Power distribution network micro-grid collaborative optimization method considering renewable energy and demand uncertainty flexibility

Abstract

The invention discloses a power distribution network and micro-grid collaborative optimization method considering renewable energy source and demand uncertainty flexibility. The method comprises the following steps: constructing an uncertainty set of demand and renewable energy source output based on historical data; respectively constructing a distribution robust optimization model and a distribution network optimization model of the micro-grid by utilizing the uncertainty set; solving the two models to obtain the electricity purchasing intentions of the micro-grid and the distribution network; collaborative optimization of the micro-grid and a distribution network model is realized by adopting an alternating direction multiplier method (ADMM), coupling constraints are decoupled, and convergence of a solving process is accelerated by dynamically adjusting a relaxation factor; and iteratively solving until a convergence standard is met, and outputting an optimal decision. According to the method, the energy utilization efficiency is improved, the reliability of system power supply is ensured, the adaptability of a power grid to uncertainty is enhanced, energy balance and efficient use are achieved, and support is provided for green, low-carbon and efficient energy system transformation.

Description

Technical Field

[0001] The present invention relates to renewable energy grid technology, and in particular to a coordinated optimization method for distribution network and microgrid considering the uncertainty flexibility of renewable energy and demand. Background Art

[0002] In the context of achieving the goals of "carbon peak and carbon neutrality", the proportion of renewable energy in the distribution network is gradually increasing, especially the widespread application of distributed photovoltaics, wind energy and energy storage systems. However, the intermittency, volatility of these renewable energies and the flexibility of the demand side pose more uncertain challenges to the operation of the distribution network. At the same time, the growth of global energy demand and the exacerbation of environmental problems urgently require the energy system to achieve a more green, low-carbon and efficient transformation.

[0003] Traditional distribution network scheduling methods mostly rely on centralized management models, often ignoring the spatio-temporal uncertainty of renewable energy and the flexible response ability of the demand side. This leads to problems such as unstable power supply, low energy utilization efficiency and large environmental burden in traditional scheduling methods when a large number of distributed resources are connected.

[0004] It should be noted that the information disclosed in the above background art section is only used for understanding the background of the present application, and thus may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The main purpose of the present invention is to overcome the defects existing in the above background art, and provide a coordinated optimization method for distribution network and microgrid considering the uncertainty flexibility of renewable energy and demand.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions:

[0007] A coordinated optimization method for distribution network and microgrid considering the uncertainty flexibility of renewable energy and demand, comprising the following steps:

[0008] a. Construct an uncertainty set of the demand and renewable energy output of the main grid and the microgrid based on historical data;

[0009] b. Use the uncertainty set to construct a distributionally robust optimization model for the microgrid;

[0010] c. Use the uncertainty set to construct a distribution network optimization model;

[0011] d. Solve the distributionally robust optimization model of the microgrid to obtain the microgrid's power purchase intention;

[0012] e. Solve the distribution network optimization model to obtain the distribution network's power purchase intention;

[0013] f. Check whether the microgrid and the intention of purchasing electricity from the distribution network meet the convergence criterion. If so, output the optimal decision of the distribution network and the microgrid. If not, return to step d for iterative solution until the convergence criterion is met;

[0014] Among them, in steps d and e, the processes of solving the distributionally robust optimization model of the microgrid and the distribution network optimization model are realized through the alternating direction method of multipliers (ADMM) for collaborative optimization, so as to decouple the coupling constraints of the microgrid and the distribution network models, and accelerate convergence by dynamically adjusting the relaxation factor, so as to achieve the energy balance and cost minimization between the distribution network and the microgrid.

[0015] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it realizes the collaborative optimization method of the distribution network and the microgrid described above.

[0016] A computer program product includes a computer program, and when the computer program is executed by a processor, it realizes the collaborative optimization method of the distribution network and the microgrid described above.

[0017] Advantages of the present invention:

[0018] The method of the present invention provides an important solution in dealing with the volatility of renewable energy and the uncertainty of the demand side through the collaborative optimization of the distribution network and the microgrid. By reasonably configuring and scheduling distributed energy resources, energy storage devices, and demand response technologies, this method effectively reduces the uncertainty impact brought by the volatility of renewable energy. This optimization method not only improves energy utilization efficiency and ensures the reliability of system power supply, but also further enhances the adaptability of the power grid to uncertainty by flexibly adjusting the demand-side load response.

