimization and fast convergence characteristics bring economic and time benefits to the power system. The distributed power trading method proposed in this invention achieves rapid modeling, ease of computation, rapid iteration, and fast convergence, saving computational resources in the power system, reducing the computational burden on the smart grid, minimizing the cost of power trading, and bringing considerable economic benefits to the power system. Attached Figure Description

[0058] Figure 1 The optimal power output diagram for the main power grid;

[0059] Figure 2 The optimal power output diagram for distributed generators;

[0060] Figure 3 Incremental cost diagram for each operator;

[0061] Figure 4 A graph showing the estimated values ​​and estimation errors;

[0062] Figure 5 The partial derivatives and power mismatch plot of the Lagrange;

[0063] Figure 6 This is a schematic diagram of a distributed electricity trading method under an energy shortage environment. Detailed Implementation

[0064] This invention mainly considers a distributed power trading method under energy shortage conditions. This strategy proposes a new power dispatch framework that combines the main power grid and distributed generation. Based on this framework, a discrete-time algorithm combining distributed estimation strategy and mining method is designed to solve the problem of distributed power trading under energy shortage conditions.

[0065] The purpose of this invention is to provide a discrete-time algorithm that integrates estimation techniques and mining methods. This algorithm is based on a novel power dispatch framework combining the main power grid and distributed generation proposed in this invention. It employs a distributed optimization method to address power shortages in a distributed manner, increasing power output even during periods of scarcity. Furthermore, the distributed algorithm proposed in this invention not only provides privacy protection and information security but also improves the scalability of the power grid. Therefore, this invention not only makes power grids with a large number of distributed generators more flexible and solves the energy shortage problem in the power system but also protects the information security and privacy of the power system.

[0066] Example 1: To achieve the above objective, the technical solution of the present invention is as follows: A distributed electricity trading method under energy shortage conditions, the strategy including the following steps:

[0067] Step 1: Construct a power dispatch game theory model that combines the main power grid and distributed generators;

[0068] Step 2: Transform the proposed game theory problem using distributed optimization techniques;

[0069] Step 3: Design a discrete-time distributed algorithm that integrates estimation techniques and mining methods, obtain the distributed iterative formula, and solve iteratively.

[0070] The power dispatch model in step 1 is specifically as follows:

[0071]

[0072] Among them, P i and G i Let $\mathbf{i}$ represent the power output of distributed generator $i$ and the power obtained by the operator from node $i$ of the main grid, respectively, which meet the output constraints of the second and third constraints in model (1). The superscripts $min$ and $max$ represent the minimum and maximum values ​​of the corresponding variables. i This represents the load demand of operator node i and Represents the operator index set.

[0073] The first constraint in model (1) is the power balance limit, and the cost function of each operator i is defined as follows:

[0074]

[0075] Where a i ,b i ,c i ,α i ,β i and γ i It is a positive parameter, C i (P i G i ) represents the electricity cost of operator i.

[0076] The added value for operator i is described as follows:

[0077]

[0078] Both r and l are positive parameters adjusted by the main power grid.

[0079] The conversion process in step 2) is as follows:

[0080] In step 1), the power dispatch model (1) is a game theory problem, which is transformed into an unconstrained optimization problem to fit the framework of the distributed algorithm. First, the local objective function F of model (1) is defined. i (P i G i ), pseudo gradient The Lagrange dual function and its locally feasible set. Secondly, under specific assumptions, the original game problem is transformed into an unconstrained optimization problem. The specific process is as follows:

[0081] ① Provide the definitions required for model transformation:

[0082] The local objective function F of model (1) i (P i G i ) is defined as:

[0083] F i (P i G i ) = C i (P i G i )-R i (P1,…,P N (4)

[0084] P i and G i is the decision variable for operator i, and the decision variables for other operators are independent variables.

[0085] pseudo gradient definition:

[0086]

[0087] Where S = [P1, G1, ..., P N G N ] T .

[0088] The Lagrange dual function is defined as:

[0089]

[0090] Where S i =[P i G i ] T ,λ i Let d be the Lagrange multiplier of operator i. i (λ i ) indicates that it is only related to λ i The relevant function, Ω i The locally feasible set is defined as follows:

[0091]

[0092] ② Assumptions required for model transformation:

[0093] pseudo gradient Composed of the gradients of the local objective function in model (1), it satisfies the following monotonicity condition.

[0094]

[0095] Where σ>0 is a strongly monotonic parameter, and Ω represents the feasible set subject to all constraints in the power dispatch model (1).

[0096] ③ Transform the original game theory problem into an unconstrained optimization problem:

[0097] Based on Lagrange duality theory, the power dispatch model (1) proposed in step 1 has the same optimal solution as formula (9):

[0098]

[0099] If for all All Lagrange multipliers λ i If the values ​​are the same, then formula (9) is transformed into formula (10):

[0100]

[0101] Where λ=[λ1,…,λ N ] T The conjugate function (objective function) F i * (λ i The following is represented:

[0102]

[0103] Based on duality theory, equation (11) is transformed into an unconstrained optimization problem:

[0104]

[0105] Where θ represents the Lagrange multiplier with respect to the consistency constraint in problem (11). I and W represent the identity matrix and the non-negative birandom matrix, respectively.

[0106] The original game model (1) is then transformed into an unconstrained optimization formula (12).

