WO2024000930A1 - 一种电力用户增值服务决策方法 - Google Patents

一种电力用户增值服务决策方法 Download PDF

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WO2024000930A1
WO2024000930A1 PCT/CN2022/125545 CN2022125545W WO2024000930A1 WO 2024000930 A1 WO2024000930 A1 WO 2024000930A1 CN 2022125545 W CN2022125545 W CN 2022125545W WO 2024000930 A1 WO2024000930 A1 WO 2024000930A1
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electricity
power
grid
value
demand
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French (fr)
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张玮
耿博
杨祥勇
温克欢
孙文静
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深圳供电局有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Definitions

  • the invention belongs to the technical field of electric power energy consumption optimization and value-added services, and specifically relates to a value-added service decision-making method for electric power users.
  • Value-added services mostly appear in the fields of mobile communications and the Internet. However, with the rapid development of Internet technology and the gradual advancement of informatization construction, value-added services have gradually penetrated into other industry fields. For example, value-added services for mining rights information can increase the value of information and meet the needs of Customer information needs; value-added privacy services for medical data can leverage masked data sets to provide value-added data analysis.
  • Some scholars have analyzed the value-added services of government information resources and proposed a basic framework for research on value-added services of government information resources from a micro level; some scholars have put forward suggestions and configurations for value-added services of electric vehicle battery reuse, which can be used as a type of information
  • Some researchers have proposed a value-added solution for runner group software business based on factor analysis, which can regularly obtain useful running news information from online running communities.
  • the customer value-added service system has many functions, such as electricity consumption information collection, monitoring and analysis, electricity load management, electric energy measurement, user behavior analysis, etc. It mainly collects, integrates and processes user electricity information, so that Users can achieve efficient query, energy-saving use, orderly control and other operations.
  • the technical problem to be solved by the present invention is to provide a value-added service decision-making method for electric power users to effectively reduce the power supply pressure of the power grid, improve the power supply level of the power grid, effectively cope with the power demand in complex environments, and effectively improve the efficiency of electric energy use.
  • the present invention provides a value-added service decision-making method for electric power users, which includes:
  • Step S1 Based on the power consumption data of the new generation measurement system, perform energy consumption optimization analysis of typical household demand response;
  • Step S2 based on typical household demand response energy consumption characteristics, build a user-side demand response optimization model under time-of-use electricity prices;
  • Step S3 Combine the operation of the new energy grid and the balance of supply and demand to make customer value-added service decisions based on energy big data and obtain value-added service methods.
  • step S1 of typical household demand response energy consumption optimization includes two stages:
  • the first stage of optimization is based on the economics of home energy management and user comfort, respectively, to obtain the objective functions that minimize the impact on electricity costs and comfort, and then combine the two objective functions to serve as users' participation in power grid emergency dispatch. home energy management optimization goals under circumstances;
  • the second stage of optimization is specifically based on the overall power purchase volatility of users based on the peak-to-valley difference. In order to reduce the risk of exceeding the limit in a certain period due to users concentrated purchase of power during the same low period of electricity price, the process of smoothing the power purchase is obtained. The second stage optimizes the objective function.
  • t end is the cut-off time of the current period
  • P grid (t) is the electricity purchased from the power grid in the current period
  • ⁇ grid (t) is the electricity price between the user and the grid during the analyzed period.
  • P grid (t)>0 ⁇ grid (t) represents electricity purchase.
  • P grid (t) ⁇ 0 ⁇ grid (t) represents electricity sales, and the electricity sales price is defined as half of the electricity purchase price at that time
  • C DG is distributed power generation. Cost and loss depreciation expenses.
  • C 3 is the total electricity cost of second-order users, Expressed as the average value of purchased power, w grid (t) is the weight of grid electricity price, which is inversely proportional to electricity price.
  • the second stage optimization objective function includes:
  • step S1 also includes setting constraints on the home energy management method, specifically:
  • P grid,max (t) represents the power exchange limit between the user and the grid in each time period
  • t load,start , t load , t load,end are the start time, running time and end time of the uninterruptible load respectively, and N is the load working time margin;
  • P air (t) is the power of the air conditioner in the t period during cooling
  • P air,max is the rated power of the air conditioner in cooling
  • T in (t) is the indoor temperature in the t period during the air conditioning cooling
  • T air,max , T air,min are the upper and lower limits of indoor temperature respectively;
  • T eh (t) is the temperature of the hot water of the water heater in the tth period
  • P eh,max is the rated heating power of the water heater
  • T eh,max and T eh,min are the upper and lower limit temperatures of the hot water of the electric water heater respectively.
  • step S2 specifically includes the following steps:
  • Step S21 construct a single-period demand response optimization model
  • Step S22 construct a multi-period demand response optimization model
  • Step S23 Set constraints on demand response under real-time electricity prices.
  • step S21 it is assumed that the user's income in the i-th hour is:
  • N(D i ) represents the total revenue generated by the user's electricity consumption in the i-th hour
  • p i D i is the user's electricity cost in the i-th hour
  • D i and p i represent the i-th hour electricity demand and the i-th hour electricity price respectively.
  • D i0 and p i0 represent the initial electricity demand at the i-th hour and the initial electricity price at the i-th hour respectively;
  • ⁇ ii is self-elasticity, defined as: p i0 is the market clearing price before demand response;
  • step S22 it is assumed that the electricity price at the jth hour is adjusted from p j0 to p j , and the user's electricity demand at the ith hour is adjusted from D i0 to D i .
  • the customer's electricity demand at the ith hour is :
  • D ⁇ 0 is the multi-time basic electricity demand
  • ⁇ ij is the mutual elasticity coefficient between the i-th hour and the j-th hour
  • p p0 and p f0 are the flat power price and peak power price in the peak-valley flat power price mechanism respectively;
  • D D is the comprehensive basic electricity demand:
  • the maximum demand D maxl is the electric energy demand when the user is running at full load
  • the minimum demand D minl is the electric energy demand when the user meets the basic safety production and life needs, that is:
  • the maximum electricity price P maxl is a maximum limit set to protect the interests of users
  • the minimum electricity price P minl is a minimum limit set to protect the interests of electricity sellers, that is:
  • step S3 specifically includes the following steps:
  • Step S31 define business scenarios, including: three major scenario classifications and six specifically implemented scenario tasks: power customer classification and profiling, comprehensive energy consumption assessment and power consumption analysis, customer-side energy value-added services;
  • Step S32 Different value-added service plans are formed according to different combinations of power supply areas, billing methods, and payment methods.
