Virtual power plant multi-period optimization scheduling method and device considering comprehensive demand response

Abstract

The invention relates to the technical field of virtual power plant scheduling, in particular to a virtual power plant multi-period optimization scheduling method and device considering comprehensive demand response. According to the method, optimization scheduling is carried out by taking the maximization of the day-ahead income and the intra-day income as the target, and the day-ahead compensation cost and the intra-day compensation cost are considered during scheduling, so that the power fluctuation caused by the fluctuation of the intra-day wind and light output is stabilized, and the influence of renewable energy sources such as wind and light on the stability of the power system of the virtual power plant is reduced; therefore, the economical efficiency and the stable operation level of the power system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plant scheduling, and in particular to a multi-period optimization scheduling method and device for a virtual power plant taking comprehensive demand response into consideration. Background Art

[0002] With the acceleration of globalization, the energy consumption and CO2 greenhouse gas emissions caused by rapid economic growth have become increasingly prominent, becoming the main driving force for the intensification of global climate change. This change has posed unprecedented threats and damage to human society and the natural environment, including the frequent occurrence of extreme climate events, the loss of biodiversity and the rise of sea levels. These impacts not only threaten human survival conditions, but also pose challenges to the stable development of the global ecological environment.

[0003] In this context, building a virtual power plant (VPP) is an effective way to solve the problems of energy waste and CO2 greenhouse gas emissions. Compared with traditional energy management systems, virtual power plants aggregate a large number of distributed energy resources, such as solar energy, wind energy, and energy storage systems, and communicate through smart grid technology to optimize the scheduling of each unit to ensure the smooth operation and low-carbon economy of the power system. With the large-scale grid connection of renewable energy such as wind and solar power, it poses a challenge to the safe and stable operation of the power system. The optimized scheduling on a single time period scale today is difficult to meet the power fluctuations caused by the fluctuations in wind and solar power output during the day. Therefore, how to smooth out the power fluctuations caused by the fluctuations in wind and solar power output during the day has become an urgent problem to be solved. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a multi-period optimization scheduling method and device for a virtual power plant taking into account comprehensive demand response, so as to solve the problem of power fluctuation caused by fluctuations in wind and solar power output during the day.

[0005] In a first aspect, an embodiment of the present invention provides a multi-period optimization scheduling method for a virtual power plant considering comprehensive demand response, the multi-period optimization scheduling method for a virtual power plant comprising:

[0006] Obtain the demand load of the day-ahead demand response and the planned energy sales amount output by each path of the virtual power plant, and calculate the planned energy purchase amount of the virtual power plant according to the demand load and the planned energy sales amount output by each path;

[0007] Calculate the planned sales revenue according to the planned energy sales volume outputted from each of the paths, and calculate the planned purchase cost according to the planned energy purchase volume;

[0008] Obtaining the equipment operation and maintenance cost and the compensation cost of the day-ahead, and calculating the day-ahead income of the virtual power plant according to the planned sales income, the planned purchase cost, the equipment operation and maintenance cost and the compensation cost of the day-ahead;

[0009] Obtaining intraday compensation costs and deviation costs, and calculating intraday returns based on the planned sales revenue, the planned purchase cost, the equipment operation and maintenance costs, the intraday compensation costs and the deviation costs;

[0010] Obtain constraints, and when the constraints are met, adjust the planned energy sales volume output by each path of the virtual power plant with the goal of maximizing the day-ahead profit and the day-ahead profit.

[0011] In a second aspect, an embodiment of the present invention provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer device is used to execute the multi-period optimization scheduling method for a virtual power plant as described in any one of the first aspects.

[0012] Compared with the prior art, the present invention has the following beneficial effects:

[0013] Obtain the demand load of the day-ahead demand response and the planned energy sales output of each channel of the virtual power plant, calculate the planned energy purchase amount of the virtual power plant according to the demand load and the planned energy sales output of each channel, calculate the planned sales revenue according to the planned energy sales output of each channel, calculate the planned purchase cost according to the planned energy purchase amount, obtain the equipment operation and maintenance cost and the day-ahead compensation cost, calculate the day-ahead revenue of the virtual power plant according to the planned sales revenue, planned purchase cost, equipment operation and maintenance cost and the day-ahead compensation cost, obtain the intraday compensation cost and deviation cost, calculate the intraday revenue according to the planned sales revenue, planned purchase cost, equipment operation and maintenance cost, intraday compensation cost and deviation cost, obtain the constraint conditions, and adjust the planned energy sales output of each channel of the virtual power plant with the maximum day-ahead revenue and the maximum intraday revenue as the goal when the constraint conditions are met. In this application, the optimization scheduling is carried out with the goal of maximizing the day-ahead revenue and the intraday revenue. During the scheduling, the day-ahead compensation cost and the intraday compensation cost are considered to smooth the power fluctuation caused by the fluctuation of the wind and solar output during the day, reduce the impact of renewable energy such as wind and solar on the stability of the virtual power plant power system, thereby improving the economy and stable operation level of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0015] Figure 1 It is a flow chart of a multi-period optimization scheduling method of a virtual power plant considering comprehensive demand response provided by an embodiment of the present invention;

[0016] Figure 2 It is a schematic diagram of the structural layout of a virtual power plant provided by an embodiment of the present invention;

[0017] Figure 3 It is a schematic diagram of a 24-hour electric load curve of a virtual power plant before and after considering comprehensive demand response provided by an embodiment of the present invention;

[0018] Figure 4 It is a schematic diagram of a 24-hour heat load curve of a virtual power plant before and after considering comprehensive demand response provided by an embodiment of the present invention;

[0019] Figure 5 It is a structural block diagram of a multi-period optimization scheduling device for a virtual power plant considering comprehensive demand response provided by an embodiment of the present invention;

[0020] Figure 6 It is a structural schematic diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0023] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0024] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0025] As used in the present specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]", depending on the context.

