Power demand side response double-layer optimization method and system for high-energy-consumption users

Abstract

The invention relates to the technical field of power system optimization, and discloses a power demand side response double-layer optimization method and system for high-energy-consumption users, and the method comprises the steps: constructing a power distribution network upper-layer model, carrying out the day-ahead prediction, and adjusting a dispatching means; constructing a load aggregator and high-energy-consumption user lower-layer model, formulating a demand response subsidy scheme, and ensuring a user production plan; constructing a single load regulation potential model and a load cluster regulation potential model to obtain an LA demand response potential model; designing a day-ahead-intra-day two-stage industrial adjustable load aggregation response strategy; and solving the double-layer optimization model by adopting an improved hunter and prey optimization algorithm to obtain an optimal scheduling strategy. The method is suitable for various high-energy-consumption industries, has wide applicability and popularization value, has important theoretical and practical value, is wide in application scene, realizes demand side management of the electric power system, improves the energy utilization efficiency, and optimizes the operation state of the electric power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization, and in particular to a double-layer optimization method and system for power demand side response for high energy consumption users. Background Art

[0002] With the increasing emphasis on renewable energy and energy conservation and emission reduction at home and abroad, the power market model represented by the rigid and carbon bubble models can no longer meet the needs of the current power market reform. At the same time, in fact, different regions have different load demands, and there are also uncertainties in new energy power generation and weather conditions. The operating conditions of power systems in different regions are also different. These reasons make it crucial to optimize the dispatch of power resources on a large spatial scale.

[0003] As an innovative response strategy for the power system, demand side response (DSR) for users in high-energy-consuming industries aims to ensure the orderly and stable operation of the power grid by real-time monitoring of system load conditions and power grid operation information on the user side through the use of modern communication, computing, and network control technologies.

[0004] Under the traditional power system operation mode, the power system must dispatch various power sources in a balanced manner according to load demand. DSR is a way to use end users to participate in the operation and dispatch of the power system, so that power resources can be balanced on both the supply and demand sides, thereby enhancing the economy, reliability and flexibility of the system.

[0005] The demand-side response mainly includes users in high-energy-consuming industries actively adjusting their electricity consumption time periods to change their electricity consumption patterns; the power grid dispatching center incentivizes or punishes users' electricity consumption on the basis of the original electricity price to achieve changes in electricity consumption patterns; new energy power plants, usually wind power or photovoltaic power generation, execute dispatching instructions based on power generation output forecasts and according to unit characteristics with the lowest peak-shaving cost, thereby achieving a balance between electricity demand and supply.

[0006] The main problem with traditional power systems in responding to the needs of users in high-energy-consuming industries is that most of the users in high-energy-consuming industries are large users of industrial production, and their production process has a large demand for electricity. Once the production process is affected, its economic benefits will drop significantly. Therefore, the flexibility in demand response is very limited. At the same time, due to the deepening reform of the power market, the existence of competition among traditional power production users will also lead to fluctuations in electricity prices, which will further affect the production stability of high-energy-consuming industries.

[0007] Therefore, how to build an efficient and fair electricity demand-side response mechanism based on the actual electricity consumption of users in high-energy-consuming industries to balance the stability, economy and environmental friendliness of power system operation is a key issue to be solved by the present invention. Summary of the invention

[0008] In view of the above existing problems, the present invention is proposed.

[0009] Therefore, the present invention provides a two-layer optimization method for power demand side response for high-energy-consuming users, proposes a two-layer optimization model for power demand side response for users in high-energy-consuming industries, and designs an improved hunter-prey optimization algorithm based on Sine chaotic mapping, dynamic cross boundary and parallel search mechanism to solve the model. The main technical problem solved is how to optimize the power demand side response of high-energy-consuming industries, and construct a two-layer optimization model, while solving the complexity and timeliness problems of model solving.

[0010] To solve the above technical problems, the present invention provides the following technical solutions, a two-layer optimization method for power demand side response for high-energy-consuming users, including: constructing an upper-layer model of the distribution network to perform day-ahead prediction and adjust the scheduling means; constructing a lower-layer model of load aggregators and high-energy-consuming users to formulate a demand response subsidy plan and ensure the user's production plan; constructing a single load regulation potential model and a load cluster regulation potential model to obtain an LA demand response potential model; designing a two-stage industrial adjustable load aggregation response strategy of day-ahead and intraday; and using an improved hunter-prey optimization algorithm to solve the two-layer optimization model to obtain the optimal scheduling strategy.

