Energy storage type virtual power plant optimization scheduling method, device, equipment and medium

Abstract

The invention relates to an energy storage type virtual power plant optimization scheduling method and device, equipment and a medium, and the method comprises the steps: building a dynamic time-of-use electricity price model based on a fuzzy membership function; based on the dynamic time-of-use electricity price model, constructing an energy storage type virtual power plant optimization model by taking energy storage equipment operation income maximization as a target; and calling the energy storage type virtual power plant optimization model based on a rolling optimization scheduling method to make a decision. The peak regulation capability and flexibility of the power grid can be improved, and the demand of power supply is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plant optimization scheduling, and in particular to an energy storage type virtual power plant optimization scheduling method, device, equipment and medium. Background Art

[0002] With the deepening reform of the new power system, large-scale distributed energy is integrated into the power grid. Due to the volatility and intermittency of renewable energy, the traditional power grid operation mode is challenged, which may lead to an imbalance in system supply and demand. Therefore, the future power grid will have higher and higher requirements for stability and flexibility. With the development of virtual power plant technology, user-side distributed wind power, photovoltaics, energy storage and adjustable load resources participate in power grid operation by aggregating and building virtual power plants. Energy storage is the user-side resource and important equipment foundation for building a new power system, and plays an important role in achieving the goals of carbon peak and carbon neutrality. Through the optimized scheduling of charging and discharging, energy storage can not only decouple energy production and consumption, increase the proportion of renewable energy consumption, but also reduce the peak load of the system and reduce the capacity investment of equipment in energy transmission and distribution links; in addition, in response to the volatility of renewable energy and the uncertainty of user energy demand, energy storage can also provide auxiliary services for the power system to maintain the safe and reliable operation of the system. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method, device, equipment and medium for optimizing the scheduling of an energy storage type virtual power plant, which can improve the peak-shaving capacity and flexibility of the power grid and ensure the demand for power supply.

[0004] The technical solution adopted by the present invention to solve the technical problem is: to provide an energy storage type virtual power plant optimization scheduling method, comprising the following steps:

[0005] Establish a dynamic time-of-use electricity price model based on fuzzy membership function;

[0006] Based on the dynamic time-of-use electricity price model, an energy storage virtual power plant optimization model is constructed with the goal of maximizing the operating income of energy storage equipment;

[0007] The energy storage virtual power plant optimization model is called to make decisions based on the rolling optimization scheduling method.

[0008] The establishment of a dynamic time-of-use electricity price model based on a fuzzy membership function specifically includes:

[0009] The electricity price is divided into peak and valley periods. When dividing, the probability that the highest peak point on the load curve is in the peak period is set to 1, and the probability that the lowest valley point is in the peak period is set to 0; the probability that the lowest valley point on the load curve is in the valley period is set to 1, and the probability that the highest peak point is in the valley period is set to 0; the probability that the remaining points on the load curve are in the peak period and valley period is determined by using a semi-trapezoidal membership function;

[0010] A dynamic time-of-use electricity price model is established based on the division results. The dynamic time-of-use electricity price model is expressed as: Among them, λ t To correct the electricity price, K, L and P are the grid's off-peak electricity price adjustment coefficient, peak electricity price adjustment coefficient and normal electricity price adjustment coefficient, respectively. They refer to the off-peak electricity price, peak electricity price and normal electricity price of the power grid respectively, v (P t l ) represents the valley membership, χ p (P t l ) represents the peak membership, τ v and τ p are the valley membership threshold and peak membership threshold respectively.

[0011] The objective function of the energy storage virtual power plant optimization model is: maxR = r 1 +r 2 +r 3 -c 1 -c 2 , where R is the operating income of the energy storage equipment, r 1 is the arbitrage profit, expressed as r 1 =BDQ 1 , r 2 The basic electricity fee reduced for users is expressed as r 2 =BYQ 2 , r 3 is the electricity subsidy issued by the government, denoted as r 3 =BNQ 3 , c 1 is the investment cost of energy storage equipment, expressed as c 2 is the operation and maintenance cost of the energy storage equipment, expressed as c 2 =BC m P max ; B is the compound interest coefficient, expressed as t r is the inflation rate, d r is the discount rate, D is the number of days per year that the energy storage equipment participates in arbitrage, Q 1 The daily income obtained by energy storage equipment from participating in arbitrage is expressed as: is the discharge power of the energy storage device in the i-th period, is the charging power of the energy storage device in the i-th period, B dis (i) is the discharge state of the energy storage device in the i-th period, B ch (i) is the charging state of the energy storage device in the i-th period, Δt i is the time step, λ(i) is the electricity price in the ith period, Y is the number of months per year that the energy storage device is used, Q 2 The basic electricity bill reduced by energy storage device users each month is expressed as P max is the rated power of the energy storage device, e b is the monthly transformer basic capacity electricity fee, P c The maximum energy storage device power required to reduce the peak load power to the average daily power, N is the number of days the energy storage device is used each year, Q is 3 is the daily energy storage equipment subsidy, expressed as p e is the government subsidy electricity price, λ is the recovery coefficient of the energy storage device, C p is the unit power cost of the energy storage device, C e is the rated capacity cost of the energy storage equipment, E max is the rated capacity of the energy storage device, T is the service life of the energy storage device, C m is the operating cost of the energy storage device per unit power.

[0012] The constraints of the energy storage virtual power plant optimization model include:

[0013] The power balance constraint is expressed as: Among them, P grid (i) is the electricity from the grid, P load (i) represents the user's load size, is the predicted power generation of renewable energy, P ess_dis (i) is the discharge capacity of the energy storage device, P ess_cha (i) is the charge capacity of the energy storage device;

[0014] The operating constraints of the state of charge of the energy storage equipment are expressed as: Among them, S OC (i) is the charge state of the energy storage device at the current moment, S OC (i-1) is the state of charge of the energy storage device at the previous moment, η d is the discharge efficiency of the energy storage device, η c is the charging efficiency of the energy storage device, P dis (i) is the discharge power of the energy storage device, P ch(i) is the charging power of the energy storage device, Δt is the time between the current moment and the previous moment, E max is the rated capacity of the energy storage device;

[0015] The charging and discharging power constraints of energy storage equipment are expressed as: B dis (i) is the discharge state of the energy storage device in the i-th period, B ch (i) is the charging state of the energy storage device in the i-th period, P max is the rated power of the energy storage device;

[0016] The state of charge constraint of the energy storage device is expressed as: S OCmin ≤S OC (i)≤S OCmax , where S OCmin and S OCmax They are the lower and upper limits of the power of the energy storage device respectively.