[0026] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0027] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0028] It should be understood that the order of execution of the steps in the following embodiments does not imply a precedence of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0029] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0030] See also Figure 1 , is a flow chart of a multi-period optimization scheduling method for a virtual power plant considering comprehensive demand response provided by an embodiment of the present invention, such as Figure 1As shown, the multi-period optimization scheduling method of a virtual power plant considering comprehensive demand response may include the following steps.

[0031] S101: Obtain the demand load of the day-ahead demand response and the planned energy sales volume of the virtual power plant output through various channels, and calculate the planned energy purchase volume of the virtual power plant based on the demand load and the planned energy sales volume of the virtual power plant output through various channels.

[0032] In step S101, the demand load is the energy required by each device, the demand load of the day-ahead demand response is the energy required by each device planned for the previous day, and the virtual power plant is an advanced energy management system that integrates decentralized energy resources (such as renewable energy generation in the form of solar photovoltaics and wind power) and energy storage systems (such as batteries and heat storage tanks) through intelligent technology. This integration involves not only energy supply equipment, but also controllable loads such as demand response and electric vehicles. Each path is a path for generating electrical energy and thermal energy, the planned energy sales volume is the electricity and thermal energy that the virtual power plant plans to sell, and the planned energy purchase volume is the electricity and thermal energy that the virtual power plant plans to purchase.

[0033] In this embodiment, the demand load of the day-ahead demand response and the planned energy sales amount of the virtual power plant output through various channels are obtained, and the planned energy purchase amount of the virtual power plant is calculated based on the demand load and the planned energy sales amount of the virtual power plant output through various channels. That is, the planned energy purchase amount of the virtual power plant is obtained by subtracting the planned energy sales amount from the demand load.

[0034] It should be noted that the energy in the virtual power plant includes electricity and heat energy, among which electricity can include wind power generation, photovoltaic power generation, battery discharge, electric vehicle discharge, and electricity produced by the combined heat and power system (CHP). The combined heat and power system is an efficient energy conversion technology that can produce electricity and heat energy at the same time. The combined heat and power unit is mainly composed of a generator, a heat recovery system and a control system. In this system, the generator generates electricity to supply users, and the waste heat generated is recycled through the heat recovery system to provide hot water or steam heating or industrial use, realizing the dual utilization of energy.

[0035] The thermal energy in the virtual power plant can include heat energy generated by the cogeneration system, heat energy generated by the gas boiler, and heat energy generated by the thermal storage tank.

[0036] See also Figure 2 , is a schematic diagram of the structural layout of a virtual power plant provided by an embodiment of the present invention, in which the upper-level power grid outputs electric load and the upper-level gas grid outputs thermal load.

[0037] It should be noted that if the planned energy sales volume is greater than the demand load, that is, the electricity and heat energy generated by the energy sold by the virtual power plant can meet the required load, then the energy purchase volume is zero, that is, there is no need to purchase additional energy.

[0038] S102: Calculate the planned sales revenue based on the planned energy sales volume output from each channel, and calculate the planned purchase cost based on the planned energy purchase volume.

[0039] In step S102, the planned sales revenue is the revenue that the virtual power plant plans to obtain from selling energy, and the planned purchase cost is the cost that the virtual power plant plans to purchase energy.

[0040] The planned sales revenue is calculated based on the planned energy sales volume output from each channel and the energy unit price of each channel, and the planned purchase cost is calculated based on the planned energy purchase volume and the energy purchase unit price.

[0041] S103: Obtain the equipment operation and maintenance cost and the compensation cost on the day before, and calculate the day-ahead income of the virtual power plant based on the planned sales income, the planned purchase cost, the equipment operation and maintenance cost and the compensation cost on the day before.

[0042] In step S103, the day-ahead equipment operation and maintenance cost is the cost of planned maintenance of each energy-generating equipment, the day-ahead compensation cost is the compensation for adjusting the electricity consumption according to different incentive responses when the corresponding electricity is planned to be used in each time period, and the day-ahead profit is the profit generated by the date plan.

[0043] In this embodiment, the equipment operation and maintenance cost and the compensation cost of the day-ahead are obtained, and the planned purchase cost, equipment operation and maintenance cost and the compensation cost of the day-ahead are subtracted from the planned sales income to calculate the day-ahead income of the virtual power plant. The calculation formula of the day-ahead income is as follows:

[0044]

[0045] Among them, F1 is the day-ahead return, t1 is the corresponding period within 24 hours of the day-ahead, and F s (t1) is the planned sales revenue per unit time period the day before, which is equal to the product of the planned energy sales volume and the unit price. BP (t1) is the planned purchase cost for the period t1 within 24 hours before the previous day. The planned purchase cost is equal to the product of the planned energy purchase amount and the unit price. EM (t1) is the equipment operation and maintenance cost in the t1 period within 24 hours before the previous day, F DR (t1) is the day-ahead compensation cost for period t1 within 24 hours before the day.