[0011] As a preferred solution of the double-layer optimization method for power demand side response for high-energy-consuming users described in the present invention, wherein: the construction of the upper-layer model of the distribution network includes, by predicting the main grid electricity price for the next day, rigid load power consumption, and the maximum output of the new energy power plant, combined with the available capacity of the energy storage equipment, the next day's power consumption reported by the LA and the adjustable range, while ensuring the safe, stable and efficient operation of the system, by adjusting the scheduling means, a double-layer optimization model with the goal of maximizing the profit and operation safety of the distribution network is constructed.

[0012] As a preferred solution of the two-layer optimization method for power demand side response for high energy consumption users described in the present invention, wherein: the user lower layer model includes modeling from the perspectives of load aggregators and high energy consumption users as the lower layer model of the two-layer scheduling optimization model;

[0013] Among them, load aggregators need to ensure that the production plans of high-energy-consuming users in the park are met while maximizing the profits of demand response resources, increase the park's kilowatt-hour output, and formulate appropriate demand response subsidy plans so that users can complete their daily production plans at the lowest electricity price.

[0014] As a preferred solution of the two-layer optimization method for power demand side response for high energy consumption users described in the present invention, wherein: the regulation potential model includes respectively constructing a single load regulation potential model and a load cluster regulation potential model for each high energy consumption industry user, and further obtaining a LA demand response potential model;

[0015] In the constructed model, the demand response electricity of LA serves as a common decision variable between the upper and lower levels. The upper-level DSO adjusts the demand while considering its own interests, which affects the lower-level LA in formulating the regulation plan. The regulation plans formulated by LA and high-energy-consuming users after considering their own interests are different from the plans issued by DSO, which affects the profits of DSO.

[0016] As a preferred scheme of the two-layer optimization method for power demand side response for high-energy-consuming users described in the present invention, the adjustable load aggregation response strategy includes using triangular fuzzy numbers to describe the uncertainty of the response, establishing an industrial adjustable load aggregation response model that considers the start-stop response speed and response time, and based on the incentive-based demand response market, with the goal of minimizing the calling cost, proposing a two-stage industrial adjustable load aggregation response strategy of day-ahead and intraday.

[0017] As a preferred scheme of the two-layer optimization method for power demand side response for high-energy-consuming users described in the present invention, the improved hunter-prey optimization algorithm includes mapping the Sine chaotic sequence to the solution space to generate the initial population of the algorithm, and designing a parallel search mechanism based on dynamic boundaries to maximize the optimization potential of each individual in the population. Specifically, the population is sorted according to the fitness value, and divided into three sub-populations using dynamic boundaries. Each sub-population adopts a different optimization strategy for parallel search.

[0018] As a preferred solution of the two-layer optimization method for power demand side response for high energy consumption users described in the present invention, the parallel search mechanism based on dynamic boundaries includes firstly sorting each individual in the population from large to small according to the fitness value, and dividing the population X into elite populations X through dynamic boundaries m and n. i,a , intermediate population X i,b and the inferior population X i,c Three sub-populations are updated simultaneously in different ways:

[0019]

[0020] Adopting the best strategy based on the strengths and weaknesses of individuals is conducive to promoting information exchange among populations and enhancing the global search capability of the algorithm.

[0021] Another object of the present invention is to provide a two-layer optimization system for electricity demand side response for high-energy-consuming users, which can ensure that the production plans of high-energy-consuming users are met, maintain production efficiency even during demand response, improve user satisfaction, and help users optimize production processes and improve production efficiency by providing flexible electricity usage plans.

[0022] As a preferred solution of the two-layer optimization system for power demand side response for high energy consumption users described in the present invention, it includes: a prediction and scheduling module, a load aggregation and user model module, a regulation potential evaluation module, a response strategy formulation module, and an optimization algorithm execution module;

[0023] The forecasting and dispatching module is responsible for making day-ahead forecasts and adjusting dispatching measures based on the available capacity of energy storage equipment and the power consumption reported by LA to ensure safe, stable and efficient operation of the distribution network;

[0024] The load aggregation and user model module constructs the lower-level models of load aggregators and high-energy-consuming users, formulates demand response subsidy plans, ensures the maximization of demand response resource profits, meets the production plans of high-energy-consuming users in the park, and ensures that users complete their daily production plans at the lowest electricity prices;