[0019] In the collaborative scheduling between the microgrid and the distribution network, the present invention realizes the balance and efficient use of energy through the reasonable utilization of energy storage devices, the regulation of the demand side, and the scheduling optimization of renewable energy. The collaborative optimization technology proposed by the present invention integrates various distributed resources and uses the distributed Bayesian optimization theory to model these resources. In addition, the present invention also uses the alternating direction method of multipliers (ADMM) based on the dynamic adjustment of the relaxation factor to support the collaborative optimization management of the distribution network and the microgrid, thereby improving the solution efficiency and stability.

[0020] Furthermore, the present invention uses the Bayesian distributionally robust optimization theory to model the distributed resources of the microgrid, and adopts the Monte Carlo integration method and the Metropolis-Hastings algorithm to accelerate the calculation process. The application of these algorithms significantly improves the calculation efficiency and enables the distribution network model to be accurately characterized.

[0021] Through the comprehensive application of these technologies, the present invention realizes the efficient collaborative optimization between the distribution network and the microgrid, providing strong technical support for the transformation of the energy system towards green, low-carbon, and efficient development.

[0022] Other beneficial effects in the embodiments of the present invention will be further described hereinafter. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flowchart of the collaborative optimization of the microgrid and the distribution network according to the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] The following makes a detailed description of the embodiments of the present invention. It should be emphasized that the following description is merely exemplary and not intended to limit the scope and application of the present invention.

[0025] To better address these challenges, the collaborative optimization of the distribution network and the microgrid has become an important solution. This method can effectively reduce the uncertainty impact brought by the volatility of renewable energy through the reasonable configuration and scheduling of distributed energy resources, energy storage devices, and demand response technologies.

[0026] In this context, considering the uncertainty of renewable energy and demand, the technology of collaborative optimization of the distribution network and the microgrid has emerged. This optimization method can not only improve the energy utilization efficiency and ensure the reliability of system power supply, but also further enhance the adaptability of the power grid to uncertainty by flexibly adjusting the demand-side load response. Especially in the collaborative scheduling between the microgrid and the distribution network, through the reasonable utilization of energy storage devices, the regulation of the demand side, and the optimization of the scheduling of renewable energy, the balance and efficient use of energy can be achieved.

[0027] However, in the traditional collaborative optimization of the distribution network and the microgrid, especially when considering the uncertainty of renewable energy and demand, although the robust optimization and traditional distributionally robust optimization methods can effectively address the uncertainty problem, they face significant challenges in terms of computational efficiency and time complexity. The robust optimization (RO) method deals with uncertainty by assuming the worst-case scenario and guarantees obtaining the optimal solution under all possible worst cases. This method emphasizes the robustness of the system, that is, even under the most adverse conditions, the system can still maintain normal operation. Although robust optimization has theoretical advantages, its time complexity in practical applications is usually too high, especially in the problem of collaborative optimization of the distribution network and the microgrid.

[0028] Distributionally Robust Optimization (DRO), as a method to enhance robust optimization, not only considers the worst-case scenario but also takes into account the probability distribution in the uncertainty space. By modeling the probability characteristics of the uncertainty set, DRO can more effectively capture the actual uncertainty and seek the optimal solution under certain probability constraint conditions. Although distributionally robust optimization can provide a more refined decision-making scheme than traditional robust optimization methods, it still faces the trade-off problem between computational efficiency and the size of the uncertainty set.

[0029] Refer to Figure 1 , an embodiment of the present invention provides a coordinated optimization method for a distribution network and a microgrid considering the flexibility of renewable energy and demand uncertainty, including the following steps:

[0030] a. Construct uncertainty sets for the demand and renewable energy output of the main grid and the microgrid based on historical data;

[0031] b. Use the uncertainty sets to construct a distributionally robust optimization model for the microgrid;

[0032] c. Use the uncertainty sets to construct a distribution network optimization model;

[0033] d. Solve the distributionally robust optimization model for the microgrid to obtain the microgrid's power purchase intention;

[0034] e. Solve the distribution network optimization model to obtain the distribution network's power purchase intention;

[0035] f. Check whether the power purchase intentions of the microgrid and the distribution network meet the convergence criteria. If so, output the optimal decisions for the distribution network and the microgrid. If not, return to step d for iterative solution until the convergence criteria are met;

[0036] Among them, in the process of solving the distributionally robust optimization model for the microgrid and the distribution network optimization model in steps d and e, the alternating direction method of multipliers (ADMM) is used to achieve coordinated optimization, decouple the coupling constraints of the microgrid and the distribution network models, and accelerate convergence by dynamically adjusting the relaxation factor, so as to achieve energy balance and cost minimization between the distribution network and the microgrid.

