Centralized distributed integrated demand response method and system for integrated energy system

Abstract

The centralized distributed integrated demand response method and system for the integrated energy system obtains the planned electricity consumption of each park, generates noise disturbance based on the differential privacy mechanism, and determines the response amount of each park by using the peak-shaving demand of the power grid and the planned electricity consumption after adding the noise disturbance; the difference between the planned electricity consumption and the response amount of each park is used as the electricity baseline value for each park to participate in the response; the reward and punishment cost of each park from the integrated energy park operator is determined based on the difference between the electricity baseline value for each park's participation in the response and the actual electricity consumption of each park; the response amount corresponding to the minimum difference between the operating cost of each park and the reward and punishment cost is used as the optimal response amount of each park; the demand response amount instruction of each park is determined with the optimization goal of minimizing the difference between the optimal response amount and the response amount of each park and maximizing the income of the integrated energy park operator, so as to solve the problems of privacy leakage, low participation willingness and heavy computational burden of industrial parks in the existing demand response mechanism.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy demand response of smart grids, and specifically relates to a centralized distributed integrated demand response method and system for an integrated energy system. Background Art

[0002] Currently, the application of integrated demand response mechanisms is still in its early stages, lacking a truly effective integrated demand response mechanism. In practice, although various approaches have been proposed, the lack of unified standards and coordination mechanisms makes existing demand response schemes difficult to fully and effectively implement, particularly for residential and commercial users. Residential and commercial users have relatively dispersed load distribution, with individual loads being relatively small and the user population being large. Consequently, demand response implementation is challenging to coordinate interests among individuals, aggregate flexible resources, and achieve low scheduling efficiency. Furthermore, demand response preferences vary significantly among different user types, increasing the complexity of flexibility management and control. Therefore, the current implementation of integrated demand response for these user groups faces significant challenges. In contrast, industrial parks have large, relatively concentrated loads, and are relatively easy to regulate. Therefore, as a target for integrated demand response, industrial parks hold great potential for implementing integrated demand response. Industrial parks not only possess strong demand response capabilities but also typically utilize multiple energy sources, including electricity, heat, cooling, and gas, forming a typical integrated energy system. In this multi-energy synergy, energy utilization within the park can be more efficiently scheduled and respond to demand fluctuations, thereby better achieving demand response goals. Based on this characteristic, industrial parks have great advantages in the application of demand response. However, despite the advantages of industrial parks in implementing comprehensive demand response, there are still some shortcomings in existing technologies. Existing demand response mechanisms are often carried out in a centralized or distributed manner, lacking effective integration and optimization of the two, resulting in the potential in response efficiency, scheduling flexibility, and resource utilization not being fully tapped. In addition, the park's energy system involves multiple energy forms, and the existing scheduling methods fail to fully consider the synergy between various energy sources, thus affecting the overall energy management efficiency of the park. Summary of the Invention

[0003] In order to address the deficiencies in the prior art, the present invention provides a centralized distributed integrated demand response method and system for an integrated energy system. This method does not require each industrial park to provide a large amount of information to the outside world, thereby protecting the privacy of each industrial park and increasing the enthusiasm of the industrial park to participate in demand response. It also delegates more calculations to each park to complete independently, thereby reducing the performance requirements for the integrated energy park operator and reducing the solution time. It solves the problems of privacy leakage of industrial parks, low willingness to participate, and heavy computational burden in the demand response mechanism of the prior art.

[0004] The present invention adopts the following technical solutions.

[0005] The present invention proposes a centralized and distributed integrated demand response method for an integrated energy system, comprising:

[0006] Obtain the planned electricity consumption of each park, generate noise perturbations based on the differential privacy mechanism, and use the peak-shaving demand of the power grid and the planned electricity consumption after adding noise perturbations to determine the response amount of each park. The difference between the planned electricity consumption and the response amount of each park is used as the electricity consumption baseline value for each park to participate in the response.

[0007] Based on the difference between the baseline electricity consumption of each park and its actual electricity consumption, the reward and penalty costs each park receives from the integrated energy park operator are determined; the response amount corresponding to the minimum difference between the operating cost of each park and the reward and penalty cost is taken as the optimal response amount for each park;

[0008] The demand response quantity instructions for each park are determined with the optimization goal of minimizing the difference between the optimal response quantity and the response quantity of each park and maximizing the income of the integrated energy park operator.

[0009] Preferably, the planned electricity consumption of each park is obtained, and noise perturbations are generated based on the differential privacy mechanism. The park adds noise perturbations to the planned electricity consumption and then reports them to the operator;

[0010] Park During peak load period The planned electricity consumption is , the park is locally Add noise perturbation , get the planned power consumption after adding noise disturbance = + ;

[0011] Among them, noise perturbation is generated based on the differential privacy mechanism, The probability distribution function of is shown in the following relationship:

[0012]

[0013] Where, Noise disturbance The probability distribution function of is the noise control parameter, .