[0046] Among them, the planned purchase cost of the t1 period within 24 hours before the day includes the planned electricity purchase cost and the planned gas purchase cost. The calculation formula of the planned purchase cost is as follows:

[0047] F BP (t1) = F B (t1)+F P (t1)

[0048] Among them, F BP (t1) is the planned purchase cost for the period t1 within 24 hours before the previous day, F B (t1) is the planned electricity purchase cost during the t1 period, F P (t1) is the planned gas purchase cost during period t1.

[0049] The calculation formula for the equipment operation and maintenance cost in the t1 period within the previous 24 hours is as follows:

[0050]

[0051] Among them, i is the device corresponding to the energy generated by the i-th path, n is the total number of devices, such as the solar photovoltaic panel corresponding to the photovoltaic power generation path, ω i is the coefficient of operation and maintenance of the i-th equipment, P i (t1) is the active power output by the i-th device in period t1.

[0052] Among them, the day-ahead compensation cost for the t1 period within 24 hours the day before includes the day-ahead price-type compensation cost and the day-ahead substitution-type compensation cost for the t1 period.

[0053] Optionally, it also includes:

[0054] Obtain the day-ahead price-type compensation cost corresponding to the price-type compensation cost and the day-ahead alternative compensation cost corresponding to the alternative compensation cost;

[0055] The day-ahead compensation cost is calculated based on the day-ahead price compensation cost and the day-ahead substitution compensation cost.

[0056] In this embodiment, the day-ahead price-type compensation cost corresponding to the price-type compensation cost and the day-ahead alternative compensation cost corresponding to the alternative compensation cost are obtained, and the day-ahead price-type compensation cost and the day-ahead alternative compensation cost are added together to calculate the day-ahead compensation cost.

[0057] Among them, the price-type compensation cost is the compensation for losses caused by changing electricity prices to encourage consumers to adjust their electricity consumption, and the substitution-type compensation cost is the compensation for losses caused by replacing energy generated by different means.

[0058] Price-based compensation costs are compensated based on price-based demand response, which influences users' electricity consumption behavior through price signals.

[0059] Dividing a day into n periods, we can construct an n×n demand elasticity coefficient matrix, as shown below:

[0060]

[0061] Among them, η ij is the price demand elasticity coefficient, i,j∈[1,n].

[0062] Set the load demand response type to L k,0 (i), the load after demand response is calculated as follows:

[0063] L k (i) = L k,0 (i)+λ L L k,0 (i)

[0064]

[0065] In the above formula (3.3), L k,0 (i), L k (i) represents the load at time i before and after considering demand response. Integrating the above mentioned formulas, it can be concretized into a matrix form as follows:

[0066]

[0067] The price-based compensation cost includes the interruptible load compensation cost for interrupting power supply to the load and the transferable load compensation cost for the load transfer power supply period, and M is an n×n demand elasticity coefficient matrix.

[0068] Among them, the interruptible load compensation cost is the compensation for the loss caused by the ability to spontaneously adjust its electricity consumption under the stimulation of changes in electricity prices in response to the signals of the electricity market. The load can be reduced to reduce electricity consumption, or the load can be increased to take advantage of the discounts during low electricity price periods.

[0069] Interruptible load change ΔP CL (t) is calculated as follows:

[0070]

[0071] In the formula, is the load before the response at time t, E CL (t,j) is the element in the tth row and jth column of the demand elasticity matrix of the interruptible load, ρ j To respond to the electricity price at the next j moments, is the response to the electricity price at the previous j moments.

[0072] The transferable load compensation cost for the load transfer power supply period is to transfer part of the load to the low electricity price during the peak electricity price period. That is, the virtual power plant operator can effectively guide the load to operate during the non-peak period of the total load of the power system, thereby achieving the effect of balancing supply and demand.

[0073] Among them, the sensitivity of electricity price changes in different periods to the consumer side load response is depicted, and the value of the element in the tth row and jth column is:

[0074]

[0075] Among them, e j (t) is the value of the element in the tth row and jth column of the demand elasticity coefficient matrix, ΔP L (t) is the load change from the response to time t, is the load after response at time t, Δρ j is the change in electricity price from the response to time j, is the response to the electricity price at the previous j moments.

[0076] Transferable load change ΔP SL (t) is:

[0077]

[0078] In the formula, is the load before the response at time t, E SL (t,j) is the element in the tth row and jth column of the demand elasticity matrix of the transferable load. is the response to the electricity price at the previous j moments.

[0079] The price compensation cost is calculated based on the change in interruptible load and transferable load. The calculation formula is as follows:

[0080]

[0081] Among them, F CS (t) is the price compensation cost, ΔP CL (t) is the interruptible load change, ΔP SL (t) is the load change of transferable type, k is the load change of level k interruptible, kcl i is the compensation cost corresponding to the i-th level interruptible load, and k(t) is the transferable load compensation rate in time period t.

[0082] It should be noted that the output power of different paths can be classified into different levels, such as 3 levels.

[0083] According to the demand load of the day-ahead demand response, the day-ahead price-type compensation cost of the planned time period t is calculated the day-ahead.

[0084] The replacement compensation cost is the loss cost caused by using different energy sources for substitution. For each period t, the replacement amount of electric and thermal load is as follows:

[0085]

[0086] Among them, Prl e (t) is the replacement amount of electric load, P2H is the electric-to-heat coefficient, pe a (t) is the electricity price in period t, ph a (t) is the hot price in period t, load e (t) is the initial electrical load in the tth period, e W is the replacement ratio of the electrical load.

