Virtual power plant resource aggregation method and system for power market power grid auxiliary service

Abstract

The invention discloses a virtual power plant resource aggregation method and system for power market power grid auxiliary services, and the method comprises the steps: carrying out the statistics and classification of all resources contained in a virtual power plant energy system, and constructing a unified model type of each resource; data acquisition configuration, including acquisition unit configuration, acquisition channel configuration and sampling point configuration; resource aggregation: constructing a resource aggregation model by adopting a data driving mode, and establishing a mathematical model reflecting resource characteristics and interaction relationships by analyzing and processing historical operation data of various resources in the virtual power plant on the basis of big data and a machine learning technology; and evaluating the aggregation efficiency, and measuring the capability of the virtual power plant in aggregating different resources to participate in market transaction and power grid service. The established virtual power plant aggregation model can aggregate the flexible resources of the multiple virtual power plants, aggregation does not change the total output and capacity of the virtual power plants, and reference is provided for virtual power plant operators to participate in power market auxiliary services.

Description

Technical Field

[0001] The present invention relates to a method and system for aggregating virtual power plant resources, and in particular to a method and system for aggregating virtual power plant resources for power grid auxiliary services in an electric power market. Background Art

[0002] In recent years, with the reform of the power market and the construction of the energy Internet, a large amount of flexible resources need to be managed according to the areas naturally demarcated by the power supply range of the substation. Various types of resources in the region have different characteristics. The rapid development of communication and control technology provides a good foundation for optimizing and regulating large-scale flexible resources. Driven by the scale benefits of aggregation, virtual power plants aggregate cross-regional and multi-type controllable distributed resources through advanced information collection, control, and communication technologies to give full play to the flexibility and complementarity of resources. The controllable distributed resources involved in the aggregation include controllable loads such as air source heat pumps, ground source heat pumps, and charging piles, as well as controllable power sources such as distributed micro-turbines and batteries, which have the characteristics of large numbers and large performance differences. Therefore, for virtual power plant operators who are responsible for their own profits and losses, in order to better participate in market transactions as an independent market operator, it is necessary to improve the aggregation management capabilities of internal resources and obtain aggregation characteristics. In addition, different ancillary service products have differentiated requirements for the time domain response performance of virtual power plant resources. Therefore, when a virtual power plant aggregates and manages a large number of controllable loads with significantly different response performances, it is necessary not only to consider the power characteristics of the controllable resources, but also the time domain response performance of the controllable resources to support different ancillary service needs. Summary of the invention

[0003] Purpose of the invention: The purpose of the present invention is to provide a method and system for aggregating virtual power plant resources for power grid auxiliary services in the power market, to achieve virtual power plant aggregation efficiency evaluation, and to improve the global responsiveness of aggregated resources and the operating economy of each virtual power plant.

[0004] Technical solution: The present invention includes the following steps: unified modeling, statistics and classification of all resources contained in the virtual power plant energy system, and construction of a unified model type for each resource; data acquisition configuration, including acquisition unit configuration, acquisition channel configuration and sampling point configuration; resource aggregation, integrating various controllable resources within the virtual power plant to form a unified dispatchable whole, and constructing a resource aggregation model in a data-driven manner, based on big data and machine learning technology, by analyzing and processing the historical operating data of various resources within the virtual power plant, to establish a mathematical model reflecting resource characteristics and interactive relationships; aggregation efficiency evaluation, measuring the ability of the virtual power plant to aggregate different resources to participate in market transactions and grid services.

[0005] The unified modeling is specifically as follows: specific model objects of each resource are established according to the unified model type, each model object corresponds to an actual physical device in the virtual power plant energy system, and at the same time, the hierarchical relationship between the model objects and the specific attribute information under the model object are determined according to the physical connection relationship between the resources.

[0006] When constructing the resource aggregation model in a data-driven manner, first collect and organize detailed data of various resources within the virtual power plant; then, clean and integrate these data through data preprocessing methods; use machine learning algorithms to conduct in-depth analysis of the data to explore the interactions and synergies between resources.

[0007] The aggregation efficiency evaluation includes the design of aggregation strategy, evaluation of aggregation response capability, network constraints and equipment operation constraints, economic analysis, and uncertainty and risk management.