[0025] The regulation potential evaluation module constructs a single load regulation potential model and a load cluster regulation potential model, estimates the power increase and decrease capabilities of the load group under the LA, and provides decision support for the double-layer optimization model;

[0026] The response strategy formulation module designs a two-stage industrial adjustable load aggregation response strategy of day-ahead and day-intraday, uses triangular fuzzy numbers to describe response uncertainty, establishes a response model that considers start-stop response speed and response time, and proposes a call cost minimization strategy based on the incentive-based demand response market;

[0027] The optimization algorithm execution module adopts an improved hunter-prey optimization algorithm to solve the double-layer optimization model, including initializing the population, adaptive parameter adjustment, updating the population position, and introducing Sine chaos mapping, dynamic cross-boundary and parallel search mechanisms to obtain the optimal scheduling strategy.

[0028] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the processor implements the steps of a two-layer optimization method for power demand side response for high energy consumption users.

[0029] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of a two-layer optimization method for power demand side response for high energy consumption users are implemented.

[0030] Beneficial effects of the invention: This patent constructs a two-layer optimization model for the power demand side response for users in high-energy-consuming industries, effectively realizes the coordination of power demand between the distribution network and high-energy-consuming users, and improves the safety, stability and efficiency of power system operation. By introducing the improved hunter-prey optimization algorithm (HPO) with Sine chaotic mapping, dynamic cross-boundary and parallel search mechanism, the complexity and timeliness of model solution are successfully solved, greatly improving the calculation efficiency and solution accuracy. Through cross-regional load scheduling, the optimal allocation of power resources is effectively achieved to adapt to the differences in energy demand in different regions in different time periods, greatly improving energy utilization efficiency and economic benefits.

[0031] The method described in this patent is applicable to various high-energy-consuming industries, has wide applicability and promotion value, and has important theoretical and practical value; this solution has a wide range of applicable scenarios, and can not only be applied to high-energy-consuming industries such as steel, chemical industry, etc., but can also be introduced into new energy systems such as smart grids and microgrids to achieve demand-side management of power systems, improve energy utilization efficiency, and optimize the operating status of power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0033] Figure 1 A schematic flow chart of a two-layer optimization method for power demand-side response for high-energy-consuming users provided in one embodiment of the present invention.

[0034] Figure 2 A demand response two-layer optimization model framework of a two-layer optimization method for power demand side response for high energy consumption users is provided in one embodiment of the present invention.

[0035] Figure 3 A Sine chaotic mapping of a double-layer optimization method for power demand side response for high energy consumption users is provided in one embodiment of the present invention.

[0036] Figure 4 A dynamic crossing boundary of a two-layer optimization method for power demand side response for high energy consumption users provided by an embodiment of the present invention.

[0037] Figure 5 An IHPO algorithm flow chart of a two-layer optimization method for power demand side response for high energy consumption users provided in one embodiment of the present invention.

[0038] Figure 6 The load dispatching result of cross-regional load high energy consumption industries of the double-layer optimization method of power demand side response for high energy consumption users provided by an embodiment of the present invention.

[0039] Figure 7 A schematic diagram of the working modules of a two-layer optimization system for power demand-side response for high-energy-consuming users provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0041] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0042] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0043] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0044] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, which are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0045] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0046] Example 1, reference Figure 1 - Figure 5 , which is the first embodiment of the present invention, and provides a two-layer optimization method for power demand side response for high energy consumption users, including:

[0047] S1: Build the upper-level model of the distribution network, make day-ahead forecasts and adjust the dispatching methods.

[0048] Furthermore, the model is firstly built from the perspective of the distribution network. The distribution network system is the upper model of the two-layer dispatching optimization model. By predicting the main grid electricity price, rigid load electricity consumption, and maximum output of new energy power plants for the next day, combined with the available capacity of the energy storage equipment, the next day's electricity consumption reported by the LA and the adjustable range, while ensuring the safe, stable and efficient operation of the system, a two-layer optimization model is constructed by adjusting the dispatching means with the goal of maximizing the profit and operation safety of the distribution network.

[0049] S2: Build the lower-level model of load aggregators and high-energy-consuming users, formulate demand response subsidy plans, and ensure user production plans.