  • the present invention not only realizes energy consumption optimization and energy efficiency improvement on the user side, but also achieves the goal of stabilizing the output of the power system for peak shaving and valley filling, providing a good reference demonstration for market-oriented smart grids;
  • this invention can help energy companies formulate dynamic price and demand response incentive policies more scientifically, and the conclusions obtained by analyzing and processing detailed energy consumption monitoring results can be used to adjust, improve, and scientifically evaluate the ongoing progress of energy companies. energy efficiency projects and more rationally design and implement future projects and allocate funds;
  • this invention can combine personalized energy-saving regulation suggestions with their own electricity consumption habits, power grid status, dynamic prices, home system energy efficiency status diagnosis, etc., under the conditions of meeting electricity and energy needs and ensuring user comfort.
  • this invention can provide a basis for engineers in enterprises, academic researchers in universities, and amateurs to strategically focus their research efforts in a certain correct direction, and can promote and promote manufacturers to accelerate the research and development of high-energy-efficiency equipment. , thereby accelerating energy efficiency technology innovation, inducing energy efficiency market reform and helping regulatory authorities to scientifically formulate relevant policies. It can also improve the energy efficiency of buildings (especially commercial buildings) (including saving energy and consumption) on the basis of identifying low energy efficiency links. can transfer both aspects) to reduce unnecessary energy bills, effectively alleviate the energy crisis, reduce environmental pollution, and slow down the greenhouse effect.
  • Figure 1 is a schematic flow chart of a value-added service decision-making method for electric power users according to an embodiment of the present invention.
  • Figure 2 is a schematic diagram of the clustering results of four typical daily load curves in the embodiment of the present invention.
  • Figure 3 is a schematic diagram comparing the load curves of industrial and commercial users before and after using the electricity package in the embodiment of the present invention.
  • the present invention first adopts two-stage optimization to realize demand-side response optimization of users in different scenarios, and then uses energy big data for analysis to provide customers with personalized energy packages according to different business scenarios. Based on this, please refer to Figure 1, an embodiment of the present invention provides a value-added service decision-making method for electric power users, including:
  • Step S1 Based on the power consumption data of the new generation measurement system, perform energy consumption optimization analysis of typical household demand response;
  • Step S2 based on typical household demand response energy consumption characteristics, build a user-side demand response optimization model under time-of-use electricity prices;
  • Step S3 Combine the operation of the new energy grid and the balance of supply and demand to make customer value-added service decisions based on energy big data and obtain value-added service methods.
  • step S1 performs energy consumption optimization for typical household demand response, which specifically includes two stages:
  • the system function of home energy management needs to comprehensively consider the economy and user comfort.
  • the goals of minimizing the impact on electricity cost and comfort are respectively obtained.
  • the two objective functions are combined to serve as the user's participation in the power grid emergency.
  • For the economic objective function first analyze the situation without risk coefficient, including the user's electricity cost and energy storage charge and discharge loss.
  • the user's total electricity cost C 1 is expressed as:
  • t end is the cut-off time of the current period
  • P grid (t) is the electricity purchased from the power grid in the current period
  • ⁇ grid (t) is the electricity price between the user and the grid during the analyzed period.
  • P grid (t)>0 ⁇ grid (t) represents electricity purchase.
  • P grid (t) ⁇ 0 ⁇ grid (t) represents electricity sales, and the electricity sales price is defined as half of the electricity purchase price at that time
  • C DG is distributed power generation. Cost and loss depreciation. For distributed renewable energy, the power generation cost is 0. For micro gas turbines, etc., the cost is the gas cost.
  • Second stage optimization The optimization objective function of the first stage will minimize the user's net expenditure cost. However, simply minimizing the user's net expenditure economic cost will lead to a problem: a certain period or certain periods of time when the load cannot be interrupted. The charge and discharge amount will be transferred to other periods when the electricity price is equal or the sum of the electricity prices is equal, which will cause a multiple solution problem. In order to solve this problem, a second-stage optimization objective function needs to be introduced.
  • the objective function is expressed as:
  • C 3 is the total electricity cost of second-order users, Expressed as the average value of purchased power, w grid (t) is the weight of grid electricity price, which is inversely proportional to electricity price.
  • the objective function is expressed as the overall power purchase volatility of users taking into account the peak-valley difference. It is to reduce the risk of exceeding the limit in a certain period due to users concentrated purchase of power during the same low electricity price period, and to achieve a smooth process of power purchase.
  • the optimization of the second stage needs to be coordinated with the optimization of the first stage. Therefore, in addition to the constraints mentioned above, it is also necessary to add:
  • optimization decision variables are the electric vehicle battery power and the starting time of the adjustable load. Therefore, other variables should continue to operate according to the results of the first stage of optimization.
  • Set constraints on home energy management methods which mainly include: (1) grid power constraints; (2) uninterruptible load constraints; (3) air conditioning load constraints; (4) water heater load constraints.
  • P grid,max (t) represents the power exchange limit between the user and the grid in each time period.
  • t load,start , t load , t load,end are the start time, running time and end time of the uninterruptible load respectively, and N is the load working time margin.
  • P air (t) is the power of the air conditioner in the t period during cooling
  • P air,max is the rated power of the air conditioner in cooling
  • T in (t) is the indoor temperature in the t period of the air conditioning cooling
  • ⁇ t is a period of time
  • T air, max , T air, min are the upper and lower limits of indoor temperature respectively.
  • T eh (t) is the temperature of the hot water of the water heater in the tth period
  • P eh,max is the rated heating power of the water heater
  • T eh,max and T eh,min are the upper and lower limit temperatures of the hot water of the electric water heater respectively.
  • Step S2 specifically includes the following steps:
  • Step S21 Construct a single-period demand response optimization model.