[0107] The distributed algorithm in step 3) is specifically as follows:

[0108]

[0109]

[0110]

[0111]

[0112] Where δ>0 represents the step size of the designed algorithm. The initial value satisfies y i (0)=-S i (0), where argmin represents the variable that makes the corresponding function reach its minimum value. W ij (k) represents the element in the i-th row and j-th column of the time-varying random matrix W(k). Function Defined as:

[0113]

[0114] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific numerical simulation implementations.

[0115] This invention was simulated based on the IEEE 14 bus to verify the performance of the proposed algorithm. Table 1 shows the main grid generator cost coefficients under the proposed scenario.

[0116]

[0117] Table 1

[0118] The coefficients, including cost factors, constraints, and initial power, are shown in Table 2. The load demand for each operator is selected as D = [205, 60, 30, 30, 28, 35]. T And the parameters r and l are chosen to be 0.00001 and 0.02 respectively.

[0119]

[0120]

[0121] Table 2

[0122] Simulation results Figure 1 As shown. Figure 1 It shows the evolution trajectory of the optimal power output of the main power grid.

[0123] from Figure 1 It can be seen that the power output converges to the optimal solution after 100 iterations, which means that the designed algorithm has a fast convergence speed in the time-varying communication graph environment.

[0124] Figure 2 The evolution trajectory of the optimal power output of the distributed generator is shown. Similarly, the evolution trajectory converges to the optimal solution after approximately 100 iterations.

[0125] Incremental costs for each operator, such as Figure 3 As shown, the consistency of incremental costs implies that each operator minimizes its costs within a game-theoretic framework.

[0126] exist Figure 4 In (a), the estimate of the sum of all power outputs. The communication graph converges to a consistent value when it is time-varying. Figure 4 (b) It was confirmed that the estimated values ​​obtained from each operator were consistent with the actual values, which reflects the good performance of the proposed algorithm in aggregating the corresponding decisions.

[0127] In this case, all optimal outputs P and G are within a given range, which means that if the convergence value of the proposed algorithm is the optimal solution, then the partial derivatives of the function... It must be equal to 0.

[0128] from Figure 5 In (a) and (b), the above optimal conditions were confirmed, and the convergence value of the proposed algorithm was verified to be the optimal solution.

[0129] exist Figure 5 In (c), it can be found that the mismatch of the entire power grid is equal to 0, which means that when the algorithm converges to the optimal solution, the algorithm can maintain the global power balance constraint.

[0130] It should be noted that the above embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Equivalent substitutions or alternatives made based on the above technical solutions shall all fall within the scope of protection of the present invention.

Claims

1. A distributed electricity trading method under energy shortage conditions, characterized in that, Includes the following steps: Step 1: Construct a power dispatch game theory model that combines the main power grid and distributed generators; Step 2: Transform the proposed game theory problem into an optimization problem using the concept of distributed optimization; Step 3: Design a discrete-time distributed algorithm, obtain the distributed iterative formula, and solve iteratively; The power dispatch game theory model in step 1) is specifically referred to as model (1). in, and Distributed generator Power output and operators from the main grid The power obtained by the nodes respectively conforms to the output constraints of the second and third constraints in model (1), with superscripts indicating that the power obtained by the nodes conforms to the output constraints of the second and third constraints in model (1). and This represents the minimum and maximum values ​​of the corresponding variable. Indicates operator node load demand and Represents the operator index set; The first constraint in model (1) is the power balance limit, and each operator The cost function is defined as follows: in and It is a positive parameter. Indicates the operator Electricity costs; For operators The added value is described as follows: in and These are all positive parameters adjusted by the main power grid; The conversion process in step 2) is as follows: In step 1), the power dispatch game theory model (1) is a game theory problem. To adapt it to the framework of the distributed algorithm, we first define the local objective function of model (1). pseudo gradient First, the Lagrange dual function and its local feasible set are identified. Second, under specific assumptions, the original game problem is transformed into an unconstrained optimization problem. The specific process is as follows: ① Provide the definitions required for model transformation: Local objective function of model (1) Defined as: pseudo gradient : in . The Lagrange dual function is defined as: in Operator Lagrange multipliers, Indicates only with The relevant functions, The locally feasible set is defined as follows: ② Assumptions required for model transformation: pseudo gradient (S) consists of the gradients of the local objective function in model (1), and satisfies the following monotonicity condition. in It is a strongly monotonic parameter, and Ω represents the feasible set subject to all constraints in the power dispatch model (1); ③ Transform the original game theory problem into an unconstrained optimization problem: Based on Lagrange duality theory, the power dispatch model (1) proposed in step 1 has the same optimal solution as formula (9): If for all All Lagrange multipliers If the values ​​are the same, then formula (9) is transformed into formula (10): in The conjugate function is the objective function. It is expressed as follows: Based on duality theory, equation (11) is transformed into an unconstrained optimization problem: in Let the Lagrange multipliers be the ones that represent the consistency constraints in formula (11). and Let them represent the identity matrix and the non-negative double random matrix, respectively. The power dispatch game theory model (1) is then transformed into an unconstrained optimization formula (12). The distributed algorithm in step 3) is specifically as follows: in This represents the step size of the designed algorithm, and the initial value satisfies... , This represents a variable that allows the corresponding function to reach its minimum value. Represents a time-varying random matrix The The first row and the second row Column elements, functions Defined as: 。

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