[0017] The method based on rolling optimization scheduling calls the energy storage virtual power plant optimization model to make a decision, specifically including:

[0018] According to the operating status of the energy storage equipment in the virtual power plant at the current moment, the system state sequence in the prediction time domain is obtained;

[0019] Based on the system state sequence in the prediction time domain, the energy storage virtual power plant optimization model is solved considering the constraints of various resources to obtain the control sequence of the system in the prediction time domain;

[0020] Applying the first control value of the control sequence to the control system;

[0021] Repeat the above steps to achieve rolling optimization of the virtual power plant’s daily scheduling.

[0022] The technical solution adopted by the present invention to solve the technical problem is: to provide an energy storage type virtual power plant optimization scheduling device, comprising:

[0023] Establishing a module for establishing a dynamic time-of-use electricity price model based on fuzzy membership function;

[0024] A construction module is used to construct an energy storage virtual power plant optimization model based on the dynamic time-of-use electricity price model with the goal of maximizing the operating income of energy storage equipment;

[0025] A calling module is used to call the energy storage virtual power plant optimization model to make decisions based on a rolling optimization scheduling method.

[0026] The establishment module includes:

[0027] The division unit is used to divide the peak and valley periods of electricity prices. When dividing, the probability that the highest peak point on the load curve is in the peak period is set to 1, and the probability that the lowest valley point is in the peak period is set to 0; the probability that the lowest valley point on the load curve is in the valley period is set to 1, and the probability that the highest peak point is in the valley period is set to 0; the probability that the remaining points on the load curve are in the peak period and the valley period is determined by using a semi-trapezoidal membership function;

[0028] The establishing unit is used to establish a dynamic time-of-use electricity price model based on the division result, and the dynamic time-of-use electricity price model is expressed as: Among them, λ t To correct the electricity price, K, L and P are the grid's off-peak electricity price adjustment coefficient, peak electricity price adjustment coefficient and normal electricity price adjustment coefficient, respectively. They refer to the off-peak electricity price, peak electricity price and normal electricity price of the power grid respectively, v (P t l ) represents the valley membership, χ p (P t l ) represents the peak membership, τ v and τ p are the valley membership threshold and peak membership threshold respectively.

[0029] The objective function of the energy storage virtual power plant optimization model constructed by the building module is: maxR = r 1 +r 2 +r 3 -c 1 -c 2 , where R is the operating income of the energy storage equipment, r 1 is the arbitrage profit, expressed as r 1 =BDQ 1 , r 2 The basic electricity fee reduced for users is expressed as r 2 =BYQ 2 , r 3 is the electricity subsidy issued by the government, denoted as r 3 =BNQ 3 , c 1 is the investment cost of energy storage equipment, expressed as c 2 is the operation and maintenance cost of the energy storage equipment, expressed as c 2 =BC m P max ; B is the compound interest coefficient, expressed as t r is the inflation rate, d r is the discount rate, D is the number of days per year that the energy storage equipment participates in arbitrage, Q 1The daily income obtained by energy storage equipment from participating in arbitrage is expressed as: is the discharge power of the energy storage device in the i-th period, is the charging power of the energy storage device in the i-th period, B dis (i) is the discharge state of the energy storage device in the i-th period, B ch (i) is the charging state of the energy storage device in the i-th period, Δt i is the time step, λ(i) is the electricity price in the ith period, Y is the number of months per year that the energy storage device is used, Q 2 The basic electricity bill reduced by energy storage device users each month is expressed as P max is the rated power of the energy storage device, e b is the monthly transformer basic capacity electricity fee, P c The maximum energy storage device power required to reduce the peak load power to the average daily power, N is the number of days the energy storage device is used each year, Q is 3 is the daily energy storage equipment subsidy, expressed as p e is the government subsidy electricity price, λ is the recovery coefficient of the energy storage device, C p is the unit power cost of the energy storage device, C e is the rated capacity cost of the energy storage equipment, E max is the rated capacity of the energy storage device, T is the service life of the energy storage device, C m is the operating cost of the energy storage device per unit power.

[0030] The constraints of the energy storage virtual power plant optimization model constructed by the building module include:

[0031] The power balance constraint is expressed as: Among them, P grid (i) is the electricity from the grid, P load (i) represents the user's load size, is the predicted power generation of renewable energy, P ess_dis (i) is the discharge capacity of the energy storage device, P ess_cha (i) is the charge capacity of the energy storage device;

[0032] The operating constraints of the state of charge of the energy storage equipment are expressed as: Among them, S OC (i) is the charge state of the energy storage device at the current moment, S OC (i-1) is the state of charge of the energy storage device at the previous moment, η d is the discharge efficiency of the energy storage device, η c is the charging efficiency of the energy storage device, P dis(i) is the discharge power of the energy storage device, P ch (i) is the charging power of the energy storage device, Δt is the time between the current moment and the previous moment, E max is the rated capacity of the energy storage device;

[0033] The charging and discharging power constraints of energy storage equipment are expressed as: B dis (i) is the discharge state of the energy storage device in the i-th period, B ch (i) is the charging state of the energy storage device in the i-th period, P max is the rated power of the energy storage device;

[0034] The state of charge constraint of the energy storage device is expressed as: S OCmin ≤S OC (i)≤S OCmax , where S OCmin and S OCmax They are the lower and upper limits of the power of the energy storage device respectively.