[0014] Preferably, the planned power consumption after adding noise disturbance in each park is The peak-shaving demand of the upper power grid is allocated to each park as the response quantity of each park, satisfying the following relationship:

[0015]

[0016] Where, For the park During peak load period The response amount, For the power grid during peak load period Peak shaving demand, For the park During peak load period Increase the planned electricity consumption after the noise disturbance.

[0017] Preferably, the difference between the planned power consumption of each park and the response amount is used as the power consumption benchmark value for each park to participate in the response, satisfying the following relationship:

[0018]

[0019] Where, For the park During peak load period The electricity consumption benchmark value for participating responses, For the park During peak load period Planned electricity consumption.

[0020] Preferably, the park receives corresponding penalties or rewards from the integrated energy park operator, satisfying the following relationship:

[0021]

[0022] In the formula, when hour, For integrated energy park operators During peak load period The reward, when hour, Provide comprehensive energy park operators with During peak load period punishment, For the park During peak load period of electricity consumption, Peak load period The unit price of reward or punishment, For the park During peak load period The disturbance compensation factor.

[0023] Preferably, the disturbance compensation factor satisfies the following relationship:

[0024] .

[0025] Preferably, the optimization objective satisfies the following relationship:

[0026]

[0027] Where, To optimize the goal, For the park During peak load period The optimal response amount, For the park During peak load period The response amount, Revenue for integrated energy park operators.

[0028]

[0029]

[0030] Where, The electricity sales revenue for the integrated energy park operator, Peak load period The unit price of electricity sales, For gas units during peak load period Output electrical power;

[0031]

[0032] Where, Heat sales revenue for integrated energy park operators, The unit price for selling heat;

[0033]

[0034] Where, Revenue from selling peak load for integrated energy park operators, Peak load period The unit price of selling peak load, For the power grid during peak load period Peak shaving demand;

[0035]

[0036] Where, For gas units during peak load period fuel costs, For gas units during peak load period Output electrical power, 、 、 are the cost coefficients corresponding to the output unit electric power, For gas units during peak load period Output thermal power, 、 、 are the cost coefficients corresponding to the output unit thermal power;

[0037]

[0038] Where, Compensation for integrated energy park operators, Provide comprehensive energy park operators with During peak load period rewards or punishments.

[0039] Preferably, when the difference between the optimal response amount and the response amount of each park is minimized and the income of the integrated energy park operator is maximized, the power consumption instructions of each park satisfy the following relationship:

[0040]

[0041] Where, For the park During peak load period The power consumption instruction, For the park During peak load period Planned electricity consumption, For the power grid during peak load period Peak shaving demand, For gas units during peak load period Output electrical power.

[0042] The present invention also proposes a centralized distributed integrated demand response system for an integrated energy system, comprising:

[0043] The park execution module is used to obtain the planned electricity consumption of each park, generate noise perturbations based on the differential privacy mechanism, and use the peak-shaving demand of the power grid and the planned electricity consumption after adding noise perturbations to determine the response amount of each park. The difference between the planned electricity consumption and the response amount of each park is used as the electricity consumption baseline value for each park to participate in the response.

[0044] The operator execution module is used to determine the reward and penalty costs each park receives from the integrated energy park operator based on the difference between the power consumption baseline value of each park's participation and the actual power consumption of each park. The response amount corresponding to the minimum difference between the operating cost of each park and the reward and penalty cost is used as the optimal response amount of each park.

[0045] The integrated execution module is used to determine the demand response quantity instructions of each park with the optimization goal of minimizing the difference between the optimal response quantity and the response quantity of each park and maximizing the income of the integrated energy park operator.

[0046] The beneficial effects of the present invention are that, compared with the prior art, at least the method proposed by the present invention optimizes the response volume and revenue of industrial park and integrated energy park operators, ensuring that industrial parks can actively participate in demand response while protecting privacy, while also improving the economic benefits of integrated energy park operators. This significantly reduces the amount of external information that each industrial park needs to provide, effectively protecting the park's operational data and privacy. Through a combination of centralized and distributed methods, the park completes data processing and demand response calculations internally, avoiding the external sharing of sensitive data, thereby enhancing the park's security and willingness to participate.

[0047] The present invention reduces the computing requirements for central operators and the load on the central system by delegating computing tasks to each park; this distributed computing method not only reduces the consumption of computing resources, but also shortens the system solution time and improves the response speed, thereby greatly improving the overall operating efficiency. Using an optimized centralized-distributed scheduling strategy, more flexible energy management can be achieved while ensuring the effectiveness of demand response, reducing the complexity of energy management within the park, while improving the park's ability and flexibility to participate in demand response. The park does not need to expose too many operational details and can independently control the calculations and decisions in the participation process, which increases the park's enthusiasm for participating in the demand response mechanism. Compared with the traditional centralized demand response model, the present invention significantly improves the park's acceptance and coordination of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Flowchart of the centralized and distributed integrated demand response method for the integrated energy system proposed by the present invention;

[0049] Figure 2 It is the total power purchase amount of the post-industrial park aggregate in the embodiment of the present invention. DETAILED DESCRIPTION

[0050] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0051] Based on the existing centralized-distributed architecture, this invention proposes an improved demand response coordination method, which not only performs collaborative optimization of upper-level profits and lower-level costs through a two-layer optimization model, but also introduces a privacy-preserving data interaction mechanism, a dynamic evaluation model of the park's response capability, and a dynamic reward and punishment response function, thereby constructing an efficient, safe, and economical integrated energy system scheduling mechanism.