[0037] In a preferred embodiment, constructing the distributionally robust optimization model for the microgrid in step b includes: the model includes an objective function that aims to minimize the Bayesian expectation of the microgrid operating cost, and the operating cost includes the day-ahead power purchase plan cost, energy storage degradation cost, day-time power purchase plan adjustment cost, generator operating cost, and renewable energy curtailment cost; the model further includes multiple constraint conditions, and the constraint conditions include the charge and discharge balance of the energy storage device, the upper and lower limits of the power purchase quantity, the upper and lower bounds of the decision variables, and the balance between renewable energy output and demand.

[0038] In a preferred embodiment, constructing the distribution network optimization model in step c includes: the model includes an objective function, which aims to minimize the distribution network operation cost, and the operation cost includes generator power generation cost, upstream power grid power purchase cost, microgrid power purchase cost, and network loss cost; the model further includes a plurality of constraint conditions, and the constraint conditions relate to the active / reactive power flow balance of bus injection and branch transmission, bus voltage level, power balance between load demand and generator output, and upper and lower limits of generator output.

[0039] In a preferred embodiment, the process of solving the microgrid distributionally robust optimization model in step d includes: using the Metropolis-Hastings algorithm and Monte Carlo integration method to accelerate the solution of the inner-layer expected value in the distributionally robust optimization model to improve the calculation efficiency and solution speed. In a further preferred embodiment, the specific process of solving the microgrid distributionally robust optimization model includes: using distributionally robust optimization to model the microgrid, including minimizing the Bayesian expectation of the operation cost, and the operation cost depends on the probability distribution of uncertainty variables; the uncertainty variables include load and renewable energy output, and their historical data samples are used to construct the probability density function; using the Metropolis-Hastings algorithm to accelerate the calculation of the inner-layer expected value, and this algorithm performs effective sampling by constructing a Markov chain to generate samples from a complex probability distribution; the proposal distribution of the Metropolis-Hastings algorithm is a uniform distribution, and the target distribution is the posterior distribution of the uncertainty variable parameters; using the Monte Carlo integration method to solve the integral to accelerate the calculation of the posterior distribution of the uncertainty variable parameters, and this method is applicable to the integral calculation of high-dimensional integrals and complex functions.

[0040] In a preferred embodiment, solving the microgrid distributionally robust optimization model in step d includes: using the alternating direction method of multipliers (ADMM) for collaborative optimization to decouple the coupling constraints of the microgrid and distribution network models; accelerating the convergence of the ADMM algorithm by dynamically adjusting the relaxation factor to improve the solution efficiency; during the solution process of the ADMM algorithm, using the Metropolis-Hastings algorithm and Monte Carlo integration method to accelerate the solution of the inner-layer expected value in the model.

[0041] In a preferred embodiment, solving the distribution network optimization model in step e includes: using the Alternating Direction Method of Multipliers (ADMM) for collaborative optimization to decouple the coupling constraints of the distribution network and microgrid models; accelerating the convergence of the ADMM algorithm by dynamically adjusting the relaxation factor, thereby improving the solution efficiency; during the solution process of the ADMM algorithm, using the Metropolis-Hastings algorithm and Monte Carlo integration method to accelerate the solution of the inner-layer expected value in the model; the generator power generation cost in the objective function of the distribution network optimization model is a quadratic function of the generator power generation, the network loss cost is a linear function of the distribution network loss, and the purchase cost from the upstream power grid and the purchase cost from the microgrid are linear functions of the purchase amount from the upstream power grid and the purchase amount from the microgrid respectively.