[0035] The calling module includes:

[0036] A prediction unit, used to obtain a system state sequence in a prediction time domain according to the operating state of the energy storage equipment in the virtual power plant at the current moment;

[0037] A solving unit, used to solve the energy storage virtual power plant optimization model based on the system state sequence in the prediction time domain and taking into account the constraints of various resources to obtain a control sequence of the system in the prediction time domain;

[0038] a control unit for applying a first control value of a control sequence to the control system;

[0039] Repeat the operations of the above units to achieve rolling optimization of the virtual power plant’s daily scheduling.

[0040] The technical solution adopted by the present invention to solve its technical problem is: to provide an electronic device, including a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above-mentioned energy storage type virtual power plant optimization scheduling method when executing the computer program.

[0041] The technical solution adopted by the present invention to solve its technical problem is: providing a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned energy storage type virtual power plant optimization scheduling method are implemented.

[0042] Beneficial Effects

[0043] Due to the adoption of the above-mentioned technical scheme, the present invention has the following advantages and positive effects compared with the prior art: the present invention establishes a dynamic time-of-use electricity price mathematical model based on fuzzy membership function, and on this basis constructs an optimization scheduling model of the energy storage type virtual power plant, and finally calls the optimization scheduling model of the energy storage type virtual power plant to make decisions through the optimization scheduling method of the energy storage type virtual power plant based on rolling optimization. This method can better handle the intelligent decision-making problem of the energy storage equipment inside the virtual power plant, and effectively make up for the shortcomings of low intelligence level of resource decision-making on the demand side of the distribution network, insufficient refinement of the control means, and excessive peak-to-valley difference of the power grid load, thereby improving the peak-shaving capacity and flexibility of the power grid and ensuring the demand for power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flow chart of the energy storage type virtual power plant optimization scheduling method according to the first embodiment of the present invention;

[0045] Figure 2 It is a rolling optimization process design diagram in the first embodiment of the present invention;

[0046] Figure 3 It is a flow chart of rolling optimization of energy storage type virtual power plant in the first embodiment of the present invention. DETAILED DESCRIPTION

[0047] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0048] The first embodiment of the present invention relates to an energy storage type virtual power plant optimization scheduling method, such as Figure 1 As shown, the following steps are included:

[0049] Step 1: Establish a dynamic time-of-use electricity price model based on fuzzy membership function.

[0050] This step constructs a dynamic time-of-use electricity price model to optimize the behavior of energy storage equipment in the energy storage virtual power plant. It uses a fuzzy membership function to divide the peak and valley periods of electricity prices, and establishes a scientific and effective time-of-use electricity price model to improve the flexibility of user-side resources.

[0051] The dynamic time-of-use electricity price model uses daily load data and renewable energy output to obtain the electricity price of user-side energy storage in different time periods.

[0052] The input and output power of the system meet the following power balance conditions, from which the net power of the system can be calculated as follows:

[0053]

[0054] Among them, P t 1 Represents the net power of the system, P t BL Indicates the load size of different users. and Respectively represent the charging power and discharging power of the energy storage device, Represents the forecast value of renewable energy power generation.

[0055] On the basis of the traditional fuzzy membership function peak-valley time division method, this implementation method considers the demand response of power users and establishes a dynamic time-of-use electricity price model based on the fuzzy membership function. First, the peak-valley time division needs to be carried out. Scientific peak-valley time division is the basis for establishing appropriate time-of-use electricity prices. Only with appropriate peak-valley time division can there be corresponding appropriate time-of-use electricity prices, and can it play a better role in peak shaving and valley filling. The division principles determined in this implementation method are: (1) The probability that the highest peak point on the load curve is in the peak time period is 1, and the probability that the lowest valley point is in the peak time period is 0. (2) The probability that the lowest valley point on the load curve is in the valley time period is 1, and the probability that the highest peak point is in the valley time period is 0. (3) The probability that the remaining points on the load curve are in the peak time period and the valley time period is determined by the semi-trapezoidal membership function. The following two formulas respectively give the calculation methods of the peak-valley membership of the load at a certain moment:

[0056]

[0057]

[0058] In the formula, and are the maximum net load power and the minimum net load power of the system respectively. According to the above formula, when the net load power reaches the lowest level, the valley membership is 1, the peak membership is 0, and the peak-valley membership thresholds are τ p and τ v , the value range is [0-1]. In this implementation, the peak membership degree χ p (P t l ) is greater than the peak membership threshold τ p The period is divided into peak time regions, and the valley membership χ v (P t l ) is greater than the valley membership threshold τ v The cycle is divided into valley period areas, and other periods are regular areas.

[0059] Therefore, the dynamic time-of-use electricity price model can be expressed as:

[0060]

[0061] Among them, λ t To revise electricity prices; They refer to the valley electricity price, peak electricity price and normal electricity price of the power grid respectively; K, L and P refer to the valley electricity price adjustment coefficient, peak electricity price adjustment coefficient and normal electricity price adjustment coefficient of the power grid respectively. Under the influence of dynamic time-of-use electricity prices, the energy storage equipment in the virtual power plant can flexibly arrange charging and discharging according to the electricity price, choose to charge during low electricity price periods and discharge during high electricity price periods to increase income.

[0062] Step 2: Based on the dynamic time-of-use electricity price model, an energy storage virtual power plant optimization model is constructed with the goal of maximizing the operating profit of energy storage equipment.

[0063] The energy storage virtual power plant optimization model constructed in this step includes objective functions and constraints. The objective function takes into account the benefits of the energy storage system and the related costs of energy storage configuration. The constraints take into account the power balance and the charging and discharging characteristics of the energy storage equipment.

[0064] Energy storage virtual power plants mainly aggregate the main resources related to energy storage, including mechanical energy storage, chemical energy storage, electromagnetic energy storage, phase change energy storage and other forms. With the rapid improvement of the economic efficiency of energy storage technology in recent years, electric energy has broken through the limitation of large-scale economic storage, and also changed the control and optimization mechanism of the industry. Energy storage equipment can be roughly divided into four categories: mechanical energy storage, such as pumped storage, flywheel energy storage, etc.; chemical energy storage, such as lead-acid batteries, sodium-sulfur batteries, etc.; electromagnetic energy storage, such as supercapacitors, superconducting energy storage, etc.; phase change energy storage. Energy storage virtual power plants can participate in the ancillary service market, and can also discharge in certain periods to sell electricity.