[0052] The centralized distributed integrated demand response method of the integrated energy system proposed in this invention is as follows: Figure 1 Shown, including:

[0053] Step 1: Obtain the planned electricity consumption of each park, generate noise disturbance based on the differential privacy mechanism, and use the peak-shaving demand of the power grid and the planned electricity consumption after adding noise disturbance to determine the response amount of each park; the difference between the planned electricity consumption and the response amount of each park is used as the electricity consumption baseline value for each park to participate in the response.

[0054] Specifically, step 1 includes:

[0055] Step 1.1: Obtain the planned electricity consumption of each park, generate noise perturbations based on the differential privacy mechanism, and report the noise perturbations to the planned electricity consumption to the operator.

[0056] Specifically, the park During peak load period The planned electricity consumption is , the park is locally Add noise perturbation , get the planned power consumption after adding noise disturbance = + ;

[0057] Among them, noise perturbation is generated based on the differential privacy mechanism, The probability distribution function of is shown in the following relationship:

[0058]

[0059] Where, Noise disturbance The probability distribution function of is the noise control parameter, ;

[0060] Noise disturbance Obeying the Laplace distribution makes Satisfying differential privacy constraints, parameters Control the size of the noise, which in turn affects the strength of privacy protection. The value will lead to stronger privacy protection, but it will also increase the deviation of the disturbed data; through the introduction of this differential privacy mechanism, the park does not need to provide its sensitive electricity consumption data to the operator, but only needs to upload the data after noise disturbance. This will not affect the effectiveness of the peak-shaving calculation while protecting the privacy of the park, thereby realizing the unification of local retention of electricity consumption data and external collaborative calculation. This method significantly reduces the privacy risk of the park when uploading data. In actual operation, the operator cannot directly access the real electricity consumption data of the park, so even in the process of participating in demand response multiple times, the privacy information of the park will not be leaked. Through this privacy protection mechanism, the park can participate in the demand response process without exposing its sensitive data, while ensuring the accuracy and efficiency of the peak-shaving calculation. Compared with the traditional centralized scheduling method, the present invention enhances the privacy protection capability of the park by minimizing data interaction, while ensuring the effectiveness and accuracy of the peak-shaving calculation.

[0061] Step 1.2: Determine the response of each park using the planned power consumption after adding noise disturbance and the peak load demand of the power grid;

[0062] Specifically, based on the planned electricity consumption after adding noise disturbance in each park The peak-shaving demand of the upper power grid is allocated to each park as the response quantity of each park, satisfying the following relationship:

[0063]

[0064] Where, For the park During peak load period The response amount, For the power grid during peak load period Peak shaving demand, For the park During peak load period Increase the planned electricity consumption after the noise disturbance.

[0065] Step 1.3: The difference between the planned electricity consumption of each park and the response amount is used as the electricity consumption benchmark value for each park to participate in the response;

[0066] The electricity consumption benchmark value of the park's participation response satisfies the following relationship:

[0067]

[0068] Where, For the park During peak load period The electricity consumption benchmark value for participating responses, For the park During peak load period Increase planned electricity consumption before the noise disturbance.

[0069] This embodiment proposes a two-tiered optimization model architecture. In this optimization model, the integrated energy park operator coordinates the energy demands of multiple industrial parks, optimizes peak-shaving response, ensures park energy consumption meets grid peak-shaving requirements, and maximizes its own profits. Industrial parks participate in demand response, adjusting power consumption according to the operator's requirements, optimizing energy consumption to ensure benchmarks are met and earn rewards or avoid penalties. The upper-level grid provides peak-shaving demand and purchases peak-shaving power from the operator.

[0070] In this embodiment, the lower-level optimization model primarily consists of industrial parks. Its goal is to adjust its own electricity consumption to a specified baseline value based on the dispatch instructions from the integrated energy park operator, ensuring participation in the grid's peak-shaving response and avoiding overuse or underuse. After receiving the peak-shaving capacity allocated by the operator, the park calculates the required electricity consumption based on its original power consumption plan and compares it with the baseline value. The park adjusts its own electricity consumption based on the operator's baseline value. If it exceeds or falls short of the baseline value, a reward or penalty is calculated based on the deviation of the adjustment amount. The park optimizes based on its own costs and reward mechanisms to ensure the lowest energy consumption cost and meet peak-shaving requirements. The integrated energy park operator needs to obtain the planned electricity consumption of each park during the peak-shaving period to allocate grid peak-shaving demand and determine the park's response capacity. However, directly obtaining the park's raw data poses the risk of privacy leakage. To reduce the amount of raw energy consumption data provided by the park to the integrated energy park operator, protect park privacy information, and balance the accuracy and efficiency of peak-shaving response calculations, the present invention introduces a data perturbation mechanism based on differential privacy during data exchange when the park reports its original power consumption plan to protect data sensitivity.