  • Single-period demand response means that the user's electricity demand in a certain period is only affected by the electricity price in the current period, and the load cannot be transferred.
  • the real-time electricity price is a node for each specified period (the period can be one hour, 30 minutes, 15 minutes, or even 5 minutes to 1 minute)
  • p i0 is the market clearing price before demand response, and the electricity price will be changed at the i-th hour. Adjust from p i0 to p i , the user will give a certain response, and the load will be adjusted from D i0 to D i .
  • the user's income in the i-th hour is:
  • N(D i ) represents the total revenue generated by the user's electricity consumption in the i-th hour
  • p i D i is the user's electricity cost in the i-th hour
  • D i and p i represent the i-th hour electricity demand and the i-th hour electricity price respectively.
  • D i0 and p i0 represent the initial electricity demand at the i-th hour and the initial electricity price at the i-th hour respectively.
  • Perform Taylor expansion on the user's total electricity income N(D i ) retain the quadratic term, omit the high-order term and define the self-elasticity as:
  • the electricity demand of the customer at hour i can be obtained as:
  • Step S22 Construct a multi-period demand response optimization model.
  • Multi-period demand response means that the user's electricity demand in a certain period of time is not only affected by the electricity price in the current period, but also the electricity price in other periods also affects the electricity demand in that period, and the load can be transferred to other time periods. Assume that the electricity price is adjusted from p j0 to p j at the jth hour, the user gives a certain response at the ith hour, and the electricity demand is adjusted from D i0 to D i . After the implementation of real-time electricity prices, the customer's electricity demand at the i-th hour is:
  • D ⁇ 0 is the multi-time basic electricity demand
  • ⁇ ij is the mutual elasticity coefficient between the i-th hour and the j-th hour
  • p p0 and p f0 are the flat electricity price and peak electricity price in the peak-valley flat electricity price mechanism respectively.
  • D D is the comprehensive basic electricity demand:
  • Step S23 Set constraints on demand response under real-time electricity prices.
  • the same electricity sales area contains different categories of users. Different categories of users have different electricity consumption characteristics and different price elasticities, so their load adjustment capabilities are also different. There are also differences in the adjustment range.
  • the maximum electricity price P maxl is a maximum limit set to protect the interests of users
  • the minimum electricity price P minl should be a minimum limit set to protect the interests of electricity sellers, that is:
  • Step S3 specifically includes the following steps:
  • Step S31 Define business scenarios.
  • the customer value-added service business based on energy big data includes three major scenario categories: power customer classification and portrait, comprehensive energy consumption assessment and electricity consumption analysis, and customer-side energy value-added service business. It is specifically divided into six realized scenario tasks, as follows Table 1 shows:
  • Quantitative analysis can accurately reflect the energy consumption characteristics of different types of users. It is based on the comprehensive energy consumption time series data of existing users in recent years and combined with the national energy strategic guidelines to analyze different types of users. Energy utilization efficiency, proportion of clean energy, prediction and assessment of energy consumption of multiple types of users, and acquisition of high-energy-consuming users with energy-saving potential.
  • Step S32 Different value-added service plans are formed according to different combinations of power supply areas, billing methods, and payment methods.
  • the "guaranteed" and “sharing" models under the spot market price difference transmission model will no longer apply.
  • Electricity sales companies must sign contracts with users at positive prices and design customized power packages for users. In the spot market competition, scientific electricity packages will be more beneficial to electricity sales companies and users.
  • the electricity sales company can form different packages based on different combinations of power supply area, billing method and payment method and provide them to users in the spot market.
  • the time-of-use electricity price provided by this embodiment is shown in Table 2.
  • Table 2 For users, the previous peak and valley electricity prices have been adopted in terms of electricity consumption, that is, three electricity prices a day, and users may be more likely to accept the electricity price packages offered during peak and valley periods.
  • the CH index value of the daily load curve of industrial and commercial users is clustered.
  • the user types are divided into four categories, the data within the cluster are closely connected, the dispersion between clusters is large, and the clustering Best results.
  • the daily load curves of all users in the same user group are superimposed and averaged to obtain the typical load curve of the corresponding class as shown in Figure 2.
  • industrial and commercial users in this area can be divided into four categories: bimodal type, peak-flat type, smooth type and peak-avoiding type.
  • Bimodal loads mainly include large-scale manufacturing industries, and the load drops significantly at noon due to employees taking breaks; peak-flat loads include companies such as garment factories, where working hours are fixed and regular; smooth loads have little load fluctuations throughout the day, and the load rate is high. Most of them are three-shift production methods, such as high-temperature furnace loads and large-capacity high-voltage motor loads; peak avoidance loads are special, and their peak power consumption periods are exactly at the low periods of the system.
  • bimodal and flat-peak loads there are more bimodal and flat-peak loads, and the proportion of peak-avoiding loads is smaller.
  • bimodal and flat-peak loads should be the main targets for the implementation of demand response projects, which can bring greater peak-shaving and valley-filling benefits.
  • the potential of smoothing and peak-avoiding users to participate in demand response projects should be further explored to achieve the goal of peak shaving and valley filling for the entire system, alleviate short-term system capacity shortages, reduce power generation costs, and delay power grid upgrades.
  • Packages A and B both occupy a certain market share among the four typical user groups.
  • the probability of bimodal, peak-flat, and smooth users to choose package D is close to zero.
  • Package D only has a certain market share among peak-avoiding users. market. Since the proportion of peak avoidance users in the total number of users is much smaller than that of other typical users, the market share of package D is only 0.96%.
  • 31.18% of users are still unwilling to change the current electricity pricing method and have not chosen any package. This may be caused by unobservable random utility factors and observable utility errors such as users' psychological factors and personal preferences.
  • the optimal package is evaluated based on the established power package evaluation model based on cost-benefit analysis to verify the economy and feasibility of the power package.
  • the probability of each type of users choosing each package (including maintaining the status quo) superimposing the load curves of all users, we can get the total daily load curve of industrial and commercial users after the implementation of the power package, and compare it with the original load curve, as shown in Figure 3 Show. It can be seen from Figure 3 that after the implementation of the power package, the total daily load curve of industrial and commercial users tends to be smooth, the peak-valley difference is significantly reduced, and the peak-cutting and valley-filling effect is more obvious.