[0065] The energy storage virtual power plant takes the energy storage power station as the core, and integrates regional distributed power sources and adjustable loads and other resources into the system. Through the intelligent control center, the virtual power plant can participate in the power trading market of main energy and auxiliary services, give full play to the output advantages of centralized energy storage power stations, improve the efficiency of distributed power sources and adjustable loads, and thus enhance the reliability of the virtual power plant. The energy storage in the virtual power plant can operate in two modes: one is to participate in grid dispatching alone; the other is to participate in dispatching and operation together with distributed power sources and loads.

[0066] When the energy storage power station participates in grid dispatching independently, it only needs to adjust the charging and discharging of the energy storage, without adjusting other distributed energy sources and loads. Energy storage can participate in peak-shaving auxiliary services, replacing the traditional peak-shaving means of coal-fired thermal power units, thereby improving the economy of the grid; at the same time, it can also participate in frequency regulation auxiliary services and support the automatic generation control (AGC) function, that is, responding in real time to the energy storage system frequency regulation power demand command issued by the upper-level dispatching system, and meeting the power command value corresponding to the AGC plan issued by the upper-level dispatching in real time. In addition, energy storage can also participate in the spot market and respond in real time to the energy storage system day-ahead / real-time peak-shaving power demand command issued by the upper-level dispatching system.

[0067] When the energy storage power station is operated in conjunction with distributed power sources and loads, the corresponding control strategy needs to be executed through the master control center to coordinate the power flow between different energy sources and loads. Energy storage, wind power and distributed photovoltaics are regarded as the main body of the virtual power plant. Through internal optimization of the virtual power plant, the overall output plan is predicted and reported to the dispatch center, and the dispatch command is only issued to the virtual power plant master control center. The master control center is responsible for real-time information collection and unified dispatch of wind power, photovoltaic and energy storage power stations. In this mode, the main goal of the energy storage system is to make up for the difference between the actual output and the predicted output of wind and solar power generation, and to improve the overall output accuracy of the virtual power plant. The energy management system of the energy storage power station controls the charging and discharging power of the energy storage power station according to the daily dispatch plan issued by the dispatch center to achieve tracking with the power generation plan. During the trough and peak periods of the grid load, the energy storage device will start the charging and discharging function and respond to the power demand command issued by the virtual power plant master control center in real time to ensure the effective implementation of peak shaving and valley filling measures.

[0068] In this implementation, the goal is to maximize the operating benefits of energy storage equipment in the virtual power plant. The user-side energy storage benefits include the arbitrage benefits of low storage and high generation in the electricity market, the policy subsidies obtained, and the reduction of electricity charges. While maximizing the benefits, the relevant costs of energy storage configuration should also be considered, including investment costs and operating costs. Therefore, the objective function of the energy storage virtual power plant optimization model in this implementation is expressed as:

[0069] maxR=r 1 +r 2 +r 3 -c 1 -c 2 ;

[0070] Among them, R is the operating income of the energy storage equipment.

[0071] r 1 is the arbitrage profit, expressed as: r 1 =BDQ 1 ; In the formula, B is the compound interest coefficient, expressed as t r is the inflation rate, dr is the discount rate, T is the useful life of the energy storage equipment, D is the number of days per year that the energy storage equipment participates in arbitrage, Q 1 The daily income obtained by energy storage equipment from participating in arbitrage is expressed as:

[0072] is the discharge power of the energy storage device in the i-th period, is the charging power of the energy storage device in the i-th period, B dis (i) is the discharge state of the energy storage device in the i-th period, which is a variable between 0 and 1. dis When (i) = 0, it means that the energy storage device has not discharged in the i-th period. dis When (i) = 1, it means that the energy storage device is discharging in the i-th period, B ch (i) is the charging state of the energy storage device in the i-th period, which is a variable between 0 and 1. ch When (i) = 0, it means that the energy storage device is not charged in the i-th period. ch When (i) = 1, it means that the energy storage device is charging in the i-th period, Δt i is the time step, λ(i) is the electricity price in the ith period, which can be obtained through the above time-of-use electricity price model.

[0073] r 2 The basic electricity fee reduced for users is expressed as r 2 =BYQ 2 In the formula, Y is the number of months per year that the energy storage device is used, Q 2 The basic electricity bill reduced by energy storage device users each month is expressed as P max is the rated power of the energy storage device, e b is the monthly transformer basic capacity electricity fee, P c The maximum energy storage device power required to reduce peak load power to average daily power.

[0074] r 3 is the electricity subsidy issued by the government, denoted as r 3 =BNQ 3 Where N is the number of days per year that the energy storage device is used, Q 3 is the daily energy storage equipment subsidy, expressed as p e The government subsidizes electricity prices.

[0075] c 1 is the investment cost of energy storage equipment, expressed as Where λ is the recovery coefficient of the energy storage device, C p is the unit power cost of the energy storage device, C eis the rated capacity cost of the energy storage equipment, E max is the rated capacity of the energy storage device.

[0076] c 2 is the operation and maintenance cost of the energy storage equipment, expressed as c 2 =BC m P max In the formula, C m is the operating cost of the energy storage device per unit power.

[0077] The energy storage virtual power plant needs to meet the following power balance conditions, namely the power balance constraints, which are expressed as:

[0078]

[0079] Among them, P grid (i) is the electricity from the grid, P load (i) represents the user's load size, is the predicted power generation of renewable energy, P ess_dis (i) is the discharge capacity of the energy storage device, P ess_cha (i) is the charge capacity of the energy storage device. The energy storage system selects the battery as the main research object.