[0087]

[0088] Among them, Prl h (t) is the heat load replacement amount in period t, load h (t) is the initial heat load, h W is the replacement ratio of thermal load. Compensation cost of electrical load replacement:

[0089] F rl-e =Prl e (t)×pe a (t)-Prl e (t)×P2H×ph a (t)

[0090] Among them, F rl-e is the compensation cost for electric load replacement, Prl e (t) is the replacement amount of electric load, pe a (t) is the electricity price in the tth period, P2H is the electricity-to-heat coefficient, ph a (t) is the heat price in period t.

[0091] Compensation costs for heat load substitution:

[0092]

[0093] Among them, F rl-h is the compensation cost for heat load replacement, Prl h (t) is the heat load replacement during period t, ph a (t) is the heat price in the t period, Prl h (t) is the heat load replacement in period t.

[0094] The calculation formula for replacement compensation cost is as follows:

[0095] F RL_TOTAL (t) = F rl_e (t)+F rl_h (t)

[0096] Among them, F RL-TORAL (t) is the replacement compensation cost in period t, F rl-e is the compensation cost of electric load replacement, F rl-e Compensation costs for electrical load replacement.

[0097] The day-ahead replacement compensation cost is calculated based on the demand load of the day-ahead demand response in the day-ahead planned period t and the planned energy sales volume of the virtual power plant output in various ways. The day-ahead price compensation cost is added to the day-ahead replacement compensation cost to calculate the day-ahead compensation cost.

[0098] S104: Obtain the intraday compensation cost and the deviation cost, and calculate the intraday income according to the planned sales income, the planned purchase cost, the equipment operation and maintenance cost, the intraday compensation cost and the deviation cost.

[0099] In step S104, the internal compensation cost is the compensation for adjusting the electricity consumption according to different incentive responses when the corresponding electricity is used in the corresponding time period of the day. The deviation cost is the large difference between the date forecast value and the actual output due to the uncertainty of the current environment. The intraday profit is the actual profit corresponding to each time period of the day.

[0100] In this embodiment, the intraday compensation cost and deviation cost are obtained, and the planned purchase cost, equipment operation and maintenance cost, intraday compensation cost and deviation cost are deducted from the planned sales income to calculate the intraday income. The calculation formula of intraday income is as follows:

[0101]

[0102] Among them, F2 is the day-ahead return, t1 is the corresponding period within the day, and F s2 (t1) is the planned sales income during the t1 period of the day, F BP (t1) is the planned purchase cost for the t1 period within the day, F EM (t1) is the equipment operation and maintenance cost during the t1 period of the day, F DR2 (t1) is the intraday compensation cost of the t1 period, F CR (t1) is the deviation cost of the intraday period t1.

[0103] The intraday compensation cost in the intraday t1 period includes the intraday price-type compensation cost, intraday incentive-type compensation cost and intraday substitution-type compensation cost in the t1 period. Among them, the calculation process of the intraday price-type compensation cost and the intraday substitution-type compensation cost is the same as the calculation process of the day-ahead price-type compensation cost and the day-ahead substitution-type compensation cost mentioned above. Substitute the actual data in the t1 period into the formula of the price-type compensation cost and the substitution-type compensation cost mentioned above to calculate the intraday price-type compensation cost and the intraday substitution-type compensation cost.

[0104] Incentive Compensation Costs The costs of compensation based on incentive-based demand response, which provides direct financial incentives or compensation to consumers in exchange for their commitment to reduce electricity consumption during specific periods of time.

[0105] In this embodiment, the intraday incentive compensation cost is the cost of compensation by incentivizing the charging and discharging of electric vehicles. Electric vehicle users sign incentive agreements with virtual power plant operators, making these vehicles not only means of transportation, but also become incentive demand response resources.

[0106] Based on the actual situation, when electric vehicles participate in the process of daily optimization scheduling, as the number of charge and discharge times increases, it will inevitably lead to battery loss, and the battery is a relatively expensive component in the electric vehicle. Therefore, in order to encourage electric vehicle users to participate in the process of daily optimization scheduling, the virtual power plant operator will compensate users for battery loss. The specific mathematical modeling is as follows:

[0107]

[0108] Among them, F EB (t) is the battery loss compensation cost of the electric vehicle at time t, that is, the intraday incentive compensation cost, n v is the number of electric vehicles; is the battery cost of the Vth electric car; is the upper limit of the number of charge / discharge cycles in the Vth electric vehicle; is the battery capacity of the Vth electric car; is the discharge depth of the battery of the Vth electric vehicle; is the discharge power of the Vth electric car at time t. is the discharge efficiency of the Vth electric vehicle; is the mileage of the Vth electric car in time period t; E v is the expected charging capacity of the Vth electric vehicle, which represents the power consumption required per kilometer of the EV.

[0109] The day-ahead compensation cost is calculated by adding the intraday price-based compensation cost, the intraday incentive-based compensation cost and the intraday substitution-based compensation cost.

[0110] In day-ahead scheduling, virtual power plants need to make plans for energy output and consumption in advance. However, due to the uncertainty of wind and solar power generation, such as light intensity, ambient temperature and wind speed, there is a large difference between the date forecast and the actual output, which poses a challenge to the safe and stable operation of virtual power plants. Therefore, the objective function of the intraday rolling optimization stage is defined as taking into account the deviation between the actual energy and the planned energy purchase amount on the basis of maximizing the intraday profit, that is, introducing the deviation cost.