  • Figure 3 shows the total load characteristics of industrial and commercial users before and after the implementation of power packages.
  • the load peak dropped by 63MW, which was 4.67% of the original total industrial and commercial load peak.
  • the system's daily peak-to-valley difference rate was 36.73%, which was a decrease of 8.25% compared to the original data.
  • the total cost required to implement the optimal power package is 83.48 million yuan/year, the total benefit brought to multiple entities is 131.69 million yuan/year, and the profit-to-cost ratio is 1.783.
  • the beneficial effect of the present invention is that: the present invention not only realizes energy consumption optimization and energy efficiency improvement on the user side, but also achieves the goal of stabilizing the output of the power system by peak-cutting and valley-filling, which provides the market with Oriented smart grid provides a good reference demonstration;
  • this invention can help energy companies formulate dynamic price and demand response incentive policies more scientifically, and the conclusions obtained by analyzing and processing detailed energy consumption monitoring results can be used to adjust, improve, and scientifically evaluate the ongoing progress of energy companies. energy efficiency projects and more rationally design and implement future projects and allocate funds;
  • this invention can combine personalized energy-saving regulation suggestions with their own electricity consumption habits, power grid status, dynamic prices, home system energy efficiency status diagnosis, etc., under the conditions of meeting electricity and energy needs and ensuring user comfort.
  • this invention can provide a basis for engineers in enterprises, academic researchers in universities, and amateurs to strategically focus their research efforts in a certain correct direction, and can promote and promote manufacturers to accelerate the research and development of high-energy-efficiency equipment. , thereby accelerating energy efficiency technology innovation, inducing energy efficiency market reform and helping regulatory authorities to scientifically formulate relevant policies. It can also improve the energy efficiency of buildings (especially commercial buildings) (including saving energy and consumption) on the basis of identifying low energy efficiency links. can transfer both aspects) to reduce unnecessary energy bills, effectively alleviate the energy crisis, reduce environmental pollution, and slow down the greenhouse effect.

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Abstract

本发明公开一种电力用户增值服务决策方法，包括：步骤S1，基于新一代量测系统的用电数据，进行典型家庭需求响应用能优化分析；步骤S2，基于典型家庭需求响应用能特征，构建分时电价下用户侧需求响应优化模型；步骤S3，结合新能源电网运行及供需平衡，进行基于能源大数据的客户增值服务决策，获取增值服务方法。本发明不仅实现了用户侧的用能优化与能效提升，而且实现了电力系统削峰填谷的出力平稳目标，为市场导向型智能电网提供了良好的参考示范。

Description

一种电力用户增值服务决策方法 技术领域
本发明属于电力用能优化与增值服务技术领域，具体涉及一种电力用户增值服务决策方法。
背景技术