[0071] Step 2: Determine the reward and punishment costs that each park receives from the integrated energy park operator based on the difference between the baseline electricity consumption of each park and its actual electricity consumption; determine the response amount corresponding to the minimum difference between the operating cost of each park and the reward and punishment cost as the optimal response amount of each park.

[0072] Each park adjusts its electricity consumption according to the benchmark value, and obtains corresponding rewards or penalties based on whether it exceeds or falls short of the benchmark value. This optimized reward and punishment mechanism based on actual response is different from the fixed incentive method. By dynamically adjusting the reward and punishment rules, the optimal response of each park can be obtained through the optimized calculation of operating costs and reward and punishment costs, thereby achieving optimal economic efficiency, enhancing the enthusiasm of the park to participate in demand response, and improving the overall energy utilization efficiency and flexibility of system operation.

[0073] The reward and punishment costs include penalty costs and reward costs; when the actual electricity consumption of each park is greater than the electricity consumption benchmark value, each park will receive penalty costs from the integrated energy park operator; when the actual electricity consumption of each park is less than the electricity consumption benchmark value, each park will receive reward costs from the integrated energy park operator; the present invention enables the park to adjust the electricity consumption according to the benchmark value, and exceeding or falling short of the benchmark value will result in corresponding rewards or penalties.

[0074] By extending the disturbance mechanism to the reward and punishment calculation link, the present invention not only improves the overall privacy protection capability of the system, but also realizes a compensation mechanism for the initial disturbance error by dynamically adjusting the reward and punishment influence coefficient, thereby enhancing the fairness of the reward and punishment mechanism and the accuracy of response scheduling without sacrificing privacy. After the integrated energy park operator of the park specifies the electricity consumption benchmark value, it is necessary to adjust its actual electricity consumption to the electricity consumption benchmark value. If the actual electricity consumption exceeds or falls short of the electricity consumption benchmark value, the park will receive corresponding penalties or rewards from the integrated energy park operator, satisfying the following relationship:

[0075]

[0076] In the formula, when hour, Provide comprehensive energy park operators with During peak load period The reward, when hour, Provide comprehensive energy park operators with During peak load period punishment, For the park During peak load period of electricity consumption, Peak load period The unit price of reward or punishment, For the park During peak load period The disturbance compensation factor satisfies the following relationship:

[0077]

[0078] In order to further compensate for the impact of the noise disturbance introduced in step 1 on the accuracy of the electricity consumption benchmark values ​​of each park participating in the response, and to ensure that parks with high noise levels receive relatively fair compensation or exemption in rewards and punishments, the present invention introduces a disturbance compensation factor in the reward and punishment calculation, and the disturbance compensation factor is coupled with the noise disturbance probability distribution through noise control parameters, realizing a joint enhancement mechanism of disturbance compensation adjustment and differential privacy, reducing the direct impact of disturbance errors on the reward and punishment mechanism, and improving the privacy protection and fairness of the reward and punishment mechanism.

[0079] When an industrial park participates in peak demand, its operation optimization needs to consider the rewards or penalties of the integrated energy park operator on the basis of the operating expenditure cost, and the objective function is to minimize the difference between the operating cost and the reward and punishment cost of each park. In the operation optimization process after the industrial park participates in peak demand, the objective function needs to comprehensively consider the operating cost and reward and punishment cost of the park, and its goal is to minimize the difference between the operating cost and the reward and punishment cost. Specifically, the optimal response quantity can be determined by solving the following relationship, where the objective function represents the key response quantity of operation optimization. The objective function satisfies the following relationship:

[0080]

[0081] Where, For the park The minimum cost, For the park operating costs;

[0082] Park The response amount corresponding to the minimum difference between the operating cost and the reward and punishment cost is used as the The optimal response .

[0083] In the embodiment, the upper-level optimization model focuses on the integrated energy park operator, with the goal of maximizing the operator's profit. Based on the original power consumption plan reported by the park and the peak-shaving demand of the power grid, the operator allocates peak-shaving tasks according to the total demand and ensures that the park's response can balance the demand of the power grid. The operator needs to consider multiple cost and benefit factors, including: electricity sales revenue (revenue from peak-shaving electricity purchased from the power grid), heat sales revenue (revenue from heat energy sold through the CHP system), fuel costs (fuel costs required for the CHP system to generate electricity and heat), and reward and penalty expenditures (rewarding or punishing the park to ensure that the park responds to peak-shaving demand as required). By adjusting the reward and penalty mechanism, the operator incentivizes the park to respond to demand while maximizing its own profits.