[0008] The design of the aggregation strategy includes classification and priority sorting of resources and optimization of the aggregation strategy.

[0009] A virtual power plant management system for power grid auxiliary services in the power market, comprising:

[0010] Adjustable resource management module: manages and maintains basic information of virtual power plant resources;

[0011] Resource aggregation management module: manages the access information of distributed resources of virtual power plants;

[0012] Resource forecasting management module: manages the transaction declaration plan of virtual power plants and their proxy distributed resources participating in the spot market and ancillary services, and provides the functions of preparing, evaluating, declaring and receiving the clearing results of the plan's peak-shaving segmented quotation, power constraints, load tracking capacity, and output curves;

[0013] Economic dispatch management module: It divides distributed resources into fine categories according to their response mode, response potential, response cost constraints, tracking capability constraints, and output curve constraints, and provides an economic dispatch algorithm call function based on a quantitative indicator system and taking constraints into consideration;

[0014] Market transaction management module: realizes data interaction with auxiliary services, power spot trading system and medium- and long-term trading system.

[0015] The resource forecast management module manages the pre-dispatch plan for market clearing based on the resource forecast results, including the functions of receiving, simulating and importing the pre-dispatch plan for the day ahead.

[0016] The market transaction management module provides the functions of receiving, confirming and maintaining electricity market information, and provides data support for virtual power plants to participate in market transactions.

[0017] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in a virtual power plant resource aggregation method for power grid auxiliary services in an electricity market.

[0018] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in a method for aggregating virtual power plant resources for power grid auxiliary services in the power market are implemented.

[0019] Beneficial effects: The virtual power plant aggregation model constructed by the present invention can aggregate the flexible resources of multiple virtual power plants. The aggregation does not change the total output and capacity of the virtual power plant, satisfies the law of energy conservation, and can provide an effective reference for virtual power plant operators to participate in auxiliary services in the electricity market. After calculating the parameters of the flexible resource cluster aggregation reference model, unified response characteristic indicator standards and specifications can be established according to different application scenarios, which is convenient for analyzing and comparing the scheduling potential and properties of various distributed resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flowchart of a virtual power plant aggregation method according to an embodiment of the present invention;

[0021] Figure 2 This is a diagram of a virtual power plant data acquisition communication network architecture according to an embodiment of the present invention;

[0022] Figure 3 It is a visual display of the resource aggregation efficiency evaluation results of the virtual power plant management system in the embodiment of the present invention. DETAILED DESCRIPTION

[0023] The present invention will be further described below in conjunction with the accompanying drawings.

[0024] The virtual power plant resource aggregation method for power market power grid auxiliary services of the present invention comprises the following steps:

[0025] Step 1: Unified modeling. All resources contained in the virtual power plant energy system are counted and classified, and a unified model type for each resource is constructed based on this. Specific model objects for each resource are established based on the unified model type, and each model object corresponds to an actual physical device in the virtual power plant energy system. At the same time, based on the physical connection relationship between resources, the hierarchical relationship between each model object and the specific attribute information under the model object are determined.

[0026] The virtual power plant resource model includes renewable energy generation, energy storage equipment, controllable loads, demand response resources, etc.

[0027] Renewable energy generation is one of the core resources of virtual power plants, including distributed generation methods such as solar energy and wind energy. These energy generation methods are intermittent and uncertain. For example, solar energy depends on sunshine conditions, and wind energy depends on wind speed. These characteristics require virtual power plants to have high flexibility and prediction accuracy during operation to ensure the stability of energy supply.

[0028] Energy storage equipment plays a key role in balancing supply and demand and improving response speed in virtual power plants. For example, battery energy storage systems can store energy when energy prices are low and release energy when demand peaks or other needs arise, thereby smoothing the uncertainty and intermittency of renewable energy and improving the system's peak load capacity.

[0029] Controllable loads mainly refer to users who can adjust power consumption according to operational needs or market price signals, such as building electricity, commercial electricity, etc. The regulation capability of these loads can support the peak load shifting of the power grid and enhance the flexibility and reliability of the system.

[0030] Demand response refers to a measure that enables end users to reduce or increase electricity consumption through economic incentives or other means. Demand response resources can quickly respond to changes in grid demand and are an important part of the regulation capability of virtual power plants.