[0042] In a preferred embodiment, the process of checking whether the power purchase intentions of the microgrid and the distribution network meet the convergence criteria in step f includes: using the Alternating Direction Method of Multipliers (ADMM) based on dynamic adjustment of the relaxation factor to decouple the coupling constraints of the distribution network model and the microgrid model; the ADMM algorithm includes initializing variables, iteratively solving the microgrid and distribution network optimization models, updating the Lagrange multipliers and the relaxation factor; during the iterative solution process, dynamically adjusting the relaxation factor to accelerate convergence and improve the solution stability, wherein the relaxation factor is updated using the primal residual and the adjustment factor; checking whether the convergence condition is met, including whether the difference in power purchase intentions is less than the preset tolerance or whether the number of iterations exceeds the preset maximum value; if the convergence condition is met, output the optimal decision of the distribution network and microgrid; if not, continue the iterative solution until the convergence criteria are met.

[0043] The present invention proposes an innovative collaborative optimization method for distribution networks and microgrids. By considering the uncertainties of renewable energy and demand, this method improves the energy utilization efficiency and the reliability of system operation. By constructing a distributionally robust optimization model for the microgrid and a distribution network optimization model, the present invention can not only cope with the volatility of renewable energy and the flexibility of the demand side, but also achieve cost minimization and energy balance. The application of the Alternating Direction Method of Multipliers (ADMM) effectively decouples the coupling constraints of the microgrid and distribution network models, accelerates the convergence of the solution process by dynamically adjusting the relaxation factor, and improves the stability and solution efficiency of the algorithm. In addition, by iteratively solving until the convergence criteria are met, this method ensures that the finally output decision is optimal, thereby realizing the efficient collaboration between the distribution network and the microgrid, enhancing the system's adaptability to uncertainties, and providing strong support for the transformation of the energy system towards green, low-carbon, and efficient.

[0044] The following further describes specific embodiments of the present invention and their algorithm examples.

[0045] A collaborative optimization method for distribution networks and microgrids considering the flexibility of renewable energy and demand uncertainty, which uses collaborative optimization technology to integrate various distributed resources, models distributed resources using distributed Bayesian optimization theory, and uses the alternating direction multiplier method based on dynamic adjustment of the relaxation factor to support the collaborative optimization management of distribution networks and microgrids. The process is as follows Figure 1 as shown.

[0046] The specific implementation steps are as follows:

[0047] (1) Construct a distributionally robust optimization model for the microgrid;

[0048] The objective function of the microgrid is to minimize the operating cost under distributionally robust Bayesian optimization. The model is as follows:

[0049]

[0050]

[0051] The objective function is to minimize the Bayesian expectation of the allowable cost of the microgrid.

[0052] The operating cost of the microgrid mainly consists of five parts: the cost of the day-ahead power purchase plan, the cost of energy storage degradation, the cost of adjusting the day-ahead power purchase plan during the day, the operating cost of the microgrid generator, and the cost of curtailment of renewable energy.

[0053] The cost of the day-ahead power purchase plan C MG,Ahead is the product of the electricity price λ t at each time period and the day-ahead power purchase quantity (if negative, it is the power sales quantity) at each time period.

[0054] is the cost of energy storage degradation, which is linearly related to the charge and discharge power at each time period. is the battery degradation cost coefficient, is the discharge loss coefficient.

[0055] C MG,In is the cost of adjusting the day-ahead power purchase plan during the day, which consists of two parts. The first part is the power purchase cost, and the second part is the default cost, is the penalty coefficient for temporary adjustment during the day, is the adjustment quantity of the day-ahead power purchase during the day.

[0056] is the power generation cost of the generator, is the power generation quantity of the generator at each time period, is the power generation cost coefficient of the generator.

[0057] is the cost of curtailment of renewable energy, is the curtailment electricity of renewable energy for each period, is the curtailment loss coefficient.

[0058] The constraints of the microgrid model are as follows:

[0059]

[0060] is the output of renewable energy, which is composed of the output of wind power renewable energy and the output of photovoltaic renewable energy constitutes. is the decision variable of the microgrid.

[0061] (2) Construct a distribution network optimization model:

[0062] The objective function of the microgrid is to minimize the cost:

[0063]

[0064] C DN = C gen + C HN + C MG + C loss (14)

[0065]

[0066] The operating cost of the microgrid mainly consists of five parts: the power generation cost of the generator, the power purchase cost from the superior power grid, the power purchase cost of the microgrid, and the network loss. The power generation cost C gen is a quadratic function of the power generation of the generator , and Gen,DN is the set of distribution network generators. The network loss cost C loss is a linear function of the distribution network loss . The power purchase cost C HN from the superior power grid and the power purchase cost C MG of the microgrid are linear functions of the power purchased from the superior power grid and the power purchased from the microgrid respectively.