增值服务大多出现在移动通信和互联网领域，但随着互联网技术的快速发展以及信息化建设的逐步推进，增值服务逐渐渗透到了其他的行业领域，例如，矿业权信息增值服务可以提高信息价值，满足客户的信息需求；医疗数据的增值隐私服务可以利用屏蔽数据集提供增值数据分析。有的学者对政府信息资源增值服务进行了分析，从微观层面提出了政府信息资源增值服务研究的基本框架；有的学者提出了电动汽车电池再利用增值服务的建议和配置，可以作为一类信息系统的蓝图，将其他废旧物品再利用作为客户解决方案。还有的学者提出了一种基于因子分析的跑步者群体软件业务增值方案，可以定期从在线跑步社区获取跑步新闻有用信息。
然而，在电力系统领域，增值服务的研究相对较少。客户增值服务系统作为一个营销自动化系统有很多功能，如用电信息采集，监控与分析，用电负荷管理，电能计量，用户行为分析等，主要通过将用户用电信息收集、整合和处理，使得用户可以实现高效查询、节能使用、有序控制等操作。
发明内容
本发明所要解决的技术问题在于，提供一种电力用户增值服务决策方法，以有效减轻电网供电压力，提高电网供电水平，有效应对复杂环境下的用电需求，切实提升电能使用效率。
为解决上述技术问题，本发明提供一种电力用户增值服务决策方法，包括：
步骤S1，基于新一代量测系统的用电数据，进行典型家庭需求响应用能优化分析；
步骤S2，基于典型家庭需求响应用能特征，构建分时电价下用户侧需求响应优化模型；
步骤S3，结合新能源电网运行及供需平衡，进行基于能源大数据的客户增值服务决策，获取增值服务方法。
进一步地，所述步骤S1进行典型家庭需求响应用能优化包括两个阶段：
第一阶段优化，具体是根据家庭能量管理的经济性与用户舒适度，分别得到用电成本与舒适度受影响程度最小为目标函数，然后将两种目标函数综合起来，作为用户参与电网紧急调度情况下的家庭能量管理优化目标；
第二阶段优化，具体是根据峰谷差的用户整体购电波动性，为降低由于用户在同一电价低谷时段集中购电而造成某一时段越限的风险，达到将购电功率平滑的流程，获得第二阶段优化目标函数。
进一步地，所述第一阶段优化中，对于经济性目标函数，用户总的用电费用C 1表示 为：
Figure PCTCN2022125545-appb-000001
其中，t end为当前时段的截止时刻，P grid(t)为当前时段从电网中购买的电量；ρ grid(t)为所分析时段用户与电网间的电价，当P grid(t)>0，ρ grid(t)表示为购电，当P grid(t)<0，ρ grid(t)表示为售电，售电价格定义为该时购电价格的一半；C DG为分布式电源发电成本及损耗折旧费。
进一步地，第二阶段优化目标函数为：
Figure PCTCN2022125545-appb-000002
Figure PCTCN2022125545-appb-000003
其中，C 3为第二阶用户总的用电费用，
Figure PCTCN2022125545-appb-000004
表示为购电功率平均值，w grid(t)为电网电价权重，与电价成反比。
进一步地，所述第二阶段优化目标函数包括：
C 3≤C 1。
进一步地，所述步骤S1还包括设置家庭能量管理方法约束条件，具体为：
电网功率约束：
|P grid(t)|≤P grid,max(t)
其中，P grid,max(t)表示用户与电网间每个时间段的功率交换限制；
不可中断负荷约束：
t load,start≤t load≤t load,end-N,t∈N *
其中，t load,start，t load，t load,end分别为不可中断负荷的开始时间、运行时间以及结束时间，N为负荷工作时间裕度；
空调负荷约束：
0≤P air(t)≤P air,max
T air,min≤T in(t)≤T air,max
其中，P air(t)为空调制冷时t时段的功率；P air,max为空调制冷额定功率；T in(t)为空调制冷时t时段的室内温度；T air,max，T air,min分别为室内温度上下限；
热水器负荷约束：
0≤P eh(t)≤P eh,max
T eh,min≤T eh(t)≤T eh,max
其中，T eh(t)为热水器热水在第t时段的温度；P eh,max为热水器加热额定功率；T eh,max、T eh,min分别为电热水器的热水上下限温度。
进一步地，所述步骤S2具体包括如下步骤：
步骤S21，构建单时段需求响应优化模型；
步骤S22，构建多时段需求响应优化模型；
步骤S23，设置实时电价下需求响应的约束条件。
进一步地，所述步骤S21中，假设用户在第i小时的收益为：
M(D i)＝N(D i)-p iD i
ΔD i＝D i-D i0
其中，N(D i)表示第i小时用户用电产生的总收入；p iD i为第i小时用户用电成本；D i和p i分别表示第i小时用电需求和第i小时电价；D i0和p i0分别表示第i小时初始用电需求和第i小时初始电价；
则第i小时客户的用电需求量为：
Figure PCTCN2022125545-appb-000005
其中，α ii为自弹性，定义为：
Figure PCTCN2022125545-appb-000006
p i0为进行需求响应前市场拟出清价；
所述步骤S22中，假设第j小时电价由p j0调整到p j，第i小时用户的用电需求量由D i0调整到D i，实施实时电价后，第i小时客户用电需求量为：
Figure PCTCN2022125545-appb-000007
其中，D λ0为多时段基础用电需求，α ij为第i个小时与第j个小时之间的互弹性系数，p p0，p f0分别为峰谷平电价机制中的平电价与峰电价；
同时考虑单时段与多时段的综合需求响应模型，得到：
Figure PCTCN2022125545-appb-000008
其中，D D为综合基础用电需求：
D D＝D i0+D λ0
进一步地，所述步骤S23中，设最大需求D maxl为用户满负荷运行时电能需求，最小需求D minl为用户满足基本安全生产生活需求时的电能需求，即：
D minl≤D i≤D maxl
设最高电价P maxl为保护用户利益而设置的一个最高限值，最低电价P minl是为了保护售电方利益而设置的一个最低限值，即：
p min≤p i≤p max。
进一步地，所述步骤S3具体包括如下步骤：
步骤S31，定义业务场景，包括：电力客户分类及画像、综合能耗评估及用电分析、客户侧用能增值服务三个大场景分类和六个具体实现的场景任务；
步骤S32，根据供电区域、计费方式和支付方式的不同组合，形成不同的增值服务方案。
实施本发明具有如下有益效果：本发明不仅实现了用户侧的用能优化与能效提升，而且实现了电力系统削峰填谷的出力平稳目标，为市场导向型智能电网提供了良好的 参考示范；
在能源公司层面，本发明能帮助能源公司更加科学地制定动态价格与需求响应激励政策，并对用能细节监测结果进行分析处理所得的结论可被用于调整、完善和科学评估能源公司正在进行的能效项目、更合理地设计施行未来项目和分配资金；