[0050] Furthermore, the model is modeled from the perspective of load aggregators and high-energy-consuming users as the lower model of the two-layer dispatch optimization model. The load aggregators need to ensure that the production plans of high-energy-consuming users in the park are met while maximizing the profit of demand response resources, increase the output of the park's kilowatt-hour value, and formulate appropriate demand response subsidy plans. Users need to complete their daily production plans at the lowest electricity price.

[0051] S3: Construct a single load regulation potential model and a load cluster regulation potential model to obtain the LA demand response potential model.

[0052] Furthermore, a single load regulation potential model and a load cluster regulation potential model are constructed for each high-energy-consuming industry user, and the LA demand response potential model is further obtained to estimate the power increase and decrease capabilities of the load group under the jurisdiction of the LA. In the constructed model, the LA's demand response power serves as a common decision variable between the upper and lower layers. The upper-level DSO, considering its own interests, will affect the lower-level LA in formulating the regulation plan. The regulation plans formulated by LA and high-energy-consuming users after considering their own interests are not exactly the same as the plans issued by the DSO, which will affect the profits of the DSO. The goals of the upper and lower layers are different, but the realization of their goals is affected by the same decision variable, namely the demand response power. Therefore, the proposed model will take into account the interests of the upper and lower layers and ultimately achieve a balance of interests. Figure 2 Shown is the overall framework of the two-layer optimization model.

[0053] S4: Design a two-stage industrial adjustable load aggregation response strategy for the day-ahead and intraday periods, and use the improved hunter-prey optimization algorithm to solve the two-level optimization model to obtain the optimal scheduling strategy.

[0054] Furthermore, considering the problem of response error when industrial adjustable loads are aggregated to participate in demand response, a two-stage industrial adjustable load aggregation response strategy of day-ahead and intraday is designed: first, triangular fuzzy numbers are used to describe the uncertainty of the response, and an industrial adjustable load aggregation response model considering the start-stop response speed and response time is established. Then, based on the incentive-based demand response market, a two-stage industrial adjustable load aggregation response strategy of day-ahead and intraday is proposed with the goal of minimizing the calling cost.

[0055] Furthermore, in order to solve the above two-layer scheduling optimization model, we first use the Karush-Kuhn-Tucker (KKT) condition to transform the two-layer problem into a single layer, that is, the objective function and constraint conditions of the lower layer model are equivalently transformed by the KKT condition and used as the constraint conditions of the upper layer model to simplify the model for calculation. First, by introducing the inequality constraint coefficient μ t 1 , μ t 2 , μ t 3 and the equality constraint coefficient ω 1 ,ω t 2 ,ω t 3 , the original lower-level objective function and constraints are constructed into a Lagrangian function L. The constructed Lagrangian function can be transformed by KKT to obtain the newly added constraints of the upper-level model.

[0056] Considering the complexity and timeliness of model solution, an improved Hunter-Prey Optimizer (HPO) based on Sine chaotic mapping, dynamic cross boundary and parallel search mechanism is designed to solve it, so as to obtain the optimal demand response two-layer optimal scheduling strategy. The detailed IHPO algorithm construction scheme is shown in the following technical solutions.

[0057] HPO is a new type of swarm intelligence optimization algorithm. It simulates the behaviors of carnivores such as lions, leopards and wolves, and prey such as deer and antelopes in nature to adaptively update and optimize problems, with characteristics such as fast convergence speed and strong optimization ability.

[0058] The algorithm initializes the population randomly, as shown in the following formula:

[0059] X = rand(Np, dim) × (ub - lb) + lb

[0060] Where Np represents the population size, dim represents the dimension of problem variables, and ub and lb represent the upper and lower limits of values respectively.

[0061] An adaptive parameter Z is used to adjust the update amplitude, and its calculation formula is

[0062] P = R 1 < C; IDX = (P == 0)

[0063]

[0064] Where R1 and R3 are random vectors within [0, 1], R2 is a random number within [0, 1], P is the index value of R1 < C, and IDX is the index value of vector R1 that satisfies (P == 0). C is an iteration parameter used to balance the relationship between global and local optimization, and its calculation formula is as follows:

[0065]

[0066] Where t is the current iteration number and T_iter is the maximum iteration number.

[0067] HPO searches for the optimal solution by iteratively updating the positions of the population. A parameter β and a random number R5 within [0, 1] are used to control the update strategy to select whether to update the population in the way of hunters or prey. The specific calculation formula is as follows. Through simulation comparison using multiple benchmark test functions, the parameter β in this scheme is set to 0.8.