[0067] The distribution network constraints are as follows:

[0068]

[0069]

[0070] U min ≤ U t,i ≤ U max (27)

[0071]

[0072] Inject active / reactive power flow into each bus. Transmit active / reactive power flow through the branch between buses k and i. Is the voltage of bus i at time t. Is the active / reactive power load of bus i at time t. U t,i Is the voltage of bus I at time t. r ij 、x ij Are the branch resistance and reactance.

[0073]

[0074] Among them, S ij,max Is the upper limit of branch power flow.

[0075] (III) Distributionally robust optimization model:

[0076] In (I), a distributionally robust optimization model is used to model the microgrid:

[0077]

[0078] Among them, the outer expectation Is the expectation with respect to the parameter θ N θ N Has the following probability density function:

[0079]

[0080] In the formula, Is the historical data sample of the uncertainty variables (load, renewable energy output) with a sample size of N. f is the assumed probability density function of the uncertainty variables. p(θ) is the prior distribution of the uncertainty variable parameter θ.

[0081] The distribution network model is as follows:

[0082] Formula (15) can be linearized by the piecewise linearization method. Therefore, the distribution network (DN) model can be expressed in the following compact form:

[0083]

[0084] Among them, an equality constraint can be regarded as two inequality constraints, and the chance constraint g(x, s) includes constraints (27) and (29). S is the distribution network uncertainty variable (renewable energy output)

[0085] The requirements for the chance constraint are as follows:

[0086]

[0087] The formula (31) is derived to be in the form under Bayesian KL-divergence distributionally robust optimization

[0088]

[0089] Therefore, the problem in Equation (30) can be equivalently transformed into:

[0090]

[0091] To accelerate the computational efficiency, the inner expectation E Q|θ employs the Metropolis-Hastings algorithm for acceleration. The Metropolis-Hastings algorithm is a type of Markov chain Monte Carlo (MCMC) method used to generate samples from a complex probability distribution. The basic idea of this algorithm is to construct a Markov chain such that the stationary distribution of the chain is the desired target distribution. Through continuous iteration, the Metropolis-Hastings algorithm can effectively sample from the target distribution.

[0092] The proposal distribution of the Metropolis-Hastings algorithm is a uniform distribution α is the range parameter, which is the parameter distribution of the sample ξ(N). The target distribution of the Metropolis-Hastings algorithm is p(θ|q (N) ).

[0093] To accelerate the computational efficiency of p(θ|q (N) ), the Monte Carlo integration method is used to solve the integral ∫ Θ f(q (N) |θ)p(θ)dθ. The Monte Carlo integration method is a numerical method that uses random sampling to estimate complex integrals. It obtains an approximate value of the integral by randomly selecting several sample points within the integration interval and then calculating the average of the function values corresponding to these points. This method is particularly suitable for high-dimensional integrals or when traditional analytical methods are difficult to solve. The advantage of the Monte Carlo integration method is that its calculation method is relatively simple and it is insensitive to the dimension of the problem. It is suitable for calculating integrals of complex functions, especially when the dimension is high, and is more efficient than other traditional numerical integration methods. As the number of samples increases, the accuracy of the calculation result will gradually improve, although increasing the number of samples will bring greater computational overhead.

[0094] In the embodiment of the present invention, it is assumed that the uncertainty variable follows a normal distribution f ∼ N(θ), where θ := (μ, δ 2 ). The prior distribution of θ is assumed to follow a normal distribution where β is the range parameter.

[0095] (4) Microgrid and distribution network collaborative optimization based on dynamically adjusted alternating direction method of multipliers with relaxation factor:

[0096] Noting that formula (12) is the coupling constraint between the distribution network model and the microgrid model, in order to decouple the above two formulas, the alternating direction method of multipliers with dynamically adjusted relaxation factor is adopted.