[0080] The operating constraints of the state of charge of the energy storage equipment are expressed as:

[0081]

[0082] Among them, S OC (i) is the charge state of the energy storage device at the current moment, S OC (i-1) is the state of charge of the energy storage device at the previous moment, η d is the discharge efficiency of the energy storage device, η c is the charging efficiency of the energy storage device, P dis (i) is the discharge power of the energy storage device, P ch (i) is the charging power of the energy storage device, and Δt is the time between the current moment and the previous moment.

[0083] The following three expressions limit the charging and discharging power of the energy storage device to the minimum and maximum allowable limits. At the same time, it must be ensured that charging and discharging are not performed at the same time. The charging and discharging power constraints of the energy storage device are expressed as:

[0084]

[0085] B dis (i) is the discharge state of the energy storage device in the i-th period, B ch (i) is the charging state of the energy storage device in the i-th period.

[0086] The state of charge constraint of energy storage equipment is expressed as:

[0087] S OCmin ≤S OC (i)≤S OCmax ;

[0088] Among them, S OCmin and S OCmax They are the lower and upper limits of the power of the energy storage device respectively.

[0089] Step 3: Call the energy storage virtual power plant optimization model to make a decision based on the rolling optimization scheduling method.

[0090] The specific steps of rolling optimization in this step are designed as follows: Figure 2 As shown. Rolling optimization is an online optimization method that is updated in real time. At each moment, the optimal control strategy is calculated for a period consisting of several future time periods. As the online optimization is repeated, the update and execution of the control strategy are also formed. Rolling optimization is not a one-time offline process, but an iterative calculation is repeatedly performed online during the sampling process. It can update data in time during the control process, reduce the impact of prediction errors on decision-making, and respond to complex and unforeseen emergencies.

[0091] At each rolling node, the optimization system needs to generate forecast results for the future time period based on the current latest data and recalculate the control changes. The specific steps of rolling optimization are:

[0092] 1. Forecast data iteration: collect the latest measured data and calculate the forecast amount in the future time period;

[0093] 2. Control optimization calculation: introduce prediction results and make optimization decisions on the objective function;

[0094] 3. Data update correction: After forming the control strategy at the current moment, the actual state of the parameters in the model is corrected through feedback from the measured data, so that the next optimization calculation is based on the latest measured data.

[0095] Rolling optimization can effectively solve the contradiction between energy storage strategies in terms of time cycle and prediction error, and assist energy storage in electricity market transactions. By formulating strategies for energy storage to participate in electricity market transactions through rolling optimization, it can effectively improve the benefits of energy storage in the electricity market and provide a reference for energy storage to participate in electricity market transactions. In the combined system of energy storage and new energy, the optimization aims to maximize the benefits of the combined system, comprehensively considers the benefits of new energy, energy storage and new energy policies in the electricity market, which can not only reduce the loss of benefits caused by the forecast deviation of new energy power generation and improve the overall benefits, but also make the output of the combined system smoother and improve the stability of the power system.

[0096] The time period of rolling optimization is the number of future time nodes involved in the optimization model. Among them, the time period is divided into prediction period and control optimization period, both of which are key parameters in optimization. The prediction period P is the time length for predicting unknown quantities in the system. The length of the prediction period P determines the prediction range of the optimization system for future data; the optimization control period C is the time range for overall optimization of future data, which represents the design length of the control strategy and also reflects the scale of optimization. In rolling optimization, the prediction period P is greater than or equal to the optimization control period C. In this embodiment, the prediction period P is equal to the optimization control period C.

[0097] In each time period, first, the node electricity price, power generation of new energy units and other parameters at each time node in the forecast period are predicted to obtain the forecast data for each time node; then, considering the goal and constraints of maximizing comprehensive benefits, the optimal charging and discharging strategy is calculated. Finally, the energy storage device charges and discharges at the current time node according to the optimization results. The total number of time nodes in the rolling optimization is J, which means that the optimization strategy is calculated J times in a complete rolling optimization, and finally J sets of strategies are obtained.

[0098] The number of time nodes in the rolling cycle is set to j, and the process of rolling optimization is as follows.

[0099] 1. At time node i, predict the time-of-use electricity price and renewable energy output data of j nodes in the period [i,i+j-1].

[0100] 2. After obtaining the predicted data, calculate the charging and discharging strategies of j nodes in the period [i,i+j-1] with the goal of maximizing the comprehensive benefit, and select the strategy of node i for use.

[0101] 3. Clear the data and perform the next round of calculation at time node i+1.

[0102] At each time step, the state quantity of the system model is established based on the decision variables of the energy storage device charging and discharging. The optimization problem aims to maximize the benefits of energy storage within the set time, considers the energy storage operation constraints, and solves the optimal control action in the future control time domain, but only selects the first moment action to execute and update the real-time state of the system. As the optimization proceeds, the time window moves forward, and the above steps are repeated to start a new round of optimization until the end of the entire scheduling cycle, and the optimal result of resource optimization in the entire cycle is obtained.

[0103] like Figure 3 As shown, the process of rolling optimization of the energy storage virtual power plant in this embodiment is as follows:

[0104] 1) Establish the basic quantity of the system model: The state quantity of the system model is the power of the energy storage battery. Correspondingly, the input quantity of the system is the charge and discharge state of the energy storage battery.

[0105] The state quantity x(k), input quantity u(k) and output quantity y(k) of the user-side energy management system are as follows:

[0106]

[0107] The objective function of optimization is to minimize the electricity cost while considering the variable incentives proposed in Chapter 3, which is defined as:

[0108]

[0109] Where: j is the number of nodes in each rolling optimization time period; k is the current computing node; R is the total benefit of energy storage within the rolling time range.

[0110] The constraints are the operating conditions of the energy storage system.

[0111] 2) At the current time k, according to the operating status of the energy storage equipment in the virtual power plant, the system state sequence {x(k+1|k), x(k+2|k),…, x(k+NT|k)} in the prediction time domain NT is obtained. This sequence represents the state of each distributed resource of the system at each future moment.