[0031] Step 2: Data acquisition configuration. Data acquisition configuration mainly includes acquisition unit configuration, acquisition channel configuration and sampling point configuration. Among them:

[0032] The configuration of the acquisition unit mainly includes:

[0033] (1) Hardware selection: Select appropriate data collection hardware, such as smart meters, sensors, data gateways, etc.

[0034] (2) Protocol adaptation: According to the meter or data acquisition terminal protocol, the acquisition unit is adapted to support the corresponding communication mode.

[0035] The acquisition channel configuration mainly includes:

[0036] (1) IP address and port number: Assign a network address and communication port to the acquisition unit.

[0037] (2) General call interval: Set the communication frequency between the acquisition unit and the master station.

[0038] (3) Data sending format: defines the encoding format of the data, such as JSON, XML, etc.

[0039] The sampling point configuration mainly includes:

[0040] (1) Measurement point name: Define easy-to-understand names for measurement points to facilitate data interpretation and analysis.

[0041] (2) Measuring point type: Define the measuring point type based on its function and measurement parameters, such as power, temperature, pressure, flow, etc.

[0042] (3) Station address and point number: Assign a unique address and number to each measuring point for easy identification and management.

[0043] Step 3: Resource aggregation. Integrate various controllable resources within the virtual power plant, such as adjustable loads, energy storage equipment, renewable energy generation units, etc., to form a unified dispatchable whole. The aggregation model can support the virtual power plant to efficiently respond to the needs of the power grid in the power market, such as participating in the peak load regulation, frequency regulation and other auxiliary service markets, optimize resource allocation, reduce costs, and improve economic benefits.

[0044] The resource aggregation model is constructed in a data-driven way, which is based on big data and machine learning technology. By analyzing and processing the historical operation data of various resources within the virtual power plant, a mathematical model reflecting the characteristics and interactive relationships of resources is established. When constructing a data-driven aggregation model, it is first necessary to collect and organize detailed data on various resources within the virtual power plant, including but not limited to the type, location, historical output, response speed, adjustment range, etc. of the resources; then, clean and integrate these data through data preprocessing methods to ensure data quality and availability; next, use machine learning algorithms to conduct in-depth analysis of the data to explore the interactions and synergies between resources.

[0045] Step 4: Aggregation efficiency assessment. Measure the ability of virtual power plants to aggregate different resources to participate in market transactions and grid services. Aggregation efficiency assessment is a complex process involving strategy design, capacity assessment, constraint considerations, economic analysis, and risk management.

[0046] Aggregation strategy design: When a power plant aggregates resources, it is necessary to design an efficient aggregation strategy, including resource classification, priority sorting, and optimization of the aggregation strategy. For example, by classifying resources with different characteristics through unified modeling, the characteristics of heterogeneous resources can be standardized, which helps to improve aggregation efficiency.

[0047] Assessment of aggregate response capability: When aggregating different resources, it is necessary to assess the response capabilities of these resources, including response time, response speed, duration, adjustment amplitude, adjustment direction, and timing matching. These assessment results are the basis for aggregate efficiency analysis, and they determine the regulation capability of the virtual power plant after aggregation.

[0048] Network constraints and equipment operation constraints: When aggregating resources, virtual power plants must consider network constraints, such as grid constraints, equipment operation constraints, etc. These constraints will affect the responsiveness of the aggregated resources.

[0049] Economic analysis: Aggregation efficiency is not only a technical issue, but also an economic issue. While ensuring aggregation efficiency, power plants must also consider minimizing costs, including resource call costs and transaction costs.

[0050] Uncertainty and risk management: When aggregating different resources, virtual power plants face uncertainty challenges such as resource output, ramp rate, power baseline, etc. Uncertainty will affect aggregation efficiency, so it is necessary to optimize aggregation efficiency through risk management and uncertainty management.

[0051] The distributed resource aggregation method proposed in the present invention aggregates various types of scattered resources on the user side into a whole, participates in the power grid auxiliary service market as a whole, and uses the response capability evaluation model to realize the virtual power plant aggregation efficiency evaluation, thereby improving the global response capability of the aggregated resources and the operating economy of each virtual power plant.