[0097] The alternating direction method of multipliers with dynamically adjusted relaxation factor (ADMM) has significant advantages in solving game problems, which can accelerate convergence, improve stability, and effectively handle complex constraints and high-dimensional problems. By flexibly adjusting the step size, ADMM can adapt to the nonlinearity, non-convexity in the game and the mutual influence among multiple participants, and improve the efficiency of solving the equilibrium solution. The introduction of the dynamic relaxation factor also enables the algorithm to cope with challenges such as distributed solution and policy changes, thus providing a more efficient and stable solution scheme, especially suitable for large-scale and complex game problems.

[0098] The specific algorithm steps are as follows:

[0099] Step 1. Initialize variables

[0100]

[0101] Step 6. Check whether the convergence condition is satisfied: or k > K

[0102] If yes, return the solution; if not satisfied, execute step 2.

[0103] where u is the Lagrange multiplier and β is the adjustment factor, is the primal residual at the (k + 1)-th step.

[0104] The embodiment of the present invention also provides a storage medium for storing a computer program, which when executed, at least executes the method as described above.

[0105] The embodiment of the present invention also provides a control device, including a processor and a storage medium for storing a computer program; wherein, the processor is used to execute the computer program to at least execute the method as described above.

[0106] The embodiment of the present invention also provides a processor, and the processor executes a computer program to at least execute the method as described above.

[0107] The storage medium can be implemented by any type of non-volatile storage device, or a combination thereof. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a ferromagnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The storage medium described in the embodiments of the present invention is intended to include, but not limited to, these and any other suitable types of memories.

[0108] In several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the couplings, direct couplings, or communication connections between the components shown or discussed can be through some interfaces, and the indirect couplings or communication connections of devices or units can be electrical, mechanical, or other forms.

[0109] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0110] In addition, in each embodiment of the present invention, the functional units can all be integrated in a processing unit, or each unit can be separately used as a unit, or two or more units can be integrated in a unit; the above integrated units can be implemented in the form of hardware, or in the form of hardware plus software functional units.

[0111] Those of ordinary skill in the art can understand that all or part of the steps to implement the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including the above method embodiments; and the foregoing storage medium includes: various media such as removable storage devices, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0112] Alternatively, if the above integrated units of the present invention are implemented in the form of software function modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solutions of the embodiments of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. And the foregoing storage medium includes: various media such as removable storage devices, ROM, RAM, magnetic disks, or optical discs that can store program codes.

[0113] The methods disclosed in several method embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0114] The features disclosed in several product embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0115] The features disclosed in several method or device embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0116] The above content is a further detailed description of the present invention in combination with specific preferred implementation manners. It cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those skilled in the technical field to which the present invention belongs, without departing from the concept of the present invention, several equivalent substitutions or obvious variations can be made, and as long as the performance or use is the same, they should all be regarded as belonging to the protection scope of the present invention.

Claims

1. A method for collaborative optimization of distribution network and microgrid considering renewable energy and demand uncertainty flexibility, characterized in that: The following steps are involved: a. Construct the uncertainty set of main grid and microgrid demand and renewable energy output based on historical data; b. constructing a microgrid distributed robust optimization model using the uncertainty set; c. constructing a distribution network optimization model using the uncertainty set; d. Solve the microgrid distribution blue stick optimization model to obtain the microgrid electricity purchase intention; e. Solving the distribution network optimization model to obtain the distribution network power purchase intention; f. Check whether the microgrid and distribution network purchase intention meet the convergence criteria. If so, output the optimal decision of the distribution network microgrid. If not, return to step d for iterative solution until the convergence criteria are met; Among them, the process of solving the microgrid distributed robust optimization model and the distribution network optimization model in step d and step e is coordinated and optimized through the alternating direction multiplier method (ADMM) to decouple the coupling constraints of the microgrid and distribution network models, and accelerate convergence by dynamically adjusting the relaxation factor, thereby achieving energy balance and cost minimization between the distribution network and the microgrid.

2. The method for collaborative optimization of distribution network and microgrid according to claim 1, characterized in that: The construction of the microgrid distributed robust optimization model in step b includes: The model includes an objective function, which aims to minimize the Bayesian expectation of the microgrid operation cost, and the operation cost includes the day-ahead power purchase plan cost, energy storage degradation cost, daytime power purchase plan adjustment cost, generator operation cost and renewable energy curtailment cost; The model further includes a plurality of constraints, including a charge and discharge balance of the energy storage device, an upper and lower limit of the amount of electricity purchased, an upper and lower limit of the decision variables, and a balance between the output and demand of renewable energy.