[0111] Optionally, it also includes:

[0112] Get the actual energy purchase and sale volume of the current day;

[0113] The power purchase deviation value is calculated based on the actual energy purchase amount and the planned energy purchase amount;

[0114] The power sales deviation value is calculated based on the actual energy sales volume and the planned energy sales volume;

[0115] The deviation cost is calculated based on the electricity purchase deviation value and the electricity sales deviation value.

[0116] In this embodiment, the deviation cost includes the power purchase deviation value and the power sales deviation value. The actual energy purchase amount and the actual energy sales amount in the current day are obtained, and the actual energy purchase amount is subtracted from the planned energy purchase amount to calculate the power purchase deviation value. The actual energy sales amount is subtracted from the planned energy sales amount to calculate the power sales deviation value. The deviation cost is calculated based on the power purchase deviation value and the power sales deviation value. The calculation formula of the deviation cost is as follows:

[0117] F CR (t) = μ × (cerrorb 2 (t)+cerrors 2 (t))

[0118] Among them, cerrorb(t) is the deviation value of electricity purchase in period t, cerrors(t) is the deviation value of electricity sales in period t, and F CR (t) is the deviation cost in period t.

[0119] Calculate the intraday profit by deducting the planned purchase cost, equipment operation and maintenance cost, intraday compensation cost and deviation cost from the planned sales profit.

[0120] S105: Obtain constraint conditions, and when the constraint conditions are met, adjust the planned energy sales volume output by each channel of the virtual power plant with the goal of maximizing the day-ahead profit and the intra-day profit.

[0121] In step S105, the constraints constrain the parameters in the day-ahead profit and the intraday profit. When the constraints are met, the planned energy sales volume output by each path of the virtual power plant is adjusted with the goal of maximizing the day-ahead profit and the intraday profit.

[0122] In this embodiment, the constraints include power balance constraints, power purchase and sales constraints, wind and solar power output constraints, and demand response constraints, where the power balance constraints include electric power constraints and thermal power constraints. The calculation formula for the electric power constraints is as follows:

[0123] P RE (t)+P CHP,e (t)+P BS,e (t) = P OP,load,e (t)+P ES1 (t)+P EV (t)+P HP,e (t)

[0124] Among them, P RE (t) is the sum of wind and solar power output power of renewable energy in period t, P CHP,e is the electric power output of the cogeneration unit during period t, P BS,e (t) is the difference between the amount of electricity purchased from the grid and the amount of electricity sold by the virtual power plant operator during period t, P OP,load,e (t) is the electric load after demand response in period t. ES1 (t) is the charging power or discharging power of the charging device during time period t. ES1 When the (t) value is positive, it indicates that the energy storage device is charging; when P ES1 When the value of (t) is negative, it means that the energy storage device is discharging. EV (t) is the charging power or discharging power of the electric vehicle during period t. EV When the (t) value is positive, it means that the electric vehicle is charging; when P EV When the value of (t) is negative, it means that the electric vehicle is discharging, P HP,e (t) is the power of the heat pump converting electrical energy into thermal energy during period t.

[0125] The calculation formula of thermal power constraint is as follows:

[0126] P GB,h (t)+P CHP,h (t)+P HP,h (t) = P OP,load,h (t)+P ES2 (t)

[0127] P OP,load,h (t) = P load,h (t)-Prlh (t)+Prl e (t)×P2H

[0128] Among them, P GB,h (t) is the thermal power output of the gas boiler during period t, P CHP,h (t) is the heat output of the cogeneration unit during period t, P HP,h (t) is the thermal power output of the heat pump during period t, P OP,load,h (t) is the heat load power after demand response, P ES2 (t) is the charging power or heat release power of the charging device during period t. ES2 When the (t) value is positive, it means that the energy storage device is charging; when P ES2 When the value of (t) is negative, it means that the energy storage device releases heat. load,h (t) is the initial thermal load power planned to be output by the virtual power plant during period t, Prl h (t) is the replacement amount of heat load converted into electrical load during period t, Prl e (t) is the substitution amount of electrical load converted into thermal load during period t, and P2H is the electricity-to-heat coefficient.

[0129] The calculation formula for the constraints of electricity purchase and sales is as follows:

[0130]

[0131] Among them, ub(t) and us(t) are the state variables of electricity purchase and sale in period t, with values ​​between 0 and 1, P e,b (t) is the amount of electricity purchased by the virtual power plant at time t, p max is the maximum transaction volume, P e,s (t) is the electricity sales amount of the virtual power plant at time t.

[0132] The calculation formula for the wind and solar power output power constraint is as follows:

[0133]

[0134] Where P WT (t) is the output power of the fan in period t, P is the upper limit of the fan output power. PV (t) is the output power of the solar panel in period t, The upper limit of its output power.

[0135] Demand response constraints include price constraints, incentive constraints and substitution constraints. The calculation formula for price constraints is as follows:

[0136]

[0137] Among them, P PDR (t) is the price-based demand response power, ΔP CL (t) is the interruptible load change,

[0138] ΔP SL (t) is the load variation of transferable type, k is the load variation of level k interruptible type, is the minimum value of the price-based demand response power, It is the maximum value of the price-based demand response power.