[0112] 3) Based on the current status of the energy storage system at time k, with minimizing the electricity cost as the optimization goal, while considering the constraints of various resources, a solution is obtained (i.e., the optimization model of the energy storage virtual power plant is solved) to obtain {u(k+1|k),u(k+2|k),…,u(k+NT|k)}, that is, the control sequence of the system in the future NT time is obtained.

[0113] 4) The first value u(k+1) of the control sequence is applied to the control system, i.e., the charging and discharging state of the energy storage at time k+1.

[0114] 5) At time k+1, the system state quantity is updated to x(k+1|k+1) according to the real-time state of the energy storage in the virtual power plant, and then the above steps are repeated to achieve rolling optimization.

[0115] It is not difficult to find that the present invention establishes a dynamic time-of-use electricity price mathematical model based on fuzzy membership function, and on this basis constructs an optimization scheduling model of the energy storage type virtual power plant. Finally, the optimization scheduling model of the energy storage type virtual power plant is called to make decisions through the optimization scheduling method of the energy storage type virtual power plant based on rolling optimization. This method can better handle the intelligent decision-making problem of the energy storage equipment inside the virtual power plant, and effectively make up for the shortcomings of low intelligence level of resource decision-making on the demand side of the distribution network, insufficient refinement of the control means, and excessive peak-to-valley difference of the power grid load, thereby improving the peak-shaving capacity and flexibility of the power grid and ensuring the demand for power supply.

[0116] The second embodiment of the present invention relates to an energy storage type virtual power plant optimization scheduling device, comprising:

[0117] Establishing a module for establishing a dynamic time-of-use electricity price model based on fuzzy membership function;

[0118] A construction module is used to construct an energy storage virtual power plant optimization model based on the dynamic time-of-use electricity price model with the goal of maximizing the operating income of energy storage equipment;

[0119] A calling module is used to call the energy storage virtual power plant optimization model to make decisions based on a rolling optimization scheduling method.

[0120] The establishment module includes:

[0121] The division unit is used to divide the peak and valley periods of electricity prices. When dividing, the probability that the highest peak point on the load curve is in the peak period is set to 1, and the probability that the lowest valley point is in the peak period is set to 0; the probability that the lowest valley point on the load curve is in the valley period is set to 1, and the probability that the highest peak point is in the valley period is set to 0; the probability that the remaining points on the load curve are in the peak period and the valley period is determined by using a semi-trapezoidal membership function;

[0122] The establishing unit is used to establish a dynamic time-of-use electricity price model based on the division result, and the dynamic time-of-use electricity price model is expressed as: Among them, λ t To correct the electricity price, K, L and P are the grid's off-peak electricity price adjustment coefficient, peak electricity price adjustment coefficient and normal electricity price adjustment coefficient, respectively. They refer to the off-peak electricity price, peak electricity price and normal electricity price of the power grid respectively, v (P t l ) represents the valley membership, χ p (P t l ) represents the peak membership, τ v and τ p are the valley membership threshold and peak membership threshold respectively.

[0123] The objective function of the energy storage virtual power plant optimization model constructed by the building module is: maxR = r 1 +r 2 +r 3 -c 1 -c 2 , where R is the operating income of the energy storage equipment, r 1 is the arbitrage profit, expressed as r 1 =BDQ 1 , r 2 The basic electricity fee reduced for users is expressed as r 2 =BYQ2 , r 3 is the electricity subsidy issued by the government, denoted as r 3 =BNQ 3 , c 1 is the investment cost of energy storage equipment, expressed as c 2 is the operation and maintenance cost of the energy storage equipment, expressed as c 2 =BC m P max ; B is the compound interest coefficient, expressed as t r is the inflation rate, d r is the discount rate, D is the number of days per year that the energy storage equipment participates in arbitrage, Q 1 The daily income obtained by energy storage equipment from participating in arbitrage is expressed as: is the discharge power of the energy storage device in the i-th period, is the charging power of the energy storage device in the i-th period, B dis (i) is the discharge state of the energy storage device in the i-th period, B ch (i) is the charging state of the energy storage device in the i-th period, Δt i is the time step, λ(i) is the electricity price in the ith period, Y is the number of months per year that the energy storage device is used, Q 2 The basic electricity bill reduced by energy storage device users each month is expressed as P max is the rated power of the energy storage device, e b is the monthly transformer basic capacity electricity fee, P c The maximum energy storage device power required to reduce the peak load power to the average daily power, N is the number of days the energy storage device is used each year, Q is 3 is the daily energy storage equipment subsidy, expressed as p e is the government subsidy electricity price, λ is the recovery coefficient of the energy storage device, C p is the unit power cost of the energy storage device, C e is the rated capacity cost of the energy storage equipment, E max is the rated capacity of the energy storage device, T is the service life of the energy storage device, C m is the operating cost of the energy storage device per unit power.

[0124] The constraints of the energy storage virtual power plant optimization model constructed by the building module include:

[0125] The power balance constraint is expressed as: Among them, P grid (i) is the electricity from the grid, P load (i) represents the user's load size, is the predicted power generation of renewable energy, P ess_dis (i) is the discharge capacity of the energy storage device, P ess_cha (i) is the charge capacity of the energy storage device;

[0126] The operating constraints of the state of charge of the energy storage equipment are expressed as: Among them, S OC (i) is the charge state of the energy storage device at the current moment, S OC (i-1) is the state of charge of the energy storage device at the previous moment, η d is the discharge efficiency of the energy storage device, η c is the charging efficiency of the energy storage device, P dis (i) is the discharge power of the energy storage device, P ch (i) is the charging power of the energy storage device, Δt is the time between the current moment and the previous moment, E max is the rated capacity of the energy storage device;

[0127] 0≤P dis (i)≤B dis (i)P max

[0128] The charging and discharging power constraint of the energy storage device is expressed as: 0≤P ch (i)≤B ch (i)P max , B dis (i) is the energy storage device i

[0129] B dis (i)+B ch (i)≤1

[0130] The discharge state of each period, B ch (i) is the charging state of the energy storage device in the i-th period, P max is the rated power of the energy storage device;

[0131] The state of charge constraint of the energy storage device is expressed as: S OCmin ≤S OC (i)≤S OCmax , where S OCmin and S OCmax They are the lower and upper limits of the power of the energy storage device respectively.