在居民用户层面，本发明可以结合自身用电习惯、电网状态、动态价格的个性化节能调控建议，家居系统能效状态诊断等，在满足用电用能需求且保证用户舒适度的条件下，可极大地提高居民用电经济效益。并向用户实时反馈每个设备的用能信息为深入挖掘电器的用能规律(包括设备工作状态间的关联性)和了解用户自身用能习惯提供可能性；
在社会层面，本发明能够为企业的工程师、高校的学术研究人员、以及业余爱好者战略性地集中研究精力于某一正确的方向提供依据，能够推动和促进制造商加速开展高能效设备的研发，从而加速能效技术革新、诱发能效市场改革和帮助监管部门科学地制定相关政策，而且能在确定低能效环节的基础上，提高楼宇建筑(尤其是商业楼宇)的能效(包括节约能耗和耗能转移两方面)，以减少不必要的能源费、有效缓解能源危机，降低环境污染、减缓温室效应。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本发明实施例一种电力用户增值服务决策方法的流程示意图。
图2是本发明实施例中四类典型日负荷曲线聚类结果示意图。
图3是本发明实施例中工商业用户在使用用电套餐前后的负荷曲线对比示意图。
具体实施方式
以下各实施例的说明是参考附图，用以示例本发明可以用以实施的特定实施例。
本发明首先采用两阶段优化实现不同场景下的用户的需求侧响应优化，之后利用能源大数据进行分析，根据不同的业务场景，为客户提供提供个性化用能套餐。基于此，请参照图1所示，本发明实施例提供一种电力用户增值服务决策方法，包括：
步骤S1，基于新一代量测系统的用电数据，进行典型家庭需求响应用能优化分析；
步骤S2，基于典型家庭需求响应用能特征，构建分时电价下用户侧需求响应优化模型；
步骤S3，结合新能源电网运行及供需平衡，进行基于能源大数据的客户增值服务决策，获取增值服务方法。
具体地，步骤S1进行典型家庭需求响应用能优化，具体包括两个阶段：
第一阶段优化：家庭能量管理的系统函数需要对经济性与用户舒适度进行综合考虑，分别得到用电成本与舒适度受影响程度最小为目标，两种目标函数综合起来，作为用户参与电网紧急调度情况下的家庭能量管理优化目标。对于经济性目标函数，首先分析其不含风险系数的情况，包括用户用电费用及储能充放电损耗，用户总的用电费用C 1表示为：
Figure PCTCN2022125545-appb-000009
其中，t end为当前时段的截止时刻，P grid(t)为当前时段从电网中购买的电量；ρ grid(t)为所分析时段用户与电网间的电价，当P grid(t)>0，ρ grid(t)表示为购电，当P grid(t)<0，ρ grid(t)表示为售电，售电价格定义为该时购电价格的一半；C DG为分布式电源发电成本及损耗折旧费，对于分布式可再生能源，其发电成本为0，对于微燃气轮机等，成本为燃气成本。
第二阶段优化：第一阶段的优化目标函数会让用户的净支出成本最小，然而单纯让用户的净支出经济成本最小，会导致一种问题：不可中断负荷的某一时段或者某几个时段的充放电电电量会转移到其他电价相等或者电价之和相等的几个时段，从而会造成多解问题，为了解决这种问题，需要引入第二阶段优化目标函数，该目标函数表述为：
Figure PCTCN2022125545-appb-000010
Figure PCTCN2022125545-appb-000011
其中，C 3为第二阶用户总的用电费用，
Figure PCTCN2022125545-appb-000012
表示为购电功率平均值，w grid(t)为电网电价权重，与电价成反比。该目标函数表示为考虑峰谷差的用户整体购电波动性，是为了降低由于用户在同一电价低谷时段集中购电而造成某一时段越限的风险，达到将购电功率平滑的流程。第二阶段的优化需要考虑到与第一阶段的优化相配合，因此除了以上所说的约束条件外，还需要加入：
C 3≤C 1。
另外，优化决策变量为电动汽车蓄电池功率和可调整负荷的启动时间，因此，其他变量应按照第一阶段优化后的结果继续运行。
设置家庭能量管理方法约束条件，主要包括：(1)电网功率约束；(2)不可中断负荷约束；(3)空调负荷约束；(4)热水器负荷约束。
(1)电网功率约束：
|P grid(t)|≤P grid,max(t)
其中，P grid,max(t)表示用户与电网间每个时间段的功率交换限制。
(2)不可中断负荷约束：
t load,start≤t load≤t load,end-N,t∈N *
其中，t load,start，t load，t load,end分别为不可中断负荷的开始时间、运行时间以及结束时间，N为负荷工作时间裕度。
(3)空调负荷约束：
0≤P air(t)≤P air,max
T air,min≤T in(t)≤T air,max
其中，P air(t)为空调制冷时t时段的功率；P air,max为空调制冷额定功率；T in(t)为空调制冷时t时段的室内温度；Δt为一个时段时间；T air,max，T air,min分别为室内温度上下限。
(4)热水器负荷约束：
0≤P eh(t)≤P eh,max
T eh,min≤T eh(t)≤T eh,max
其中，T eh(t)为热水器热水在第t时段的温度；P eh,max为热水器加热额定功率；T eh,max、T eh,min分别为电热水器的热水上下限温度。
步骤S2具体包括如下步骤：
步骤S21，构建单时段需求响应优化模型。单时段需求响应指用户在某一时段的用电需求只受当前时段电价影响，负荷不可以转移。假设实时电价每规定时段(该时段可以为一小时、30分钟、15分钟甚至5分钟至1分钟)为一个节点，且p i0为进行需求响应前市场拟出清价，在第i小时将电价由p i0调整到p i，用户给予一定响应，负荷量由D i0调整到D i，假设用户在第i小时的收益为：
M(D i)＝N(D i)-p iD i
ΔD i＝D i-D i0
其中，N(D i)表示第i小时用户用电产生的总收入；p iD i为第i小时用户用电成本；D i和p i分别表示第i小时用电需求和第i小时电价；D i0和p i0分别表示第i小时初始用电需求和第i小时初始电价。为了使用户收益最大化(求极大值)，就需使
Figure PCTCN2022125545-appb-000013
为0。并对用户的用电总收入N(D i)进行泰勒展开，保留二次项，略去高阶项并定义自弹性为：
Figure PCTCN2022125545-appb-000014
经过推导，可得到第i小时客户的用电需求量为：
Figure PCTCN2022125545-appb-000015
步骤S22，构建多时段需求响应优化模型。多时段需求响应，指用户在某一时段内的用电需求除了受当前时段电价的影响，并且其他时段电价也影响该时段用电需求，负荷可以转移到其他时间段。假设第j小时电价由p j0调整到p j，第i小时用户给予一定响应，用电需求量由D i0调整到D i。实施实时电价后，第i小时客户用电需求量为：
Figure PCTCN2022125545-appb-000016
其中，D λ0为多时段基础用电需求，α ij为第i个小时与第j个小时之间的互弹性系数，p p0，p f0分别为峰谷平电价机制中的平电价与峰电价。