[0139] The calculation formula of incentive type incentive constraint is as follows:

[0140]

[0141] in, is the storage power of the Vth electric vehicle in time period t, and They are respectively the upper and lower limits of the storage power of this electric vehicle. represents the charging power of the Vth electric vehicle in time period t, The upper limit of charging power. represents the discharge power of the Vth electric vehicle in time period t, is the upper limit of discharge power. Indicates whether the Vth electric car is in a charging or discharging state during time period t. If so, it is 1, otherwise it is 0. Indicates whether the Vth electric vehicle is connected to the grid within time period t, and They are the total power of the Vth electric car from beginning to end. and are the charging efficiency and discharging efficiency of the Vth electric vehicle, is the distance traveled by the Vth electric vehicle in time period t. It should be noted that in the intraday scheduling link, 15-minute time intervals are used for scheduling. By narrowing the scheduling time window, the output of wind and solar power generation can be predicted more accurately. In order to adapt to the 15-minute time interval, the calculation of the power constraint needs to be multiplied by 0.25. That is, the hour unit is converted to a 15-minute time unit, because 1 hour is equal to 4 15-minute intervals, so it is multiplied by 0.25.

[0142] The calculation formula of the substitution constraint is as follows:

[0143] Prl e,min (t)≤Prl e (t)≤Prl e,max (t)

[0144] Prlh,min (t)≤Prl h (t)≤Prl h,max (t)

[0145] Among them, Prl e (t) is the replacement amount of electrical load converted into thermal load during period t, Prl h (t) is the replacement amount of heat load converted into electrical load during period t, Prl e,min (t) is the minimum value of the replacement amount of electrical load converted into thermal load during period t, Prl e,max (t) is the maximum value of the replacement amount of electrical load converted into thermal load during period t, Prl h,min (t) is the minimum value of the replacement amount of thermal load converted into electrical load during period t, Prl h,max (t) is the maximum value of the replacement amount of thermal load converted into electrical load during period t.

[0146] Under the condition of satisfying the constraints, the planned energy sales volume of each output path of the virtual power plant is adjusted with the goal of maximizing the day-ahead profit and the intraday profit. That is, when F1 and F2 take the maximum value, the planned energy sales volume of each output path of the virtual power plant is adjusted.

[0147] See also Figure 3 , is a schematic diagram of a 24-hour electric load curve of a virtual power plant before and after considering comprehensive demand response provided by an embodiment of the present invention, Figure 3 In the figure, the horizontal axis is time, the vertical axis is power, the red curve is the output power of the optimized electric load, and the blue curve is the output power of the electric load before optimization. By comparing the electric load before and after the figure, it can be seen that after considering the comprehensive demand response, the peak-to-valley difference of the electric load is reduced by 24.85% compared with that without considering the comprehensive demand response, achieving the goal of peak shaving and valley filling. The main reason is that the demand response strategy prompts users to adjust their energy use strategies according to changes in electricity prices, thereby optimizing the allocation and utilization of power resources. By considering the comprehensive demand response, the purpose of optimizing the electric load curve can be achieved, which verifies the effectiveness of this method.

[0148] See also Figure 4 , is a schematic diagram of a 24-hour heat load curve of a virtual power plant before and after considering comprehensive demand response, provided by an embodiment of the present invention. Figure 4 In the figure, the horizontal axis is time, the vertical axis is power, the red curve is the output power of the heat load after optimization, and the blue curve is the output power of the heat load before optimization.

[0149] From the comparison of heat loads before and after in the figure, it can be seen that after considering the comprehensive demand response, the peak-to-valley difference of the heat load is reduced by 19.06% compared with that without considering the comprehensive demand response, achieving the goal of peak shaving and valley filling. In terms of thermal energy management, by using diversified energy equipment such as cogeneration units, gas boilers and heat storage devices, the comprehensive demand response can flexibly allocate energy supply during different demand periods, ensuring the stability and high efficiency of energy supply. During the peak period of heat load, the thermal energy demand is met through the coordinated work of gas boilers and heat storage devices and gas turbines.

[0150] As shown in Table 1, the dispatch results under four scenarios with different demand responses are given.

[0151] Table 1

[0152]

[0153]

[0154] Among them, scenario 1 does not consider comprehensive demand response, that is, compensation cost is not calculated; scenario 2 only considers price-based demand response, that is, only price-based compensation cost is calculated; scenario 3 considers price-based and incentive-based demand response, that is, price-based compensation cost and incentive-based compensation cost are considered; scenario 4 is the comprehensive demand response considered in this method, that is, price-based compensation cost, substitution-based compensation cost and incentive-based compensation cost are considered. From the comparison results in Table 1, it can be seen that since the day is based on the day-ahead dispatch, the output plan of each unit set within the day remains unchanged, and the flexibility of electric vehicles is considered to smooth the total deviation of power purchase and sale, the equipment operation and maintenance cost is 38,196 yuan. From the perspective of price-based compensation cost, as more types of demand response are considered, the virtual power plant operator will have corresponding subsidies, and its cost will also increase from 0 to 37,308.68 yuan. From the perspective of deviation cost, as more types of demand response are considered, the corresponding load curve will be better optimized, making the deviation gradually smaller, so the deviation penalty cost gradually decreases from 6,275.17 yuan to 1,856.73 yuan.

[0155] As shown in Table 2, the scheduling results are given in two scenarios: ignoring the deviation cost and not using the day-ahead-intraday multi-period optimization scheduling.