3. The method for collaborative optimization of distribution network and microgrid according to claim 1, characterized in that: The construction of the distribution network optimization model in step c includes: The model includes an objective function, which aims to minimize the distribution network operation cost, and the operation cost includes the generator power generation cost, the upper power grid power purchase cost, the microgrid power purchase cost and the network loss cost; The model further includes a plurality of constraints related to active / reactive power flow balance of bus injection and branch transmission, bus voltage level, power balance of load demand and generator output, and upper and lower limits of generator output.

4. The method for collaborative optimization of distribution network and microgrid according to claim 1, characterized in that: The process of solving the microgrid distributed robust optimization model in step d includes: The Metropolis-Hastings algorithm and the Monte Carlo integration method are used to accelerate the solution of the inner layer expected value in the distributed robust optimization model to improve the calculation efficiency and solution speed.

5. The method for collaborative optimization of distribution network and microgrid according to claim 4, characterized in that: The process of solving the microgrid distributed robust optimization model in step d includes: Modeling the microgrid using distributed robust optimization, including minimizing the Bayesian expectation of operating costs that depend on the probability distribution of uncertainty variables; The uncertain variables include load and renewable energy output, and their historical data samples are used to construct a probability density function; The Metropolis-Hastings algorithm is used to accelerate the computation of inner layer expectations, which generates samples from complex probability distributions by constructing Markov chains for efficient sampling. The proposed distribution of the Metropolis-Hastings algorithm is a uniform distribution, and the target distribution is the posterior distribution of the uncertainty variable parameter; The Monte Carlo integration method is used to solve the integral to accelerate the calculation of the posterior distribution of the uncertain variable parameters. The method is suitable for the integral calculation of high-dimensional integrals and complex functions.

6. The method for collaborative optimization of distribution network and microgrid according to claim 1, characterized in that: Solving the microgrid distributed robust optimization model in step d includes: The alternating direction method of multipliers (ADMM) is used for collaborative optimization to decouple the coupling constraints of the microgrid and distribution network models. The convergence of the ADMM algorithm is accelerated by dynamically adjusting the relaxation factor, thereby improving the solution efficiency; In the process of solving the ADMM algorithm, the Metropolis-Hastings algorithm and the Monte Carlo integration method are used to accelerate the solution of the inner expected value in the model.

7. The method for coordinated optimization of distribution network and microgrid according to claim 1, characterized in that: Solving the distribution network optimization model in step e includes: The alternating direction method of multipliers (ADMM) is used for collaborative optimization to decouple the coupling constraints of the distribution network and microgrid models. The convergence of the ADMM algorithm is accelerated by dynamically adjusting the relaxation factor, thereby improving the solution efficiency; In the process of solving the ADMM algorithm, the Metropolis-Hastings algorithm and Monte Carlo integration method are used to accelerate the solution of the inner expected value in the model; The generator power generation cost in the objective function of the distribution network optimization model is a quadratic function of the generator power generation, the network loss cost is a linear function of the distribution network loss, and the power purchase cost of the upper power grid and the power purchase cost of the microgrid are linear functions of the power purchased from the upper power grid and the power purchased from the microgrid respectively.

8. The method for collaborative optimization of distribution network and microgrid according to claim 1, characterized in that: The process of checking whether the power purchase intention of the microgrid and the distribution network meets the convergence standard in step f includes: The coupling constraints between the distribution network model and the microgrid model are decoupled using the alternating direction multiplier method (ADMM) based on dynamic adjustment of relaxation factors. The ADMM algorithm includes initializing variables, iteratively solving microgrid and distribution network optimization models, and updating Lagrange multipliers and relaxation factors; In the iterative solution process, the relaxation factor is dynamically adjusted to accelerate convergence and improve solution stability, wherein the relaxation factor is updated using the original residual and the adjustment factor; Check whether the convergence conditions are met, including whether the difference in the power purchase intention is less than the preset tolerance or whether the number of iterations exceeds the preset maximum value; If the convergence conditions are met, the optimal decision of the distribution network microgrid is output; if not, the iterative solution continues until the convergence criteria are met.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for collaborative optimization of a distribution network and a microgrid is implemented as described in any one of claims 1 to 8.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for collaborative optimization of a distribution network and a microgrid is implemented as described in any one of claims 1 to 8.

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