[0052] A virtual power plant management system for power grid auxiliary services in the power market of the present invention comprises:

[0053] Adjustable resource management module: manages and maintains basic information of virtual power plant resources, including resource types, resource agent information, market participation information, etc.; provides functions for adding, modifying, and deleting virtual power plant and resource rated capacity, maximum output, minimum output, adjustment rate, and other information; provides resource archive query function to maintain the enterprise business archive information of agent resources, including enterprise information, resource types, business archives, etc., to help enterprises better manage user resources;

[0054] Resource aggregation management module: manages the access information of distributed resources of virtual power plants, provides functions such as equipment maintenance, feature definition, and operation mode configuration of distributed resources, and supports aggregation statistics, analysis, and graphical display of load characteristics of aggregated resources according to power generation units, power consumption units, and energy storage units, including power statistics management and aggregation optimization management;

[0055] Resource forecast management module: manages the transaction declaration plan of virtual power plants and their proxy distributed resources participating in the spot market and ancillary services, and provides the peak-shaving segment quotation, power constraints, load tracking capacity, preparation, evaluation, declaration and clearing result reception functions of the plan; manages the pre-dispatch plan of market clearing based on resource forecast results, including the reception, simulation and import functions of the pre-dispatch plan on the day before;

[0056] Economic dispatch management module: According to the response mode (manual or automatic), response potential, response cost constraint, tracking capacity constraint, and output curve constraint of distributed resources, it provides a function of calling an economic dispatch algorithm based on a quantitative indicator system and taking constraints into consideration, and realizes interactive response dispatch management of timely dispatch order reception, confirmation, plan selection optimization, dispatch order decomposition, and execution tracking. Including day-ahead dispatch, intraday dispatch, peak load management, aggregate power monitoring, real-time power monitoring, etc.

[0057] The market transaction management module realizes data interaction with auxiliary services, electricity spot trading systems and medium- and long-term trading systems, and provides the functions of receiving, confirming and maintaining electricity market information such as auxiliary services and spot markets, including market announcements, load forecasts, transaction constraints, load gaps and other information, and provides data support for virtual power plants to participate in market transactions, including market information disclosure and transaction announcement reminders.

[0058] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in a virtual power plant resource aggregation method for power grid auxiliary services in an electricity market.

[0059] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in a method for aggregating virtual power plant resources for power grid auxiliary services in the power market are implemented.

[0060] Example 1

[0061] This embodiment provides a method for aggregating flexible resources of virtual power plants for grid ancillary services in a flat power market. Figure 1 As shown, including:

[0062] Step 1. The present invention considers the following centralized virtual power plant adjustable resources: virtual power plant photovoltaic system, virtual power plant electric energy storage system, virtual power plant charging pile, virtual power plant building lighting socket, virtual power plant building HVAC, virtual power plant building electrical equipment, virtual power plant building gas equipment, virtual power plant gas trigeneration unit, virtual power plant ground source heat pump system, virtual power plant air source heat pump system, virtual power plant water storage system. According to the role of each resource, it can be divided into the following three categories:

[0063] Generator resources: such as photovoltaic power generation systems, wind power generation systems, micro generators, lithium bromide refrigerators, fuel cells, etc.

[0064] Energy storage resources: such as energy storage systems, electric vehicles, HVAC, gas grids, heat grids, transferable loads, etc.

[0065] Flexible load resources: such as air source heat pumps, ground source heat pumps, building lighting sockets, building HVAC, building electrical equipment, building gas equipment, etc.

[0066] Step 2: Configure the data collection of energy equipment. The communication network architecture of data collection is as follows: Figure 2 As shown. According to the communication capabilities and requirements of different energy devices in the virtual power plant, select the appropriate IoT communication method and communication protocol and configure the device to collect information. At the same time, when configuring the relevant information of data collection, it is necessary to specify the frequency of data collection. The communication methods and transmission protocols corresponding to common collection systems or meters are shown in Table 1 below.