[0068]

[0069] Where X(t+1) is the position of the individual after the update, X(t) is the position before the update, R4 is a random number in [0,1], T pos is the position of the global optimal individual. Where μ represents the average position of individuals in the population, P pos represents the position of the prey, and the calculation formulas are as follows:

[0070]

[0071] P pos =X i |i is sorted D euc (kbest)

[0072]

[0073] Where X i Indicates the position of individual i in the population, kbest is a decreasing control parameter, and kbest = N at the beginning of the algorithm. euc (i) represents the distance between each individual and the average position. The individuals in the population are divided into groups according to D euc (i) Sort to get the prey position P pos .X i,j represents the position of individual i in the jth dimension, μ j is the average value of the j-th dimension position.

[0074] Taking into account the problems of poor initial population diversity and decreased convergence performance in the later stage of the algorithm in the traditional HPO algorithm, this scheme proposes an improved HPO algorithm (IHPO) based on Sine chaotic mapping, dynamic cross boundary and parallel search mechanism. In order to improve the convergence speed of the algorithm, the Sine chaotic sequence is mapped to the solution space to generate the initial population of the algorithm. A parallel search mechanism based on dynamic boundary is further designed to maximize the optimization potential of each individual in the population. Specifically, the population is sorted according to the fitness value and divided into three sub-populations using dynamic boundaries. Each sub-population adopts different optimization strategies for parallel search.

[0075] Since traditional HPO generates the initial population in a random way, it is difficult to ensure the diversity of the population. Uneven or overly dense distribution of individuals will affect the convergence speed of the algorithm and the quality of the solution. However, chaotic sequences have good ergodicity and arbitrariness, which can solve the problems of the above methods. Therefore, introducing Sine chaotic mapping to initialize the population based on traditional HPO can effectively improve the algorithm search efficiency and solution accuracy.

[0076] Sine mapping is one of the classic representatives of chaotic mapping. Compared with chaotic mapping such as Tent mapping and Logistic mapping, it has the advantages of simple structure, good chaotic characteristics and fast generation speed. It can evenly map the initial individuals to the entire solution space without repetition, thereby ensuring the quality and diversity of the initial population. The specific generation formula of Sine chaotic sequence is shown in the following formula.

[0077] x i+1 =ρsin(πx i ),x∈[-1,1]

[0078] Where ρ is the chaos parameter, which is usually set to 1. At this time, the sequence will be in a completely chaotic state, and the values ​​of the sequence will be evenly distributed between [0,1]. The generated chaotic sequence is mapped to the individual solution space, as shown below, to obtain a high-quality initial population.

[0079] X i =lb+(1+x i )×(ub-lb) / 2

[0080] In order to finely divide the algorithm population and maximize the search ability of each individual, our company proposed a dynamic boundary mechanism of HPO. This mechanism dynamically divides the population into layers and performs parallel searches between each layer, which can effectively improve the diversity of the population. The classification process is shown in the following formula. First, each individual in the population is sorted from large to small according to the fitness value, and the population X is divided into elite population X through dynamic boundaries m and n. i,a , intermediate population X i,b and the inferior population X i,c Three sub-populations, the three sub-populations are updated simultaneously in different ways.

[0081]

[0082] The following introduces the specific update method after being divided into three sub-populations.

[0083] (1) Intermediate population: traditional HPO update method

[0084] For the intermediate population X i,b , the individual fitness is between good and bad, and we choose not to impose too much intervention on its update strategy, so the original HPO individual update formula is still used.

[0085] (2) Elite population: elite guidance strategy

[0086] Targeting the elite population X with higher fitness in the population i,a, and use the elite guidance strategy to update it, try to play the leading role of the global optimal solution without destroying the position of the dominant individual, and make more full development of the dominant position in the solution space. The specific method is to introduce individual extreme values ​​on the basis of the HPO update formula, so as to accelerate the convergence speed of the head population.

[0087]

[0088] (3) Disadvantaged population: destructive disturbance strategy

[0089] For the disadvantaged population X i,c , its individual can hardly play an effective role in the optimization of the algorithm, so a more "radical" update strategy is needed. The combination of reverse learning and Gaussian mutation is used to update X i,c The population is updated with destructive disturbance. Reverse learning is to randomly generate a reverse solution in the solution space according to the position of the original solution, and its randomness is controlled by the random number k. The Gaussian mutation strategy can effectively enhance the local and global optimization ability of the algorithm and has been widely used in the improvement strategies of various metaheuristic algorithms. The destructive disturbance update based on reverse learning and Gaussian mutation is shown in the following formula.

