EV-V2G and fixed energy storage cooperative discharge scheduling method and device

Abstract

The invention provides an EV-V2G and fixed energy storage cooperative discharge scheduling method and device, relates to the technical field of power system scheduling, and solves the technical problem that it is difficult to realize global optimal scheduling of a power distribution network through EV-V2G and fixed energy storage cooperative discharge in a multi-source uncertainty scene in the prior art. The method specifically comprises the following steps: acquiring a scheduling power parameter; constructing a collaborative optimization problem based on the scheduling power parameters; the collaborative optimization problem is divided into an EV-V2G discharge sub-problem and a fixed energy storage discharge sub-problem; converting a non-convex problem of each round of iteration of the fixed energy storage discharge problem into a standard convex optimization problem based on an iterative conversion method of continuous convex approximation; and outputting the optimal EV-V2G discharge power and the optimal fixed energy storage discharge power by alternately solving the EV-V2G discharge sub-problem and the standard convex optimization problem. The method is used for EV-V2G and fixed energy storage discharge scheduling.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system dispatching, and in particular to an EV-V2G and fixed energy storage collaborative discharge scheduling method and device. BACKGROUND

[0002] It is of great significance to promote the safe, economic and efficient operation of new power systems in the scenarios of high penetration of new energy and large-scale access of electric vehicles. In the prior art, early distribution network dispatching focuses on fixed load forecasting and deterministic power flow analysis, and is gradually developing towards multi-source collaboration, data-driven and intelligent optimization. Domestic V2G demonstration stations are being built, distribution automation platforms are being developed, and multi-source fusion theory research is being carried out. However, the prior art still has deficiencies in EV-V2G and fixed energy storage collaborative discharge scheduling, and does not fully consider that the collaborative process of the two can effectively improve the new energy consumption capacity, the flexibility and resilience of the distribution network in dealing with power fluctuations, and save grid costs. At the same time, the coupling of multi-source uncertainty makes it difficult to achieve global optimal scheduling. Therefore, the prior art cannot achieve global optimal scheduling of the distribution network through the collaborative discharge of EV-V2G and fixed energy storage in a multi-source uncertainty scenario. SUMMARY

[0003] The present application provides an EV-V2G and fixed energy storage collaborative discharge scheduling method and device, which solves the technical problem that the prior art cannot achieve global optimal scheduling of the distribution network through the collaborative discharge of EV-V2G and fixed energy storage in a multi-source uncertainty scenario.

[0004] To achieve the above-mentioned purpose, the present application adopts the following technical solution: In a first aspect, an EV-V2G and fixed energy storage collaborative discharge scheduling method is provided, comprising: obtaining scheduling power parameters; the scheduling power parameters include power shortage, EV-V2G cluster maximum available discharge capacity, fixed energy storage initial remaining capacity, EV-V2G unit discharge compensation cost, fixed energy storage unit discharge operation and maintenance cost, fixed energy storage maximum discharge power, and fixed energy storage minimum allowed remaining capacity; constructing a collaborative optimization problem based on the scheduling power parameters; splitting the collaborative optimization problem into an EV-V2G discharge sub-problem and a fixed energy storage discharge sub-problem; based on a continuous convex approximation iterative transformation method, transforming the non-convex problem of each round of iteration of the fixed energy storage discharge sub-problem into a standard convex optimization problem; and outputting optimal EV-V2G discharge power and optimal fixed energy storage discharge power by alternately solving the EV-V2G discharge sub-problem and the standard convex optimization problem.

[0005] In combination with the first aspect described above, in a possible implementation manner, the collaborative optimization problem satisfies the following formula:

[0006] wherein, EV-V2G discharging power, fixed energy storage discharging power, EV-V2G unit discharging compensation cost, fixed energy storage unit discharging operation and maintenance cost, t is a scheduling time index, power shortage of the distribution network, maximum available discharging capacity of the EV-V2G cluster, maximum discharging power of the fixed energy storage, remaining capacity of the fixed energy storage, discharging efficiency of the fixed energy storage, zero-power reference efficiency coefficient, efficiency decay coefficient, initial remaining capacity of the fixed energy storage, minimum allowed remaining capacity of the fixed energy storage.

[0007] In combination with the first aspect, in a possible implementation manner, the EV-V2G discharging sub-problem satisfies the following formula:

[0008] wherein, the fixed energy storage discharging power is a fixed value.

[0009] In combination with the first aspect, in a possible implementation manner, the fixed energy storage discharging sub-problem satisfies the following formula:

[0010] wherein, the EV-V2G discharging power is a fixed value.

[0011] In combination with the first aspect, in a possible implementation manner, based on a continuous convex approximation iterative transformation method, a non-convex problem of each round of iteration of the fixed energy storage discharging sub-problem is transformed into a standard convex optimization problem, including: performing a global lower convex approximation on the non-convex constraint (C4) at by first-order Taylor expansion to obtain a linear energy balance constraint equation; substituting the linear energy balance constraint equation into the non-convex constraint (C4) to obtain a standard convex optimization problem.