[0067] Table 1 Communication methods and transmission protocols corresponding to common acquisition systems or meters

[0068]

[0069] Taking Modbus acquisition protocol as an example, Figure 3 The schematic structure diagram of the data acquisition scenario built based on the Modbus protocol is shown. The data acquisition platform communicates with the PLC through the Modbus agent to obtain analog information such as flow, pressure, and opening degree of the underlying equipment.

[0070] Step 3: Perform virtual power plant peak load resource aggregation calculation based on the real-time values ​​of each collection point. The resource aggregation calculation process is oriented to the energy supply and consumption end. Taking the resource situation of the virtual power plant covered in step 1 of the implementation method as an example, the specific process is as follows:

[0071] (1) First, determine the type of aggregation model, including conventional partitions; partition aggregation model with special naming methods (for example: Jiangbei, Jiangnan, etc.); partition aggregation model + resource type (for example: Jiangbei distributed photovoltaic, Jiangbei electric vehicles, etc.). Among them, the partition aggregation model includes "sequence number" and "partition aggregation name".

[0072] (2) Secondly, determine the resource aggregation method.

[0073] (21) For the full aggregation of virtual power plant operators: All instruction information of the operators required for statistics in the [Operator] field is summarized and displayed in a table in the form of a list. The corresponding 288 curves also adopt a unified horizontal axis, and the vertical axes are added together to obtain a total curve.

[0074] (22) Resource aggregation for operators within a certain partition:

[0075] For regular partitions, the statistics are summarized according to the instructions of the specified statistical partition in [Partition to which it belongs], that is, the information of two or three types of models in the partition is summarized. You can select "All types" according to [Resource type].

[0076] For example, when the instruction list contains:

[0077]

[0078] When calculating the full amount of virtual power plant 3, first select instructions 3, 4, 5, and 6 for calculation and display the results in the instruction information table. For 3 and 4, the partition information displays "Yangjiang", for 5, the partition information displays "Jiangnan", and for 6, the partition information displays "Jiangbei".

[0079] When calculating the instructions of virtual power plant 3 in Jin, instructions 3 and 4 are selected and displayed in the instruction information table, where the partition information shows "Yangjiang".

[0080] When calculating the total amount of a partition aggregation, you can use the data table whose first 6 digits of the instruction number are 103022 for calculation, and do not calculate it here.

[0081] Step 4: resource aggregation efficiency evaluation. The response capability evaluation index used in the present invention can be:

[0082] (1) Adjustment direction, that is, the relative position of the operating point and the operating base point after adjustment, including upward adjustment and downward adjustment.

[0083] (2) Adjustment amplitude refers to the maximum deviation value that can be achieved relative to the current operating point, including the upward adjustment amplitude A+ and the downward adjustment amplitude A-.

[0084] Increase A+: From the command "actual start time" to the "command set end time", the cumulative sum of [min{actual power, baseline power + set adjustment amount × effective upper limit coefficient}-min(actual power, baseline power + set adjustment amount × effective lower limit coefficient)]. Take the minimum value of the actual power value and the baseline power × effective power upper limit value, and the minimum value of the actual power value and the baseline power × effective power lower limit value at time t.

[0085] Downward adjustment A-: From the command "actual start time" to the "command set end time", the cumulative sum of [max{actual power, baseline power + set adjustment amount × effective lower limit coefficient}-max(actual power, baseline power + set adjustment amount × effective upper limit coefficient)]. Take the maximum value of the actual power value and the baseline power × the effective power lower limit value, and the maximum value of the actual power value and the baseline power × the effective power upper limit value at time t.

[0086] (3) Actual adjustment time. The actual adjustment time includes response time and duration. It refers to the maximum time to maintain the state from receiving the adjustment instruction to fully meeting the adjustment requirements.

[0087] Increase. From the command "actual start time" to "set end time", the time of "actual power" in the interval of [set adjustment amount × effective lower limit coefficient, set adjustment amount × effective upper limit coefficient] is accumulated.

[0088] Downward adjustment. From the command "actual start time" to "set end time", the time of "actual power" in the interval of [set adjustment amount × effective upper limit coefficient, set adjustment amount × effective lower limit coefficient] is accumulated.

[0089] (4) Ramp rate refers to the change in the operating point per unit time Δt during the regulation process. The operating point in distributed resources refers to the resource output during operation; the operating point in aggregated VPP refers to something different.