[0132] The calling module includes:

[0133] A prediction unit, used to obtain a system state sequence in a prediction time domain according to the operating state of the energy storage equipment in the virtual power plant at the current moment;

[0134] A solving unit, used to solve the energy storage virtual power plant optimization model based on the system state sequence in the prediction time domain and taking into account the constraints of various resources to obtain a control sequence of the system in the prediction time domain;

[0135] a control unit for applying a first control value of a control sequence to the control system;

[0136] Repeat the operations of the above units to achieve rolling optimization of the virtual power plant’s daily scheduling.

[0137] The third embodiment of the present invention relates to an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned energy storage type virtual power plant optimization scheduling method when executing the computer program of the first embodiment.

[0138] The fourth embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the energy storage type virtual power plant optimization scheduling method of the first embodiment.

[0139] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0140] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0141] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction method, which is implemented in the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0142] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0143] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for optimizing and dispatching an energy storage virtual power plant, characterized in that: The following steps are involved: Establish a dynamic time-of-use electricity price model based on fuzzy membership function; Based on the dynamic time-of-use electricity price model, an energy storage virtual power plant optimization model is constructed with the goal of maximizing the operating income of energy storage equipment; The energy storage virtual power plant optimization model is called to make decisions based on the rolling optimization scheduling method.

2. The energy storage type virtual power plant optimization scheduling method according to claim 1 is characterized in that: The establishment of a dynamic time-of-use electricity price model based on a fuzzy membership function specifically includes: The electricity price is divided into peak and valley periods. When dividing, the probability that the highest peak point on the load curve is in the peak period is set to 1, and the probability that the lowest valley point is in the peak period is set to 0; the probability that the lowest valley point on the load curve is in the valley period is set to 1, and the probability that the highest peak point is in the valley period is set to 0; the probability that the remaining points on the load curve are in the peak period and valley period is determined by using a semi-trapezoidal membership function; A dynamic time-of-use electricity price model is established based on the division results. The dynamic time-of-use electricity price model is expressed as: Among them, λ t To correct the electricity price, K, L and P are the grid's off-peak electricity price adjustment coefficient, peak electricity price adjustment coefficient and normal electricity price adjustment coefficient, respectively. They refer to the off-peak electricity price, peak electricity price and normal electricity price of the power grid respectively. represents the valley membership, represents the peak membership, τ v and τ p are the valley membership threshold and peak membership threshold respectively.

3. The energy storage type virtual power plant optimization scheduling method according to claim 1 is characterized in that: The objective function of the energy storage virtual power plant optimization model is: maxR = r1 + r2 + r3 - c1 - c2, where R is the operating income of the energy storage equipment, r1 is the arbitrage income, expressed as r1 = BDQ1, r2 is the basic electricity fee reduced by the user, expressed as r2 = BYQ2, r3 is the electricity fee subsidy issued by the government, expressed as r3 = BNQ3, and c1 is the investment cost of the energy storage equipment, expressed as c2 is the operation and maintenance cost of the energy storage equipment, expressed as c2=BC m P max ; B is the compound interest coefficient, expressed as t r is the inflation rate, d r is the discount rate, D is the number of days that the energy storage device participates in arbitrage each year, and Q1 is the daily income obtained by the energy storage device from participating in arbitrage, expressed as: is the discharge power of the energy storage device in the i-th period, is the charging power of the energy storage device in the i-th period, B dis (i) is the discharge state of the energy storage device in the i-th period, B ch (i) is the charging state of the energy storage device in the i-th period, Δt i is the time step, λ(i) is the electricity price in the i-th period, Y is the number of months the energy storage device is used each year, and Q2 is the basic electricity fee reduced by the energy storage device user each month, which can be expressed as P max is the rated power of the energy storage device, e b is the monthly transformer basic capacity electricity fee, P c The maximum energy storage device power required to reduce the peak load power to the average daily power, N is the annual use days of the energy storage device, Q3 is the daily energy storage device subsidy, expressed as p e is the government subsidy electricity price, λ is the recovery coefficient of the energy storage device, C p is the unit power cost of the energy storage device, C e is the rated capacity cost of the energy storage equipment, E max is the rated capacity of the energy storage device, T is the service life of the energy storage device, C m is the operating cost of the energy storage device per unit power.

4. The energy storage virtual power plant optimization scheduling method according to claim 1 is characterized in that: The constraints of the energy storage virtual power plant optimization model include: The power balance constraint is expressed as: Among them, P grid (i) is the electricity from the grid, P load (i) represents the user's load size, is the predicted power generation of renewable energy, P ess_dis (i) is the discharge capacity of the energy storage device, P ess_cha (i) is the charge capacity of the energy storage device; The operating constraints of the state of charge of the energy storage equipment are expressed as: Among them, S OC (i) is the charge state of the energy storage device at the current moment, S OC (i-1) is the state of charge of the energy storage device at the previous moment, η d is the discharge efficiency of the energy storage device, η c is the charging efficiency of the energy storage device, P dis (i) is the discharge power of the energy storage device, P ch (i) is the charging power of the energy storage device, Δt is the time between the current moment and the previous moment, E max is the rated capacity of the energy storage device; The charging and discharging power constraints of energy storage equipment are expressed as: B dis (i) is the discharge state of the energy storage device in the i-th period, B ch (i) is the charging state of the energy storage device in the i-th period, P max is the rated power of the energy storage device; The state of charge constraint of the energy storage device is expressed as: S OCmin ≤S OC (i)≤S OCmax , where S OCmin and S OCmax They are the lower and upper limits of the power of the energy storage device respectively.