[0012] In combination with the first aspect, in a possible implementation manner, by alternately solving the EV-V2G discharging sub-problem and the standard convex optimization problem, the optimal EV-V2G discharging power and the optimal fixed energy storage discharging power are output, including: initializing the iteration number k=0, and setting a convergence threshold; fixing , solving the EV-V2G discharging sub-problem to obtain the kth round of EV-V2G optimal discharging power ; fixing , the k+1th round of fixed energy optimal discharging power is obtained by solving the standard convex optimization problem The iteration cost difference is calculated, and if the iteration cost difference is less than the convergence threshold, the optimal EV-V2G discharging power and the optimal fixed energy discharging power are output is the optimal EV-V2G discharging power, is the optimal fixed energy discharging power; otherwise, k=k+1, and the alternating iteration is continued until the iteration cost difference is less than the convergence threshold.

[0013] In combination with the first aspect, in a possible implementation manner, the EV-V2G discharging sub-problem and the standard convex optimization problem are solved by using a Mosek solver.

[0014] In combination with the first aspect, in a possible implementation manner, the linear energy balance constraint equation satisfies the following equation:

[0015] wherein, , , .

[0016] In combination with the first aspect, in a possible implementation manner, after the optimal EV-V2G discharging power and the optimal fixed energy discharging power are output, the method further includes: recording the correspondence between the optimal EV-V2G discharging power and the optimal fixed energy discharging power corresponding to each scheduling period t.

[0017] The second aspect provides an EV-V2G and fixed energy cooperative discharging scheduling device, which includes a communication unit and a processing unit; the communication unit is configured to obtain scheduling power parameters; the scheduling power parameters include a power shortage, a maximum available discharging capacity of an EV-V2G cluster, an initial residual capacity of fixed energy, an EV-V2G unit discharging compensation cost, a fixed energy unit discharging operation and maintenance cost, a maximum discharging power of fixed energy, and a minimum allowed residual capacity of fixed energy; the processing unit is configured to construct a cooperative optimization problem based on the scheduling power parameters; split the cooperative optimization problem into an EV-V2G discharging sub-problem and a fixed energy discharging sub-problem; convert a non-convex problem of each iteration of the fixed energy discharging sub-problem into a standard convex optimization problem based on an iterative conversion method of continuous convex approximation; and output an optimal EV-V2G discharging power and an optimal fixed energy discharging power by alternately solving the EV-V2G discharging sub-problem and the standard convex optimization problem.

[0018] In a third aspect, the present application provides an EV-V2G and fixed energy storage collaborative discharging scheduling device, comprising: a processor and a storage medium; the storage medium comprises instructions, and the processor is configured to execute the instructions to implement the method described in the first aspect and any possible implementation manner of the first aspect. The EV-V2G and fixed energy storage collaborative discharging scheduling device can be an electronic device or a chip in an electronic device.

[0019] In a fourth aspect, the present application provides an EV-V2G and fixed energy storage collaborative discharging scheduling system, comprising: a data acquisition device and an electronic device; wherein the data acquisition device is configured to acquire a scheduling power parameter; the electronic device is configured to construct a collaborative optimization problem based on the scheduling power parameter; split the collaborative optimization problem into an EV-V2G discharging sub-problem and a fixed energy storage discharging sub-problem; convert a non-convex problem of each iteration of the fixed energy storage discharging sub-problem into a standard convex optimization problem based on a continuous convex approximation iterative conversion method; and output an optimal EV-V2G discharging power and an optimal fixed energy storage discharging power by alternately solving the EV-V2G discharging sub-problem and the standard convex optimization problem.

[0020] In a fifth aspect, the present application provides a computer readable storage medium, which stores instructions, and when the instructions are executed on an EV-V2G and fixed energy storage collaborative discharging scheduling device, the EV-V2G and fixed energy storage collaborative discharging scheduling device executes the method described in the first aspect and any possible implementation manner of the first aspect.

[0021] In a sixth aspect, the present application provides a computer program product comprising instructions, and when the computer program product is executed on an EV-V2G and fixed energy storage collaborative discharging scheduling device, the EV-V2G and fixed energy storage collaborative discharging scheduling device executes the method described in the first aspect and any possible implementation manner of the first aspect.

[0022] The present application provides an EV-V2G and fixed energy storage collaborative discharging scheduling method and device, which can effectively solve the non-convexity of the fixed energy storage discharging sub-problem by using a continuous convex approximation iterative conversion method, and realize efficient collaborative scheduling of the EV-V2G and the fixed energy storage by using an alternating optimization strategy, to accurately output optimal discharging power and ensure that the power shortage of the power distribution network is reliably filled, while significantly improving the economy and flexibility of the scheduling, enhancing the adaptability of the new power distribution network to multi-source uncertainty, and solving the technical problem that the prior art cannot realize global optimal scheduling of the power distribution network through collaborative discharging of the EV-V2G and the fixed energy storage in a multi-source uncertainty scenario.