[0090]

[0091] Where k is set to a random number between [0,1], μ represents the mean, and σ 2 Indicates the variance. Considering the value range of weights and wavelet factors, this scheme is set to μ=0, λ=1.

[0092] Example 2, reference Figure 6 , which is an embodiment of the present invention, provides a two-layer optimization method for power demand side response for high-energy-consuming users. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0093] The figure shows the load dispatch results of two cross-regional high-load energy-consuming industries. It can be seen that this solution effectively achieves peak load reduction and valley filling. By reducing the load during peak periods and increasing the load during valley periods, the goal of balancing power supply and demand is achieved. This not only helps to improve the stability and reliability of the power system, but also achieves obvious advantages in energy utilization and economic benefits, providing a feasible example for future energy sustainable development.

[0094] Based on the simulation results of cross-regional load high-energy-consuming industries, a strategy for improving energy efficiency in high-energy-consuming industries through cross-regional load scheduling is proposed. This strategy mainly relies on optimizing the allocation of power resources to adapt to the differences in energy demand in different regions at different time periods. The implementation of this strategy involves dynamic adjustment of power loads in high-energy-consuming industries.

[0095] Through the IHPO algorithm, we have accurately analyzed the energy demand patterns and supply capacity of each region, and successfully achieved the optimal energy configuration. The implementation results show that through this cross-regional load dispatch, our company has achieved an improvement of about 25% in overall efficiency indicators. The successful implementation of this strategy demonstrates our innovative thinking in energy management and dispatch, and provides a sustainable development path for high-energy-consuming industries.

[0096] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0097] Embodiment 3, the third embodiment of the present invention, is different from the first two embodiments in that:

[0098] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0099] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0100] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0101] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0102] Example 4, reference Figure 7 , which is an embodiment of the present invention, provides a two-layer optimization system for power demand side response for high energy consumption users, characterized by: comprising a prediction and scheduling module 1, a load aggregation and user model module 2, a regulation potential assessment module 3, a response strategy formulation module 4, and an optimization algorithm execution module 5;

[0103] Forecasting and dispatching module 1 is responsible for making day-ahead forecasts and adjusting dispatching measures based on the available capacity of energy storage equipment and the power consumption reported by LA to ensure safe, stable and efficient operation of the distribution network;

[0104] Load aggregation and user model module 2, builds the lower-level model of load aggregators and high-energy-consuming users, formulates demand response subsidy plans, ensures the maximization of demand response resource profits, meets the production plans of high-energy-consuming users in the park, and ensures that users complete their daily production plans at the lowest electricity price;

[0105] Regulation potential assessment module 3 builds a single load regulation potential model and a load cluster regulation potential model to estimate the power increase and decrease capabilities of the load group under the jurisdiction of LA, providing decision support for the two-layer optimization model;

[0106] Response strategy formulation module 4 designs a two-stage industrial adjustable load aggregation response strategy for the day-ahead and day-intraday periods, uses triangular fuzzy numbers to describe response uncertainty, establishes a response model that considers the start-stop response speed and response time, and proposes a call cost minimization strategy based on the incentive-based demand response market;

[0107] The optimization algorithm execution module 5 adopts the improved hunter-prey optimization algorithm to solve the two-layer optimization model, including initializing the population, adaptive parameter adjustment, updating the population position, and introducing Sine chaos mapping, dynamic cross-boundary and parallel search mechanism to obtain the optimal scheduling strategy.

[0108] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A two-layer optimization method for power demand side response for high energy consumption users, characterized by: include, Build the upper-level model of the distribution network, make day-ahead forecasts and adjust dispatching measures; Build load aggregators and high-energy-consuming users’ lower-level models, formulate demand response subsidy programs, and ensure user production plans; Construct a single load regulation potential model and a load cluster regulation potential model to obtain the LA demand response potential model; Design a two-stage day-ahead and day-intraday industrial adjustable load aggregation response strategy; The improved hunter-prey optimization algorithm is used to solve the two-layer optimization model and obtain the optimal scheduling strategy.