[0090] For example, the peak-shaving scenario evaluates the response effect of distributed photovoltaic, energy storage, electric vehicles, smart buildings, and microgrid resources participating in the electricity market. It can combine the evaluation standards to analyze whether the power reported by different resources and the actual regulated power meet the standards and the compliance ratio, analyze the deviation between the regulation capacity reported by different resources and the actual regulation capacity, analyze the deviation between the reported regulation time and the actual regulation duration, analyze the deviation between the reported regulation start time and the actual regulation start time, and mark the resources for subsequent evaluation of the resource regulation capacity.

Claims

1. A virtual power plant resource aggregation method for power grid auxiliary services in the power market, characterized in that: The following steps are involved: Unified modeling, statistics and classification of all resources contained in the virtual power plant energy system, and based on this, a unified model type for each resource is constructed; data acquisition configuration, including acquisition unit configuration, acquisition channel configuration and sampling point configuration; resource aggregation, integrating various controllable resources within the virtual power plant to form a unified dispatchable whole, and constructing a resource aggregation model in a data-driven manner. Based on big data and machine learning technology, a mathematical model reflecting resource characteristics and interactive relationships is established by analyzing and processing the historical operating data of various resources within the virtual power plant; aggregation efficiency evaluation, measuring the ability of the virtual power plant to aggregate different resources to participate in market transactions and grid services.

2. A virtual power plant resource aggregation method for power market grid auxiliary services according to claim 1, characterized in that: The unified modeling is specifically as follows: specific model objects of each resource are established according to the unified model type, each model object corresponds to an actual physical device in the virtual power plant energy system, and at the same time, the hierarchical relationship between the model objects and the specific attribute information under the model object are determined according to the physical connection relationship between the resources.

3. A virtual power plant resource aggregation method for power market grid auxiliary services according to claim 1, characterized in that: When constructing the resource aggregation model in a data-driven manner, firstly, detailed data of various resources within the virtual power plant are collected and sorted; then, the data are cleaned and integrated through a data preprocessing method; Use machine learning algorithms to conduct in-depth analysis of data and explore the interactions and synergies between resources.

4. A virtual power plant resource aggregation method for power market grid auxiliary services according to claim 1, characterized in that: The aggregation efficiency evaluation includes the design of aggregation strategy, evaluation of aggregation response capability, network constraints and equipment operation constraints, economic analysis, and uncertainty and risk management.

5. A virtual power plant resource aggregation method for power market grid auxiliary services according to claim 4, characterized in that: The design of the aggregation strategy includes classification and priority sorting of resources and optimization of the aggregation strategy.

6. A virtual power plant management system for power grid auxiliary services in the power market, characterized in that: include: Adjustable resource management module: manages and maintains basic information of virtual power plant resources; Resource aggregation management module: manages the access information of distributed resources of virtual power plants; Resource forecasting management module: manages the transaction declaration plan of virtual power plants and their proxy distributed resources participating in the spot market and ancillary services, and provides the functions of preparing, evaluating, declaring and receiving the clearing results of the plan's peak-shaving segmented quotation, power constraints, load tracking capacity, and output curves; Economic dispatch management module: It divides distributed resources into fine categories according to their response mode, response potential, response cost constraints, tracking capability constraints, and output curve constraints, and provides an economic dispatch algorithm call function based on a quantitative indicator system and taking constraints into consideration; Market transaction management module: realizes data interaction with auxiliary services, power spot trading system and medium- and long-term trading system.

7. A virtual power plant management system for power market grid auxiliary services according to claim 6, characterized in that: The resource forecast management module manages the pre-dispatch plan for market clearing based on the resource forecast results, including the functions of receiving, simulating and importing the pre-dispatch plan for the day ahead.

8. A virtual power plant management system for power market grid auxiliary services according to claim 6, characterized in that: The market transaction management module provides the functions of receiving, confirming and maintaining electricity market information, and provides data support for virtual power plants to participate in market transactions.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the steps in the virtual power plant resource aggregation method for power market grid auxiliary services as described in any one of claims 1 to 5 are implemented.

10. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method for aggregating virtual power plant resources for power market grid auxiliary services as described in any one of claims 1 to 5 are implemented.

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