[0084] Step 3: Determine the demand response quantity instructions for each park with the optimization goal of minimizing the difference between the optimal response quantity and the response quantity of each park and maximizing the income of the integrated energy park operator.

[0085] The optimization objective satisfies the following relationship:

[0086]

[0087] Where, To optimize the goal, revenue for integrated energy park operators;

[0088] In an ideal state, noise perturbations generated based on the differential privacy mechanism only protect data privacy and security without causing errors in the response of each park, satisfying

[0089]

[0090] Therefore, in the centralized distributed integrated demand response optimization of the integrated energy system, the present invention introduces the optimal response amount of each park and the minimum difference between the response amounts as an optimization goal, and this optimization goal is the main task of each park, thereby realizing a distributed demand response. Moreover, the planned power consumption after adding noise disturbance calculated in step 1 is a corrected data that includes privacy protection. Since the added noise disturbance is controllable and can be compensated by rewards and penalties, the planned power consumption after adding noise disturbance can be directly used in subsequent steps to allocate response amounts and calculate rewards and penalties, which ensures privacy without affecting system efficiency and calculation accuracy.

[0091] In the present invention, although the differential privacy mechanism is mainly directly applied in step 1 to generate noise disturbance, its role is not limited to determining the electricity consumption baseline value of each park participating in the response under the condition of effectively protecting privacy, but also indirectly affects the response judgment and reward and punishment results of the entire optimization process in the process of using the difference of the electricity consumption baseline value to determine the subsequent reward and punishment mechanism. It has a continuous and global impact, and ensures the privacy security of the park throughout the entire process.

[0092] In addition, maximizing the revenue of the integrated energy park operator is another optimization goal, and this optimization goal is the main task of the integrated energy park operator. A centralized integrated demand response based on distributed demand response is realized to meet the following requirements:

[0093]

[0094] The electricity sales revenue of the integrated energy park operator satisfies the following relationship:

[0095]

[0096] Where, The electricity sales revenue for the integrated energy park operator, Peak load period The unit price of electricity sales, For gas units during peak load period Output electrical power;

[0097] The heat sales revenue of the integrated energy park operator satisfies the following relationship:

[0098]

[0099] Where, Heat sales revenue for integrated energy park operators, The unit price for selling heat;

[0100] When the power grid needs to perform peak regulation, it can purchase the required peak regulation capacity from the integrated energy park operator. Specifically, the peak regulation demand of the power grid is provided by the power grid and needs to be balanced by the peak regulation response capacity of multiple parks. The optimal response capacity of each park is derived based on the objective of minimizing the difference between its costs, rewards or penalties. When the park adjusts its electricity consumption, the optimization objective function will generate a corresponding optimal response capacity, that is, the amount of electricity required for the park to participate in peak regulation. The income of the integrated energy park operator from selling peak regulation capacity satisfies the following relationship:

[0101]

[0102] Where, Revenue from selling peak load for integrated energy park operators, Peak load period The unit price of selling peak load, For the power grid during peak load period Peak shaving demand;

[0103] The operator of a comprehensive energy park owns a CHP unit, which generates heat and electricity by burning natural gas. Its main power generation equipment is a gas turbine and a gas boiler. Based on the variable efficiency characteristics of the gas turbine and boiler, the relationship between their output power and fuel cost is expressed as a quadratic function, satisfying the following relationship:

[0104]

[0105] Where, For gas units during peak load period fuel costs, For gas units during peak load period Output electrical power, 、 、 are the cost coefficients corresponding to the output unit electric power, For gas units during peak load period Output thermal power, 、 、 are the cost coefficients corresponding to the output unit thermal power;

[0106] In the centralized distributed integrated demand response, the integrated energy park operator needs to compensate the parks participating in the response. The compensation expenditure of the integrated energy park operator is the sum of the reward and punishment costs of all parks, satisfying the following relationship:

[0107]

[0108] Where, Compensation expenses for integrated energy park operators.

[0109] The peak load demand of the upper power grid is partially met by the power generation of the integrated energy park operator. Therefore, when the difference between the optimal response and the response of each park is minimized and the income of the integrated energy park operator is maximized, the power consumption instructions of each park meet the following relationship:

[0110]

[0111] Where, For the park During peak load period The power consumption instruction, For the park During peak load period Planned electricity consumption, For the power grid during peak load period Peak shaving demand, For gas units during peak load period Output electrical power.

[0112] In the embodiment, through the dual optimization mechanism, the response of the park and the revenue of the operator can reach a dynamic balance; each time the park adjusts its power consumption according to the benchmark value given by the operator, and reports its adjusted power consumption to the operator; the operator adjusts the strategy through the reward and penalty mechanism according to the response of the park to ensure that the park meets the peak-shaving demand of the power grid; through multiple rounds of iteration, the park and the operator gradually optimize their respective objective functions, and finally reach an equilibrium point to meet the peak-shaving demand of the power grid while ensuring the interests of all parties.