[0023] It should be understood that the description of technical features, technical solutions, advantages or similar language in the present application does not imply that all features and advantages can be realized in any single embodiment. On the contrary, it can be understood that the description of a feature or advantage means that the specific technical feature, technical solution or advantage is included in at least one embodiment. Therefore, the description of technical features, technical solutions or advantages in the specification does not necessarily refer to the same embodiment. Further, the technical features, technical solutions and advantages described in the embodiments can be combined in any appropriate manner. Those skilled in the art will understand that the embodiments can be implemented without one or more specific technical features, technical solutions or advantages of the specific embodiments. In other embodiments, additional technical features and advantages can be identified in specific embodiments that do not embody all embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 A system architecture diagram of an EV-V2G and fixed energy storage cooperative discharge scheduling system provided by an embodiment of the present application; Figure 2 A flowchart of an EV-V2G and fixed energy storage cooperative discharge scheduling method provided by an embodiment of the present application; Figure 3 A flowchart of another EV-V2G and fixed energy storage cooperative discharge scheduling method provided by an embodiment of the present application; Figure 4 A structure diagram of an EV-V2G and fixed energy storage cooperative discharge scheduling device provided by an embodiment of the present application; Figure 5 A hardware structure diagram of an EV-V2G and fixed energy storage cooperative discharge scheduling device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0025] In the description of the present application, unless otherwise specified, " / " means "or", for example, A / B can mean A or B. "And / or" in this document is only a description of the relationship between the associated objects, which means that there can be three relationships, for example, A and / or B, which means that there are three cases of A alone, A and B together, and B alone. In addition, "at least one" means one or more, and "multiple" means two or more. "First", "second", etc. do not limit the quantity and execution order, and "first", "second", etc. do not necessarily mean different.

[0026] It should be noted that the terms "exemplary" or "for example" when used in this specification mean "serving as an example, instance, or illustration," and should not necessarily be construed as preferred or advantageous over other embodiments or suitable designs. Rather, use of these terms is intended to present concepts in a concrete manner.

[0027] The EV-V2G and fixed energy storage cooperative discharging scheduling method provided by the embodiments of the present application can be applied to an EV-V2G and fixed energy storage cooperative discharging scheduling system as shown in the figure. Figure 1 The system comprises a data acquisition device 101 and an electronic device 102.

[0028] The data acquisition device 101 is configured to acquire scheduling power parameters, and the scheduling power parameters comprise a power shortage, a maximum available discharging capacity of an EV-V2G cluster, an initial residual capacity of a fixed energy storage, a unit discharging compensation cost of the EV-V2G, a unit discharging operation and maintenance cost of the fixed energy storage, a maximum discharging power of the fixed energy storage, and a minimum allowed residual capacity of the fixed energy storage. The electronic device 102 is configured to construct a cooperative optimization problem based on the scheduling power parameters, split the cooperative optimization problem into an EV-V2G discharging problem and a fixed energy storage discharging problem, convert a non-convex problem of each round of iteration of the fixed energy storage discharging problem into a standard convex optimization problem based on a continuous convex approximation iterative conversion method, and output an optimal EV-V2G discharging power and an optimal fixed energy storage discharging power by alternately solving the EV-V2G discharging problem and the standard convex optimization problem.

[0029] To solve the technical problem that the prior art is difficult to achieve global optimal scheduling of the power distribution network through the coordinated discharge of EV-V2G and fixed energy storage in a multi-source uncertainty scenario, the embodiment of the present application provides an EV-V2G and fixed energy storage coordinated discharge scheduling method, which comprises: acquiring scheduling power parameters; constructing a collaborative optimization problem based on the scheduling power parameters; splitting the collaborative optimization problem into an EV-V2G discharge subproblem and a fixed energy storage discharge subproblem; converting the non-convex problem of each iteration of the fixed energy storage discharge subproblem into a standard convex optimization problem based on a continuous convex approximation iterative conversion method; outputting optimal EV-V2G discharge power and optimal fixed energy storage discharge power by alternately solving the EV-V2G discharge subproblem and the standard convex optimization problem, based on which, the non-convexity of the fixed energy storage discharge subproblem is effectively solved through the continuous convex approximation iterative conversion method, and the efficient coordinated scheduling of EV-V2G and fixed energy storage is realized in combination with the alternating optimization strategy, so as to accurately output the optimal discharge power and ensure that the power shortage of the power distribution network is reliably filled, while significantly improving the economy and flexibility of the scheduling, enhancing the adaptability of the new power distribution network to multi-source uncertainty, and solving the technical problem that the prior art is difficult to achieve global optimal scheduling of the power distribution network through the coordinated discharge of EV-V2G and fixed energy storage in a multi-source uncertainty scenario.

[0030] As shown in Figure 2 , the EV-V2G and fixed energy storage coordinated discharge scheduling method provided by the embodiment of the present application comprises: S201, acquiring scheduling power parameters.

[0031] The scheduling power parameters are basic data supporting the construction of the collaborative optimization problem, including the power shortage of the power distribution network at each time , the maximum available discharge capacity of the EV-V2G cluster at each time , the initial remaining capacity of the fixed energy storage , the unit discharge compensation cost of EV-V2G , the unit discharge operation and maintenance cost of fixed energy storage , the maximum discharge power of fixed energy storage , and the minimum allowed remaining capacity of fixed energy storage .

[0032] In the embodiment of the present application, the data acquisition device collects the above-mentioned parameters through the power distribution network scheduling platform, the EV-V2G aggregation management system and the fixed energy storage local monitoring device, wherein is calculated by the difference between the power distribution network load prediction module and the new energy output prediction module, is aggregated by real-time statistics of the state of charge, discharge efficiency and available number of EVs accessed through the V2G platform.

[0033] It should be noted that the collection of scheduling power parameters needs to ensure real-time and accuracy. The prediction error should be controlled within 5%. The update cycle does not exceed 15 minutes to ensure the accuracy of subsequent optimization problems.

[0034] Based on the above steps, the obtained scheduling power parameters cover the core influencing factors of coordinated discharge scheduling, providing accurate data support for the construction and solution of subsequent optimization problems.

[0035] S202, Constructing a collaborative optimization problem based on scheduling power parameters.