[0156] Table 2

[0157]

[0158] Scenario 5 uses the multi-period optimization scheduling from day-ahead to day-intraday, but the optimization objective function of the virtual power plant in the day-intraday stage does not take into account the deviation cost, and optimizes the scheduling; Scenario 6 does not use the multi-period optimization scheduling from day-ahead to day-intraday, and the virtual power plant only optimizes the scheduling in the day-ahead stage. By comparing Table 2, it can be seen that since the deviation essentially comes from the difference between the wind and solar output power predicted during the day and the day-ahead, there is no deviation in the day-ahead scheduling stage, and the deviation penalty cost of scenario 6 is 0. Because the deviation cost is not considered in the objective function of scenario 5, there is a large deviation from the planned purchase cost expected on the day-ahead, and its deviation cost is the largest. Comparing scenario 4 and scenario 6, scenario 6 is only optimized in the day-ahead stage, achieving a total profit of 20,726.79 yuan, and scenario 4 achieves a total profit of 19,636.17 yuan. Although the total profit of scenario 4 is slightly lower than that of scenario 6, due to the comprehensive consideration of the deviation, that is, sacrificing part of the economy in exchange for the stable operation of the virtual power plant. Both scenarios 4 and 5 adopt two-stage optimization scheduling. The intraday optimization scheduling in scenario 5 does not take into account the deviation between the intraday and the day-ahead planned purchase costs, which increases the deviation cost by 980.83 yuan compared with scenario 4, and its total revenue decreases by 4597.76 yuan compared with scenario 4. In summary, introducing deviation costs and adopting multi-period optimization scheduling strategies can effectively improve the economic benefits of virtual power plants.

[0159] Obtain the demand load of the day-ahead demand response and the planned energy sales output of each channel of the virtual power plant, calculate the planned energy purchase amount of the virtual power plant according to the demand load and the planned energy sales output of each channel, calculate the planned sales revenue according to the planned energy sales output of each channel, calculate the planned purchase cost according to the planned energy purchase amount, obtain the equipment operation and maintenance cost and the day-ahead compensation cost, calculate the day-ahead revenue of the virtual power plant according to the planned sales revenue, planned purchase cost, equipment operation and maintenance cost and the day-ahead compensation cost, obtain the intraday compensation cost and deviation cost, calculate the intraday revenue according to the planned sales revenue, planned purchase cost, equipment operation and maintenance cost, intraday compensation cost and deviation cost, obtain the constraint conditions, and adjust the planned energy sales output of each channel of the virtual power plant with the maximum day-ahead revenue and the maximum intraday revenue as the goal when the constraint conditions are met. In this application, the optimization scheduling is carried out with the goal of maximizing the day-ahead revenue and the intraday revenue. During the scheduling, the day-ahead compensation cost and the intraday compensation cost are considered to smooth the power fluctuation caused by the fluctuation of the wind and solar output during the day, reduce the impact of renewable energy such as wind and solar on the stability of the virtual power plant power system, thereby improving the economy and stable operation level of the power system.

[0160] See also Figure 5 , Figure 51 is a block diagram of a multi-period optimization scheduling device for a virtual power plant considering comprehensive demand response provided by an embodiment of the present invention. For ease of explanation, only the parts related to the embodiment of the present application are shown. Figure 5 The virtual power plant multi-period optimization scheduling device 50 includes a first acquisition module 51, a first calculation module 52, a second calculation module 53, a third calculation module 54, and an adjustment module 55.

[0161] The first acquisition module 51 is used to obtain the demand load of the day-ahead demand response and the planned energy sales volume of the virtual power plant through various output channels, and calculate the planned energy purchase volume of the virtual power plant based on the demand load and the planned energy sales volume of the virtual power plant through various output channels.

[0162] The first calculation module 52 is used to calculate the planned sales revenue according to the planned energy sales volume outputted from each channel, and to calculate the planned purchase cost according to the planned energy purchase volume.

[0163] The second calculation module 53 is used to obtain the equipment operation and maintenance cost and the compensation cost of the day before, and calculate the day-ahead income of the virtual power plant according to the planned sales income, the planned purchase cost, the equipment operation and maintenance cost and the compensation cost of the day before.

[0164] The third calculation module 54 is used to obtain the intraday compensation cost and the deviation cost, and calculate the intraday income according to the planned sales income, the planned purchase cost, the equipment operation and maintenance cost, the intraday compensation cost and the deviation cost.

[0165] The adjustment module 55 is used to obtain constraint conditions and, when the constraint conditions are met, adjust the planned energy sales volume output by each channel of the virtual power plant with the goal of maximizing the day-ahead profit and the intra-day profit.

[0166] Optionally, the virtual power plant multi-period optimization scheduling device 50 further includes:

[0167] The second acquisition module is used to acquire the day-ahead price-type compensation cost corresponding to the price-type compensation cost and the day-ahead alternative compensation cost corresponding to the alternative compensation cost.

[0168] The fourth calculation module is used to calculate the day-ahead compensation cost based on the day-ahead price compensation cost, the day-ahead incentive compensation cost and the day-ahead replacement compensation cost.

[0169] Optionally, the virtual power plant multi-period optimization scheduling device 50 further includes:

[0170] The third acquisition module is used to obtain the intraday price-type compensation cost corresponding to the price-type compensation cost, the intraday incentive-type compensation cost corresponding to the incentive-type compensation cost, and the intraday alternative compensation cost corresponding to the alternative compensation cost.

[0171] The fifth calculation module is used to calculate the day-ahead compensation cost based on the intraday price-type compensation cost, the intraday incentive-type compensation cost and the intraday substitution-type compensation cost.