[0113] In traditional centralized demand response mechanisms, energy optimization for all industrial parks is typically centrally dispatched and managed by the integrated energy park operator. While this centralized dispatch approach ensures a unified response to global peak-shaving demand, it also has some drawbacks. First, centralized dispatch can lead to a mismatch between local park energy demand and global targets, particularly when considering the parks' individual energy needs, geographic distribution, and operating conditions. This can underutilize the park's flexibility. Second, centralized dispatch requires large-scale data aggregation and information transmission, which can reduce the timeliness of system responses and increase computational complexity. In contrast to centralized demand response, distributed response mechanisms allow each park to make independent decisions based on its own energy needs and external incentives. Distributed response offers the advantage of increased park flexibility and response speed while reducing information transmission lags. However, distributed response also has some drawbacks. In particular, decision-making between parks can be inconsistent, making it difficult to achieve a global optimal solution. Furthermore, insufficient coordination between parks can make it difficult to accurately meet peak-shaving demand, thereby impacting grid stability.

[0114] The technical solution of this invention utilizes dual optimization at both the lower and upper levels. In the lower-level optimization model, park operators first allocate peak-shaving capacity to each park based on the grid's peak-shaving needs and the park's original electricity consumption plan. In the upper-level model, operators must consider not only the revenue from grid peak-shaving services but also the fuel costs, electricity and heat sales revenues, and compensation expenses of their own CHP (combined heat and power) systems. This allows them to fine-tune the park's electricity and thermal energy needs, thereby minimizing park operating costs and improving incentive benefits while meeting grid peak-shaving needs. By deeply exploring the relationship between centralized and distributed scheduling, parks can more flexibly participate in demand response, avoiding the problems of insufficient coordination between different levels and complex calculations found in traditional methods. Furthermore, this mechanism enables integrated energy park operators to maximize profits, improve economic efficiency, and enhance their market competitiveness.

[0115] Take, for example, a 35kV industrial park cluster, which includes five industrial parks. Table 1 lists the number of devices in each industrial park's integrated energy system, and Table 2 provides detailed equipment parameters. Table 3 summarizes the interruptible load parameters for each industrial park, and Table 4 shows the time-of-use electricity price. The unit calorific value prices for natural gas and industrial park CHP steam are 0.349 yuan / kWh and 0.465 yuan / kWh, respectively.

[0116] Table 1 Equipment quantity in each park

[0117]

[0118] Table 2 Equipment Specific Parameters

[0119]

[0120] Table 3 Interruptible load parameters of each park

[0121]

[0122] Table 4 Time-of-use electricity prices

[0123]

[0124] By adopting the method proposed in the present invention, in order to protect the privacy information such as the number and parameters of equipment in the park, each industrial park will not report all the equipment parameters in the park to the upper-level integrated energy park operator. Therefore, the integrated energy park operator cannot uniformly calculate the feasible domain. Instead, each park calculates its own feasible domain range. In order to protect its own privacy, the park needs to provide its own computing power. After calculating its own feasible domain range, each park reports it to the integrated energy park operator. The integrated energy park operator then aggregates the feasible domains of each park in the aggregate. After obtaining the aggregation results, the integrated energy park operator reports to the upper-level power grid. After obtaining the total power purchase plan and feasible domain range of the park aggregate, the upper-level power grid proposes a peak reduction of 2000kW in time period "4" based on actual needs, and sends this demand to the integrated energy park operator. The integrated energy park operator performs centralized-distributed optimization based on this demand and the process of the method proposed in the present invention. After the response, the total power purchase of the industrial park aggregate is as follows. Figure 2 As shown, each park uses energy according to the optimized energy consumption plan finally calculated by itself and reports it to the integrated energy park operator.

[0125] The integrated energy park operator first sets the base price for peak-shaving compensation or penalty electricity for users at 0.35 yuan / kWh, increasing it by 0.01 yuan / kWh each time. After 10 iterations, the final peak-shaving compensation price is 0.26 yuan / kWh. At this point, the cost of participating in the integrated demand response for Park 1 is 110,286 yuan, Park 2 is 79,603 yuan, Park 3 is 62,425 yuan, Park 4 is 30,315 yuan, and Park 5 is 36,042 yuan, resulting in a profit of 52,961 yuan for the integrated energy park operator.

[0126] Park 1 was selected as an example for analyzing its peak-shaving demand response. As part of the entire park aggregate, utilizing a centralized-distributed integrated demand response approach to participate in the peak-shaving demand response mandated by the upper power grid, the peak-shaving requirement during time period 4 led to a significant increase in gas turbine power generation to meet the industrial park's electrical load. The integrated energy park operator also provides a certain level of peak-shaving capacity within the centralized-distributed integrated demand response approach, reaching up to 1,000 kW. As can be seen, since production AC loads comprise the majority of the park's production load, battery energy storage is the primary means of regulating energy consumption, while conversion to other energy sources is a supplementary means of enhancing the park's energy flexibility.