[0036] Among them, the collaborative optimization problem refers to a mathematical optimization model that aims to minimize the total cost of collaborative discharge between EV-V2G and stationary energy storage, while simultaneously satisfying constraints such as power deficit filling in the distribution network and equipment operating boundaries.

[0037] In this embodiment, the electronic device, based on the scheduling power parameters obtained in S201, clearly defines the optimization objective as minimizing the sum of EV-V2G discharge compensation cost and fixed energy storage operation and maintenance cost within 24 hours. At the same time, it identifies the constraints such as the total discharge power satisfying the power deficit, the EV-V2G and fixed energy storage discharge power boundary, and the fixed energy storage nonlinear energy balance, and constructs a complete collaborative optimization problem.

[0038] Alternatively, the collaborative optimization problem satisfies the following formula:

[0039] in, For EV-V2G discharge power, To fix the energy storage discharge power, For the unit discharge compensation cost of EV-V2G, To define the fixed unit discharge operation and maintenance cost of energy storage, t is the scheduling time index. To address the power deficit in the distribution network, For the maximum available discharge capacity of the EV-V2G cluster, To fix the maximum discharge power of energy storage, To fix the remaining energy storage capacity, To maintain a fixed energy storage discharge efficiency, The zero-power reference efficiency coefficient The efficiency attenuation coefficient, To fix the initial remaining capacity of energy storage, The minimum allowable remaining capacity for stationary energy storage.

[0040] It should be noted that the scheduling duration is 1 hour, and the day is divided into 24 scheduling durations, as shown in the formula. The objective function directly reflects the economic requirements of scheduling; the formula Ensure that the power deficit in the distribution network is reliably filled. is the power gap that the distribution network must fill (such as insufficient new energy output, sudden load increase, etc.), and As the two core discharge subjects, they need to jointly bear the gap. When one party's discharge power is insufficient, the other party needs to make up for it. When one party's discharge power is redundant, the other party can appropriately reduce it; formula 、 comply with the physical boundary constraints of the device discharge power, formula is the core nonlinear constraint, which represents the characteristics of the fixed energy storage discharge efficiency changing with power; For the fixed energy storage discharge efficiency, when the discharge power tends to 0, the internal loss of the energy storage tends to zero, and the efficiency tends to the ideal maximum value , is the efficiency attenuation coefficient. The larger the discharge power, the larger the current, and the ohmic loss grows in square level. The polarization loss and side reaction loss also intensify simultaneously, resulting in a linear decline in actual efficiency, is the actual consumed capacity; formula 、 ensures the safe operation of the fixed energy storage.

[0041] Based on the above steps, the collaborative optimization problem constructed not only takes into account the economy and reliability of scheduling, but also conforms to the actual operating characteristics of the fixed energy storage through nonlinear constraints.

[0042] S203, split the collaborative optimization problem into an EV-V2G discharge problem and a fixed energy storage discharge problem.

[0043] Among them, the EV-V2G discharge problem refers to a sub-problem of optimizing only the EV-V2G discharge power when determining the fixed energy storage discharge power; the fixed energy storage discharge problem refers to a sub-problem of optimizing only the fixed energy storage discharge power and the remaining capacity state when fixing the EV-V2G discharge power.

[0044] In the embodiments of the present application, based on the variable coupling characteristics of the collaborative optimization problem, the bi-variable optimization problem is split into two single-variable dominated sub-problems: after splitting, the EV-V2G discharge problem aims to minimize its own discharge compensation cost, and the constraint conditions inherit the constraints related to in the collaborative optimization problem; the fixed energy storage discharge problem aims to minimize its own operation and maintenance cost, and the constraint conditions inherit the constraints related to in the collaborative optimization problem.

[0045] Optionally, the EV-V2G discharge problem satisfies the following formula:

[0046] wherein, a fixed energy storage discharging power.

[0047] Optionally, the fixed energy storage discharging problem satisfies the following formula:

[0048] wherein, an EV-V2G discharging power.

[0049] It should be noted that the core of the splitting strategy is to use the idea of "alternating fixed variables" to transform the originally coupled two-variable optimization problem into two sub-problems that can be iteratively solved, wherein the EV-V2G discharging problem is a standard linear programming problem and can be directly and efficiently solved, and the fixed energy storage discharging problem needs to be further transformed due to the fractional nonlinear term of formula (C4).

[0050] Based on the above steps, the splitting of the sub-problems effectively reduces the solution complexity of the original collaborative optimization problem, while retaining the core operating constraints of each device, so that the two sub-problems can be independently solved and can achieve the global optimization goal through iterative coupling.

[0051] S204, based on the iterative transformation method of continuous convex approximation, the non-convex problem of the fixed energy storage discharging problem in each iteration is transformed into a standard convex optimization problem.

[0052] wherein, the continuous convex approximation (SCA) refers to an iterative transformation method of transforming a non-convex function into a convex function near the current iteration point by successive first-order Taylor expansion, and then transforming a non-convex optimization problem into a convex optimization problem.

[0053] In the embodiments of the present application, the electronic device firstly defines the nonlinear term , calculates the first derivative of at the current iteration point , then performs first-order Taylor expansion on , obtains a convex approximation function, and then linearizes the non-convex energy balance constraint to obtain a linear energy balance constraint equation, substitutes the linear energy balance constraint equation into the non-convex constraint (C4) to obtain a standard convex optimization problem (linear programming).