[0172] Optionally, the virtual power plant multi-period optimization scheduling device 50 further includes:

[0173] The fourth acquisition module is used to obtain the actual energy purchase amount and the actual energy sales amount in the current day.

[0174] The sixth calculation module is used to calculate the power purchase deviation value according to the actual energy purchase amount and the planned energy purchase amount.

[0175] The seventh calculation module is used to calculate the power sales deviation value according to the actual energy sales amount and the planned energy sales amount.

[0176] The eighth calculation module is used to calculate the deviation cost according to the power purchase deviation value and the power sale deviation value.

[0177] It should be noted that the information interaction, execution process and other contents between the above-mentioned modules are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0178] Figure 6 Schematic diagram of the structure of a computer device provided by Embodiment 5 of the present invention. Figure 6 As shown, the computer device of this embodiment includes: at least one processor ( Figure 6 Only one is shown), a memory, and a computer program stored in the memory and executable on at least one processor, and when the processor executes the computer program, the steps in any of the above-mentioned embodiments of the multi-period optimization scheduling method for a virtual power plant are implemented.

[0179] The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that Figure 6 This is merely an example of a computer device and does not constitute a limitation on the computer device. The computer device may include more or fewer components than those shown in the figure, or a combination of certain components, or different components. For example, it may also include a network interface, a display screen, and an input device.

[0180] The processor may be a CPU, or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0181] The memory includes a readable storage medium, an internal memory, etc., wherein the internal memory may be the memory of a computer device, and the internal memory provides an environment for the operation of an operating system and computer-readable instructions in the readable storage medium. The readable storage medium may be a hard disk of a computer device, and in other embodiments may also be an external storage device of a computer device, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on a computer device. Further, the memory may also include both an internal storage unit of a computer device and an external storage device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as program codes of computer programs, etc. The memory may also be used to temporarily store data that has been output or is to be output.

[0182] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the above-mentioned method embodiment, which will not be repeated here. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiment when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device capable of carrying computer program code, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0183] The present application implements all or part of the processes in the above-mentioned embodiment method, and may also be completed through a computer program product. When the computer program product runs on a computer device, the computer device can implement the steps in the above-mentioned method embodiment when executing the computer program product.

[0184] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0185] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0186] In the embodiments provided in the present application, it should be understood that the disclosed devices / computer equipment and methods can be implemented in other ways. For example, the device / computer equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0187] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0188] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A multi-period optimization scheduling method for a virtual power plant considering comprehensive demand response, characterized in that: The multi-period optimization scheduling method of the virtual power plant includes: Obtain the demand load of the day-ahead demand response and the planned energy sales amount output by each path of the virtual power plant, and calculate the planned energy purchase amount of the virtual power plant according to the demand load and the planned energy sales amount output by each path; Calculate the planned sales revenue according to the planned energy sales volume outputted from each of the paths, and calculate the planned purchase cost according to the planned energy purchase volume; Obtaining equipment operation and maintenance costs and day-ahead compensation costs, and calculating the day-ahead revenue of the virtual power plant based on the planned sales revenue, the planned purchase cost, the equipment operation and maintenance costs and the day-ahead compensation costs; Obtaining intraday compensation costs and deviation costs, and calculating intraday returns based on the planned sales revenue, the planned purchase cost, the equipment operation and maintenance costs, the intraday compensation costs and the deviation costs; Obtain constraints, and when the constraints are met, adjust the planned energy sales volume output by each path of the virtual power plant with the goal of maximizing the day-ahead profit and the intra-day profit.

2. The multi-period optimization scheduling method of a virtual power plant according to claim 1, characterized in that: Also includes: Obtain the day-ahead price-type compensation cost corresponding to the price-type compensation cost and the day-ahead alternative compensation cost corresponding to the alternative compensation cost; The day-ahead compensation cost is calculated based on the day-ahead price-type compensation cost and the day-ahead replacement-type compensation cost.

3. The multi-period optimization scheduling method of a virtual power plant according to claim 1, characterized in that: Also includes: Obtain the intraday price-type compensation cost corresponding to the price-type compensation cost, the intraday incentive-type compensation cost corresponding to the incentive-type compensation cost, and the intraday alternative compensation cost corresponding to the alternative compensation cost; The day-ahead compensation cost is calculated based on the intraday price-type compensation cost, the intraday incentive-type compensation cost and the intraday substitution-type compensation cost.

4. The multi-period optimization scheduling method of a virtual power plant according to claim 3, characterized in that: The price-type compensation cost includes the interruptible load compensation cost for interrupting the power supply to the load and the transferable load compensation cost for the load transfer power supply period.

5. The multi-period optimization scheduling method of a virtual power plant according to claim 1, characterized in that: Also includes: Get the actual energy purchase and sale volume of the current day; Calculating a power purchase deviation value according to the actual energy purchase amount and the planned energy purchase amount; Calculating a power sales deviation value based on the actual energy sales amount and the planned energy sales amount; The deviation cost is calculated based on the electricity purchase deviation value and the electricity sales deviation value.

6. The multi-period optimization scheduling method of a virtual power plant according to claim 1, characterized in that: The constraints include power balance constraints, power purchase and sales constraints, wind and solar power generation output power constraints and demand response constraints.

7. A computer device, characterized in that: The computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, and the computer device is used to execute the multi-period optimization scheduling method for a virtual power plant as described in any one of claims 1 to 6.

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