[0127] The present invention also proposes a centralized distributed integrated demand response system for an integrated energy system, comprising:

[0128] The park execution module is used to obtain the planned electricity consumption of each park, generate noise perturbations based on the differential privacy mechanism, and use the peak-shaving demand of the power grid and the planned electricity consumption after adding noise perturbations to determine the response amount of each park. The difference between the planned electricity consumption and the response amount of each park is used as the electricity consumption baseline value for each park to participate in the response.

[0129] The operator execution module is used to determine the reward and penalty costs each park receives from the integrated energy park operator based on the difference between the power consumption baseline value of each park's participation and the actual power consumption of each park. The response amount corresponding to the minimum difference between the operating cost of each park and the reward and penalty cost is used as the optimal response amount of each park.

[0130] The integrated execution module is used to determine the demand response quantity instructions of each park with the optimization goal of minimizing the difference between the optimal response quantity and the response quantity of each park and maximizing the income of the integrated energy park operator.

[0131] In the centralized and distributed integrated demand response system of the integrated energy system, the integrated energy park operator allocates the peak-shaving capacity according to the park's electricity consumption based on the peak-shaving demand of the upper power grid and the original power consumption plan reported by the park, and determines the peak-shaving capacity that each park should respond to. Based on the park's peak-shaving capacity, the park's electricity consumption baseline value is further calculated. The architectural design not only enables centralized scheduling by the integrated energy park operator, but also introduces distributed computing and an adaptive coordination mechanism between parks. While meeting the peak-shaving demand of the power grid, it can reduce the park's need to provide data externally, protect the park's privacy, and enhance the park's independent optimization capabilities. In addition, the distributed computing model delegates most computing tasks to the park, effectively reducing the computing burden of the integrated energy park operator and improving response speed. Each park adjusts its electricity consumption according to the benchmark value, and obtains corresponding rewards or penalties based on whether it exceeds or falls short of the benchmark value. This optimized reward and punishment mechanism based on actual response is different from the fixed incentive method. By dynamically adjusting the reward and punishment rules, the optimal response of each park can be obtained through the optimized calculation of operating costs and reward and punishment costs, thereby achieving optimal economic efficiency, enhancing the enthusiasm of the park to participate in demand response, and improving the overall energy utilization efficiency and flexibility of system operation.

[0132] Based on the park's response, the integrated energy park operator optimizes for profit maximization. Through a two-tiered optimization model, the park optimizes its own costs, while the integrated energy park operator optimizes profits, ultimately meeting peak-shaving demand and maximizing the interests of all parties. Furthermore, in terms of optimal scheduling, the two-tiered optimization model not only optimizes the demand response of each park but also further integrates the combined heat and power (CHP) system to achieve coordinated optimized scheduling of electricity and heat energy. The two-tiered optimization model's optimization objectives encompass not only the park's electricity costs but also comprehensively consider the park's incentive and penalty costs, the fuel costs of gas-fired units, and the operator's electricity and heat sales revenue, making the entire system more economical and rational.

[0133] Finally, in terms of market applicability, the technical solution of this application is not only applicable to demand response optimization scheduling in integrated energy systems, but can also be widely used in the intelligent scheduling of industrial parks, virtual power plants, and microgrids. Unlike the comparative documents that only focus on the regulation of regional integrated energy systems, this application uses a two-layer optimization model to enable integrated energy systems to more flexibly adapt to market changes, improve energy utilization efficiency, and maximize the benefits of operators and parks while ensuring grid stability.

[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A centralized and distributed integrated demand response method for an integrated energy system, characterized in that: include: Obtain the planned electricity consumption of each park, generate noise perturbations based on the differential privacy mechanism, and use the peak-shaving demand of the power grid and the planned electricity consumption after adding noise perturbations to determine the response amount of each park. The difference between the planned electricity consumption and the response amount of each park is used as the electricity consumption baseline value for each park to participate in the response. Based on the difference between the baseline electricity consumption of each park and its actual electricity consumption, the reward and penalty costs each park receives from the integrated energy park operator are determined; the response amount corresponding to the minimum difference between the operating cost of each park and the reward and penalty cost is taken as the optimal response amount for each park; The optimization goal is to minimize the difference between the optimal response quantity and the response quantity of each park and maximize the income of the integrated energy park operator, and determine the demand response quantity instruction of each park. The optimization goal satisfies the following relationship: Where, To optimize the goal, For the park During peak load period The optimal response amount, For the park During peak load period The response amount, revenue for integrated energy park operators; Where, The electricity sales revenue for the integrated energy park operator, Peak load period The unit price of electricity sales, For gas units during peak load period Output electrical power; Where, Heat sales revenue for integrated energy park operators, The unit price for selling heat; Where, Revenue from selling peak load for integrated energy park operators, Peak load period The unit price of selling peak load, For the power grid during peak load period Peak shaving demand; Where, For gas units during peak load period fuel costs, For gas units during peak load period Output electrical power, 、 、 are the cost coefficients corresponding to the output unit electric power, For gas units during peak load period Output thermal power, 、 、 are the cost coefficients corresponding to the output unit thermal power; Where, Compensation for integrated energy park operators, For integrated energy park operators During peak load period rewards or punishments; When the difference between the optimal response amount and the response amount of each park is minimized and the income of the integrated energy park operator is maximized, the power consumption instructions of each park satisfy the following relationship: Where, For the park During peak load period The power consumption instruction, For the park During peak load period Planned electricity consumption, For the power grid during peak load period Peak shaving demand, For gas units during peak load period Output electrical power.