[0054] Optionally, the linear energy balance constraint equation satisfies the following equation:

[0055] wherein, , , .

[0056] It should be noted that, since As a strictly convex function (second derivative constant positive), its first-order Taylor expansion is a global lower convex approximation, ensuring that the approximation function of each iteration is a convex function, and the transformed subproblem is a standard linear programming problem, which can be quickly solved by a convex optimization solver; during the iteration process, the Taylor expansion point Gradually approaches the optimal solution of the original non-convex problem, ensuring the convergence of the algorithm.

[0057] Based on the above steps, the iterative transformation method of continuous convex approximation effectively solves the non-convexity problem of the fixed energy storage discharging problem, has high solving efficiency for the transformed convex problem, and at the same time guarantees the accuracy, and provides key technical support for the landing of the alternating optimization strategy.

[0058] S205, output the optimal EV-V2G discharging power and the optimal fixed energy storage discharging power by alternately solving the EV-V2G discharging problem and the standard convex optimization problem.

[0059] Among them, the alternating solving strategy refers to a solving method that solves the EV-V2G discharging problem and the transformed fixed energy storage convex subproblem in turn through multiple iterations until the convergence condition is met.

[0060] In the embodiments of the present application, the electronic device first initializes the iteration number k=0, sets the convergence threshold and the initial value of the fixed energy storage discharging power ; in the kth iteration, the fixed is solved to obtain the EV-V2G subproblem , and the fixed is solved to obtain the fixed energy storage subproblem ; the iteration cost difference is calculated , if is less than the convergence threshold, the output is the optimal EV-V2G discharging power, and the output is the optimal fixed energy storage discharging power; otherwise, let k=k+1, and continue to alternately iterate until the iteration cost difference is less than the convergence threshold.

[0061] Optionally, the iteration cost difference satisfies the following formula:

[0062] It should be noted that both subproblems are standard convex optimization problems and can be solved by a solver. The Mosek solver is used to solve the EV-V2G discharging problem and the standard convex optimization problem. In the alternating solving process, the optimal solutions of the two subproblems are mutually coupled and mutually approached, and the solution of each iteration is closer to the global optimal solution than the previous iteration. Since both subproblems are convex optimization problems, the iteration sequence is monotonically decreasing and has a lower bound, ensuring that the global optimal solution is finally converged, avoiding local optimal traps, and the convergence threshold The value of the scheduling power parameter can be adjusted according to actual scheduling accuracy requirements.

[0063] Based on the above steps, the alternating solution strategy efficiently integrates the linear solution advantage of the EV-V2G sub-problem and the convex transformation result of the fixed energy storage sub-problem, and the output optimal discharge power not only meets the power shortage demand of the power distribution network, but also minimizes the total discharge cost, considering economy and reliability.

[0064] Based on the above technical solution, the scheduling power parameter is first acquired, and then a cooperative optimization problem that fits the actual situation is constructed, the complexity of solving is reduced through sub-problem splitting, the non-convex problem is transformed through continuous convex approximation, and finally the global optimal discharge power is output through alternating solution, effectively solving the non-convexity and variable coupling problem in the cooperative discharge of EV-V2G and fixed energy storage, significantly improving the economy, solving efficiency and reliability of the scheduling, and solving the technical problem that the existing technology cannot realize the global optimal scheduling of the power distribution network through the cooperative discharge of EV-V2G and fixed energy storage in a multi-source uncertainty scenario.

[0065] In a possible manner, in combination with Figure 2 As shown in S205, the EV-V2G and fixed energy storage cooperative discharge scheduling method provided by the embodiment of the application further includes S301: Figure 3 S301, record the optimal EV-V2G discharge power and optimal fixed energy storage discharge power corresponding relationship corresponding to each scheduling period t. S301, record the optimal EV-V2G discharge power and optimal fixed energy storage discharge power corresponding relationship corresponding to each scheduling period t.

[0066] Wherein, the scheduling period t is the time unit (t∈{1,2,...,24}) of 24-hour time sequence scheduling, and the optimal discharge power corresponding relationship refers to the corresponding data combination of the optimal EV-V2G discharge power and the optimal fixed energy storage discharge power at each t moment, including time sequence index, two types of power values and related information such as power distribution network power shortage at corresponding moment.

[0067] In the embodiment of the application, after the electronic device outputs the optimal discharge power through S205, the data recording process is automatically started, and the optimal EV-V2G discharge power and the optimal fixed energy storage discharge power at each t moment and the corresponding (t), EESS_remain(t) and other key data are stored in the power distribution network scheduling database in a structured format according to the time sequence index, optimal power data and state parameters, and the recording period is consistent with the scheduling period (1 record per hour).

[0068] It should be pointed out that the recording process needs to ensure the integrity and consistency of the data, and the power data of each time sequence node needs to be completely matched with the final result of the iterative solution, and the constraint satisfaction at that moment is also stored, providing complete basis for data tracing.

[0069] Based on the above steps, the recorded optimal power correspondence provides historical data support for the iterative optimization of the scheduling strategy, and realizes the traceability of the scheduling process, facilitating the analysis of the scheduling effect and the troubleshooting of potential problems by the operation and maintenance personnel.

[0070] The above describes the scheme of the embodiments of the application mainly from the perspective of device implementation. It can be understood that, in order to implement the above functions, each device, for example, the EV-V2G and fixed energy storage cooperative discharging scheduling device, contains at least one of the corresponding hardware structure and software module for executing each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the application can be realized in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the application.