在实施实时电价情况下，若要获得使客户获得最大用电收益的用电消耗量，须同时考虑自身价格弹性和交叉价格弹性，即同时考虑单时段与多时段的综合需求响应模型，得到：
Figure PCTCN2022125545-appb-000017
其中，D D为综合基础用电需求：
D D＝D i0+D λ0
步骤S23，设置实时电价下需求响应的约束条件。在同一个售电区域内，包含不同类别的用户，不同类别的用户用电特性不一样，价格弹性也不同，从而负荷调节能力也不同。同时调节范围也有差异。设最大需求D maxl为用户满负荷运行时电能需求，最小需求D minl为用户满足基本安全生产生活需求时的电能需求，即：
D minl≤D i≤D maxl
设最高电价P maxl为保护用户利益而设置的一个最高限值，最低电价P minl应该是为了保护售电方利益而设置的一个最低限值，即：
p min≤p i≤p max。
步骤S3具体包括如下步骤：
步骤S31，定义业务场景。基于能源大数据的客户增值服务业务包含：电力客户分类及画像、综合能耗评估及用电分析、客户侧用能增值服务业务三个大场景分类，具体分为六个实现的场景任务，如下表1所示：
表1：典型的业务场景
Figure PCTCN2022125545-appb-000018
(1)基于客户用电行为精准分析，能够结合其他客户行为数据(包括缴费行为、投诉行为等)，以商业定位、能耗体量等综合用能状况对客户进行分类。
(2)对各类客户进行画像，力求精准刻画客户的行为偏好、信用风险、客户价值等隐性特征，提升企业对客户需求的洞察力，为营销和服务的策划、执行、评估、优化提供宝贵的指引。
(3)用户的综合能耗量化指标计算，针对新形式下的多表一体化采集方案，建立以用电采集为支撑，多种能源(如电、水、气)为一体的综合能耗分析体系，给出用户的综合能耗量化指标。
(4)用户能耗预测及高能耗用户评估，定量分析能够精确反应不同类型用户的能源消耗特性，以现有用户近年来的综合能源消耗时间序列数据为基础，结合国家能源战略方针分析不同类型能源利用效率、清洁能源占比，进行多类型用户能耗预测和评估，获得具有节能潜力的高能耗用户。
(5)个性化电费套餐智能推送，结合客户用能特性，应用客户分类和客户画像技术以及两阶段优化策略输出结果，进行个性化电费套餐推送，实现用户合理用电和电力公司最优盈利的目的。
(6)电力客户远程最佳用电模式分析及节能降耗建议，基于用户用电行为分析结果，用能优化结构、结果以及能耗共享数据库，通过与其他同类用户用能情况横向对比和用户自身历史数据的纵向对比，规划当前场景内的最佳用电模式，给出用户能效升级建议。
步骤S32，根据供电区域、计费方式和支付方式的不同组合，形成不同的增值服务 方案。现货市场价差传导模式下的“保底”、“分成”模式将不再适用，售电公司要以正价格形式与用户签约，并为用户设计定制化的用电套餐。现货市场竞争中，用电套餐科学化，将更有利于售电公司和用户。售电公司可以根据供电区域、计费方式和支付方式的不同组合形成不同的套餐并提供给在现货市场的用户。
本实施例提供的分时电价如表2所示。对用户来说，用电思维上已经沿用之前峰平谷的电价，即一天三个电价，而用户或许会更容易去接受峰平谷时段给的用电价格套餐。
表2：分时电价
Figure PCTCN2022125545-appb-000019
以深圳市的某综合区域内的工商业用户为研究对象工商业用户的日负荷曲线进行聚类的CH指标值，用户类型分为四类时，簇内数据联系紧密，簇间分散性大，聚类效果最优。将同一用户群内的所有用户的日负荷曲线叠加取平均，得到如图2所示相应类的典型负荷曲线。从图2看出，该区域工商业用户可被分为四类:双峰型、峰平型、平滑型和避峰型。双峰型负荷主要包括大型制造工业，中午由于员工休息负荷明显下降；峰平型负荷包括制衣厂等公司，工作时间固定且规律；平滑型负荷全天负荷波动不大，负荷率较高，多为三班制生产方式，如高温炼炉负荷、大容量高压电机负荷；避峰型负荷比较特殊，其用电高峰时段正好处于系统的低谷时段。
从图2还可以看出，双峰型和峰平型负荷数量较多，避峰型负荷所占比例较少。从需求侧的角度看，双峰型和峰平型负荷应是需求响应项目实施的主要对象，可以带来较大的削峰填谷效益。同时也应进一步挖掘平滑型和避峰型用户参与需求响应项目的潜力，以完成整个系统削峰填谷的目标，缓解短期内系统容量短缺、降低发电成本、延缓电网升级。
表3：4种不同类型的用户群体得到的具体最优电力套餐方案
Figure PCTCN2022125545-appb-000020
从表3看出，不同类型用户选择其对应套餐的概率最大，均大于维持现状的概率。套餐A和B在4种典型用户群体中均占有一定的市场份额，双峰型、峰平型、平滑型用户选择套餐D的概率接近于零，套餐D仅在避峰型用户中拥有一定的市场。由于避峰型用户在用户总体中占的比例远小于其他典型用户，故套餐D的市场份额仅为0.96％。实施电力套餐后，仍有31.18％的用户不愿意变更现在的电费计价方式，没有选择任何套餐，可能是用户的心理因素、个人偏好等不可观测的随机效用因素及可观测的效用误差导致的。
根据所建立的基于成本—效益分析的电力套餐评估模型对最优套餐进行评估，以验证电力套餐的经济性及可行性。考虑各类型用户选择各套餐(包括维持现状)的概率，将所有用户的负荷曲线叠加，可得到推行电力套餐后工商业用户的总日负荷曲线，并将其与原始负荷曲线对比，如图3所示。由图3可知实行电力套餐后，工商业用户的总日负荷曲线趋于平滑，峰谷差明显减小，削峰填谷效果较为明显。
图3给出了实行电力套餐前后工商业用户总负荷特征。实施电力套餐后，负荷峰值的下降量为63MW，为原始工商业总负荷峰值的4.67％，系统日峰谷差率为36.73％，相比原始数据下降了8.25％。实施最优电力套餐需投入的总成本为8348万元/年，给多元主体带来的总效益为13169万元/年，益本比为1.783。
通过上述说明可知，与现有技术相比，本发明的有益效果在于：本发明不仅实现了用户侧的用能优化与能效提升，而且实现了电力系统削峰填谷的出力平稳目标，为市场导向型智能电网提供了良好的参考示范；
在能源公司层面，本发明能帮助能源公司更加科学地制定动态价格与需求响应激励政策，并对用能细节监测结果进行分析处理所得的结论可被用于调整、完善和科学评估能源公司正在进行的能效项目、更合理地设计施行未来项目和分配资金；
在居民用户层面，本发明可以结合自身用电习惯、电网状态、动态价格的个性化节能调控建议，家居系统能效状态诊断等，在满足用电用能需求且保证用户舒适度的条件下，可极大地提高居民用电经济效益。并向用户实时反馈每个设备的用能信息为深入挖掘电器的用能规律(包括设备工作状态间的关联性)和了解用户自身用能习惯提供可能性；
在社会层面，本发明能够为企业的工程师、高校的学术研究人员、以及业余爱好者战略性地集中研究精力于某一正确的方向提供依据，能够推动和促进制造商加速开展高能效设备的研发，从而加速能效技术革新、诱发能效市场改革和帮助监管部门科学地制定相关政策，而且能在确定低能效环节的基础上，提高楼宇建筑(尤其是商业楼宇)的能效(包括节约能耗和耗能转移两方面)，以减少不必要的能源费、有效缓解能源危机，降低环境污染、减缓温室效应。