2. The two-layer optimization method for power demand side response for high energy consumption users according to claim 1, characterized in that: The construction of the upper-level model of the distribution network includes, by predicting the main grid electricity price for the next day, the rigid load electricity consumption, and the maximum output of the new energy power plant, combined with the available capacity of the energy storage equipment, the next day's electricity consumption reported by the LA and the adjustable range, while ensuring the safe, stable and efficient operation of the system, by adjusting the dispatching means, a two-layer optimization model is constructed with the goal of maximizing the profit and operation safety of the distribution network.

3. The two-layer optimization method for power demand side response for high energy consumption users as claimed in claim 2, characterized in that: The user lower layer model includes modeling from the perspectives of load aggregators and high energy consumption users, as a lower layer model of the two-layer scheduling optimization model; Among them, load aggregators need to ensure that the production plans of high-energy-consuming users in the park are met while maximizing the profits of demand response resources, increase the park's kilowatt-hour output, and formulate appropriate demand response subsidy plans so that users can complete their daily production plans at the lowest electricity price.

4. The two-layer optimization method for power demand side response for high energy consumption users as claimed in claim 3 is characterized by: The regulation potential model includes constructing a single load regulation potential model and a load cluster regulation potential model for each high energy consumption industry user, and further obtaining a LA demand response potential model; In the constructed model, the demand response electricity of LA serves as a common decision variable between the upper and lower levels. The upper-level DSO adjusts the demand while considering its own interests, which affects the lower-level LA in formulating the regulation plan. The regulation plans formulated by LA and high-energy-consuming users after considering their own interests are different from the plans issued by DSO, which affects the profits of DSO.

5. The two-layer optimization method for power demand side response for high energy consumption users as claimed in claim 4, characterized in that: The adjustable load aggregation response strategy includes using triangular fuzzy numbers to describe the uncertainty of the response, establishing an industrial adjustable load aggregation response model that takes into account the start-stop response speed and response time, and based on the incentive-based demand response market, with the goal of minimizing the call cost, proposing a two-stage day-ahead and day-intraday industrial adjustable load aggregation response strategy.

6. The two-layer optimization method for power demand side response for high energy consumption users as claimed in claim 5, characterized in that: The improved hunter-prey optimization algorithm includes mapping the Sine chaotic sequence to the solution space to generate the initial population of the algorithm, designing a parallel search mechanism based on dynamic boundaries to maximize the optimization potential of each individual in the population, specifically sorting the population according to the fitness value, dividing it into three sub-populations using dynamic boundaries, and each sub-population adopts a different optimization strategy for parallel search.

7. The double-layer optimization method for power demand side response for high energy consumption users according to claim 6, characterized in that: The parallel search mechanism based on dynamic boundaries includes firstly sorting each individual in the population from large to small according to the fitness value, and dividing the population X into elite populations X and N through dynamic boundaries m and n. i,a , intermediate population X i,b and the inferior population X i,c Three sub-populations are updated simultaneously in different ways: Adopting the best strategy based on the strengths and weaknesses of individuals is conducive to promoting information exchange among populations and enhancing the global search capability of the algorithm.

8. A system using the double-layer optimization method for power demand side response for high energy consumption users as claimed in any one of claims 1 to 7, characterized in that: It includes prediction and scheduling module, load aggregation and user model module, regulation potential assessment module, response strategy formulation module, and optimization algorithm execution module; The forecasting and dispatching module is responsible for making day-ahead forecasts and adjusting dispatching measures based on the available capacity of energy storage equipment and the power consumption reported by LA to ensure safe, stable and efficient operation of the distribution network; The load aggregation and user model module constructs the lower-level models of load aggregators and high-energy-consuming users, formulates demand response subsidy plans, ensures the maximization of demand response resource profits, meets the production plans of high-energy-consuming users in the park, and ensures that users complete their daily production plans at the lowest electricity prices; The regulation potential evaluation module constructs a single load regulation potential model and a load cluster regulation potential model, estimates the power increase and decrease capabilities of the load group under the LA, and provides decision support for the double-layer optimization model; The response strategy formulation module designs a two-stage industrial adjustable load aggregation response strategy of day-ahead and day-intraday, uses triangular fuzzy numbers to describe response uncertainty, establishes a response model that considers start-stop response speed and response time, and proposes a call cost minimization strategy based on the incentive-based demand response market; The optimization algorithm execution module adopts an improved hunter-prey optimization algorithm to solve the double-layer optimization model, including initializing the population, adaptive parameter adjustment, updating the population position, and introducing Sine chaos mapping, dynamic cross-boundary and parallel search mechanisms to obtain the optimal scheduling strategy.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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