[0071] The embodiments of the application can divide the functional units of the EV-V2G and fixed energy storage cooperative discharging scheduling device according to the above method examples. For example, each functional unit can be divided according to each function, or two or more functions can be integrated in one processing unit. The integrated unit can be realized in the form of hardware or software functional unit. It should be noted that the division of units in the embodiments of the application is illustrative, and is only a logical functional division. When actually implemented, there can be another division method.

[0072] In the case of using integrated units, Figure 4 A possible structure diagram of the EV-V2G and fixed energy storage cooperative discharging scheduling device (denoted as EV-V2G and fixed energy storage cooperative discharging scheduling device 40) involved in the above embodiments is shown, which includes a processing unit 401 and a communication unit 402, and can also include a storage unit 403. Figure 4 The structure diagram shown can be used to illustrate the structure of the EV-V2G and fixed energy storage cooperative discharging scheduling device involved in the above embodiments.

[0073] When Figure 4The shown structural schematic is used to show the structure of the EV-V2G and fixed energy storage cooperative discharge scheduling device involved in the above embodiment, the processing unit 401 is used to control and manage the action of the EV-V2G and fixed energy storage cooperative discharge scheduling device, the communication unit 402 is used for the EV-V2G and fixed energy storage cooperative discharge scheduling device to communicate with other devices, and the storage unit 403 is used to store the program code and data of the EV-V2G and fixed energy storage cooperative discharge scheduling device.

[0074] For example, the communication unit 402 is used to obtain the scheduling power parameter. The processing unit 401 is used to construct a cooperative optimization problem based on the scheduling power parameter; split the cooperative optimization problem into an EV-V2G discharge problem and a fixed energy storage discharge problem; based on a continuous convex approximation iterative transformation method, transform the non-convex problem of each iteration of the fixed energy storage discharge problem into a standard convex optimization problem; and output the optimal EV-V2G discharge power and the optimal fixed energy storage discharge power by alternately solving the EV-V2G discharge problem and the standard convex optimization problem.

[0075] In a possible implementation, the cooperative optimization problem satisfies the following formula:

[0076] Wherein, is the EV-V2G discharge power, is the fixed energy storage discharge power, is the EV-V2G unit discharge compensation cost, is the fixed energy storage unit discharge operation and maintenance cost, t is the scheduling time index, is the power shortage of the power distribution network, is the maximum available discharge capacity of the EV-V2G cluster, is the maximum discharge power of the fixed energy storage, is the remaining capacity of the fixed energy storage, is the discharge efficiency of the fixed energy storage, is the zero-power reference efficiency coefficient, is the efficiency decay coefficient, is the initial remaining capacity of the fixed energy storage, is the minimum allowed remaining capacity of the fixed energy storage.

[0077] In a possible implementation, the EV-V2G discharge problem satisfies the following formula:

[0078] Wherein, is the fixed energy storage discharge power with a fixed value.

[0079] In a possible implementation, the fixed energy storage discharging problem satisfies the following formula:

[0080] wherein, is the EV-V2G discharging power.

[0081] In a possible implementation, the processing unit 401 is further configured to convert the non-convex problem of each iteration of the fixed energy storage discharging problem into a standard convex optimization problem based on the iterative conversion method of the successive convex approximation, including: globally approximating the non-convex constraint (C4) at by first-order Taylor expansion to obtain a linear energy balance constraint equation; and substituting the linear energy balance constraint equation into the non-convex constraint (C4) to obtain the standard convex optimization problem.

[0082] In a possible implementation, the processing unit 401 is further configured to output the optimal EV-V2G discharging power and the optimal fixed energy storage discharging power by alternately solving the EV-V2G discharging problem and the standard convex optimization problem, including: initializing the iteration number k = 0 and setting a convergence threshold; fixing , solving the EV-V2G discharging problem to obtain the kth iteration optimal EV-V2G discharging power ; fixing , solving the standard convex optimization problem to obtain the (k+1)th iteration optimal fixed energy storage discharging power calculating the iteration cost difference, and if the iteration cost difference is less than the convergence threshold, outputting as the optimal EV-V2G discharging power, as the optimal fixed energy storage discharging power; otherwise, setting k = k+1 and continuing the alternating iteration until the iteration cost difference is less than the convergence threshold.

[0083] In a possible implementation, the EV-V2G discharging problem and the standard convex optimization problem are both solved by using the Mosek solver.

[0084] In a possible implementation, the linear energy balance constraint equation satisfies the following equation:

[0085] wherein, , , .

[0086] In a possible implementation, the processing unit 401 is further configured to, after outputting the optimal EV-V2G discharging power and the optimal fixed energy storage discharging power, the method further includes: recording the correspondence between the optimal EV-V2G discharging power and the optimal fixed energy storage discharging power corresponding to each scheduling period t.

[0087] The processing unit 401 can be a processor or a controller, and the communication unit 402 can be a communication interface, a transceiver, a transceiver, a transceiver circuit, a transceiver device, etc. The communication interface is collectively referred to, and can include one or more interfaces. The storage unit 403 can be a memory. When the EV-V2G and fixed energy storage cooperative discharge scheduling device 40 is a chip, the processing unit 401 can be a processor or a controller, and the communication unit 402 can be an input interface and / or an output interface, a pin or a circuit, etc. The storage unit 403 can be a storage unit (for example, a register, a cache, etc.) within the chip, or a storage unit (for example, a read-only memory (ROM), a random access memory (RAM), etc.) located outside the chip.