2. The centralized and distributed integrated demand response method for an integrated energy system according to claim 1, characterized in that: Obtain the planned electricity consumption of each park, generate noise perturbations based on the differential privacy mechanism, and report the noise perturbations to the operator after adding them to the planned electricity consumption. Park During peak load period The planned electricity consumption is , the park is locally Add noise perturbation , get the planned power consumption after adding noise disturbance = + ; Among them, noise perturbation is generated based on the differential privacy mechanism, The probability distribution function of is shown in the following relationship: Where, Noise disturbance The probability distribution function of is the noise control parameter, .

3. The centralized and distributed integrated demand response method for an integrated energy system according to claim 2, characterized in that: Planned electricity consumption after adding noise disturbance in each park The peak-shaving demand of the upper power grid is allocated to each park as the response quantity of each park, satisfying the following relationship: Where, For the park During peak load period The response amount, For the power grid during peak load period Peak shaving demand, For the park During peak load period Increase the planned electricity consumption after the noise disturbance.

4. The centralized and distributed integrated demand response method for an integrated energy system according to claim 3, characterized in that: The difference between the planned power consumption and the response amount of each park is used as the power consumption benchmark value for each park to participate in the response, satisfying the following relationship: Where, For the park During peak load period The electricity consumption benchmark value for participating responses, For the park During peak load period Planned electricity consumption.

5. The centralized and distributed integrated demand response method for an integrated energy system according to claim 4, characterized in that: The park receives corresponding penalties or rewards from the integrated energy park operator, satisfying the following relationship: In the formula, when hour, For integrated energy park operators During peak load period The reward, when hour, For integrated energy park operators During peak load period punishment, For the park During peak load period of electricity consumption, Peak load period The unit price of reward or punishment, For the park During peak load period The disturbance compensation factor.

6. The centralized and distributed integrated demand response method for an integrated energy system according to claim 5, characterized in that: The disturbance compensation factor satisfies the following relationship: 。 7. A centralized distributed integrated demand response system for an integrated energy system, characterized in that: include: The park execution module is used to obtain the planned electricity consumption of each park, generate noise perturbations based on the differential privacy mechanism, and use the peak-shaving demand of the power grid and the planned electricity consumption after adding noise perturbations to determine the response amount of each park. The difference between the planned electricity consumption and the response amount of each park is used as the electricity consumption baseline value for each park to participate in the response. The operator execution module is used to determine the reward and penalty costs each park receives from the integrated energy park operator based on the difference between the power consumption baseline value of each park's participation and the actual power consumption of each park. The response amount corresponding to the minimum difference between the operating cost of each park and the reward and penalty cost is used as the optimal response amount of each park. The integrated execution module is used to determine the demand response quantity instructions for each park with the optimization goal of minimizing the difference between the optimal response quantity and the response quantity of each park and maximizing the revenue of the integrated energy park operator; wherein the optimization goal satisfies the following relationship: Where, To optimize the goal, For the park During peak load period The optimal response amount, For the park During peak load period The response amount, revenue for integrated energy park operators; Where, The electricity sales revenue for the integrated energy park operator, Peak load period The unit price of electricity sales, For gas units during peak load period Output electrical power; Where, Heat sales revenue for integrated energy park operators, The unit price for selling heat; Where, Revenue from selling peak load for integrated energy park operators, Peak load period The unit price of selling peak load, For the power grid during peak load period Peak shaving demand; Where, For gas units during peak load period fuel costs, For gas units during peak load period Output electrical power, 、 、 are the cost coefficients corresponding to the output unit electric power, For gas units during peak load period Output thermal power, 、 、 are the cost coefficients corresponding to the output unit thermal power; Where, Compensation for integrated energy park operators, Provide comprehensive energy park operators with During peak load period rewards or punishments; When the difference between the optimal response amount and the response amount of each park is minimized and the income of the integrated energy park operator is maximized, the power consumption instructions of each park satisfy the following relationship: Where, For the park During peak load period The power consumption instruction, For the park During peak load period Planned electricity consumption, For the power grid during peak load period Peak shaving demand, For gas units during peak load period Output electrical power.

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