以上所揭露的仅为本发明较佳实施例而已，当然不能以此来限定本发明的权利范围，因此依本发明权利要求所作的等同变化，仍属本发明所涵盖的范围。

Claims (10)

  1. 一种电力用户增值服务决策方法，其特征在于，包括：
    步骤S1，基于新一代量测系统的用电数据，进行典型家庭需求响应用能优化分析；
    步骤S2，基于典型家庭需求响应用能特征，构建分时电价下用户侧需求响应优化模型；
    步骤S3，结合新能源电网运行及供需平衡，进行基于能源大数据的客户增值服务决策，获取增值服务方法。
  2. 根据权利要求1所述的电力用户增值服务决策方法，其特征在于，所述步骤S1进行典型家庭需求响应用能优化包括两个阶段：
    第一阶段优化，具体是根据家庭能量管理的经济性与用户舒适度，分别得到用电成本与舒适度受影响程度最小为目标函数，然后将两种目标函数综合起来，作为用户参与电网紧急调度情况下的家庭能量管理优化目标；
    第二阶段优化，具体是根据峰谷差的用户整体购电波动性，为降低由于用户在同一电价低谷时段集中购电而造成某一时段越限的风险，达到将购电功率平滑的流程，获得第二阶段优化目标函数。
  3. 根据权利要求2所述的电力用户增值服务决策方法，其特征在于，所述第一阶段优化中，对于经济性目标函数，用户总的用电费用C 1表示为：
    Figure PCTCN2022125545-appb-100001
    其中，t end为当前时段的截止时刻，P grid(t)为当前时段从电网中购买的电量；ρ grid(t)为所分析时段用户与电网间的电价，当P grid(t)＞0，ρ grid(t)表示为购电，当P grid(t)＜0，ρ grid(t)表示为售电，售电价格定义为该时购电价格的一半；C DG为分布式电源发电成本及损耗折旧费。
  4. 根据权利要求3所述的电力用户增值服务决策方法，其特征在于，第二阶段优化目标函数为：
    Figure PCTCN2022125545-appb-100002
    Figure PCTCN2022125545-appb-100003
    其中，C 3为第二阶用户总的用电费用，
    Figure PCTCN2022125545-appb-100004
    表示为购电功率平均值，w grid(t)为电网电价权重，与电价成反比。
  5. 根据权利要求4所述的电力用户增值服务决策方法，其特征在于，所述第二阶段优化目标函数包括：
    C 3≤C 1。
  6. 根据权利要求1所述的电力用户增值服务决策方法，其特征在于，所述步骤S1还包括设置家庭能量管理方法约束条件，具体为：
    电网功率约束：
    |P grid(t)|≤P grid,max(t)
    其中，P grid,max(t)表示用户与电网间每个时间段的功率交换限制；
    不可中断负荷约束：
    t load,start≤t load≤t load,end-N,t∈N *
    其中，t load,start，t load，t load,end分别为不可中断负荷的开始时间、运行时间以及结束时间，N为负荷工作时间裕度；
    空调负荷约束：
    0≤P air(t)≤P air,max
    T air,min≤T in(t)≤T air,max
    其中，P air(t)为空调制冷时t时段的功率；P air,max为空调制冷额定功率；T in(t)为空调制冷时t时段的室内温度；T air,max，T air,min分别为室内温度上下限；
    热水器负荷约束：
    0≤P eh(t)≤P eh,max
    T eh,min≤T eh(t)≤T eh,max
    其中，T eh(t)为热水器热水在第t时段的温度；P eh,max为热水器加热额定功率；T eh,max、T eh,min分别为电热水器的热水上下限温度。
  7. 根据权利要求1所述的电力用户增值服务决策方法，其特征在于，所述步骤S2具体包括如下步骤：
    步骤S21，构建单时段需求响应优化模型；
    步骤S22，构建多时段需求响应优化模型；
    步骤S23，设置实时电价下需求响应的约束条件。
  8. 根据权利要求7所述的电力用户增值服务决策方法，其特征在于，所述步骤S21中，假设用户在第i小时的收益为：
    M(D i)＝N(D i)-p iD i
    ΔD i＝D i-D i0
    其中，N(D i)表示第i小时用户用电产生的总收入；p iD i为第i小时用户用电成本；D i和p i分别表示第i小时用电需求和第i小时电价；D i0和p i0分别表示第i小时初始用电需求和第i小时初始电价；
    则第i小时客户的用电需求量为：
    Figure PCTCN2022125545-appb-100005
    其中，α ii为自弹性，定义为：
    Figure PCTCN2022125545-appb-100006
    p i0为进行需求响应前市场拟出清价；
    所述步骤S22中，假设第j小时电价由p j0调整到p j，第i小时用户的用电需求量由D i0调整到D i，实施实时电价后，第i小时客户用电需求量为：
    Figure PCTCN2022125545-appb-100007
    其中，D λ0为多时段基础用电需求，α ij为第i个小时与第j个小时之间的互弹性系数，p p0， p f0分别为峰谷平电价机制中的平电价与峰电价；
    同时考虑单时段与多时段的综合需求响应模型，得到：
    Figure PCTCN2022125545-appb-100008
    其中，D D为综合基础用电需求：
    D D＝D i0+D λ0。
  9. 根据权利要求8所述的电力用户增值服务决策方法，其特征在于，所述步骤S23中，设最大需求D maxl为用户满负荷运行时电能需求，最小需求D minl为用户满足基本安全生产生活需求时的电能需求，即：
    D minl≤D i≤D maxl
    设最高电价P maxl为保护用户利益而设置的一个最高限值，最低电价P minl是为了保护售电方利益而设置的一个最低限值，即：
    p min≤p i≤p max。
  10. 根据权利要求1所述的电力用户增值服务决策方法，其特征在于，所述步骤S3具体包括如下步骤：
    步骤S31，定义业务场景，包括：电力客户分类及画像、综合能耗评估及用电分析、客户侧用能增值服务三个大场景分类和六个具体实现的场景任务；
    步骤S32，根据供电区域、计费方式和支付方式的不同组合，形成不同的增值服务方案。
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