[0088] The communication unit can also be referred to as a transceiving unit. The antenna and control circuit with transceiving function in the EV-V2G and fixed energy storage cooperative discharge scheduling device 40 can be regarded as the communication unit 402 of the EV-V2G and fixed energy storage cooperative discharge scheduling device 40, and the processor with processing function can be regarded as the processing unit 401 of the EV-V2G and fixed energy storage cooperative discharge scheduling device 40. Optionally, the device for realizing the receiving function in the communication unit 402 can be regarded as a communication unit, and the communication unit is used to execute the receiving steps in the embodiments of the application, and the communication unit can be a receiver, a receiver, a receiving circuit, etc. The device for realizing the sending function in the communication unit 402 can be regarded as a sending unit, and the sending unit is used to execute the sending steps in the embodiments of the application, and the sending unit can be a transmitter, a transmitter, a sending circuit, etc.

[0089] Figure 4 The integrated units in the above-mentioned embodiments can be stored in a computer readable storage medium if they are realized in the form of software function modules and sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the application can be embodied in the form of software products, and the computer software products are stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in the embodiments of the application. The storage medium storing the computer software product includes a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk, and various media that can store program codes.

[0090] Figure 4 The units in the above-mentioned embodiments can also be referred to as modules, for example, the processing unit can be referred to as a processing module.

[0091] This application also provides a hardware structure diagram of an EV-V2G and stationary energy storage collaborative discharge scheduling device (denoted as EV-V2G and stationary energy storage collaborative discharge scheduling device 50), see [link to relevant documentation]. Figure 5 The EV-V2G and stationary energy storage coordinated discharge scheduling device 50 includes a processor 501, and optionally, a memory 502 connected to the processor 501.

[0092] In the first possible implementation, see Figure 5 The EV-V2G and stationary energy storage coordinated discharge scheduling device 50 also includes a transceiver 503. The processor 501, memory 502, and transceiver 503 are connected via a bus. The transceiver 503 is used to communicate with other devices or communication networks. Optionally, the transceiver 503 may include a transmitter and a receiver. The device in the transceiver 503 that implements the receiving function can be considered as a receiver, which is used to perform the receiving steps in the embodiments of this application. The device in the transceiver 503 that implements the transmitting function can be considered as a transmitter, which is used to perform the transmitting steps in the embodiments of this application.

[0093] Based on the first possible implementation method Figure 5 The schematic diagram shown can be used to illustrate the structure of the EV-V2G and stationary energy storage collaborative discharge scheduling device involved in the above embodiments.

[0094] in, Figure 5 The diagram can also illustrate the system chip in the EV-V2G and stationary energy storage collaborative discharge scheduling device. In this case, the actions performed by the aforementioned EV-V2G and stationary energy storage collaborative discharge scheduling device can be implemented by this system chip. The specific actions performed can be found above and will not be repeated here.

[0095] In implementation, each step of the method provided in this embodiment can be completed by integrated logic circuits in the processor or by instructions in software form. The steps of the method disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.

[0096] The processor in the present application can include, but is not limited to, at least one of the following: a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a microcontroller unit (MCU), or an artificial intelligence processor, and the like, each of which is a computing device running software, and each of which can include one or more cores for executing software instructions to perform operations or processing. The processor can be a separate semiconductor chip, or can be integrated with other circuits as a semiconductor chip, for example, can be integrated with other circuits (such as coding and decoding circuits, hardware acceleration circuits, or various bus and interface circuits) to form a SoC (system on chip), or can be integrated as a built-in processor in an ASIC. The ASIC integrated with the processor can be packaged separately or packaged together with other circuits. In addition to including cores for executing software instructions to perform operations or processing, the processor can further include necessary hardware accelerators, such as field programmable gate arrays (FPGAs), PLDs (programmable logic devices), or logic circuits implementing special logic operations.

[0097] The memory in the embodiments of the present application can include at least one of the following types: a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM). In some scenarios, the memory can also be a compact disc read-only memory (CD-ROM) or other optical disk storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto.

[0098] The embodiments of the present application also provide a computer readable storage medium including instructions, which, when executed on a computer, cause the computer to perform any of the above methods.

[0099] The embodiments of the present application also provide a computer program product including instructions, which, when executed on a computer, cause the computer to perform any of the above methods.

[0100] The embodiment of the present application further provides a chip, which comprises a processor and an interface circuit, the interface circuit is coupled with the processor, the processor is used for running computer programs or instructions to realize the method described above, and the interface circuit is used for communicating with other modules outside the chip.

[0101] In the above embodiments, the implementation can be achieved by software, hardware, firmware or any combination thereof, entirely or partially. When implemented by software, the implementation can be achieved in the form of a computer program product, entirely or partially. The computer program product comprises one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the flow or function described in the embodiments of the present application is generated, entirely or partially. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices. The computer instructions can be stored in a computer readable storage medium or transferred from one computer readable storage medium to another, for example, the computer instructions can be transferred from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode. The computer readable storage medium can be any available medium that can be accessed by a computer or data storage device including one or more servers, data centers, etc. integrated with the medium. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (solid state disk, SSD)) and the like.