5. The energy storage type virtual power plant optimization scheduling method according to claim 1 is characterized in that: The method based on rolling optimization scheduling calls the energy storage virtual power plant optimization model to make a decision, specifically including: According to the operating status of the energy storage equipment in the virtual power plant at the current moment, the system state sequence in the prediction time domain is obtained; Based on the system state sequence in the prediction time domain, the energy storage virtual power plant optimization model is solved considering the constraints of various resources to obtain the control sequence of the system in the prediction time domain; Applying the first control value of the control sequence to the control system; Repeat the above steps to achieve rolling optimization of the virtual power plant’s daily scheduling.

6. An energy storage type virtual power plant optimization and dispatching device, characterized in that: include: Establishing a module for establishing a dynamic time-of-use electricity price model based on fuzzy membership function; A construction module is used to construct an energy storage virtual power plant optimization model based on the dynamic time-of-use electricity price model with the goal of maximizing the operating income of energy storage equipment; A calling module is used to call the energy storage virtual power plant optimization model to make decisions based on a rolling optimization scheduling method.

7. The energy storage type virtual power plant optimization and dispatching device according to claim 6 is characterized in that: The establishment module includes: The division unit is used to divide the peak and valley periods of electricity prices. When dividing, the probability that the highest peak point on the load curve is in the peak period is set to 1, and the probability that the lowest valley point is in the peak period is set to 0; the probability that the lowest valley point on the load curve is in the valley period is set to 1, and the probability that the highest peak point is in the valley period is set to 0; the probability that the remaining points on the load curve are in the peak period and the valley period is determined by using a semi-trapezoidal membership function; The establishing unit is used to establish a dynamic time-of-use electricity price model based on the division result, and the dynamic time-of-use electricity price model is expressed as: Among them, λ t To correct the electricity price, K, L and P are the grid's off-peak electricity price adjustment coefficient, peak electricity price adjustment coefficient and normal electricity price adjustment coefficient, respectively. They refer to the off-peak electricity price, peak electricity price and normal electricity price of the power grid respectively. represents the valley membership, represents the peak membership, τ v and τ p are the valley membership threshold and peak membership threshold respectively.

8. The energy storage type virtual power plant optimization and dispatching device according to claim 6 is characterized in that: The objective function of the energy storage virtual power plant optimization model constructed by the construction module is: maxR=r1+r2+r3-c1-c2, where R is the operating income of the energy storage equipment, r1 is the arbitrage income, expressed as r1=BDQ1, r2 is the basic electricity fee reduced by the user, expressed as r2=BYQ2, r3 is the electricity fee subsidy issued by the government, expressed as r3=BNQ3, and c1 is the investment cost of the energy storage equipment, expressed as c2 is the operation and maintenance cost of the energy storage equipment, expressed as c2=BC m P max ; B is the compound interest coefficient, expressed as t r is the inflation rate, d r is the discount rate, D is the number of days that the energy storage device participates in arbitrage each year, and Q1 is the daily income obtained by the energy storage device from participating in arbitrage, expressed as: is the discharge power of the energy storage device in the i-th period, is the charging power of the energy storage device in the i-th period, B dis (i) is the discharge state of the energy storage device in the i-th period, B ch (i) is the charging state of the energy storage device in the i-th period, Δt i is the time step, λ(i) is the electricity price in the i-th period, Y is the number of months the energy storage device is used each year, and Q2 is the basic electricity fee reduced by the energy storage device user each month, which can be expressed as P max is the rated power of the energy storage device, e b is the monthly transformer basic capacity electricity fee, P c The maximum energy storage device power required to reduce the peak load power to the average daily power, N is the annual use days of the energy storage device, Q3 is the daily energy storage device subsidy, expressed as p e is the government subsidy electricity price, λ is the recovery coefficient of the energy storage device, C p is the unit power cost of the energy storage device, C e is the rated capacity cost of the energy storage equipment, E max is the rated capacity of the energy storage device, T is the service life of the energy storage device, C m is the operating cost of the energy storage device per unit power.

9. The energy storage type virtual power plant optimization and dispatching device according to claim 6, characterized in that: The constraints of the energy storage virtual power plant optimization model constructed by the building module include: The power balance constraint is expressed as: Among them, P grid (i) is the electricity from the grid, P load (i) represents the user's load size, is the predicted power generation of renewable energy, P ess_dis (i) is the discharge capacity of the energy storage device, P ess_cha (i) is the charge capacity of the energy storage device; The operating constraints of the state of charge of the energy storage equipment are expressed as: Among them, S OC (i) is the charge state of the energy storage device at the current moment, S OC (i-1) is the state of charge of the energy storage device at the previous moment, η d is the discharge efficiency of the energy storage device, η c is the charging efficiency of the energy storage device, P dis (i) is the discharge power of the energy storage device, P ch (i) is the charging power of the energy storage device, Δt is the time between the current moment and the previous moment, E max is the rated capacity of the energy storage device; The charging and discharging power constraints of energy storage equipment are expressed as: B dis (i) is the discharge state of the energy storage device in the i-th period, B ch (i) is the charging state of the energy storage device in the i-th period, P max is the rated power of the energy storage device; The state of charge constraint of the energy storage device is expressed as: S OCmin ≤S OC (i)≤S OCmax , where S OCmin and S OCmax They are the lower and upper limits of the power of the energy storage device respectively.

10. The energy storage type virtual power plant optimization and dispatching device according to claim 6, characterized in that: The calling module includes: A prediction unit, used to obtain a system state sequence in a prediction time domain according to the operating state of the energy storage equipment in the virtual power plant at the current moment; A solving unit, used to solve the energy storage virtual power plant optimization model based on the system state sequence in the prediction time domain and taking into account the constraints of various resources to obtain a control sequence of the system in the prediction time domain; a control unit for applying a first control value of a control sequence to the control system; Repeat the operations of the above units to achieve rolling optimization of the virtual power plant’s daily scheduling.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the energy storage type virtual power plant optimization scheduling method as described in any one of claims 1-5 are implemented.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the energy storage type virtual power plant optimization scheduling method as described in any one of claims 1-5 are implemented.

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