[0102] Although the present application is described herein in conjunction with various embodiments, other variations of the disclosed embodiments can be understood and implemented by those skilled in the art with reference to the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "one" does not exclude a plurality. A single processor or other unit can implement several functions listed in the claims. Some measures described in mutually different dependent claims can be combined and produce good results.

[0103] Although the present application has been described in connection with certain specific features and embodiments thereof, it is to be understood that it is provided as an example to the best of the applicant's knowledge and that various modifications and combinations of the described features and embodiments are possible and are within the spirit and scope of the application. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense, and all such modifications and variations are considered within the scope of the present application as defined by the following claims and their equivalents. Obviously, many modifications and variations of the present application are possible in light of the above teachings. It is, therefore, to be understood that within the scope of the claims and their equivalents, the present application can be practiced otherwise than as specifically described.

Claims

1. An EV-V2G and fixed energy storage cooperative discharge scheduling method, characterized in that, The method comprises the following steps: obtaining a scheduling power parameter; the scheduling power parameter comprises a power shortage, an EV-V2G cluster maximum available discharge capacity, a fixed energy storage initial residual capacity, an EV-V2G unit discharge compensation cost, a fixed energy storage unit discharge operation and maintenance cost, a fixed energy storage maximum discharge power, and a fixed energy storage minimum allowable residual capacity; constructing a collaborative optimization problem based on the scheduling power parameter; splitting the collaborative optimization problem into an EV-V2G discharge problem and a fixed energy storage discharge problem; based on a continuous convex approximation iterative transformation method, transforming a non-convex problem of each iteration of the fixed energy storage discharge problem into a standard convex optimization problem; outputting optimal EV-V2G discharge power and optimal fixed energy storage discharge power by alternately solving the EV-V2G discharge problem and the standard convex optimization problem.

2. The method of claim 1, wherein, The collaborative optimization problem satisfies the following formula: wherein, is the EV-V2G discharging power, is the fixed energy storage discharging power, is the EV-V2G unit discharging compensation cost, is the fixed energy storage unit discharging operation and maintenance cost, t is the scheduling time index, is the power shortage of the distribution network, is the maximum available discharging capacity of the EV-V2G cluster, is the maximum discharging power of the fixed energy storage, is the remaining capacity of the fixed energy storage, is the discharging efficiency of the fixed energy storage, is the zero-power reference efficiency coefficient, is the efficiency decay coefficient, is the initial remaining capacity of the fixed energy storage, is the minimum allowed remaining capacity of the fixed energy storage.

3. The method of claim 2, wherein, The EV-V2G discharge problem satisfies the following formula: wherein, a fixed energy storage discharge power that is a fixed value.

4. The method of claim 3, wherein, The fixed energy storage discharge problem satisfies the following formula: wherein, EV-V2G discharging power that is a fixed value.

5. The method of claim 4, wherein, The continuous convex approximation iterative transformation method for transforming the non-convex problem of each iteration of the fixed energy storage discharge problem into a standard convex optimization problem comprises: The non-convex constraint (C4) is globally under-approximated by a first order Taylor expansion at resulting in a linear energy balance constraint equation; substituting the linear energy balance constraint equation into the non-convex constraint (C4) to obtain a standard convex optimization problem.

6. The method of claim 5, wherein, The method of alternately solving the EV-V2G discharge problem and the standard convex optimization problem to output optimal EV-V2G discharge power and optimal fixed energy storage discharge power comprises: initializing the iteration number k=0 and setting a convergence threshold; Fixing , solving the EV-V2G discharging sub-problem to obtain the kth round EV-V2G optimal discharging power ; fixing solving the standard convex optimization problem to obtain the (k+1)th round fixed energy storage optimal discharge power calculating an iteration cost difference value, outputting if the iteration cost difference value is less than the convergence threshold value is the optimal EV-V2G discharge power, is the optimal fixed energy storage discharge power; otherwise, let k=k+1, and continue the alternating iteration until the iteration cost difference value is less than the convergence threshold value.

7. The method of claim 6, wherein, both solving the EV-V2G discharge problem and solving the standard convex optimization problem are solved by using a Mosek solver.

8. The method of claim 5, wherein, The linear energy balance constraint equation satisfies the following equation: wherein , , .

9. The method of claim 1, wherein, After outputting the optimal EV-V2G discharge power and the optimal fixed energy storage discharge power, the method further comprises recording the corresponding relationship between the optimal EV-V2G discharge power and the optimal fixed energy storage discharge power corresponding to each scheduling period t.

10. An EV-V2G and fixed energy storage cooperative discharging scheduling device, characterized in that, The device comprises a communication unit and a processing unit. The communication unit is configured to obtain a scheduling power parameter; the scheduling power parameter comprises a power shortage, an EV-V2G cluster maximum available discharge capacity, a fixed energy storage initial residual capacity, an EV-V2G unit discharge compensation cost, a fixed energy storage unit discharge operation and maintenance cost, a fixed energy storage maximum discharge power, and a fixed energy storage minimum allowable residual capacity. The processing unit is configured to construct a collaborative optimization problem based on the scheduling power parameter; split the collaborative optimization problem into an EV-V2G discharge problem and a fixed energy storage discharge problem; based on a continuous convex approximation iterative transformation method, transform a non-convex problem of each iteration of the fixed energy storage discharge problem into a standard convex optimization problem; and output optimal EV-V2G discharge power and optimal fixed energy storage discharge power by alternately solving the EV-V2G discharge problem and the standard convex optimization problem.

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